[ { "path": ".gitignore", "content": "__pycache__\n*.pyc\n*.egg-info/\n*.ipynb_checkpoints/\n*.pt" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Code of Conduct\n\nFacebook has adopted a Code of Conduct that we expect project participants to adhere to.\nPlease read the [full text](https://code.fb.com/codeofconduct/)\nso that you can understand what actions will and will not be tolerated.\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to `CPA` \nWe want to make contributing to this project as easy and transparent as\npossible.\n\n## Pull Requests\nWe actively welcome your pull requests.\n\n1. Fork the repo and create your branch from `master`.\n2. If you've added code that should be tested, add tests.\n3. If you've changed APIs, update the documentation.\n4. Ensure the test suite passes.\n5. Make sure your code lints.\n6. If you haven't already, complete the Contributor License Agreement (\"CLA\").\n\n## Contributor License Agreement (\"CLA\")\nIn order to accept your pull request, we need you to submit a CLA. You only need\nto do this once to work on any of Facebook's open source projects.\n\nComplete your CLA here: \n\n## Issues\nWe use GitHub issues to track public bugs. Please ensure your description is\nclear and has sufficient instructions to be able to reproduce the issue.\n\nFacebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe\ndisclosure of security bugs. In those cases, please go through the process\noutlined on that page and do not file a public issue.\n\n## License\nBy contributing to `CPA`, you agree that your contributions\nwill be licensed under the LICENSE file in the root directory of this source\ntree.\n" }, { "path": "LICENSE", "content": "The MIT License\n\nCopyright (c) Facebook, Inc. and its affiliates.\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n" }, { "path": "README.md", "content": "# CPA - Compositional Perturbation Autoencoder\n\n# This code in not being maintained anymore, please use the new implementation [here](https://github.com/theislab/cpa).\n\n\n## What is CPA?\n![Screenshot](Figure1.png)\n\n`CPA` is a framework to learn effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug response across different cell types, doses and drug combinations. CPA allows:\n\n* Out-of-distribution predicitons of unseen drug combinations at various doses and among different cell types.\n* Learn interpretable drug and cell type latent spaces.\n* Estimate dose response curve for each perturbation and their combinations.\n* Access the uncertainty of the estimations of the model.\n\n## Package Structure\n\nThe repository is centered around the `cpa` module:\n\n* [`cpa.train`](cpa/train.py) contains scripts to train the model.\n* [`cpa.api`](cpa/api.py) contains user friendly scripts to interact with the model via scanpy.\n* [`cpa.plotting`](cpa/plotting.py) contains scripts to plotting functions.\n* [`cpa.model`](cpa/model.py) contains modules of cpa model.\n* [`cpa.data`](cpa/data.py) contains data loader, which transforms anndata structure to a class compatible with cpa model.\n\nAdditional files and folders:\n\n* [`datasets`](datasets/) contains both versions of the data: raw and pre-processed.\n* [`preprocessing`](preprocessing/) contains notebooks to reproduce the datasets pre-processing from raw data.\n\n## Usage\n\n- As a first step, download the contents of `datasets/` and `pretrained_models/` from [this tarball](https://dl.fbaipublicfiles.com/dlp/cpa_binaries.tar).\n\n\nTo learn how to use this repository, check \n[`./notebooks/demo.ipynb`](notebooks/demo.ipynb), and the following scripts:\n\n\n* Note that hyperparameters in the `demo.ipynb` are set as default but might not work work for new datasets.\n## Examples and Reproducibility\nyou can find more example and hyperparamters tuning scripts and also reproducbility notebooks for the plots in the paper in the [`reproducibility`](https://github.com/theislab/cpa-reproducibility) repo.\n\n## Curation of your own data to train CPA\n\n* To prepare your data to train CPA, you need to add specific fields to adata object and perfrom data split. Examples on how to add \nnecessary fields for multiple datasets used in the paper can be found in [`preprocessing/`](/https://github.com/facebookresearch/CPA/tree/master/preprocessing) folder.\n\n## Training a model\n\nThere are two ways to train a cpa model:\n\n* Using the command line, e.g.: `python -m cpa.train --data datasets/GSM_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm`\n* From jupyter notebook: example in [`./notebooks/demo.ipynb`](notebooks/demo.ipynb)\n\n\n## Documentation\n\nCurrently you can access the documentation via `help` function in IPython. For example:\n\n```python\nfrom cpa.api import API\n\nhelp(API)\n\nfrom cpa.plotting import CPAVisuals\n\nhelp(CPAVisuals)\n\n```\n\nA separate page with the documentation is coming soon.\n\n## Support and contribute\n\nIf you have a question or noticed a problem, you can post an [`issue`](https://github.com/facebookresearch/CPA/issues/new).\n\n## Reference\n\nPlease cite the following publication if you find CPA useful in your research.\n```\n@article{lotfollahi2023predicting,\n title={Predicting cellular responses to complex perturbations in high-throughput screens},\n author={Lotfollahi, Mohammad and Klimovskaia Susmelj, Anna and De Donno, Carlo and Hetzel, Leon and Ji, Yuge and Ibarra, Ignacio L and Srivatsan, Sanjay R and Naghipourfar, Mohsen and Daza, Riza M and Martin, Beth and others},\n journal={Molecular Systems Biology},\n pages={e11517},\n year={2023}\n}\n```\n\nThe paper titled **Predicting cellular responses to complex perturbations in high-throughput screens** can be found [here](https://www.biorxiv.org/content/10.1101/2021.04.14.439903v2](https://www.embopress.org/doi/full/10.15252/msb.202211517).\n## License\n\nThis source code is released under the MIT license, included [here](LICENSE).\n" }, { "path": "cpa/__init__.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom cpa.api import API\nfrom cpa.plotting import CPAVisuals\n" }, { "path": "cpa/api.py", "content": "import copy\nimport itertools\nimport os\nimport pprint\nimport time\nfrom collections import defaultdict\nfrom typing import Optional, Union, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport scanpy as sc\nimport torch\nfrom torch.distributions import (\n NegativeBinomial,\n Normal\n)\nfrom cpa.train import evaluate, prepare_cpa\nfrom cpa.helper import _convert_mean_disp_to_counts_logits\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics.pairwise import cosine_distances, euclidean_distances\nfrom tqdm import tqdm\n\nclass API:\n \"\"\"\n API for CPA model to make it compatible with scanpy.\n \"\"\"\n\n def __init__(\n self,\n data,\n perturbation_key=\"condition\",\n covariate_keys=[\"cell_type\"],\n split_key=\"split\",\n dose_key=\"dose_val\",\n control=None,\n doser_type=\"mlp\",\n decoder_activation=\"linear\",\n loss_ae=\"gauss\",\n patience=200,\n seed=0,\n pretrained=None,\n device=\"cuda\",\n save_dir=\"/tmp/\", # directory to save the model\n hparams={},\n only_parameters=False,\n ):\n \"\"\"\n Parameters\n ----------\n data : str or `AnnData`\n AnndData object or a full path to the file in the .h5ad format.\n covariate_keys : list (default: ['cell_type'])\n List of names in the .obs of AnnData that should be used as\n covariates.\n split_key : str (default: 'split')\n Name of the column in .obs of AnnData to use for splitting the\n dataset into train, test and validation.\n perturbation_key : str (default: 'condition')\n Name of the column in .obs of AnnData to use for perturbation\n variable.\n dose_key : str (default: 'dose_val')\n Name of the column in .obs of AnnData to use for continious\n covariate.\n doser_type : str (default: 'mlp')\n Type of the nonlinearity in the latent space for the continious\n covariate encoding: sigm, logsigm, mlp.\n decoder_activation : str (default: 'linear')\n Last layer of the decoder.\n loss_ae : str (default: 'gauss')\n Loss (currently only gaussian loss is supported).\n patience : int (default: 200)\n Patience for early stopping.\n seed : int (default: 0)\n Random seed.\n pretrained : str (default: None)\n Full path to the pretrained model.\n only_parameters : bool (default: False)\n Whether to load only arguments or also weights from pretrained model.\n save_dir : str (default: '/tmp/')\n Folder to save the model.\n device : str (default: 'cpu')\n Device for model computations. If None, will try to use CUDA if\n available.\n hparams : dict (default: {})\n Parameters for the architecture of the CPA model.\n control: str\n Obs columns with booleans that identify control. If it is not provided\n the model will look for them in adata.obs[\"control\"]\n \"\"\"\n\n args = locals()\n del args[\"self\"]\n\n if not (pretrained is None):\n state, self.used_args, self.history = torch.load(\n pretrained, map_location=torch.device(device)\n )\n self.args = self.used_args\n self.args[\"data\"] = data\n self.args[\"covariate_keys\"] = covariate_keys\n self.args[\"device\"] = device\n self.args[\"control\"] = control\n if only_parameters:\n state = None\n print(f\"Loaded ARGS of the model from:\\t{pretrained}\")\n else:\n print(f\"Loaded pretrained model from:\\t{pretrained}\")\n else:\n state = None\n self.args = args\n\n self.model, self.datasets = prepare_cpa(self.args, state_dict=state)\n if not (pretrained is None) and (not only_parameters):\n self.model.history = self.history\n self.args[\"save_dir\"] = save_dir\n self.args[\"hparams\"] = self.model.hparams\n\n if not (save_dir is None):\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n\n dataset = self.datasets[\"training\"]\n self.perturbation_key = dataset.perturbation_key\n self.dose_key = dataset.dose_key\n self.covariate_keys = covariate_keys # very important, specifies the order of\n # covariates during training\n self.min_dose = dataset.drugs[dataset.drugs > 0].min().item()\n self.max_dose = dataset.drugs[dataset.drugs > 0].max().item()\n\n self.var_names = dataset.var_names\n\n self.unique_perts = list(dataset.perts_dict.keys())\n\n self.unique_covars = {}\n for cov in dataset.covars_dict:\n self.unique_covars[cov] = list(dataset.covars_dict[cov].keys())\n self.num_drugs = dataset.num_drugs\n\n self.perts_dict = dataset.perts_dict\n self.covars_dict = dataset.covars_dict\n\n self.drug_ohe = torch.Tensor(list(dataset.perts_dict.values()))\n\n self.covars_ohe = {}\n for cov in dataset.covars_dict:\n self.covars_ohe[cov] = torch.LongTensor(\n list(dataset.covars_dict[cov].values())\n )\n\n self.emb_covars = {}\n for cov in dataset.covars_dict:\n self.emb_covars[cov] = None\n self.emb_perts = None\n self.seen_covars_perts = None\n self.comb_emb = None\n self.control_cat = None\n\n self.seen_covars_perts = {}\n for k in self.datasets.keys():\n self.seen_covars_perts[k] = np.unique(self.datasets[k].pert_categories)\n\n self.measured_points = {}\n self.num_measured_points = {}\n for k in self.datasets.keys():\n self.measured_points[k] = {}\n self.num_measured_points[k] = {}\n for pert in np.unique(self.datasets[k].pert_categories):\n num_points = len(np.where(self.datasets[k].pert_categories == pert)[0])\n self.num_measured_points[k][pert] = num_points\n\n *cov_list, drug, dose = pert.split(\"_\")\n cov = \"_\".join(cov_list)\n if not (\"+\" in dose):\n dose = float(dose)\n if cov in self.measured_points[k].keys():\n if drug in self.measured_points[k][cov].keys():\n self.measured_points[k][cov][drug].append(dose)\n else:\n self.measured_points[k][cov][drug] = [dose]\n else:\n self.measured_points[k][cov] = {drug: [dose]}\n\n self.measured_points[\"all\"] = copy.deepcopy(self.measured_points[\"training\"])\n for cov in self.measured_points[\"ood\"].keys():\n for pert in self.measured_points[\"ood\"][cov].keys():\n if pert in self.measured_points[\"training\"][cov].keys():\n self.measured_points[\"all\"][cov][pert] = (\n self.measured_points[\"training\"][cov][pert].copy()\n + self.measured_points[\"ood\"][cov][pert].copy()\n )\n else:\n self.measured_points[\"all\"][cov][pert] = self.measured_points[\n \"ood\"\n ][cov][pert].copy()\n\n def load_from_old(self, pretrained):\n \"\"\"\n Parameters\n ----------\n pretrained : str\n Full path to the pretrained model.\n \"\"\"\n print(f\"Loaded pretrained model from:\\t{pretrained}\")\n state, self.used_args, self.history = torch.load(\n pretrained, map_location=torch.device(self.args[\"device\"])\n )\n self.model.load_state_dict(state_dict)\n self.model.history = self.history\n\n def print_args(self):\n pprint.pprint(self.args)\n\n def load(self, pretrained):\n \"\"\"\n Parameters\n ----------\n pretrained : str\n Full path to the pretrained model.\n \"\"\" # TODO fix compatibility\n print(f\"Loaded pretrained model from:\\t{pretrained}\")\n state, self.used_args, self.history = torch.load(\n pretrained, map_location=torch.device(self.args[\"device\"])\n )\n self.model.load_state_dict(state_dict)\n\n def train(\n self,\n max_epochs=1,\n checkpoint_freq=20,\n run_eval=False,\n max_minutes=60,\n filename=\"model.pt\",\n batch_size=None,\n save_dir=None,\n seed=0,\n ):\n \"\"\"\n Parameters\n ----------\n max_epochs : int (default: 1)\n Maximum number epochs for training.\n checkpoint_freq : int (default: 20)\n Checkoint frequencty to save intermediate results.\n run_eval : bool (default: False)\n Whether or not to run disentanglement and R2 evaluation during training.\n max_minutes : int (default: 60)\n Maximum computation time in minutes.\n filename : str (default: 'model.pt')\n Name of the file without the directoty path to save the model.\n Name should be with .pt extension.\n batch_size : int, optional (default: None)\n Batch size for training. If None, uses default batch size specified\n in hparams.\n save_dir : str, optional (default: None)\n Full path to the folder to save the model. If None, will use from\n the path specified during init.\n seed : int (default: None)\n Random seed. If None, uses default random seed specified during init.\n \"\"\"\n args = locals()\n del args[\"self\"]\n\n if batch_size is None:\n batch_size = self.model.hparams[\"batch_size\"]\n args[\"batch_size\"] = batch_size\n self.args[\"batch_size\"] = batch_size\n\n if save_dir is None:\n save_dir = self.args[\"save_dir\"]\n print(\"Results will be saved to the folder:\", save_dir)\n\n self.datasets.update(\n {\n \"loader_tr\": torch.utils.data.DataLoader(\n self.datasets[\"training\"], batch_size=batch_size, shuffle=True\n )\n }\n )\n\n self.model.train()\n\n start_time = time.time()\n pbar = tqdm(range(max_epochs), ncols=80)\n try:\n for epoch in pbar:\n epoch_training_stats = defaultdict(float)\n\n for data in self.datasets[\"loader_tr\"]:\n genes, drugs, covariates = data[0], data[1], data[2:]\n minibatch_training_stats = self.model.update(\n genes, drugs, covariates\n )\n\n for key, val in minibatch_training_stats.items():\n epoch_training_stats[key] += val\n\n for key, val in epoch_training_stats.items():\n epoch_training_stats[key] = val / len(self.datasets[\"loader_tr\"])\n if not (key in self.model.history.keys()):\n self.model.history[key] = []\n self.model.history[key].append(epoch_training_stats[key])\n self.model.history[\"epoch\"].append(epoch)\n\n ellapsed_minutes = (time.time() - start_time) / 60\n self.model.history[\"elapsed_time_min\"] = ellapsed_minutes\n\n # decay learning rate if necessary\n # also check stopping condition: patience ran out OR\n # time ran out OR max epochs achieved\n stop = ellapsed_minutes > max_minutes or (epoch == max_epochs - 1)\n\n pbar.set_description(\n f\"Rec: {epoch_training_stats['loss_reconstruction']:.4f}, \"\n + f\"AdvPert: {epoch_training_stats['loss_adv_drugs']:.2f}, \"\n + f\"AdvCov: {epoch_training_stats['loss_adv_covariates']:.2f}\"\n )\n\n if (epoch % checkpoint_freq) == 0 or stop:\n if run_eval == True:\n evaluation_stats = evaluate(self.model, self.datasets)\n for key, val in evaluation_stats.items():\n if not (key in self.model.history.keys()):\n self.model.history[key] = []\n self.model.history[key].append(val)\n self.model.history[\"stats_epoch\"].append(epoch)\n stop = stop or self.model.early_stopping(\n np.mean(evaluation_stats[\"test\"])\n )\n else:\n stop = stop or self.model.early_stopping(\n np.mean(epoch_training_stats[\"test\"])\n )\n evaluation_stats = None\n\n if stop:\n self.save(f\"{save_dir}{filename}\")\n pprint.pprint(\n {\n \"epoch\": epoch,\n \"training_stats\": epoch_training_stats,\n \"evaluation_stats\": evaluation_stats,\n \"ellapsed_minutes\": ellapsed_minutes,\n }\n )\n\n print(f\"Stop epoch: {epoch}\")\n break\n\n\n except KeyboardInterrupt:\n self.save(f\"{save_dir}{filename}\")\n\n self.save(f\"{save_dir}{filename}\")\n\n def save(self, filename):\n \"\"\"\n Parameters\n ----------\n filename : str\n Full path to save pretrained model.\n \"\"\"\n torch.save((self.model.state_dict(), self.args, self.model.history), filename)\n self.history = self.model.history\n print(f\"Model saved to: {filename}\")\n\n def _init_pert_embeddings(self):\n dose = 1.0\n self.emb_perts = (\n self.model.compute_drug_embeddings_(\n dose * self.drug_ohe.to(self.model.device)\n )\n .cpu()\n .clone()\n .detach()\n .numpy()\n )\n\n def get_drug_embeddings(self, dose=1.0, return_anndata=True):\n \"\"\"\n Parameters\n ----------\n dose : int (default: 1.0)\n Dose at which to evaluate latent embedding vector.\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n\n Returns\n -------\n If return_anndata is True, returns anndata object. Otherwise, doesn't\n return anything. Always saves embeddding in self.emb_perts.\n \"\"\"\n self._init_pert_embeddings()\n\n emb_perts = (\n self.model.compute_drug_embeddings_(\n dose * self.drug_ohe.to(self.model.device)\n )\n .cpu()\n .clone()\n .detach()\n .numpy()\n )\n\n if return_anndata:\n adata = sc.AnnData(emb_perts)\n adata.obs[self.perturbation_key] = self.unique_perts\n return adata\n\n def _init_covars_embeddings(self):\n combo_list = []\n for covars_key in self.covariate_keys:\n combo_list.append(self.unique_covars[covars_key])\n if self.emb_covars[covars_key] is None:\n i_cov = self.covariate_keys.index(covars_key)\n self.emb_covars[covars_key] = dict(\n zip(\n self.unique_covars[covars_key],\n self.model.covariates_embeddings[i_cov](\n self.covars_ohe[covars_key].to(self.model.device).argmax(1)\n )\n .cpu()\n .clone()\n .detach()\n .numpy(),\n )\n )\n self.emb_covars_combined = {}\n for combo in list(itertools.product(*combo_list)):\n combo_name = \"_\".join(combo)\n for i, cov in enumerate(combo):\n covars_key = self.covariate_keys[i]\n if i == 0:\n emb = self.emb_covars[covars_key][cov]\n else:\n emb += self.emb_covars[covars_key][cov]\n self.emb_covars_combined[combo_name] = emb\n\n def get_covars_embeddings_combined(self, return_anndata=True):\n \"\"\"\n Parameters\n ----------\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n\n Returns\n -------\n If return_anndata is True, returns anndata object. Otherwise, doesn't\n return anything. Always saves embeddding in self.emb_covars.\n \"\"\"\n self._init_covars_embeddings()\n if return_anndata:\n adata = sc.AnnData(np.array(list(self.emb_covars_combined.values())))\n adata.obs[\"covars\"] = self.emb_covars_combined.keys()\n return adata\n\n def get_covars_embeddings(self, covars_tgt, return_anndata=True):\n \"\"\"\n Parameters\n ----------\n covars_tgt : str\n Name of covariate for which to return AnnData\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n\n Returns\n -------\n If return_anndata is True, returns anndata object. Otherwise, doesn't\n return anything. Always saves embeddding in self.emb_covars.\n \"\"\"\n self._init_covars_embeddings()\n\n if return_anndata:\n adata = sc.AnnData(np.array(list(self.emb_covars[covars_tgt].values())))\n adata.obs[covars_tgt] = self.emb_covars[covars_tgt].keys()\n return adata\n\n def _get_drug_encoding(self, drugs, doses=None):\n \"\"\"\n Parameters\n ----------\n drugs : str\n Drugs combination as a string, where individual drugs are separated\n with a plus.\n doses : str, optional (default: None)\n Doses corresponding to the drugs combination as a string. Individual\n drugs are separated with a plus.\n\n Returns\n -------\n One hot encodding for a mixture of drugs.\n \"\"\"\n\n drug_mix = np.zeros([1, self.num_drugs])\n atomic_drugs = drugs.split(\"+\")\n doses = str(doses)\n\n if doses is None:\n doses_list = [1.0] * len(atomic_drugs)\n else:\n doses_list = [float(d) for d in str(doses).split(\"+\")]\n for j, drug in enumerate(atomic_drugs):\n drug_mix += doses_list[j] * self.perts_dict[drug]\n\n return drug_mix\n\n def mix_drugs(self, drugs_list, doses_list=None, return_anndata=True):\n \"\"\"\n Gets a list of drugs combinations to mix, e.g. ['A+B', 'B+C'] and\n corresponding doses.\n\n Parameters\n ----------\n drugs_list : list\n List of drug combinations, where each drug combination is a string.\n Individual drugs in the combination are separated with a plus.\n doses_list : str, optional (default: None)\n List of corresponding doses, where each dose combination is a string.\n Individual doses in the combination are separated with a plus.\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n\n Returns\n -------\n If return_anndata is True, returns anndata structure of the combinations,\n otherwise returns a np.array of corresponding embeddings.\n \"\"\"\n\n drug_mix = np.zeros([len(drugs_list), self.num_drugs])\n for i, drug_combo in enumerate(drugs_list):\n drug_mix[i] = self._get_drug_encoding(drug_combo, doses=doses_list[i])\n\n emb = (\n self.model.compute_drug_embeddings_(\n torch.Tensor(drug_mix).to(self.model.device)\n )\n .cpu()\n .clone()\n .detach()\n .numpy()\n )\n\n if return_anndata:\n adata = sc.AnnData(emb)\n adata.obs[self.perturbation_key] = drugs_list\n adata.obs[self.dose_key] = doses_list\n return adata\n else:\n return emb\n\n def latent_dose_response(\n self, perturbations=None, dose=None, contvar_min=0, contvar_max=1, n_points=100\n ):\n \"\"\"\n Parameters\n ----------\n perturbations : list\n List containing two names for which to return complete pairwise\n dose-response.\n doses : np.array (default: None)\n Doses values. If None, default values will be generated on a grid:\n n_points in range [contvar_min, contvar_max].\n contvar_min : float (default: 0)\n Minimum dose value to generate for default option.\n contvar_max : float (default: 0)\n Maximum dose value to generate for default option.\n n_points : int (default: 100)\n Number of dose points to generate for default option.\n Returns\n -------\n pd.DataFrame\n \"\"\"\n # dosers work only for atomic drugs. TODO add drug combinations\n self.model.eval()\n\n if perturbations is None:\n perturbations = self.unique_perts\n\n if dose is None:\n dose = np.linspace(contvar_min, contvar_max, n_points)\n n_points = len(dose)\n\n df = pd.DataFrame(columns=[self.perturbation_key, self.dose_key, \"response\"])\n for drug in perturbations:\n d = np.where(self.perts_dict[drug] == 1)[0][0]\n this_drug = torch.Tensor(dose).to(self.model.device).view(-1, 1)\n if self.model.doser_type == \"mlp\":\n response = (\n (self.model.dosers[d](this_drug).sigmoid() * this_drug.gt(0))\n .cpu()\n .clone()\n .detach()\n .numpy()\n .reshape(-1)\n )\n else:\n response = (\n self.model.dosers.one_drug(this_drug.view(-1), d)\n .cpu()\n .clone()\n .detach()\n .numpy()\n .reshape(-1)\n )\n\n df_drug = pd.DataFrame(\n list(zip([drug] * n_points, dose, list(response))),\n columns=[self.perturbation_key, self.dose_key, \"response\"],\n )\n df = pd.concat([df, df_drug])\n\n return df\n\n def latent_dose_response2D(\n self,\n perturbations,\n dose=None,\n contvar_min=0,\n contvar_max=1,\n n_points=100,\n ):\n \"\"\"\n Parameters\n ----------\n perturbations : list, optional (default: None)\n List of atomic drugs for which to return latent dose response.\n Currently drug combinations are not supported.\n doses : np.array (default: None)\n Doses values. If None, default values will be generated on a grid:\n n_points in range [contvar_min, contvar_max].\n contvar_min : float (default: 0)\n Minimum dose value to generate for default option.\n contvar_max : float (default: 0)\n Maximum dose value to generate for default option.\n n_points : int (default: 100)\n Number of dose points to generate for default option.\n Returns\n -------\n pd.DataFrame\n \"\"\"\n # dosers work only for atomic drugs. TODO add drug combinations\n\n assert len(perturbations) == 2, \"You should provide a list of 2 perturbations.\"\n\n self.model.eval()\n\n if dose is None:\n dose = np.linspace(contvar_min, contvar_max, n_points)\n n_points = len(dose)\n\n df = pd.DataFrame(columns=perturbations + [\"response\"])\n response = {}\n\n for drug in perturbations:\n d = np.where(self.perts_dict[drug] == 1)[0][0]\n this_drug = torch.Tensor(dose).to(self.model.device).view(-1, 1)\n if self.model.doser_type == \"mlp\":\n response[drug] = (\n (self.model.dosers[d](this_drug).sigmoid() * this_drug.gt(0))\n .cpu()\n .clone()\n .detach()\n .numpy()\n .reshape(-1)\n )\n else:\n response[drug] = (\n self.model.dosers.one_drug(this_drug.view(-1), d)\n .cpu()\n .clone()\n .detach()\n .numpy()\n .reshape(-1)\n )\n\n l = 0\n for i in range(len(dose)):\n for j in range(len(dose)):\n df.loc[l] = [\n dose[i],\n dose[j],\n response[perturbations[0]][i] + response[perturbations[1]][j],\n ]\n l += 1\n\n return df\n\n def compute_comb_emb(self, thrh=30):\n \"\"\"\n Generates an AnnData object containing all the latent vectors of the\n cov+dose*pert combinations seen during training.\n Called in api.compute_uncertainty(), stores the AnnData in self.comb_emb.\n\n Parameters\n ----------\n Returns\n -------\n \"\"\"\n if self.seen_covars_perts[\"training\"] is None:\n raise ValueError(\"Need to run parse_training_conditions() first!\")\n\n emb_covars = self.get_covars_embeddings_combined(return_anndata=True)\n\n # Generate adata with all cov+pert latent vect combinations\n tmp_ad_list = []\n for cov_pert in self.seen_covars_perts[\"training\"]:\n if self.num_measured_points[\"training\"][cov_pert] > thrh:\n *cov_list, pert_loop, dose_loop = cov_pert.split(\"_\")\n cov_loop = \"_\".join(cov_list)\n emb_perts_loop = []\n if \"+\" in pert_loop:\n pert_loop_list = pert_loop.split(\"+\")\n dose_loop_list = dose_loop.split(\"+\")\n for _dose in pd.Series(dose_loop_list).unique():\n tmp_ad = self.get_drug_embeddings(dose=float(_dose))\n tmp_ad.obs[\"pert_dose\"] = tmp_ad.obs.condition + \"_\" + _dose\n emb_perts_loop.append(tmp_ad)\n\n emb_perts_loop = emb_perts_loop[0].concatenate(emb_perts_loop[1:])\n X = emb_covars.X[\n emb_covars.obs.covars == cov_loop\n ] + np.expand_dims(\n emb_perts_loop.X[\n emb_perts_loop.obs.pert_dose.isin(\n [\n pert_loop_list[i] + \"_\" + dose_loop_list[i]\n for i in range(len(pert_loop_list))\n ]\n )\n ].sum(axis=0),\n axis=0,\n )\n if X.shape[0] > 1:\n raise ValueError(\"Error with comb computation\")\n else:\n emb_perts = self.get_drug_embeddings(dose=float(dose_loop))\n X = (\n emb_covars.X[emb_covars.obs.covars == cov_loop]\n + emb_perts.X[emb_perts.obs.condition == pert_loop]\n )\n tmp_ad = sc.AnnData(X=X)\n tmp_ad.obs[\"cov_pert\"] = \"_\".join([cov_loop, pert_loop, dose_loop])\n tmp_ad_list.append(tmp_ad)\n\n self.comb_emb = tmp_ad_list[0].concatenate(tmp_ad_list[1:])\n\n def compute_uncertainty(self, cov, pert, dose, thrh=30):\n \"\"\"\n Compute uncertainties for the queried covariate+perturbation combination.\n The distance from the closest condition in the training set is used as a\n proxy for uncertainty.\n\n Parameters\n ----------\n cov: dict\n Provide a value for each covariate (eg. cell_type) as a dictionaty\n for the queried uncertainty (e.g. cov_dict={'cell_type': 'A549'}).\n pert: string\n Perturbation for the queried uncertainty. In case of combinations the\n format has to be 'pertA+pertB'\n dose: string\n String which contains the dose of the perturbation queried. In case\n of combinations the format has to be 'doseA+doseB'\n\n Returns\n -------\n min_cos_dist: float\n Minimum cosine distance with the training set.\n min_eucl_dist: float\n Minimum euclidean distance with the training set.\n closest_cond_cos: string\n Closest training condition wrt cosine distances.\n closest_cond_eucl: string\n Closest training condition wrt euclidean distances.\n \"\"\"\n\n if self.comb_emb is None:\n self.compute_comb_emb(thrh=30)\n\n drug_ohe = torch.Tensor(self._get_drug_encoding(pert, doses=dose)).to(\n self.model.device\n )\n\n pert = drug_ohe.expand([1, self.drug_ohe.shape[1]])\n\n drug_emb = self.model.compute_drug_embeddings_(pert).detach().cpu().numpy()\n\n cond_emb = drug_emb\n for cov_key in cov:\n cond_emb += self.emb_covars[cov_key][cov[cov_key]]\n\n cos_dist = cosine_distances(cond_emb, self.comb_emb.X)[0]\n min_cos_dist = np.min(cos_dist)\n cos_idx = np.argmin(cos_dist)\n closest_cond_cos = self.comb_emb.obs.cov_pert[cos_idx]\n\n eucl_dist = euclidean_distances(cond_emb, self.comb_emb.X)[0]\n min_eucl_dist = np.min(eucl_dist)\n eucl_idx = np.argmin(eucl_dist)\n closest_cond_eucl = self.comb_emb.obs.cov_pert[eucl_idx]\n\n return min_cos_dist, min_eucl_dist, closest_cond_cos, closest_cond_eucl\n\n def predict(\n self,\n genes,\n cov,\n pert,\n dose,\n uncertainty=True,\n return_anndata=True,\n sample=False,\n n_samples=1,\n ):\n \"\"\"Predict values of control 'genes' conditions specified in df.\n\n Parameters\n ----------\n genes : np.array\n Control cells.\n cov: dict of lists\n Provide a value for each covariate (eg. cell_type) as a dictionaty\n for the queried uncertainty (e.g. cov_dict={'cell_type': 'A549'}).\n pert: list\n Perturbation for the queried uncertainty. In case of combinations the\n format has to be 'pertA+pertB'\n dose: list\n String which contains the dose of the perturbation queried. In case\n of combinations the format has to be 'doseA+doseB'\n\n uncertainty: bool (default: True)\n Compute uncertainties for the generated cells.\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n sample : bool (default: False)\n If sample is True, returns samples from gausssian distribution with\n mean and variance estimated by the model. Otherwise, returns just\n means and variances estimated by the model.\n n_samples : int (default: 10)\n Number of samples to sample if sampling is True.\n Returns\n -------\n If return_anndata is True, returns anndata structure. Otherwise, returns\n np.arrays for gene_means, gene_vars and a data frame for the corresponding\n conditions df_obs.\n\n \"\"\"\n\n assert len(dose) == len(pert), \"Check the length of pert, dose\"\n for cov_key in cov:\n assert len(cov[cov_key]) == len(pert), \"Check the length of covariates\"\n\n df = pd.concat(\n [\n pd.DataFrame({self.perturbation_key: pert, self.dose_key: dose}),\n pd.DataFrame(cov),\n ],\n axis=1,\n )\n\n self.model.eval()\n num = genes.shape[0]\n dim = genes.shape[1]\n genes = torch.Tensor(genes).to(self.model.device)\n\n gene_means_list = []\n gene_vars_list = []\n df_list = []\n\n for i in range(len(df)):\n comb_name = pert[i]\n dose_name = dose[i]\n covar_name = {}\n for cov_key in cov:\n covar_name[cov_key] = cov[cov_key][i]\n\n drug_ohe = torch.Tensor(\n self._get_drug_encoding(comb_name, doses=dose_name)\n ).to(self.model.device)\n\n drugs = drug_ohe.expand([num, self.drug_ohe.shape[1]])\n\n covars = []\n for cov_key in self.covariate_keys:\n covar_ohe = torch.Tensor(\n self.covars_dict[cov_key][covar_name[cov_key]]\n ).to(self.model.device)\n covars.append(covar_ohe.expand([num, covar_ohe.shape[0]]).clone())\n\n gene_reconstructions = (\n self.model.predict(genes, drugs, covars).cpu().clone().detach().numpy()\n )\n\n if sample:\n df_list.append(\n pd.DataFrame(\n [df.loc[i].values] * num * n_samples, columns=df.columns\n )\n )\n if self.args['loss_ae'] == 'gauss':\n dist = Normal(\n torch.Tensor(gene_reconstructions[:, :dim]),\n torch.Tensor(gene_reconstructions[:, dim:]),\n )\n elif self.args['loss_ae'] == 'nb':\n counts, logits = _convert_mean_disp_to_counts_logits(\n torch.clamp(\n torch.Tensor(gene_reconstructions[:, :dim]),\n min=1e-8,\n max=1e8,\n ),\n torch.clamp(\n torch.Tensor(gene_reconstructions[:, dim:]),\n min=1e-8,\n max=1e8,\n )\n )\n dist = NegativeBinomial(\n total_count=counts,\n logits=logits\n )\n sampled_gexp = (\n dist.sample(torch.Size([n_samples]))\n .cpu()\n .detach()\n .numpy()\n .reshape(-1, dim)\n )\n sampled_gexp[sampled_gexp < 0] = 0 #set negative values to 0, since gexp can't be negative\n gene_means_list.append(sampled_gexp)\n else:\n df_list.append(\n pd.DataFrame([df.loc[i].values] * num, columns=df.columns)\n )\n\n gene_means_list.append(gene_reconstructions[:, :dim])\n\n if uncertainty:\n (\n cos_dist,\n eucl_dist,\n closest_cond_cos,\n closest_cond_eucl,\n ) = self.compute_uncertainty(\n cov=covar_name, pert=comb_name, dose=dose_name\n )\n df_list[-1] = df_list[-1].assign(\n uncertainty_cosine=cos_dist,\n uncertainty_euclidean=eucl_dist,\n closest_cond_cosine=closest_cond_cos,\n closest_cond_euclidean=closest_cond_eucl,\n )\n\n gene_vars_list.append(gene_reconstructions[:, dim:])\n\n gene_means = np.concatenate(gene_means_list)\n gene_vars = np.concatenate(gene_vars_list)\n df_obs = pd.concat(df_list)\n del df_list, gene_means_list, gene_vars_list\n\n if return_anndata:\n adata = sc.AnnData(gene_means)\n adata.var_names = self.var_names\n adata.obs = df_obs\n if not sample:\n adata.layers[\"variance\"] = gene_vars\n\n adata.obs.index = adata.obs.index.astype(str) # type fix\n del gene_means, gene_vars, df_obs\n return adata\n else:\n return gene_means, gene_vars, df_obs\n\n def get_latent(\n self,\n genes,\n cov,\n pert,\n dose,\n return_anndata=True,\n ):\n \"\"\"Get latent values of control 'genes' with conditions specified in df.\n\n Parameters\n ----------\n genes : np.array\n Control cells.\n cov: dict of lists\n Provide a value for each covariate (eg. cell_type) as a dictionaty\n for the queried uncertainty (e.g. cov_dict={'cell_type': 'A549'}).\n pert: list\n Perturbation for the queried uncertainty. In case of combinations the\n format has to be 'pertA+pertB'\n dose: list\n String which contains the dose of the perturbation queried. In case\n of combinations the format has to be 'doseA+doseB'\n return_anndata : bool, optional (default: True)\n Return embedding wrapped into anndata object.\n\n Returns\n -------\n If return_anndata is True, returns anndata structure. Otherwise, returns\n np.arrays for latent and a data frame for the corresponding\n conditions df_obs.\n\n \"\"\"\n\n assert len(dose) == len(pert), \"Check the length of pert, dose\"\n for cov_key in cov:\n assert len(cov[cov_key]) == len(pert), \"Check the length of covariates\"\n\n df = pd.concat(\n [\n pd.DataFrame({self.perturbation_key: pert, self.dose_key: dose}),\n pd.DataFrame(cov),\n ],\n axis=1,\n )\n\n self.model.eval()\n num = genes.shape[0]\n genes = torch.Tensor(genes).to(self.model.device)\n\n latent_list = []\n df_list = []\n\n for i in range(len(df)):\n comb_name = pert[i]\n dose_name = dose[i]\n covar_name = {}\n for cov_key in cov:\n covar_name[cov_key] = cov[cov_key][i]\n\n drug_ohe = torch.Tensor(\n self._get_drug_encoding(comb_name, doses=dose_name)\n ).to(self.model.device)\n\n drugs = drug_ohe.expand([num, self.drug_ohe.shape[1]])\n\n covars = []\n for cov_key in self.covariate_keys:\n covar_ohe = torch.Tensor(\n self.covars_dict[cov_key][covar_name[cov_key]]\n ).to(self.model.device)\n covars.append(covar_ohe.expand([num, covar_ohe.shape[0]]).clone())\n\n _, latent_treated = self.model.predict(\n genes,\n drugs, \n covars,\n return_latent_treated=True,\n )\n\n latent_treated = latent_treated.cpu().clone().detach().numpy()\n\n df_list.append(\n pd.DataFrame([df.loc[i].values] * num, columns=df.columns)\n )\n\n latent_list.append(latent_treated)\n\n latent = np.concatenate(latent_list)\n df_obs = pd.concat(df_list)\n del df_list\n\n if return_anndata:\n adata = sc.AnnData(latent)\n adata.obs = df_obs\n adata.obs.index = adata.obs.index.astype(str) # type fix\n return adata\n else:\n return latent, df_obs\n\n def get_response(\n self,\n genes_control=None,\n doses=None,\n contvar_min=None,\n contvar_max=None,\n n_points=10,\n ncells_max=100,\n perturbations=None,\n control_name=\"test\",\n ):\n \"\"\"Decoded dose response data frame.\n\n Parameters\n ----------\n genes_control : np.array (deafult: None)\n Genes for which to predict values. If None, take from 'test_control'\n split in datasets.\n doses : np.array (default: None)\n Doses values. If None, default values will be generated on a grid:\n n_points in range [contvar_min, contvar_max].\n contvar_min : float (default: 0)\n Minimum dose value to generate for default option.\n contvar_max : float (default: 0)\n Maximum dose value to generate for default option.\n n_points : int (default: 100)\n Number of dose points to generate for default option.\n perturbations : list (default: None)\n List of perturbations for dose response\n\n Returns\n -------\n pd.DataFrame\n of decoded response values of genes and average response.\n \"\"\"\n\n if genes_control is None:\n genes_control = self.datasets[\"test\"].subset_condition(control=True).genes\n\n if contvar_min is None:\n contvar_min = 0\n if contvar_max is None:\n contvar_max = self.max_dose\n\n self.model.eval()\n if doses is None:\n doses = np.linspace(contvar_min, contvar_max, n_points)\n\n if perturbations is None:\n perturbations = self.unique_perts\n\n response = pd.DataFrame(\n columns=self.covariate_keys\n + [self.perturbation_key, self.dose_key, \"response\"]\n + list(self.var_names)\n )\n\n if ncells_max < len(genes_control):\n ncells_max = min(ncells_max, len(genes_control))\n idx = torch.LongTensor(\n np.random.choice(range(len(genes_control)), ncells_max, replace=False)\n )\n genes_control = genes_control[idx]\n\n j = 0\n for covar_combo in self.emb_covars_combined:\n cov_dict = {}\n for i, cov_val in enumerate(covar_combo.split(\"_\")):\n cov_dict[self.covariate_keys[i]] = [cov_val]\n print(cov_dict)\n for _, drug in enumerate(perturbations):\n if not (drug in self.datasets[control_name].subset_condition(control=True).ctrl_name):\n for dose in doses:\n # TODO handle covars\n\n gene_means, _, _ = self.predict(\n genes_control,\n cov=cov_dict,\n pert=[drug],\n dose=[dose],\n return_anndata=False,\n )\n predicted_data = np.mean(gene_means, axis=0).reshape(-1)\n response.loc[j] = (\n covar_combo.split(\"_\")\n + [drug, dose, np.linalg.norm(predicted_data)]\n + list(predicted_data)\n )\n j += 1\n return response\n\n def get_response_reference(self, perturbations=None):\n\n \"\"\"Computes reference values of the response.\n\n Parameters\n ----------\n dataset : CompPertDataset\n The file location of the spreadsheet\n perturbations : list (default: None)\n List of perturbations for dose response\n\n Returns\n -------\n pd.DataFrame\n of decoded response values of genes and average response.\n \"\"\"\n if perturbations is None:\n perturbations = self.unique_perts\n\n reference_response_curve = pd.DataFrame(\n columns=self.covariate_keys\n + [self.perturbation_key, self.dose_key, \"split\", \"num_cells\", \"response\"]\n + list(self.var_names)\n )\n\n dataset_ctr = self.datasets[\"training\"].subset_condition(control=True)\n\n i = 0\n for split in [\"training\", \"ood\"]:\n if split == 'ood':\n dataset = self.datasets[split]\n else:\n dataset = self.datasets[\"training\"].subset_condition(control=False)\n for pert in self.seen_covars_perts[split]:\n *covars, drug, dose_val = pert.split(\"_\")\n if drug in perturbations:\n if not (\"+\" in dose_val):\n dose = float(dose_val)\n else:\n dose = dose_val\n\n idx = np.where((dataset.pert_categories == pert))[0]\n\n if len(idx):\n y_true = dataset.genes[idx, :].numpy().mean(axis=0)\n reference_response_curve.loc[i] = (\n covars\n + [drug, dose, split, len(idx), np.linalg.norm(y_true)]\n + list(y_true)\n )\n\n i += 1\n\n reference_response_curve = reference_response_curve.replace(\n \"training_treated\", \"train\"\n )\n return reference_response_curve\n\n def get_response2D(\n self,\n perturbations,\n covar,\n genes_control=None,\n doses=None,\n contvar_min=None,\n contvar_max=None,\n n_points=10,\n ncells_max=100,\n #fixed_drugs=\"\",\n #fixed_doses=\"\",\n ):\n \"\"\"Decoded dose response data frame.\n\n Parameters\n ----------\n perturbations : list\n List of length 2 of perturbations for dose response.\n covar : dict\n Name of a covariate for which to compute dose-response.\n genes_control : np.array (deafult: None)\n Genes for which to predict values. If None, take from 'test_control'\n split in datasets.\n doses : np.array (default: None)\n Doses values. If None, default values will be generated on a grid:\n n_points in range [contvar_min, contvar_max].\n contvar_min : float (default: 0)\n Minimum dose value to generate for default option.\n contvar_max : float (default: 0)\n Maximum dose value to generate for default option.\n n_points : int (default: 100)\n Number of dose points to generate for default option.\n\n Returns\n -------\n pd.DataFrame\n of decoded response values of genes and average response.\n \"\"\"\n\n assert len(perturbations) == 2, \"You should provide a list of 2 perturbations.\"\n\n if contvar_min is None:\n contvar_min = self.min_dose\n\n if contvar_max is None:\n contvar_max = self.max_dose\n\n self.model.eval()\n # doses = torch.Tensor(np.linspace(contvar_min, contvar_max, n_points))\n if doses is None:\n doses = np.linspace(contvar_min, contvar_max, n_points)\n\n # genes_control = dataset.genes[dataset.indices['control']]\n if genes_control is None:\n genes_control = self.datasets[\"test\"].subset_condition(control=True).genes\n\n ncells_max = min(ncells_max, len(genes_control))\n idx = torch.LongTensor(np.random.choice(range(len(genes_control)), ncells_max))\n genes_control = genes_control[idx]\n\n response = pd.DataFrame(\n columns=perturbations+[\"response\"]+list(self.var_names)\n )\n\n drug = perturbations[0] + \"+\" + perturbations[1]\n\n dose_vals = [f\"{d[0]}+{d[1]}\" for d in itertools.product(*[doses, doses])]\n dose_comb = [list(d) for d in itertools.product(*[doses, doses])]\n\n i = 0\n if not (drug in self.datasets['training'].subset_condition(control=True).ctrl_name):\n for dose in dose_vals:\n gene_means, _, _ = self.predict(\n genes_control,\n cov=covar,\n pert=[drug],# + fixed_drugs],\n dose=[dose],# + fixed_doses],\n return_anndata=False,\n )\n predicted_data = np.mean(gene_means, axis=0).reshape(-1)\n response.loc[i] = (\n dose_comb[i]\n + [np.linalg.norm(predicted_data)]\n + list(predicted_data)\n )\n i += 1\n\n # i = 0\n # if not (drug in [\"Vehicle\", \"EGF\", \"unst\", \"control\", \"ctrl\"]):\n # for dose in dose_vals:\n # gene_means, _, _ = self.predict(\n # genes_control,\n # cov=covar,\n # pert=[drug + fixed_drugs],\n # dose=[dose + fixed_doses],\n # return_anndata=False,\n # )\n\n # predicted_data = np.mean(gene_means, axis=0).reshape(-1)\n\n # response.loc[i] = (\n # dose_comb[i]\n # + [np.linalg.norm(predicted_data)]\n # + list(predicted_data)\n # )\n # i += 1\n\n return response\n\n def evaluate_r2(self, dataset, genes_control, adata_random=None):\n \"\"\"\n Measures different quality metrics about an CPA `autoencoder`, when\n tasked to translate some `genes_control` into each of the drug/cell_type\n combinations described in `dataset`.\n\n Considered metrics are R2 score about means and variances for all genes, as\n well as R2 score about means and variances about differentially expressed\n (_de) genes.\n \"\"\"\n self.model.eval()\n scores = pd.DataFrame(\n columns=self.covariate_keys\n + [\n self.perturbation_key,\n self.dose_key,\n \"R2_mean\",\n \"R2_mean_DE\",\n \"R2_var\",\n \"R2_var_DE\",\n \"model\",\n \"num_cells\",\n ]\n )\n\n num, dim = genes_control.size(0), genes_control.size(1)\n\n total_cells = len(dataset)\n\n icond = 0\n for pert_category in np.unique(dataset.pert_categories):\n # pert_category category contains: 'celltype_perturbation_dose' info\n de_idx = np.where(\n dataset.var_names.isin(np.array(dataset.de_genes[pert_category]))\n )[0]\n\n idx = np.where(dataset.pert_categories == pert_category)[0]\n *covars, pert, dose = pert_category.split(\"_\")\n cov_dict = {}\n for i, cov_key in enumerate(self.covariate_keys):\n cov_dict[cov_key] = [covars[i]]\n\n if len(idx) > 0:\n mean_predict, var_predict, _ = self.predict(\n genes_control,\n cov=cov_dict,\n pert=[pert],\n dose=[dose],\n return_anndata=False,\n sample=False,\n )\n\n # estimate metrics only for reasonably-sized drug/cell-type combos\n y_true = dataset.genes[idx, :].numpy()\n\n # true means and variances\n yt_m = y_true.mean(axis=0)\n yt_v = y_true.var(axis=0)\n # predicted means and variances\n yp_m = mean_predict.mean(0)\n yp_v = var_predict.mean(0)\n #yp_v = np.var(mean_predict, axis=0)\n\n mean_score = r2_score(yt_m, yp_m)\n var_score = r2_score(yt_v, yp_v)\n\n mean_score_de = r2_score(yt_m[de_idx], yp_m[de_idx])\n var_score_de = r2_score(yt_v[de_idx], yp_v[de_idx])\n\n scores.loc[icond] = pert_category.split(\"_\") + [\n mean_score,\n mean_score_de,\n var_score,\n var_score_de,\n \"cpa\",\n len(idx),\n ]\n icond += 1\n if adata_random is not None:\n yp_m_bl = np.mean(adata_random, axis=0)\n yp_v_bl = np.var(adata_random, axis=0)\n\n mean_score_bl = r2_score(yt_m, yp_m_bl)\n var_score_bl = r2_score(yt_v, yp_v_bl)\n\n mean_score_de_bl = r2_score(yt_m[de_idx], yp_m_bl[de_idx])\n var_score_de_bl = r2_score(yt_v[de_idx], yp_v_bl[de_idx])\n\n\n scores.loc[icond] = pert_category.split(\"_\") + [\n mean_score_bl,\n mean_score_de_bl,\n var_score_bl,\n var_score_de_bl,\n \"baseline\",\n len(idx),\n ]\n icond += 1\n return scores\n\ndef get_reference_from_combo(perturbations_list, datasets, splits=[\"training\", \"ood\"]):\n \"\"\"\n A simple function that produces a pd.DataFrame of individual\n drugs-doses combinations used among the splits (for a fixed covariate).\n \"\"\"\n df_list = []\n for split_name in splits:\n full_dataset = datasets[split_name]\n ref = {\"num_cells\": []}\n for pp in perturbations_list:\n ref[pp] = []\n\n ndrugs = len(perturbations_list)\n for pert_cat in np.unique(full_dataset.pert_categories):\n _, pert, dose = pert_cat.split(\"_\")\n pert_list = pert.split(\"+\")\n if set(pert_list) == set(perturbations_list):\n dose_list = dose.split(\"+\")\n ncells = len(\n full_dataset.pert_categories[\n full_dataset.pert_categories == pert_cat\n ]\n )\n for j in range(ndrugs):\n ref[pert_list[j]].append(float(dose_list[j]))\n ref[\"num_cells\"].append(ncells)\n print(pert, dose, ncells)\n df = pd.DataFrame.from_dict(ref)\n df[\"split\"] = split_name\n df_list.append(df)\n\n return pd.concat(df_list)\n\n\ndef linear_interp(y1, y2, x1, x2, x):\n a = (y1 - y2) / (x1 - x2)\n b = y1 - a * x1\n y = a * x + b\n return y\n\n\ndef evaluate_r2_benchmark(cpa_api, datasets, pert_category, pert_category_list):\n scores = pd.DataFrame(\n columns=[\n cpa_api.covars_key,\n cpa_api.perturbation_key,\n cpa_api.dose_key,\n \"R2_mean\",\n \"R2_mean_DE\",\n \"R2_var\",\n \"R2_var_DE\",\n \"num_cells\",\n \"benchmark\",\n \"method\",\n ]\n )\n\n de_idx = np.where(\n datasets[\"ood\"].var_names.isin(\n np.array(datasets[\"ood\"].de_genes[pert_category])\n )\n )[0]\n idx = np.where(datasets[\"ood\"].pert_categories == pert_category)[0]\n y_true = datasets[\"ood\"].genes[idx, :].numpy()\n # true means and variances\n yt_m = y_true.mean(axis=0)\n yt_v = y_true.var(axis=0)\n\n icond = 0\n if len(idx) > 0:\n for pert_category_predict in pert_category_list:\n if \"+\" in pert_category_predict:\n pert1, pert2 = pert_category_predict.split(\"+\")\n idx_pred1 = np.where(datasets[\"training\"].pert_categories == pert1)[0]\n idx_pred2 = np.where(datasets[\"training\"].pert_categories == pert2)[0]\n\n y_pred1 = datasets[\"training\"].genes[idx_pred1, :].numpy()\n y_pred2 = datasets[\"training\"].genes[idx_pred2, :].numpy()\n\n x1 = float(pert1.split(\"_\")[2])\n x2 = float(pert2.split(\"_\")[2])\n x = float(pert_category.split(\"_\")[2])\n yp_m1 = y_pred1.mean(axis=0)\n yp_m2 = y_pred2.mean(axis=0)\n yp_v1 = y_pred1.var(axis=0)\n yp_v2 = y_pred2.var(axis=0)\n\n yp_m = linear_interp(yp_m1, yp_m2, x1, x2, x)\n yp_v = linear_interp(yp_v1, yp_v2, x1, x2, x)\n\n # yp_m = (y_pred1.mean(axis=0) + y_pred2.mean(axis=0))/2\n # yp_v = (y_pred1.var(axis=0) + y_pred2.var(axis=0))/2\n\n else:\n idx_pred = np.where(\n datasets[\"training\"].pert_categories == pert_category_predict\n )[0]\n print(pert_category_predict, len(idx_pred))\n y_pred = datasets[\"training\"].genes[idx_pred, :].numpy()\n # predicted means and variances\n yp_m = y_pred.mean(axis=0)\n yp_v = y_pred.var(axis=0)\n\n mean_score = r2_score(yt_m, yp_m)\n var_score = r2_score(yt_v, yp_v)\n\n mean_score_de = r2_score(yt_m[de_idx], yp_m[de_idx])\n var_score_de = r2_score(yt_v[de_idx], yp_v[de_idx])\n scores.loc[icond] = pert_category.split(\"_\") + [\n mean_score,\n mean_score_de,\n var_score,\n var_score_de,\n len(idx),\n pert_category_predict,\n \"benchmark\",\n ]\n icond += 1\n\n return scores\n" }, { "path": "cpa/data.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport warnings\n\nimport numpy as np\nimport torch\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\nfrom typing import Union\n\nimport pandas as pd\nimport scanpy as sc\nimport scipy\nfrom cpa.helper import rank_genes_groups\nfrom sklearn.preprocessing import OneHotEncoder\n\n\ndef ranks_to_df(data, key=\"rank_genes_groups\"):\n \"\"\"Converts an `sc.tl.rank_genes_groups` result into a MultiIndex dataframe.\n\n You can access various levels of the MultiIndex with `df.loc[[category]]`.\n\n Params\n ------\n data : `AnnData`\n key : str (default: 'rank_genes_groups')\n Field in `.uns` of data where `sc.tl.rank_genes_groups` result is\n stored.\n \"\"\"\n d = data.uns[key]\n dfs = []\n for k in d.keys():\n if k == \"params\":\n continue\n series = pd.DataFrame.from_records(d[k]).unstack()\n series.name = k\n dfs.append(series)\n\n return pd.concat(dfs, axis=1)\n\n\ndef check_adata(adata, special_fields):\n replaced = False\n for sf in special_fields:\n if sf in adata.obs:\n flag = 0\n for el in adata.obs[sf].values:\n if \"_\" in str(el):\n flag += 1\n if flag:\n print(\n f\"WARNING. Special characters ('_') were found in: '{sf}'.\",\n \"They will be replaced with '-'.\",\n \"Be careful, it may lead to errors downstream.\",\n )\n adata.obs[sf] = [s.replace(\"_\", \"-\") for s in adata.obs[sf].values]\n replaced = True\n\n return adata, replaced\n\n\nindx = lambda a, i: a[i] if a is not None else None\n\n\nclass Dataset:\n def __init__(\n self,\n data,\n perturbation_key=None,\n dose_key=None,\n covariate_keys=None,\n split_key=\"split\",\n control=None,\n ):\n if type(data) == str:\n data = sc.read(data)\n #Assert that keys are present in the adata object\n assert perturbation_key in data.obs.columns, f\"Perturbation {perturbation_key} is missing in the provided adata\"\n for key in covariate_keys:\n assert key in data.obs.columns, f\"Covariate {key} is missing in the provided adata\"\n assert dose_key in data.obs.columns, f\"Dose {dose_key} is missing in the provided adata\"\n assert split_key in data.obs.columns, f\"Split {split_key} is missing in the provided adata\"\n assert not (split_key is None), \"split_key can not be None\"\n\n #If covariate keys is empty list create dummy covariate\n if len(covariate_keys) == 0:\n print(\"Adding a dummy covariate...\")\n data.obs['dummy_cov'] = 'dummy_cov'\n covariate_keys = ['dummy_cov']\n\n self.perturbation_key = perturbation_key\n self.dose_key = dose_key\n\n if scipy.sparse.issparse(data.X):\n self.genes = torch.Tensor(data.X.A)\n else:\n self.genes = torch.Tensor(data.X)\n\n self.var_names = data.var_names\n\n if isinstance(covariate_keys, str):\n covariate_keys = [covariate_keys]\n self.covariate_keys = covariate_keys\n\n data, replaced = check_adata(\n data, [perturbation_key, dose_key] + covariate_keys\n )\n\n for cov in covariate_keys:\n if not (cov in data.obs):\n data.obs[cov] = \"unknown\"\n\n if split_key in data.obs:\n pass\n else:\n print(\"Performing automatic train-test split with 0.25 ratio.\")\n from sklearn.model_selection import train_test_split\n\n data.obs[split_key] = \"train\"\n idx = list(range(len(data)))\n idx_train, idx_test = train_test_split(\n data.obs_names, test_size=0.25, random_state=42\n )\n data.obs[split_key].loc[idx_train] = \"train\"\n data.obs[split_key].loc[idx_test] = \"test\"\n\n if \"control\" in data.obs:\n self.ctrl = data.obs[\"control\"].values\n else:\n print(f\"Assigning control values for {control}\")\n assert_msg = \"Please provide a name for control condition.\"\n assert not (control is None), assert_msg\n data.obs[\"control\"] = 0\n if dose_key in data.obs:\n pert, dose = control.split(\"_\")\n data.obs.loc[\n (data.obs[perturbation_key] == pert) & (data.obs[dose_key] == dose),\n \"control\",\n ] = 1\n else:\n pert = control\n data.obs.loc[(data.obs[perturbation_key] == pert), \"control\"] = 1\n\n self.ctrl = data.obs[\"control\"].values\n assert_msg = \"Cells to assign as control not found! Please check the name of control variable.\"\n assert sum(self.ctrl), assert_msg\n print(f\"Assigned {sum(self.ctrl)} control cells\")\n\n if perturbation_key is not None:\n if dose_key is None:\n raise ValueError(\n f\"A 'dose_key' is required when provided a 'perturbation_key'({perturbation_key}).\"\n )\n if not (dose_key in data.obs):\n print(\n f\"Creating a default entrance for dose_key {dose_key}:\",\n \"1.0 per perturbation\",\n )\n dose_val = []\n for i in range(len(data)):\n pert = data.obs[perturbation_key].values[i].split(\"+\")\n dose_val.append(\"+\".join([\"1.0\"] * len(pert)))\n data.obs[dose_key] = dose_val\n\n if not (\"cov_drug_dose_name\" in data.obs) or replaced:\n print(\"Creating 'cov_drug_dose_name' field.\")\n cov_drug_dose_name = []\n for i in range(len(data)):\n comb_name = \"\"\n for cov_key in self.covariate_keys:\n comb_name += f\"{data.obs[cov_key].values[i]}_\"\n comb_name += f\"{data.obs[perturbation_key].values[i]}_{data.obs[dose_key].values[i]}\"\n cov_drug_dose_name.append(comb_name)\n data.obs[\"cov_drug_dose_name\"] = cov_drug_dose_name\n\n if not (\"rank_genes_groups_cov\" in data.uns) or replaced:\n print(\"Ranking genes for DE genes.\")\n rank_genes_groups(data, groupby=\"cov_drug_dose_name\")\n\n self.pert_categories = np.array(data.obs[\"cov_drug_dose_name\"].values)\n self.de_genes = data.uns[\"rank_genes_groups_cov\"]\n\n self.drugs_names = np.array(data.obs[perturbation_key].values)\n self.dose_names = np.array(data.obs[dose_key].values)\n\n # get unique drugs\n drugs_names_unique = set()\n for d in self.drugs_names:\n [drugs_names_unique.add(i) for i in d.split(\"+\")]\n self.drugs_names_unique = np.array(list(drugs_names_unique))\n\n # save encoder for a comparison with Mo's model\n # later we need to remove this part\n encoder_drug = OneHotEncoder(sparse=False)\n encoder_drug.fit(self.drugs_names_unique.reshape(-1, 1))\n\n # Store as attribute for molecular featurisation\n self.encoder_drug = encoder_drug\n\n self.perts_dict = dict(\n zip(\n self.drugs_names_unique,\n encoder_drug.transform(self.drugs_names_unique.reshape(-1, 1)),\n )\n )\n\n # get drug combinations\n drugs = []\n for i, comb in enumerate(self.drugs_names):\n drugs_combos = encoder_drug.transform(\n np.array(comb.split(\"+\")).reshape(-1, 1)\n )\n dose_combos = str(data.obs[dose_key].values[i]).split(\"+\")\n for j, d in enumerate(dose_combos):\n if j == 0:\n drug_ohe = float(d) * drugs_combos[j]\n else:\n drug_ohe += float(d) * drugs_combos[j]\n drugs.append(drug_ohe)\n self.drugs = torch.Tensor(drugs)\n\n atomic_ohe = encoder_drug.transform(self.drugs_names_unique.reshape(-1, 1))\n\n self.drug_dict = {}\n for idrug, drug in enumerate(self.drugs_names_unique):\n i = np.where(atomic_ohe[idrug] == 1)[0][0]\n self.drug_dict[i] = drug\n else:\n self.pert_categories = None\n self.de_genes = None\n self.drugs_names = None\n self.dose_names = None\n self.drugs_names_unique = None\n self.perts_dict = None\n self.drug_dict = None\n self.drugs = None\n\n if isinstance(covariate_keys, list) and covariate_keys:\n if not len(covariate_keys) == len(set(covariate_keys)):\n raise ValueError(f\"Duplicate keys were given in: {covariate_keys}\")\n self.covariate_names = {}\n self.covariate_names_unique = {}\n self.covars_dict = {}\n self.covariates = []\n for cov in covariate_keys:\n self.covariate_names[cov] = np.array(data.obs[cov].values)\n self.covariate_names_unique[cov] = np.unique(self.covariate_names[cov])\n\n names = self.covariate_names_unique[cov]\n encoder_cov = OneHotEncoder(sparse=False)\n encoder_cov.fit(names.reshape(-1, 1))\n\n self.covars_dict[cov] = dict(\n zip(list(names), encoder_cov.transform(names.reshape(-1, 1)))\n )\n\n names = self.covariate_names[cov]\n self.covariates.append(\n torch.Tensor(encoder_cov.transform(names.reshape(-1, 1))).float()\n )\n else:\n self.covariate_names = None\n self.covariate_names_unique = None\n self.covars_dict = None\n self.covariates = None\n\n if perturbation_key is not None:\n self.ctrl_name = list(\n np.unique(data[data.obs[\"control\"] == 1].obs[self.perturbation_key])\n )\n else:\n self.ctrl_name = None\n\n if self.covariates is not None:\n self.num_covariates = [\n len(names) for names in self.covariate_names_unique.values()\n ]\n else:\n self.num_covariates = [0]\n self.num_genes = self.genes.shape[1]\n self.num_drugs = len(self.drugs_names_unique) if self.drugs is not None else 0\n self.is_control = data.obs[\"control\"].values.astype(bool)\n self.indices = {\n \"all\": list(range(len(self.genes))),\n \"control\": np.where(data.obs[\"control\"] == 1)[0].tolist(),\n \"treated\": np.where(data.obs[\"control\"] != 1)[0].tolist(),\n \"train\": np.where(data.obs[split_key] == \"train\")[0].tolist(),\n \"test\": np.where(data.obs[split_key] == \"test\")[0].tolist(),\n \"ood\": np.where(data.obs[split_key] == \"ood\")[0].tolist(),\n }\n\n def subset(self, split, condition=\"all\"):\n idx = list(set(self.indices[split]) & set(self.indices[condition]))\n return SubDataset(self, idx)\n\n def __getitem__(self, i):\n return (\n self.genes[i],\n indx(self.drugs, i),\n *[indx(cov, i) for cov in self.covariates],\n )\n\n def __len__(self):\n return len(self.genes)\n\n\nclass SubDataset:\n \"\"\"\n Subsets a `Dataset` by selecting the examples given by `indices`.\n \"\"\"\n\n def __init__(self, dataset, indices):\n self.perturbation_key = dataset.perturbation_key\n self.dose_key = dataset.dose_key\n self.covariate_keys = dataset.covariate_keys\n\n self.perts_dict = dataset.perts_dict\n self.covars_dict = dataset.covars_dict\n\n self.genes = dataset.genes[indices]\n self.drugs = indx(dataset.drugs, indices)\n self.covariates = [indx(cov, indices) for cov in dataset.covariates]\n\n self.drugs_names = indx(dataset.drugs_names, indices)\n self.pert_categories = indx(dataset.pert_categories, indices)\n self.covariate_names = {}\n for cov in self.covariate_keys:\n self.covariate_names[cov] = indx(dataset.covariate_names[cov], indices)\n\n self.var_names = dataset.var_names\n self.de_genes = dataset.de_genes\n self.ctrl_name = indx(dataset.ctrl_name, 0)\n\n self.num_covariates = dataset.num_covariates\n self.num_genes = dataset.num_genes\n self.num_drugs = dataset.num_drugs\n self.is_control = dataset.is_control[indices]\n\n def __getitem__(self, i):\n return (\n self.genes[i],\n indx(self.drugs, i),\n *[indx(cov, i) for cov in self.covariates],\n )\n\n def subset_condition(self, control=True):\n idx = np.where(self.is_control == control)[0].tolist()\n return SubDataset(self, idx)\n\n def __len__(self):\n return len(self.genes)\n\n\ndef load_dataset_splits(\n data: str,\n perturbation_key: Union[str, None],\n dose_key: Union[str, None],\n covariate_keys: Union[list, str, None],\n split_key: str,\n control: Union[str, None],\n return_dataset: bool = False,\n):\n\n dataset = Dataset(\n data, perturbation_key, dose_key, covariate_keys, split_key, control\n )\n\n splits = {\n \"training\": dataset.subset(\"train\", \"all\"),\n \"test\": dataset.subset(\"test\", \"all\"),\n \"ood\": dataset.subset(\"ood\", \"all\"),\n }\n\n if return_dataset:\n return splits, dataset\n else:\n return splits\n" }, { "path": "cpa/helper.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport warnings\n\nimport numpy as np\nimport pandas as pd\nimport scanpy as sc\nfrom sklearn.metrics import r2_score\nfrom scipy.sparse import issparse\nfrom scipy.stats import wasserstein_distance\nimport torch\n\nwarnings.filterwarnings(\"ignore\")\n\nimport sys\n\nif not sys.warnoptions:\n warnings.simplefilter(\"ignore\")\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\ndef _convert_mean_disp_to_counts_logits(mu, theta, eps=1e-6):\n r\"\"\"NB parameterizations conversion\n Parameters\n ----------\n mu :\n mean of the NB distribution.\n theta :\n inverse overdispersion.\n eps :\n constant used for numerical log stability. (Default value = 1e-6)\n Returns\n -------\n type\n the number of failures until the experiment is stopped\n and the success probability.\n \"\"\"\n assert (mu is None) == (\n theta is None\n ), \"If using the mu/theta NB parameterization, both parameters must be specified\"\n logits = (mu + eps).log() - (theta + eps).log()\n total_count = theta\n return total_count, logits\n\n\ndef rank_genes_groups_by_cov(\n adata,\n groupby,\n control_group,\n covariate,\n pool_doses=False,\n n_genes=50,\n rankby_abs=True,\n key_added=\"rank_genes_groups_cov\",\n return_dict=False,\n):\n\n \"\"\"\n Function that generates a list of differentially expressed genes computed\n separately for each covariate category, and using the respective control\n cells as reference.\n\n Usage example:\n\n rank_genes_groups_by_cov(\n adata,\n groupby='cov_product_dose',\n covariate_key='cell_type',\n control_group='Vehicle_0'\n )\n\n Parameters\n ----------\n adata : AnnData\n AnnData dataset\n groupby : str\n Obs column that defines the groups, should be\n cartesian product of covariate_perturbation_cont_var,\n it is important that this format is followed.\n control_group : str\n String that defines the control group in the groupby obs\n covariate : str\n Obs column that defines the main covariate by which we\n want to separate DEG computation (eg. cell type, species, etc.)\n n_genes : int (default: 50)\n Number of DEGs to include in the lists\n rankby_abs : bool (default: True)\n If True, rank genes by absolute values of the score, thus including\n top downregulated genes in the top N genes. If False, the ranking will\n have only upregulated genes at the top.\n key_added : str (default: 'rank_genes_groups_cov')\n Key used when adding the dictionary to adata.uns\n return_dict : str (default: False)\n Signals whether to return the dictionary or not\n\n Returns\n -------\n Adds the DEG dictionary to adata.uns\n\n If return_dict is True returns:\n gene_dict : dict\n Dictionary where groups are stored as keys, and the list of DEGs\n are the corresponding values\n\n \"\"\"\n\n gene_dict = {}\n cov_categories = adata.obs[covariate].unique()\n for cov_cat in cov_categories:\n print(cov_cat)\n # name of the control group in the groupby obs column\n control_group_cov = \"_\".join([cov_cat, control_group])\n\n # subset adata to cells belonging to a covariate category\n adata_cov = adata[adata.obs[covariate] == cov_cat]\n\n # compute DEGs\n sc.tl.rank_genes_groups(\n adata_cov,\n groupby=groupby,\n reference=control_group_cov,\n rankby_abs=rankby_abs,\n n_genes=n_genes,\n )\n\n # add entries to dictionary of gene sets\n de_genes = pd.DataFrame(adata_cov.uns[\"rank_genes_groups\"][\"names\"])\n for group in de_genes:\n gene_dict[group] = de_genes[group].tolist()\n\n adata.uns[key_added] = gene_dict\n\n if return_dict:\n return gene_dict\n\n\ndef rank_genes_groups(\n adata,\n groupby,\n pool_doses=False,\n n_genes=50,\n rankby_abs=True,\n key_added=\"rank_genes_groups_cov\",\n return_dict=False,\n):\n\n \"\"\"\n Function that generates a list of differentially expressed genes computed\n separately for each covariate category, and using the respective control\n cells as reference.\n\n Usage example:\n\n rank_genes_groups_by_cov(\n adata,\n groupby='cov_product_dose',\n covariate_key='cell_type',\n control_group='Vehicle_0'\n )\n\n Parameters\n ----------\n adata : AnnData\n AnnData dataset\n groupby : str\n Obs column that defines the groups, should be\n cartesian product of covariate_perturbation_cont_var,\n it is important that this format is followed.\n control_group : str\n String that defines the control group in the groupby obs\n covariate : str\n Obs column that defines the main covariate by which we\n want to separate DEG computation (eg. cell type, species, etc.)\n n_genes : int (default: 50)\n Number of DEGs to include in the lists\n rankby_abs : bool (default: True)\n If True, rank genes by absolute values of the score, thus including\n top downregulated genes in the top N genes. If False, the ranking will\n have only upregulated genes at the top.\n key_added : str (default: 'rank_genes_groups_cov')\n Key used when adding the dictionary to adata.uns\n return_dict : str (default: False)\n Signals whether to return the dictionary or not\n\n Returns\n -------\n Adds the DEG dictionary to adata.uns\n\n If return_dict is True returns:\n gene_dict : dict\n Dictionary where groups are stored as keys, and the list of DEGs\n are the corresponding values\n\n \"\"\"\n\n covars_comb = []\n for i in range(len(adata)):\n cov = \"_\".join(adata.obs[\"cov_drug_dose_name\"].values[i].split(\"_\")[:-2])\n covars_comb.append(cov)\n adata.obs[\"covars_comb\"] = covars_comb\n\n gene_dict = {}\n for cov_cat in np.unique(adata.obs[\"covars_comb\"].values):\n adata_cov = adata[adata.obs[\"covars_comb\"] == cov_cat]\n control_group_cov = (\n adata_cov[adata_cov.obs[\"control\"] == 1].obs[groupby].values[0]\n )\n\n # compute DEGs\n sc.tl.rank_genes_groups(\n adata_cov,\n groupby=groupby,\n reference=control_group_cov,\n rankby_abs=rankby_abs,\n n_genes=n_genes,\n )\n\n # add entries to dictionary of gene sets\n de_genes = pd.DataFrame(adata_cov.uns[\"rank_genes_groups\"][\"names\"])\n for group in de_genes:\n gene_dict[group] = de_genes[group].tolist()\n\n adata.uns[key_added] = gene_dict\n\n if return_dict:\n return gene_dict\n\n# def evaluate_r2_(adata, pred_adata, condition_key, sampled=False):\n# r2_list = []\n# if issparse(adata.X): \n# adata.X = adata.X.A\n# if issparse(pred_adata.X): \n# pred_adata.X = pred_adata.X.A\n# for cond in pred_adata.obs[condition_key].unique():\n# adata_ = adata[adata.obs[condition_key] == cond]\n# pred_adata_ = pred_adata[pred_adata.obs[condition_key] == cond]\n# r2_mean = r2_score(adata_.X.mean(0), pred_adata_.X.mean(0))\n# if sampled:\n# r2_var = r2_score(adata_.X.var(0), pred_adata_.X.var(0))\n# else:\n# r2_var = r2_score(\n# adata_.X.var(0), \n# pred_adata_.layers['variance'].var(0)\n# )\n# r2_list.append(\n# {\n# 'condition': cond,\n# 'r2_mean': r2_mean,\n# 'r2_var': r2_var,\n# }\n# )\n# r2_df = pd.DataFrame(r2_list).set_index('condition')\n# return r2_df\n\ndef evaluate_r2_(adata, pred_adata, condition_key, sampled=False, de_genes_dict=None):\n r2_list = []\n if issparse(adata.X): \n adata.X = adata.X.A\n if issparse(pred_adata.X): \n pred_adata.X = pred_adata.X.A\n for cond in pred_adata.obs[condition_key].unique():\n adata_ = adata[adata.obs[condition_key] == cond]\n pred_adata_ = pred_adata[pred_adata.obs[condition_key] == cond]\n r2_mean = r2_score(adata_.X.mean(0), pred_adata_.X.mean(0))\n if sampled:\n r2_var = r2_score(adata_.X.var(0), pred_adata_.X.var(0))\n else:\n r2_var = r2_score(\n adata_.X.var(0), \n pred_adata_.layers['variance'].var(0)\n )\n r2_list.append(\n {\n 'condition': cond,\n 'r2_mean': r2_mean,\n 'r2_var': r2_var,\n }\n )\n if de_genes_dict:\n de_genes = de_genes_dict[cond]\n sub_adata_ = adata_[:, de_genes]\n sub_pred_adata_ = pred_adata_[:, de_genes]\n r2_mean_deg = r2_score(sub_adata_.X.mean(0), sub_pred_adata_.X.mean(0))\n if sampled:\n r2_var_deg = r2_score(sub_adata_.X.var(0), sub_pred_adata_.X.var(0))\n else:\n r2_var_deg = r2_score(\n sub_adata_.X.var(0), \n sub_pred_adata_.layers['variance'].var(0)\n )\n r2_list[-1]['r2_mean_deg'] = r2_mean_deg\n r2_list[-1]['r2_var_deg'] = r2_var_deg\n r2_df = pd.DataFrame(r2_list).set_index('condition')\n return r2_df\n \ndef evaluate_mmd(adata, pred_adata, condition_key, de_genes_dict=None):\n mmd_list = []\n for cond in pred_adata.obs[condition_key].unique():\n adata_ = adata[adata.obs[condition_key] == cond].copy()\n pred_adata_ = pred_adata[pred_adata.obs[condition_key] == cond].copy()\n if issparse(adata_.X): \n adata_.X = adata_.X.A\n if issparse(pred_adata_.X): \n pred_adata_.X = pred_adata_.X.A\n\n mmd = mmd_loss_calc(torch.Tensor(adata_.X), torch.Tensor(pred_adata_.X))\n mmd_list.append(\n {\n 'condition': cond,\n 'mmd': mmd.detach().cpu().numpy()\n }\n )\n if de_genes_dict:\n de_genes = de_genes_dict[cond]\n sub_adata_ = adata_[:, de_genes]\n sub_pred_adata_ = pred_adata_[:, de_genes]\n mmd_deg = mmd_loss_calc(torch.Tensor(sub_adata_.X), torch.Tensor(sub_pred_adata_.X))\n mmd_list[-1]['mmd_deg'] = mmd_deg.detach().cpu().numpy()\n mmd_df = pd.DataFrame(mmd_list).set_index('condition')\n return mmd_df\n\ndef evaluate_emd(adata, pred_adata, condition_key, de_genes_dict=None):\n emd_list = []\n for cond in pred_adata.obs[condition_key].unique():\n adata_ = adata[adata.obs[condition_key] == cond].copy()\n pred_adata_ = pred_adata[pred_adata.obs[condition_key] == cond].copy()\n if issparse(adata_.X): \n adata_.X = adata_.X.A\n if issparse(pred_adata_.X): \n pred_adata_.X = pred_adata_.X.A\n wd = []\n for i, _ in enumerate(adata_.var_names):\n wd.append(\n wasserstein_distance(torch.Tensor(adata_.X[:, i]), torch.Tensor(pred_adata_.X[:, i]))\n )\n emd_list.append(\n {\n 'condition': cond,\n 'emd': np.mean(wd)\n }\n )\n if de_genes_dict:\n de_genes = de_genes_dict[cond]\n sub_adata_ = adata_[:, de_genes]\n sub_pred_adata_ = pred_adata_[:, de_genes]\n wd_deg = []\n for i, _ in enumerate(sub_adata_.var_names):\n wd_deg.append(\n wasserstein_distance(torch.Tensor(sub_adata_.X[:, i]), torch.Tensor(sub_pred_adata_.X[:, i]))\n )\n emd_list[-1]['emd_deg'] = np.mean(wd_deg)\n emd_df = pd.DataFrame(emd_list).set_index('condition')\n return emd_df\n\ndef pairwise_distance(x, y):\n x = x.view(x.shape[0], x.shape[1], 1)\n y = torch.transpose(y, 0, 1)\n output = torch.sum((x - y) ** 2, 1)\n output = torch.transpose(output, 0, 1)\n return output\n\n\ndef gaussian_kernel_matrix(x, y, alphas):\n \"\"\"Computes multiscale-RBF kernel between x and y.\n Parameters\n ----------\n x: torch.Tensor\n Tensor with shape [batch_size, z_dim].\n y: torch.Tensor\n Tensor with shape [batch_size, z_dim].\n alphas: Tensor\n Returns\n -------\n Returns the computed multiscale-RBF kernel between x and y.\n \"\"\"\n\n dist = pairwise_distance(x, y).contiguous()\n dist_ = dist.view(1, -1)\n\n alphas = alphas.view(alphas.shape[0], 1)\n beta = 1. / (2. * alphas)\n\n s = torch.matmul(beta, dist_)\n\n return torch.sum(torch.exp(-s), 0).view_as(dist)\n\n\ndef mmd_loss_calc(source_features, target_features):\n \"\"\"Initializes Maximum Mean Discrepancy(MMD) between source_features and target_features.\n - Gretton, Arthur, et al. \"A Kernel Two-Sample Test\". 2012.\n Parameters\n ----------\n source_features: torch.Tensor\n Tensor with shape [batch_size, z_dim]\n target_features: torch.Tensor\n Tensor with shape [batch_size, z_dim]\n Returns\n -------\n Returns the computed MMD between x and y.\n \"\"\"\n alphas = [\n 1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 5, 10, 15, 20, 25, 30, 35, 100,\n 1e3, 1e4, 1e5, 1e6\n ]\n alphas = torch.autograd.Variable(torch.FloatTensor(alphas)).to(device=source_features.device)\n\n cost = torch.mean(gaussian_kernel_matrix(source_features, source_features, alphas))\n cost += torch.mean(gaussian_kernel_matrix(target_features, target_features, alphas))\n cost -= 2 * torch.mean(gaussian_kernel_matrix(source_features, target_features, alphas))\n\n return cost" }, { "path": "cpa/model.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom http.client import RemoteDisconnected\nimport json\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nclass NBLoss(torch.nn.Module):\n def __init__(self):\n super(NBLoss, self).__init__()\n\n def forward(self, mu, y, theta, eps=1e-8):\n \"\"\"Negative binomial negative log-likelihood. It assumes targets `y` with n\n rows and d columns, but estimates `yhat` with n rows and 2d columns.\n The columns 0:d of `yhat` contain estimated means, the columns d:2*d of\n `yhat` contain estimated variances. This module assumes that the\n estimated mean and inverse dispersion are positive---for numerical\n stability, it is recommended that the minimum estimated variance is\n greater than a small number (1e-3).\n Parameters\n ----------\n yhat: Tensor\n Torch Tensor of reeconstructed data.\n y: Tensor\n Torch Tensor of ground truth data.\n eps: Float\n numerical stability constant.\n \"\"\"\n if theta.ndimension() == 1:\n # In this case, we reshape theta for broadcasting\n theta = theta.view(1, theta.size(0))\n log_theta_mu_eps = torch.log(theta + mu + eps)\n res = (\n theta * (torch.log(theta + eps) - log_theta_mu_eps)\n + y * (torch.log(mu + eps) - log_theta_mu_eps)\n + torch.lgamma(y + theta)\n - torch.lgamma(theta)\n - torch.lgamma(y + 1)\n )\n res = _nan2inf(res)\n return -torch.mean(res)\n \ndef _nan2inf(x):\n return torch.where(torch.isnan(x), torch.zeros_like(x) + np.inf, x)\n\nclass MLP(torch.nn.Module):\n \"\"\"\n A multilayer perceptron with ReLU activations and optional BatchNorm.\n \"\"\"\n\n def __init__(self, sizes, batch_norm=True, last_layer_act=\"linear\"):\n super(MLP, self).__init__()\n layers = []\n for s in range(len(sizes) - 1):\n layers += [\n torch.nn.Linear(sizes[s], sizes[s + 1]),\n torch.nn.BatchNorm1d(sizes[s + 1])\n if batch_norm and s < len(sizes) - 2\n else None,\n torch.nn.ReLU(),\n ]\n\n layers = [l for l in layers if l is not None][:-1]\n self.activation = last_layer_act\n if self.activation == \"linear\":\n pass\n elif self.activation == \"ReLU\":\n self.relu = torch.nn.ReLU()\n else:\n raise ValueError(\"last_layer_act must be one of 'linear' or 'ReLU'\")\n\n self.network = torch.nn.Sequential(*layers)\n\n def forward(self, x):\n if self.activation == \"ReLU\":\n x = self.network(x)\n dim = x.size(1) // 2\n return torch.cat((self.relu(x[:, :dim]), x[:, dim:]), dim=1)\n return self.network(x)\n\n\nclass GeneralizedSigmoid(torch.nn.Module):\n \"\"\"\n Sigmoid, log-sigmoid or linear functions for encoding dose-response for\n drug perurbations.\n \"\"\"\n\n def __init__(self, dim, device, nonlin=\"sigmoid\"):\n \"\"\"Sigmoid modeling of continuous variable.\n Params\n ------\n nonlin : str (default: logsigm)\n One of logsigm, sigm.\n \"\"\"\n super(GeneralizedSigmoid, self).__init__()\n self.nonlin = nonlin\n self.beta = torch.nn.Parameter(\n torch.ones(1, dim, device=device), requires_grad=True\n )\n self.bias = torch.nn.Parameter(\n torch.zeros(1, dim, device=device), requires_grad=True\n )\n\n def forward(self, x):\n if self.nonlin == \"logsigm\":\n c0 = self.bias.sigmoid()\n return (torch.log1p(x) * self.beta + self.bias).sigmoid() - c0\n elif self.nonlin == \"sigm\":\n c0 = self.bias.sigmoid()\n return (x * self.beta + self.bias).sigmoid() - c0\n else:\n return x\n\n def one_drug(self, x, i):\n if self.nonlin == \"logsigm\":\n c0 = self.bias[0][i].sigmoid()\n return (torch.log1p(x) * self.beta[0][i] + self.bias[0][i]).sigmoid() - c0\n elif self.nonlin == \"sigm\":\n c0 = self.bias[0][i].sigmoid()\n return (x * self.beta[0][i] + self.bias[0][i]).sigmoid() - c0\n else:\n return x\n\n\nclass CPA(torch.nn.Module):\n \"\"\"\n Our main module, the CPA autoencoder\n \"\"\"\n\n def __init__(\n self,\n num_genes,\n num_drugs,\n num_covariates,\n device=\"cuda\",\n seed=0,\n patience=5,\n loss_ae=\"gauss\",\n doser_type=\"mlp\",\n decoder_activation=\"linear\",\n hparams=\"\",\n ):\n super(CPA, self).__init__()\n # set generic attributes\n self.num_genes = num_genes\n self.num_drugs = num_drugs\n self.num_covariates = num_covariates\n self.device = device\n self.seed = seed\n self.loss_ae = loss_ae\n # early-stopping\n self.patience = patience\n self.best_score = -1e3\n self.patience_trials = 0\n\n # set hyperparameters\n self.set_hparams_(hparams)\n\n # set models\n self.encoder = MLP(\n [num_genes]\n + [self.hparams[\"autoencoder_width\"]] * self.hparams[\"autoencoder_depth\"]\n + [self.hparams[\"dim\"]]\n )\n\n self.decoder = MLP(\n [self.hparams[\"dim\"]]\n + [self.hparams[\"autoencoder_width\"]] * self.hparams[\"autoencoder_depth\"]\n + [num_genes * 2],\n last_layer_act=decoder_activation,\n )\n\n self.adversary_drugs = MLP(\n [self.hparams[\"dim\"]]\n + [self.hparams[\"adversary_width\"]] * self.hparams[\"adversary_depth\"]\n + [num_drugs]\n )\n\n self.loss_adversary_drugs = torch.nn.BCEWithLogitsLoss()\n self.doser_type = doser_type\n if doser_type == \"mlp\":\n self.dosers = torch.nn.ModuleList()\n for _ in range(num_drugs):\n self.dosers.append(\n MLP(\n [1]\n + [self.hparams[\"dosers_width\"]] * self.hparams[\"dosers_depth\"]\n + [1],\n batch_norm=False,\n )\n )\n else:\n self.dosers = GeneralizedSigmoid(num_drugs, self.device, nonlin=doser_type)\n\n if self.num_covariates == [0]:\n pass\n else:\n assert 0 not in self.num_covariates\n self.adversary_covariates = []\n self.loss_adversary_covariates = []\n self.covariates_embeddings = (\n []\n ) # TODO: Continue with checking that dict assignment is possible via covaraites names and if dict are possible to use in optimisation\n for num_covariate in self.num_covariates:\n self.adversary_covariates.append(\n MLP(\n [self.hparams[\"dim\"]]\n + [self.hparams[\"adversary_width\"]]\n * self.hparams[\"adversary_depth\"]\n + [num_covariate]\n )\n )\n self.loss_adversary_covariates.append(torch.nn.CrossEntropyLoss())\n self.covariates_embeddings.append(\n torch.nn.Embedding(num_covariate, self.hparams[\"dim\"])\n )\n self.covariates_embeddings = torch.nn.Sequential(\n *self.covariates_embeddings\n )\n\n self.drug_embeddings = torch.nn.Embedding(self.num_drugs, self.hparams[\"dim\"])\n # losses\n if self.loss_ae == \"nb\":\n self.loss_autoencoder = NBLoss()\n elif self.loss_ae == 'gauss':\n self.loss_autoencoder = nn.GaussianNLLLoss()\n\n self.iteration = 0\n\n self.to(self.device)\n\n # optimizers\n has_drugs = self.num_drugs > 0\n has_covariates = self.num_covariates[0] > 0\n get_params = lambda model, cond: list(model.parameters()) if cond else []\n _parameters = (\n get_params(self.encoder, True)\n + get_params(self.decoder, True)\n + get_params(self.drug_embeddings, has_drugs)\n )\n for emb in self.covariates_embeddings:\n _parameters.extend(get_params(emb, has_covariates))\n\n self.optimizer_autoencoder = torch.optim.Adam(\n _parameters,\n lr=self.hparams[\"autoencoder_lr\"],\n weight_decay=self.hparams[\"autoencoder_wd\"],\n )\n\n _parameters = get_params(self.adversary_drugs, has_drugs)\n for adv in self.adversary_covariates:\n _parameters.extend(get_params(adv, has_covariates))\n\n self.optimizer_adversaries = torch.optim.Adam(\n _parameters,\n lr=self.hparams[\"adversary_lr\"],\n weight_decay=self.hparams[\"adversary_wd\"],\n )\n\n if has_drugs:\n self.optimizer_dosers = torch.optim.Adam(\n self.dosers.parameters(),\n lr=self.hparams[\"dosers_lr\"],\n weight_decay=self.hparams[\"dosers_wd\"],\n )\n\n # learning rate schedulers\n self.scheduler_autoencoder = torch.optim.lr_scheduler.StepLR(\n self.optimizer_autoencoder, step_size=self.hparams[\"step_size_lr\"]\n )\n\n self.scheduler_adversary = torch.optim.lr_scheduler.StepLR(\n self.optimizer_adversaries, step_size=self.hparams[\"step_size_lr\"]\n )\n\n if has_drugs:\n self.scheduler_dosers = torch.optim.lr_scheduler.StepLR(\n self.optimizer_dosers, step_size=self.hparams[\"step_size_lr\"]\n )\n\n self.history = {\"epoch\": [], \"stats_epoch\": []}\n\n def set_hparams_(self, hparams):\n \"\"\"\n Set hyper-parameters to default values or values fixed by user for those\n hyper-parameters specified in the JSON string `hparams`.\n \"\"\"\n\n self.hparams = {\n \"dim\": 128,\n \"dosers_width\": 128,\n \"dosers_depth\": 2,\n \"dosers_lr\": 4e-3,\n \"dosers_wd\": 1e-7,\n \"autoencoder_width\": 128,\n \"autoencoder_depth\": 3,\n \"adversary_width\": 64,\n \"adversary_depth\": 2,\n \"reg_adversary\": 60,\n \"penalty_adversary\": 60,\n \"autoencoder_lr\": 3e-4,\n \"adversary_lr\": 3e-4,\n \"autoencoder_wd\": 4e-7,\n \"adversary_wd\": 4e-7,\n \"adversary_steps\": 3,\n \"batch_size\": 256,\n \"step_size_lr\": 45,\n }\n\n # the user may fix some hparams\n if hparams != \"\":\n if isinstance(hparams, str):\n self.hparams.update(json.loads(hparams))\n else:\n self.hparams.update(hparams)\n\n return self.hparams\n\n def move_inputs_(self, genes, drugs, covariates):\n \"\"\"\n Move minibatch tensors to CPU/GPU.\n \"\"\"\n if genes.device.type != self.device:\n genes = genes.to(self.device)\n if drugs is not None:\n drugs = drugs.to(self.device)\n if covariates is not None:\n covariates = [cov.to(self.device) for cov in covariates]\n return (genes, drugs, covariates)\n\n def compute_drug_embeddings_(self, drugs):\n \"\"\"\n Compute sum of drug embeddings, each of them multiplied by its\n dose-response curve.\n \"\"\"\n\n if self.doser_type == \"mlp\":\n doses = []\n for d in range(drugs.size(1)):\n this_drug = drugs[:, d].view(-1, 1)\n doses.append(self.dosers[d](this_drug).sigmoid() * this_drug.gt(0))\n return torch.cat(doses, 1) @ self.drug_embeddings.weight\n else:\n return self.dosers(drugs) @ self.drug_embeddings.weight\n\n def predict(\n self, \n genes, \n drugs, \n covariates, \n return_latent_basal=False,\n return_latent_treated=False,\n ):\n \"\"\"\n Predict \"what would have the gene expression `genes` been, had the\n cells in `genes` with cell types `cell_types` been treated with\n combination of drugs `drugs`.\n \"\"\"\n\n genes, drugs, covariates = self.move_inputs_(genes, drugs, covariates)\n if self.loss_ae == 'nb':\n genes = torch.log1p(genes)\n\n latent_basal = self.encoder(genes)\n\n latent_treated = latent_basal\n\n if self.num_drugs > 0:\n latent_treated = latent_treated + self.compute_drug_embeddings_(drugs)\n if self.num_covariates[0] > 0:\n for i, emb in enumerate(self.covariates_embeddings):\n emb = emb.to(self.device)\n latent_treated = latent_treated + emb(\n covariates[i].argmax(1)\n ) #argmax because OHE\n\n gene_reconstructions = self.decoder(latent_treated)\n if self.loss_ae == 'gauss':\n # convert variance estimates to a positive value in [1e-3, \\infty)\n dim = gene_reconstructions.size(1) // 2\n gene_means = gene_reconstructions[:, :dim]\n gene_vars = F.softplus(gene_reconstructions[:, dim:]).add(1e-3)\n #gene_vars = gene_reconstructions[:, dim:].exp().add(1).log().add(1e-3)\n\n if self.loss_ae == 'nb':\n gene_means = F.softplus(gene_means).add(1e-3)\n #gene_reconstructions[:, :dim] = torch.clamp(gene_reconstructions[:, :dim], min=1e-4, max=1e4)\n #gene_reconstructions[:, dim:] = torch.clamp(gene_reconstructions[:, dim:], min=1e-4, max=1e4)\n gene_reconstructions = torch.cat([gene_means, gene_vars], dim=1)\n \n if return_latent_basal:\n if return_latent_treated:\n return gene_reconstructions, latent_basal, latent_treated\n else:\n return gene_reconstructions, latent_basal\n if return_latent_treated:\n return gene_reconstructions, latent_treated\n return gene_reconstructions\n\n def early_stopping(self, score):\n \"\"\"\n Decays the learning rate, and possibly early-stops training.\n \"\"\"\n self.scheduler_autoencoder.step()\n self.scheduler_adversary.step()\n self.scheduler_dosers.step()\n\n if score > self.best_score:\n self.best_score = score\n self.patience_trials = 0\n else:\n self.patience_trials += 1\n\n return self.patience_trials > self.patience\n\n def update(self, genes, drugs, covariates):\n \"\"\"\n Update CPA's parameters given a minibatch of genes, drugs, and\n cell types.\n \"\"\"\n genes, drugs, covariates = self.move_inputs_(genes, drugs, covariates)\n gene_reconstructions, latent_basal = self.predict(\n genes,\n drugs,\n covariates,\n return_latent_basal=True,\n )\n\n dim = gene_reconstructions.size(1) // 2\n gene_means = gene_reconstructions[:, :dim]\n gene_vars = gene_reconstructions[:, dim:]\n reconstruction_loss = self.loss_autoencoder(gene_means, genes, gene_vars)\n adversary_drugs_loss = torch.tensor([0.0], device=self.device)\n if self.num_drugs > 0:\n adversary_drugs_predictions = self.adversary_drugs(latent_basal)\n adversary_drugs_loss = self.loss_adversary_drugs(\n adversary_drugs_predictions, drugs.gt(0).float()\n )\n\n adversary_covariates_loss = torch.tensor(\n [0.0], device=self.device\n )\n if self.num_covariates[0] > 0:\n adversary_covariate_predictions = []\n for i, adv in enumerate(self.adversary_covariates):\n adv = adv.to(self.device)\n adversary_covariate_predictions.append(adv(latent_basal))\n adversary_covariates_loss += self.loss_adversary_covariates[i](\n adversary_covariate_predictions[-1], covariates[i].argmax(1)\n )\n\n # two place-holders for when adversary is not executed\n adversary_drugs_penalty = torch.tensor([0.0], device=self.device)\n adversary_covariates_penalty = torch.tensor([0.0], device=self.device)\n\n if self.iteration % self.hparams[\"adversary_steps\"]:\n\n def compute_gradients(output, input):\n grads = torch.autograd.grad(output, input, create_graph=True)\n grads = grads[0].pow(2).mean()\n return grads\n\n if self.num_drugs > 0:\n adversary_drugs_penalty = compute_gradients(\n adversary_drugs_predictions.sum(), latent_basal\n )\n\n if self.num_covariates[0] > 0:\n adversary_covariates_penalty = torch.tensor([0.0], device=self.device)\n for pred in adversary_covariate_predictions:\n adversary_covariates_penalty += compute_gradients(\n pred.sum(), latent_basal\n ) # TODO: Adding up tensor sum, is that right?\n\n self.optimizer_adversaries.zero_grad()\n (\n adversary_drugs_loss\n + self.hparams[\"penalty_adversary\"] * adversary_drugs_penalty\n + adversary_covariates_loss\n + self.hparams[\"penalty_adversary\"] * adversary_covariates_penalty\n ).backward()\n self.optimizer_adversaries.step()\n else:\n self.optimizer_autoencoder.zero_grad()\n if self.num_drugs > 0:\n self.optimizer_dosers.zero_grad()\n (\n reconstruction_loss\n - self.hparams[\"reg_adversary\"] * adversary_drugs_loss\n - self.hparams[\"reg_adversary\"] * adversary_covariates_loss\n ).backward()\n self.optimizer_autoencoder.step()\n if self.num_drugs > 0:\n self.optimizer_dosers.step()\n self.iteration += 1\n\n return {\n \"loss_reconstruction\": reconstruction_loss.item(),\n \"loss_adv_drugs\": adversary_drugs_loss.item(),\n \"loss_adv_covariates\": adversary_covariates_loss.item(),\n \"penalty_adv_drugs\": adversary_drugs_penalty.item(),\n \"penalty_adv_covariates\": adversary_covariates_penalty.item(),\n }\n\n @classmethod\n def defaults(self):\n \"\"\"\n Returns the list of default hyper-parameters for CPA\n \"\"\"\n\n return self.set_hparams_(self, \"\")\n" }, { "path": "cpa/plotting.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom collections import defaultdict\n\nimport matplotlib.font_manager\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom adjustText import adjust_text\nfrom sklearn.decomposition import KernelPCA\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nFONT_SIZE = 10\nfont = {\"size\": FONT_SIZE}\n\nmatplotlib.rc(\"font\", **font)\nmatplotlib.rc(\"ytick\", labelsize=FONT_SIZE)\nmatplotlib.rc(\"xtick\", labelsize=FONT_SIZE)\n\n\nclass CPAVisuals:\n \"\"\"\n A wrapper for automatic plotting CompPert latent embeddings and dose-response\n curve. Sets up prefix for all files and default dictionaries for atomic\n perturbations and cell types.\n \"\"\"\n\n def __init__(\n self,\n cpa,\n fileprefix=None,\n perts_palette=None,\n covars_palette=None,\n plot_params={\"fontsize\": None},\n ):\n \"\"\"\n Parameters\n ----------\n cpa : CompPertAPI\n Variable from API class.\n fileprefix : str, optional (default: None)\n Prefix (with path) to the filename to save all embeddings in a\n standartized manner. If None, embeddings are not saved to file.\n perts_palette : dict (default: None)\n Dictionary of colors for the embeddings of perturbations. Keys\n correspond to perturbations and values to their colors. If None,\n default dicitonary will be set up.\n covars_palette : dict (default: None)\n Dictionary of colors for the embeddings of covariates. Keys\n correspond to covariates and values to their colors. If None,\n default dicitonary will be set up.\n \"\"\"\n\n self.fileprefix = fileprefix\n\n self.perturbation_key = cpa.perturbation_key\n self.dose_key = cpa.dose_key\n self.covariate_keys = cpa.covariate_keys\n self.measured_points = cpa.measured_points\n\n self.unique_perts = cpa.unique_perts\n self.unique_covars = cpa.unique_covars\n\n if perts_palette is None:\n self.perts_palette = dict(\n zip(self.unique_perts, get_palette(len(self.unique_perts)))\n )\n else:\n self.perts_palette = perts_palette\n\n if covars_palette is None:\n self.covars_palette = {}\n for cov in self.unique_covars:\n self.covars_palette[cov] = dict(\n zip(\n self.unique_covars[cov],\n get_palette(len(self.unique_covars[cov]), palette_name=\"tab10\"),\n )\n )\n else:\n self.covars_palette = covars_palette\n\n if plot_params[\"fontsize\"] is None:\n self.fontsize = FONT_SIZE\n else:\n self.fontsize = plot_params[\"fontsize\"]\n\n def plot_latent_embeddings(\n self,\n emb,\n titlename=\"Example\",\n kind=\"perturbations\",\n palette=None,\n labels=None,\n dimred=\"KernelPCA\",\n filename=None,\n show_text=True,\n ):\n \"\"\"\n Parameters\n ----------\n emb : np.array\n Multi-dimensional embedding of perturbations or covariates.\n titlename : str, optional (default: 'Example')\n Title.\n kind : int, optional, optional (default: 'perturbations')\n Specify if this is embedding of perturbations, covariates or some\n other. If it is perturbations or covariates, it will use default\n saved dictionaries for colors.\n palette : dict, optional (default: None)\n If embedding of kind not perturbations or covariates, the user can\n specify color dictionary for the embedding.\n labels : list, optional (default: None)\n Labels for the embeddings.\n dimred : str, optional (default: 'KernelPCA')\n Dimensionality reduction method for plotting low dimensional\n representations. Options: 'KernelPCA', 'UMAPpre', 'UMAPcos', None.\n If None, uses first 2 dimensions of the embedding.\n filename : str (default: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n \"\"\"\n if filename is None:\n if self.fileprefix is None:\n filename = None\n file_name_similarity = None\n else:\n filename = f\"{self.fileprefix}_emebdding.png\"\n file_name_similarity = f\"{self.fileprefix}_emebdding_similarity.png\"\n else:\n file_name_similarity = filename.split(\".\")[0] + \"_similarity.png\"\n\n if labels is None:\n if kind == \"perturbations\":\n palette = self.perts_palette\n labels = self.unique_perts\n elif kind in self.unique_covars:\n palette = self.covars_palette[kind]\n labels = self.unique_covars[kind]\n\n if len(emb) < 2:\n print(f\"Embedding contains only {len(emb)} vectors. Not enough to plot.\")\n else:\n plot_embedding(\n fast_dimred(emb, method=dimred),\n labels,\n show_lines=True,\n show_text=show_text,\n col_dict=palette,\n title=titlename,\n file_name=filename,\n fontsize=self.fontsize,\n )\n\n plot_similarity(\n emb,\n labels,\n col_dict=palette,\n fontsize=self.fontsize,\n file_name=file_name_similarity,\n )\n\n def plot_contvar_response2D(\n self,\n df_response2D,\n df_ref=None,\n levels=15,\n figsize=(4, 4),\n xlims=(0, 1.03),\n ylims=(0, 1.03),\n palette=\"coolwarm\",\n response_name=\"response\",\n title_name=None,\n fontsize=None,\n postfix=\"\",\n filename=None,\n alpha=0.4,\n sizes=(40, 160),\n logdose=False,\n ):\n\n \"\"\"\n Parameters\n ----------\n df_response2D : pd.DataFrame\n Data frame with responses of combinations with columns=(dose1, dose2,\n response).\n levels: int, optional (default: 15)\n Number of levels for contour plot.\n response_name : str (default: 'response')\n Name of column in df_response to plot as response.\n alpha: float (default: 0.4)\n Transparency of the background contour.\n figsize: tuple (default: (4,4))\n Size of the figure in inches.\n palette : dict, optional (default: None)\n Colors dictionary for perturbations to plot.\n title_name : str, optional (default: None)\n Title for the plot.\n postfix : str, optional (defualt: '')\n Postfix to add to the output file name to save the model.\n filename : str, optional (defualt: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n logdose: bool (default: False)\n If True, dose values will be log10. 0 values will be mapped to\n minumum value -1,e.g.\n if smallest non-zero dose was 0.001, 0 will be mapped to -4.\n \"\"\"\n sns.set_style(\"white\")\n\n if (filename is None) and not (self.fileprefix is None):\n filename = f\"{self.fileprefix}_{postfix}response2D.png\"\n if fontsize is None:\n fontsize = self.fontsize\n\n x_name, y_name = df_response2D.columns[:2]\n\n x = df_response2D[x_name].values\n y = df_response2D[y_name].values\n\n if logdose:\n x = log10_with0(x)\n y = log10_with0(y)\n\n z = df_response2D[response_name].values\n\n n = int(np.sqrt(len(x)))\n\n X = x.reshape(n, n)\n Y = y.reshape(n, n)\n Z = z.reshape(n, n)\n\n fig, ax = plt.subplots(figsize=figsize)\n\n CS = ax.contourf(X, Y, Z, cmap=palette, levels=levels, alpha=alpha)\n CS = ax.contour(X, Y, Z, levels=15, cmap=palette)\n ax.clabel(CS, inline=1, fontsize=fontsize)\n ax.set(xlim=(0, 1), ylim=(0, 1))\n ax.axis(\"equal\")\n ax.axis(\"square\")\n ax.yaxis.set_tick_params(labelsize=fontsize)\n ax.xaxis.set_tick_params(labelsize=fontsize)\n ax.set_xlabel(x_name, fontsize=fontsize, fontweight=\"bold\")\n ax.set_ylabel(y_name, fontsize=fontsize, fontweight=\"bold\")\n ax.set_xlim(xlims)\n ax.set_ylim(ylims)\n\n # sns.despine(left=False, bottom=False, right=True)\n sns.despine()\n\n if not (df_ref is None):\n sns.scatterplot(\n x=x_name,\n y=y_name,\n hue=\"split\",\n size=\"num_cells\",\n sizes=sizes,\n alpha=1.0,\n palette={\"train\": \"#000000\", \"training\": \"#000000\", \"ood\": \"#e41a1c\"},\n data=df_ref,\n ax=ax,\n )\n ax.legend_.remove()\n\n ax.set_title(title_name, fontweight=\"bold\", fontsize=fontsize)\n plt.tight_layout()\n\n if filename:\n save_to_file(fig, filename)\n\n def plot_contvar_response(\n self,\n df_response,\n response_name=\"response\",\n var_name=None,\n df_ref=None,\n palette=None,\n title_name=None,\n postfix=\"\",\n xlabelname=None,\n filename=None,\n logdose=False,\n fontsize=None,\n measured_points=None,\n bbox=(1.35, 1.0),\n figsize=(7.0, 4.0),\n ):\n \"\"\"\n Parameters\n ----------\n df_response : pd.DataFrame\n Data frame of responses.\n response_name : str (default: 'response')\n Name of column in df_response to plot as response.\n var_name : str, optional (default: None)\n Name of conditioning variable, e.g. could correspond to covariates.\n df_ref : pd.DataFrame, optional (default: None)\n Reference values. Fields for plotting should correspond to\n df_response.\n palette : dict, optional (default: None)\n Colors dictionary for perturbations to plot.\n title_name : str, optional (default: None)\n Title for the plot.\n postfix : str, optional (defualt: '')\n Postfix to add to the output file name to save the model.\n filename : str, optional (defualt: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n logdose: bool (default: False)\n If True, dose values will be log10. 0 values will be mapped to\n minumum value -1,e.g.\n if smallest non-zero dose was 0.001, 0 will be mapped to -4.\n figsize: tuple (default: (7., 4.))\n Size of output figure\n \"\"\"\n if (filename is None) and not (self.fileprefix is None):\n filename = f\"{self.fileprefix}_{postfix}response.png\"\n\n if fontsize is None:\n fontsize = self.fontsize\n\n if logdose:\n dose_name = f\"log10-{self.dose_key}\"\n df_response[dose_name] = log10_with0(df_response[self.dose_key].values)\n if not (df_ref is None):\n df_ref[dose_name] = log10_with0(df_ref[self.dose_key].values)\n else:\n dose_name = self.dose_key\n\n if var_name is None:\n if len(self.unique_covars) > 1:\n var_name = self.covars_key\n else:\n var_name = self.perturbation_key\n\n if palette is None:\n if var_name == self.perturbation_key:\n palette = self.perts_palette\n elif var_name in self.covariate_keys:\n palette = self.covars_palette[var_name]\n\n plot_dose_response(\n df_response,\n dose_name,\n var_name,\n xlabelname=xlabelname,\n df_ref=df_ref,\n response_name=response_name,\n title_name=title_name,\n use_ref_response=(not (df_ref is None)),\n col_dict=palette,\n plot_vertical=False,\n f1=figsize[0],\n f2=figsize[1],\n fname=filename,\n logscale=measured_points,\n measured_points=measured_points,\n bbox=bbox,\n fontsize=fontsize,\n figformat=\"png\",\n )\n\n def plot_scatter(\n self,\n df,\n x_axis,\n y_axis,\n hue=None,\n size=None,\n style=None,\n figsize=(4.5, 4.5),\n title=None,\n palette=None,\n filename=None,\n alpha=0.75,\n sizes=(30, 90),\n text_dict=None,\n postfix=\"\",\n fontsize=14,\n ):\n\n sns.set_style(\"white\")\n\n if (filename is None) and not (self.fileprefix is None):\n filename = f\"{self.fileprefix}_scatter{postfix}.png\"\n\n if fontsize is None:\n fontsize = self.fontsize\n\n fig = plt.figure(figsize=figsize)\n ax = plt.gca()\n sns.scatterplot(\n x=x_axis,\n y=y_axis,\n hue=hue,\n style=style,\n size=size,\n sizes=sizes,\n alpha=alpha,\n palette=palette,\n data=df,\n )\n\n ax.legend_.remove()\n ax.set_xlabel(x_axis, fontsize=fontsize)\n ax.set_ylabel(y_axis, fontsize=fontsize)\n ax.xaxis.set_tick_params(labelsize=fontsize)\n ax.yaxis.set_tick_params(labelsize=fontsize)\n ax.set_title(title)\n if not (text_dict is None):\n texts = []\n for label in text_dict.keys():\n texts.append(\n ax.text(\n text_dict[label][0],\n text_dict[label][1],\n label,\n fontsize=fontsize,\n )\n )\n\n adjust_text(\n texts, arrowprops=dict(arrowstyle=\"-\", color=\"black\", lw=0.1), ax=ax\n )\n\n plt.tight_layout()\n\n if filename:\n save_to_file(fig, filename)\n\n\ndef log10_with0(x):\n mx = np.min(x[x > 0])\n x[x == 0] = mx / 10\n return np.log10(x)\n\n\ndef get_palette(n_colors, palette_name=\"Set1\"):\n\n try:\n palette = sns.color_palette(palette_name)\n except:\n print(\"Palette not found. Using default palette tab10\")\n palette = sns.color_palette()\n while len(palette) < n_colors:\n palette += palette\n\n return palette\n\n\ndef fast_dimred(emb, method=\"KernelPCA\"):\n \"\"\"\n Takes high dimensional embeddings and produces a 2-dimensional representation\n for plotting.\n emb: np.array\n Embeddings matrix.\n method: str (default: 'KernelPCA')\n Method for dimensionality reduction: KernelPCA, UMAPpre, UMAPcos, tSNE.\n If None return first 2 dimensions of the embedding vector.\n \"\"\"\n if method is None:\n return emb[:, :2]\n elif method == \"KernelPCA\":\n similarity_matrix = cosine_similarity(emb)\n np.fill_diagonal(similarity_matrix, 1.0)\n X = KernelPCA(n_components=2, kernel=\"precomputed\").fit_transform(\n similarity_matrix\n )\n else:\n raise NotImplementedError\n\n return X\n\n\ndef plot_dose_response(\n df,\n contvar_key,\n perturbation_key,\n df_ref=None,\n response_name=\"response\",\n use_ref_response=False,\n palette=None,\n col_dict=None,\n fontsize=8,\n measured_points=None,\n interpolate=True,\n f1=7,\n f2=3.0,\n bbox=(1.35, 1.0),\n ref_name=\"origin\",\n title_name=\"None\",\n plot_vertical=True,\n fname=None,\n logscale=None,\n xlabelname=None,\n figformat=\"png\",\n):\n\n \"\"\"Plotting decoding of the response with respect to dose.\n\n Params\n ------\n df : `DataFrame`\n Table with columns=[perturbation_key, contvar_key, response_name].\n The last column is always \"response\".\n contvar_key : str\n Name of the column in df for values to use for x axis.\n perturbation_key : str\n Name of the column in df for the perturbation or covariate to plot.\n response_name: str (default: response)\n Name of the column in df for values to use for y axis.\n df_ref : `DataFrame` (default: None)\n Table with the same columns as in df to plot ground_truth or another\n condition for comparison. Could\n also be used to just extract reference values for x-axis.\n use_ref_response : bool (default: False)\n A flag indicating if to use values for y axis from df_ref (True) or j\n ust to extract reference values for x-axis.\n col_dict : dictionary (default: None)\n Dictionary with colors for each value in perturbation_key.\n bbox : tuple (default: (1.35, 1.))\n Coordinates to adjust the legend.\n plot_vertical : boolean (default: False)\n Flag if to plot reference values for x axis from df_ref dataframe.\n f1 : float (default: 7.0))\n Width in inches for the plot.\n f2 : float (default: 3.0))\n Hight in inches for the plot.\n fname : str (default: None)\n Name of the file to export the plot. The name comes without format\n extension.\n format : str (default: png)\n Format for the file to export the plot.\n \"\"\"\n sns.set_style(\"white\")\n if use_ref_response and not (df_ref is None):\n df[ref_name] = \"predictions\"\n df_ref[ref_name] = \"observations\"\n if interpolate:\n df_plt = pd.concat([df, df_ref])\n else:\n df_plt = df\n else:\n df_plt = df\n df_plt = df_plt.reset_index()\n\n atomic_drugs = np.unique(df[perturbation_key].values)\n if palette is None:\n current_palette = get_palette(len(list(atomic_drugs)))\n\n if col_dict is None:\n col_dict = dict(zip(list(atomic_drugs), current_palette))\n\n fig = plt.figure(figsize=(f1, f2))\n ax = plt.gca()\n if use_ref_response:\n sns.lineplot(\n x=contvar_key,\n y=response_name,\n palette=col_dict,\n hue=perturbation_key,\n style=ref_name,\n dashes=[(1, 0), (2, 1)],\n legend=\"full\",\n style_order=[\"predictions\", \"observations\"],\n data=df_plt,\n ax=ax,\n )\n\n df_ref = df_ref.replace(\"training_treated\", \"train\")\n sns.scatterplot(\n x=contvar_key,\n y=response_name,\n hue=\"split\",\n size=\"num_cells\",\n sizes=(10, 100),\n alpha=1.0,\n palette={\"train\": \"#000000\", \"training\": \"#000000\", \"ood\": \"#e41a1c\"},\n data=df_ref,\n ax=ax,\n )\n sns.despine()\n ax.legend_.remove()\n else:\n sns.lineplot(\n x=contvar_key,\n y=response_name,\n palette=col_dict,\n hue=perturbation_key,\n data=df_plt,\n ax=ax,\n )\n ax.legend(loc=\"upper right\", bbox_to_anchor=bbox, fontsize=fontsize)\n sns.despine()\n if not (title_name is None):\n ax.set_title(title_name, fontsize=fontsize, fontweight=\"bold\")\n ax.grid(\"off\")\n\n if xlabelname is None:\n ax.set_xlabel(contvar_key, fontsize=fontsize)\n else:\n ax.set_xlabel(xlabelname, fontsize=fontsize)\n\n ax.set_ylabel(f\"{response_name}\", fontsize=fontsize)\n\n ax.xaxis.set_tick_params(labelsize=fontsize)\n ax.yaxis.set_tick_params(labelsize=fontsize)\n\n if not (logscale is None):\n ax.set_xticks(np.log10(logscale))\n ax.set_xticklabels(logscale, rotation=90)\n\n if not (df_ref is None):\n atomic_drugs = np.unique(df_ref[perturbation_key].values)\n for drug in atomic_drugs:\n x = df_ref[df_ref[perturbation_key] == drug][contvar_key].values\n m1 = np.min(df[df[perturbation_key] == drug][response_name].values)\n m2 = np.max(df[df[perturbation_key] == drug][response_name].values)\n\n if plot_vertical:\n for x_dot in x:\n ax.plot(\n [x_dot, x_dot],\n [m1, m2],\n \":\",\n color=\"black\",\n linewidth=0.5,\n alpha=0.5,\n )\n fig.tight_layout()\n if fname:\n plt.savefig(f\"{fname}.{figformat}\", format=figformat, dpi=600)\n\n return fig\n\n\ndef plot_uncertainty_comb_dose(\n cpa_api,\n cov,\n pert,\n N=11,\n metric=\"cosine\",\n measured_points=None,\n cond_key=\"condition\",\n figsize=(4, 4),\n vmin=None,\n vmax=None,\n sizes=(40, 160),\n df_ref=None,\n xlims=(0, 1.03),\n ylims=(0, 1.03),\n fixed_drugs=\"\",\n fixed_doses=\"\",\n title=\"\",\n filename=None,\n):\n \"\"\"Plotting uncertainty for a single perturbation at a dose range for a\n particular covariate.\n\n Params\n ------\n cpa_api\n Api object for the model class.\n cov : dict\n Name of covariate.\n pert : str\n Name of the perturbation.\n N : int\n Number of dose values.\n metric: str (default: 'cosine')\n Metric to evaluate uncertainty.\n measured_points : dict (default: None)\n A dicitionary of dictionaries. Per each covariate a dictionary with\n observed doses per perturbation, e.g. {'covar1': {'pert1':\n [0.1, 0.5, 1.0], 'pert2': [0.3]}\n cond_key : str (default: 'condition')\n Name of the variable to use for plotting.\n filename : str (default: None)\n Full path to the file to export the plot. File extension should be\n included.\n\n Returns\n -------\n pd.DataFrame of uncertainty estimations.\n \"\"\"\n\n cov_name = \"_\".join([cov[cov_key] for cov_key in cpa_api.covariate_keys])\n df_list = []\n for i in np.round(np.linspace(0, 1, N), decimals=2):\n for j in np.round(np.linspace(0, 1, N), decimals=2):\n df_list.append(\n {\n \"covariates\": cov_name,\n \"condition\": pert + fixed_drugs,\n \"dose_val\": str(i) + \"+\" + str(j) + fixed_doses,\n }\n )\n df_pred = pd.DataFrame(df_list)\n uncert_cos = []\n uncert_eucl = []\n closest_cond_cos = []\n closest_cond_eucl = []\n for i in range(df_pred.shape[0]):\n (\n uncert_cos_,\n uncert_eucl_,\n closest_cond_cos_,\n closest_cond_eucl_,\n ) = cpa_api.compute_uncertainty(\n cov=cov, pert=df_pred.iloc[i][\"condition\"], dose=df_pred.iloc[i][\"dose_val\"]\n )\n uncert_cos.append(uncert_cos_)\n uncert_eucl.append(uncert_eucl_)\n closest_cond_cos.append(closest_cond_cos_)\n closest_cond_eucl.append(closest_cond_eucl_)\n\n df_pred[\"uncertainty_cosine\"] = uncert_cos\n df_pred[\"uncertainty_eucl\"] = uncert_eucl\n df_pred[\"closest_cond_cos\"] = closest_cond_cos\n df_pred[\"closest_cond_eucl\"] = closest_cond_eucl\n doses = df_pred.dose_val.apply(lambda x: x.split(\"+\"))\n X = np.array(doses.apply(lambda x: x[0]).astype(float)).reshape(N, N)\n Y = np.array(doses.apply(lambda x: x[1]).astype(float)).reshape(N, N)\n Z = np.array(df_pred[f\"uncertainty_{metric}\"].values.astype(float)).reshape(N, N)\n\n fig, ax = plt.subplots(1, 1, figsize=figsize)\n CS = ax.contourf(X, Y, Z, cmap=\"coolwarm\", levels=20, alpha=1, vmin=vmin, vmax=vmax)\n\n ax.set_xlabel(pert.split(\"+\")[0], fontweight=\"bold\")\n ax.set_ylabel(pert.split(\"+\")[1], fontweight=\"bold\")\n\n if not (df_ref is None):\n sns.scatterplot(\n x=pert.split(\"+\")[0],\n y=pert.split(\"+\")[1],\n hue=\"split\",\n size=\"num_cells\",\n sizes=sizes,\n alpha=1.0,\n palette={\"train\": \"#000000\", \"training\": \"#000000\", \"ood\": \"#e41a1c\"},\n data=df_ref,\n ax=ax,\n )\n ax.legend_.remove()\n\n if measured_points:\n ticks = measured_points[cov_name][pert]\n xticks = [float(x.split(\"+\")[0]) for x in ticks]\n yticks = [float(x.split(\"+\")[1]) for x in ticks]\n ax.set_xticks(xticks)\n ax.set_xticklabels(xticks, rotation=90)\n ax.set_yticks(yticks)\n fig.colorbar(CS)\n sns.despine()\n ax.axis(\"equal\")\n ax.axis(\"square\")\n ax.set_xlim(xlims)\n ax.set_ylim(ylims)\n ax.set_title(title, fontsize=10, fontweight='bold')\n plt.tight_layout()\n\n if filename:\n plt.savefig(filename, dpi=600)\n\n return df_pred\n\n\ndef plot_uncertainty_dose(\n cpa_api,\n cov,\n pert,\n N=11,\n metric=\"cosine\",\n measured_points=None,\n cond_key=\"condition\",\n log=False,\n min_dose=None,\n filename=None,\n):\n \"\"\"Plotting uncertainty for a single perturbation at a dose range for a\n particular covariate.\n\n Params\n ------\n cpa_api\n Api object for the model class.\n cov : str\n Name of covariate.\n pert : str\n Name of the perturbation.\n N : int\n Number of dose values.\n metric: str (default: 'cosine')\n Metric to evaluate uncertainty.\n measured_points : dict (default: None)\n A dicitionary of dictionaries. Per each covariate a dictionary with\n observed doses per perturbation, e.g. {'covar1': {'pert1':\n [0.1, 0.5, 1.0], 'pert2': [0.3]}\n cond_key : str (default: 'condition')\n Name of the variable to use for plotting.\n log : boolean (default: False)\n A flag if to plot on a log scale.\n min_dose : float (default: None)\n Minimum dose for the uncertainty estimate.\n filename : str (default: None)\n Full path to the file to export the plot. File extension should be included.\n\n Returns\n -------\n pd.DataFrame of uncertainty estimations.\n \"\"\"\n\n df_list = []\n if log:\n if min_dose is None:\n min_dose = 1e-3\n N_val = np.round(np.logspace(np.log10(min_dose), np.log10(1), N), decimals=10)\n else:\n if min_dose is None:\n min_dose = 0\n N_val = np.round(np.linspace(min_dose, 1.0, N), decimals=3)\n\n cov_name = \"_\".join([cov[cov_key] for cov_key in cpa_api.covariate_keys])\n\n for i in N_val:\n df_list.append({\"covariates\": cov_name, \"condition\": pert, \"dose_val\": repr(i)})\n\n df_pred = pd.DataFrame(df_list)\n uncert_cos = []\n uncert_eucl = []\n closest_cond_cos = []\n closest_cond_eucl = []\n\n for i in range(df_pred.shape[0]):\n (\n uncert_cos_,\n uncert_eucl_,\n closest_cond_cos_,\n closest_cond_eucl_,\n ) = cpa_api.compute_uncertainty(\n cov=cov, pert=df_pred.iloc[i][\"condition\"], dose=df_pred.iloc[i][\"dose_val\"]\n )\n uncert_cos.append(uncert_cos_)\n uncert_eucl.append(uncert_eucl_)\n closest_cond_cos.append(closest_cond_cos_)\n closest_cond_eucl.append(closest_cond_eucl_)\n\n df_pred[\"uncertainty_cosine\"] = uncert_cos\n df_pred[\"uncertainty_eucl\"] = uncert_eucl\n df_pred[\"closest_cond_cos\"] = closest_cond_cos\n df_pred[\"closest_cond_eucl\"] = closest_cond_eucl\n\n x = df_pred.dose_val.values.astype(float)\n y = df_pred[f\"uncertainty_{metric}\"].values.astype(float)\n fig, ax = plt.subplots(1, 1)\n ax.plot(x, y)\n ax.set_xlabel(pert)\n ax.set_ylabel(\"Uncertainty\")\n ax.set_title(cov_name)\n if log:\n ax.set_xscale(\"log\")\n if measured_points:\n ticks = measured_points[cov_name][pert]\n ax.set_xticks(ticks)\n ax.set_xticklabels(ticks, rotation=90)\n else:\n plt.draw()\n ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n\n sns.despine()\n plt.tight_layout()\n\n if filename:\n plt.savefig(filename)\n\n return df_pred\n\n\ndef save_to_file(fig, file_name, file_format=None):\n if file_format is None:\n if file_name.split(\".\")[-1] in [\"png\", \"pdf\"]:\n file_format = file_name.split(\".\")[-1]\n savename = file_name\n else:\n file_format = \"pdf\"\n savename = f\"{file_name}.{file_format}\"\n else:\n savename = file_name\n\n fig.savefig(savename, format=file_format, dpi=600)\n print(f\"Saved file to: {savename}\")\n\n\ndef plot_embedding(\n emb,\n labels=None,\n col_dict=None,\n title=None,\n show_lines=False,\n show_text=False,\n show_legend=True,\n axis_equal=True,\n circle_size=40,\n circe_transparency=1.0,\n line_transparency=0.8,\n line_width=1.0,\n fontsize=9,\n fig_width=4,\n fig_height=4,\n file_name=None,\n file_format=None,\n labels_name=None,\n width_ratios=[7, 1],\n bbox=(1.3, 0.7),\n):\n sns.set_style(\"white\")\n\n # create data structure suitable for embedding\n df = pd.DataFrame(emb, columns=[\"dim1\", \"dim2\"])\n if not (labels is None):\n if labels_name is None:\n labels_name = \"labels\"\n df[labels_name] = labels\n\n fig = plt.figure(figsize=(fig_width, fig_height))\n ax = plt.gca()\n\n sns.despine(left=False, bottom=False, right=True)\n\n if (col_dict is None) and not (labels is None):\n col_dict = get_colors(labels)\n\n sns.scatterplot(\n x=\"dim1\",\n y=\"dim2\",\n hue=labels_name,\n palette=col_dict,\n alpha=circe_transparency,\n edgecolor=\"none\",\n s=circle_size,\n data=df,\n ax=ax,\n )\n\n try:\n ax.legend_.remove()\n except:\n pass\n\n if show_lines:\n for i in range(len(emb)):\n if col_dict is None:\n ax.plot(\n [0, emb[i, 0]],\n [0, emb[i, 1]],\n alpha=line_transparency,\n linewidth=line_width,\n c=None,\n )\n else:\n ax.plot(\n [0, emb[i, 0]],\n [0, emb[i, 1]],\n alpha=line_transparency,\n linewidth=line_width,\n c=col_dict[labels[i]],\n )\n\n if show_text and not (labels is None):\n texts = []\n labels = np.array(labels)\n unique_labels = np.unique(labels)\n for label in unique_labels:\n idx_label = np.where(labels == label)[0]\n texts.append(\n ax.text(\n np.mean(emb[idx_label, 0]),\n np.mean(emb[idx_label, 1]),\n label,\n #fontsize=fontsize,\n )\n )\n\n adjust_text(\n texts, arrowprops=dict(arrowstyle=\"-\", color=\"black\", lw=0.1), ax=ax\n )\n\n if axis_equal:\n ax.axis(\"equal\")\n ax.axis(\"square\")\n\n if title:\n ax.set_title(title, fontweight=\"bold\")\n\n ax.set_xlabel(\"dim1\"),# fontsize=fontsize)\n ax.set_ylabel(\"dim2\"),# fontsize=fontsize)\n #ax.xaxis.set_tick_params(labelsize=fontsize)\n #ax.yaxis.set_tick_params(labelsize=fontsize)\n\n plt.tight_layout()\n\n if file_name:\n save_to_file(fig, file_name, file_format)\n\n return plt\n\n\ndef get_colors(labels, palette=None, palette_name=None):\n n_colors = len(labels)\n if palette is None:\n palette = get_palette(n_colors, palette_name)\n col_dict = dict(zip(labels, palette[:n_colors]))\n return col_dict\n\n\ndef plot_similarity(\n emb,\n labels=None,\n col_dict=None,\n fig_width=4,\n fig_height=4,\n cmap=\"coolwarm\",\n fmt=\"png\",\n fontsize=7,\n file_format=None,\n file_name=None,\n):\n\n # first we take construct similarity matrix\n # add another similarity\n similarity_matrix = cosine_similarity(emb)\n\n df = pd.DataFrame(\n similarity_matrix,\n columns=labels,\n index=labels,\n )\n\n if col_dict is None:\n col_dict = get_colors(labels)\n\n network_colors = pd.Series(df.columns, index=df.columns).map(col_dict)\n\n sns_plot = sns.clustermap(\n df,\n cmap=cmap,\n center=0,\n row_colors=network_colors,\n col_colors=network_colors,\n mask=False,\n metric=\"euclidean\",\n figsize=(fig_height, fig_width),\n vmin=-1,\n vmax=1,\n fmt=file_format,\n )\n\n sns_plot.ax_heatmap.xaxis.set_tick_params(labelsize=fontsize)\n sns_plot.ax_heatmap.yaxis.set_tick_params(labelsize=fontsize)\n sns_plot.ax_heatmap.axis(\"equal\")\n sns_plot.cax.yaxis.set_tick_params(labelsize=fontsize)\n\n if file_name:\n save_to_file(sns_plot, file_name, file_format)\n\n\nfrom scipy import sparse, stats\nfrom sklearn.metrics import r2_score\n\n\ndef mean_plot(\n adata,\n pred,\n condition_key,\n exp_key,\n path_to_save=\"./reg_mean.pdf\",\n gene_list=None,\n deg_list=None,\n show=False,\n title=None,\n verbose=False,\n x_coeff=0.30,\n y_coeff=0.8,\n fontsize=11,\n R2_type=\"R2\",\n figsize=(3.5, 3.5),\n **kwargs,\n):\n \"\"\"\n Plots mean matching.\n\n # Parameters\n adata: `~anndata.AnnData`\n Contains real v\n pred: `~anndata.AnnData`\n Contains predicted values.\n condition_key: Str\n adata.obs key to look for x-axis and y-axis condition\n exp_key: Str\n Condition in adata.obs[condition_key] to be ploted\n path_to_save: basestring\n Path to save the plot.\n gene_list: list\n List of gene names to be plotted.\n deg_list: list\n List of DEGs to compute R2\n show: boolean\n if True plots the figure\n Verbose: boolean\n If true prints the value\n title: Str\n Title of the plot\n x_coeff: float\n Shifts R2 text horizontally by x_coeff\n y_coeff: float\n Shifts R2 text vertically by y_coeff\n show: bool\n if `True`: will show to the plot after saving it.\n fontsize: int\n Font size for R2 texts\n R2_type: Str\n How to compute R2 value, should be either Pearson R2 or R2 (sklearn)\n\n Returns:\n Calluated R2 values\n \"\"\"\n\n r2_types = [\"R2\", \"Pearson R2\"]\n if R2_type not in r2_types:\n raise ValueError(\"R2 caclulation should be one of\" + str(r2_types))\n if sparse.issparse(adata.X):\n adata.X = adata.X.A\n if sparse.issparse(pred.X):\n pred.X = pred.X.A\n diff_genes = deg_list\n real = adata[adata.obs[condition_key] == exp_key]\n pred = pred[pred.obs[condition_key] == exp_key]\n if diff_genes is not None:\n if hasattr(diff_genes, \"tolist\"):\n diff_genes = diff_genes.tolist()\n real_diff = adata[:, diff_genes][adata.obs[condition_key] == exp_key]\n pred_diff = pred[:, diff_genes][pred.obs[condition_key] == exp_key]\n x_diff = np.average(pred_diff.X, axis=0)\n y_diff = np.average(real_diff.X, axis=0)\n if R2_type == \"R2\":\n r2_diff = r2_score(y_diff, x_diff)\n if R2_type == \"Pearson R2\":\n m, b, pearson_r_diff, p_value_diff, std_err_diff = stats.linregress(\n y_diff, x_diff\n )\n r2_diff = pearson_r_diff ** 2\n if verbose:\n print(f\"Top {len(diff_genes)} DEGs var: \", r2_diff)\n x = np.average(pred.X, axis=0)\n y = np.average(real.X, axis=0)\n if R2_type == \"R2\":\n r2 = r2_score(y, x)\n if R2_type == \"Pearson R2\":\n m, b, pearson_r, p_value, std_err = stats.linregress(y, x)\n r2 = pearson_r ** 2\n if verbose:\n print(\"All genes var: \", r2)\n df = pd.DataFrame({f\"{exp_key}_true\": x, f\"{exp_key}_pred\": y})\n\n plt.figure(figsize=figsize)\n ax = sns.regplot(x=f\"{exp_key}_true\", y=f\"{exp_key}_pred\", data=df)\n ax.tick_params(labelsize=fontsize)\n if \"range\" in kwargs:\n start, stop, step = kwargs.get(\"range\")\n ax.set_xticks(np.arange(start, stop, step))\n ax.set_yticks(np.arange(start, stop, step))\n ax.set_xlabel(\"true\", fontsize=fontsize)\n ax.set_ylabel(\"pred\", fontsize=fontsize)\n if gene_list is not None:\n for i in gene_list:\n j = adata.var_names.tolist().index(i)\n x_bar = x[j]\n y_bar = y[j]\n plt.text(x_bar, y_bar, i, fontsize=fontsize, color=\"black\")\n plt.plot(x_bar, y_bar, \"o\", color=\"red\", markersize=5)\n if title is None:\n plt.title(f\"\", fontsize=fontsize, fontweight=\"bold\")\n else:\n plt.title(title, fontsize=fontsize, fontweight=\"bold\")\n ax.text(\n max(x) - max(x) * x_coeff,\n max(y) - y_coeff * max(y),\n r\"$\\mathrm{R^2_{\\mathrm{\\mathsf{all\\ genes}}}}$= \" + f\"{r2:.2f}\",\n fontsize=fontsize,\n )\n if diff_genes is not None:\n ax.text(\n max(x) - max(x) * x_coeff,\n max(y) - (y_coeff + 0.15) * max(y),\n r\"$\\mathrm{R^2_{\\mathrm{\\mathsf{DEGs}}}}$= \" + f\"{r2_diff:.2f}\",\n fontsize=fontsize,\n )\n plt.savefig(f\"{path_to_save}\", bbox_inches=\"tight\", dpi=100)\n if show:\n plt.show()\n plt.close()\n if diff_genes is not None:\n return r2, r2_diff\n else:\n return r2\n\n\ndef plot_r2_matrix(pred, adata, de_genes=None, **kwds):\n \"\"\"Plots a pairwise R2 heatmap between predicted and control conditions.\n\n Params\n ------\n pred : `AnnData`\n Must have the field `cov_drug_dose_name`\n adata : `AnnData`\n Original gene expression data, with the field `cov_drug_dose_name`.\n de_genes : `dict`\n Dictionary of de_genes, where the keys\n match the categories in `cov_drug_dose_name`\n \"\"\"\n r2s_mean = defaultdict(list)\n r2s_var = defaultdict(list)\n conditions = pred.obs[\"cov_drug_dose_name\"].cat.categories\n for cond in conditions:\n if de_genes:\n degs = de_genes[cond]\n y_pred = pred[:, degs][pred.obs[\"cov_drug_dose_name\"] == cond].X\n y_true_adata = adata[:, degs]\n else:\n y_pred = pred[pred.obs[\"cov_drug_dose_name\"] == cond].X\n y_true_adata = adata\n\n # calculate r2 between pairwise\n for cond_real in conditions:\n y_true = y_true_adata[\n y_true_adata.obs[\"cov_drug_dose_name\"] == cond_real\n ].X.toarray()\n r2s_mean[cond_real].append(\n r2_score(y_true.mean(axis=0), y_pred.mean(axis=0))\n )\n r2s_var[cond_real].append(r2_score(y_true.var(axis=0), y_pred.var(axis=0)))\n\n for r2_dict in [r2s_mean, r2s_var]:\n r2_df = pd.DataFrame.from_dict(r2_dict, orient=\"index\")\n r2_df.columns = conditions\n\n plt.figure(figsize=(5, 5))\n p = sns.heatmap(\n data=r2_df,\n vmin=max(r2_df.min(0).min(), 0),\n cmap=\"Blues\",\n cbar=False,\n annot=True,\n fmt=\".2f\",\n annot_kws={\"fontsize\": 5},\n **kwds,\n )\n plt.xticks(fontsize=6)\n plt.yticks(fontsize=6)\n plt.xlabel(\"y_true\")\n plt.ylabel(\"y_pred\")\n plt.show()\n\n\ndef arrange_history(history):\n\n print(history.keys())\n\n\nclass CPAHistory:\n \"\"\"\n A wrapper for automatic plotting history of CPA model..\n \"\"\"\n\n def __init__(self, cpa_api, fileprefix=None):\n \"\"\"\n Parameters\n ----------\n cpa_api : dict\n cpa api instance\n fileprefix : str, optional (default: None)\n Prefix (with path) to the filename to save all embeddings in a\n standartized manner. If None, embeddings are not saved to file.\n \"\"\"\n self.history = cpa_api.history\n self.time = self.history[\"elapsed_time_min\"]\n self.losses_list = [\n \"loss_reconstruction\",\n \"loss_adv_drugs\",\n \"loss_adv_covariates\",\n ]\n self.penalties_list = [\"penalty_adv_drugs\", \"penalty_adv_covariates\"]\n\n subset_keys = [\"epoch\"] + self.losses_list + self.penalties_list\n\n self.losses = pd.DataFrame(\n dict((k, self.history[k]) for k in subset_keys if k in self.history)\n )\n\n self.header = [\"mean\", \"mean_DE\", \"var\", \"var_DE\"]\n self.eval_metrics = False\n if 'perturbation disentanglement' in list (self.history): #check that metrics were evaluated\n self.eval_metrics = True\n self.metrics = pd.DataFrame(columns=[\"epoch\", \"split\"] + self.header)\n for split in [\"training\", \"test\", \"ood\"]:\n df_split = pd.DataFrame(np.array(self.history[split]), columns=self.header)\n df_split[\"split\"] = split\n df_split[\"epoch\"] = self.history[\"stats_epoch\"]\n self.metrics = pd.concat([self.metrics, df_split])\n self.covariate_names = list(cpa_api.datasets[\"training\"].covariate_names)\n self.disent = pd.DataFrame(\n dict(\n (k, self.history[k])\n for k in \n ['perturbation disentanglement'] \n + [f'{cov} disentanglement' for cov in self.covariate_names]\n if k in self.history\n )\n )\n self.disent[\"epoch\"] = self.history[\"stats_epoch\"]\n self.fileprefix = fileprefix\n\n def print_time(self):\n print(f\"Computation time: {self.time:.0f} min\")\n\n def plot_losses(self, filename=None):\n \"\"\"\n Parameters\n ----------\n filename : str (default: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n \"\"\"\n if filename is None:\n if self.fileprefix is None:\n filename = None\n else:\n filename = f\"{self.fileprefix}_history_losses.png\"\n\n fig, ax = plt.subplots(1, 4, sharex=True, sharey=False, figsize=(12, 3))\n\n i = 0\n for i in range(4):\n if i < 3:\n ax[i].plot(\n self.losses[\"epoch\"].values, self.losses[self.losses_list[i]].values\n )\n ax[i].set_title(self.losses_list[i], fontweight=\"bold\")\n else:\n ax[i].plot(\n self.losses[\"epoch\"].values, self.losses[self.penalties_list].values\n )\n ax[i].set_title(\"Penalties\", fontweight=\"bold\")\n sns.despine()\n plt.tight_layout()\n\n if filename:\n save_to_file(fig, filename)\n\n def plot_r2_metrics(self, epoch_min=0, filename=None):\n \"\"\"\n Parameters\n ----------\n epoch_min : int (default: 0)\n Epoch from which to show metrics history plot. Done for readability.\n\n filename : str (default: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n \"\"\"\n\n assert self.eval_metrics == True, 'The evaluation metrics were not computed'\n\n if filename is None:\n if self.fileprefix is None:\n filename = None\n else:\n filename = f\"{self.fileprefix}_history_metrics.png\"\n\n df = self.metrics.melt(id_vars=[\"epoch\", \"split\"])\n col_dict = dict(\n zip([\"training\", \"test\", \"ood\"], [\"#377eb8\", \"#4daf4a\", \"#e41a1c\"])\n )\n fig, axs = plt.subplots(2, 2, sharex=True, sharey=False, figsize=(7, 5))\n ax = plt.gca()\n i = 0\n for i1 in range(2):\n for i2 in range(2):\n sns.lineplot(\n data=df[\n (df[\"variable\"] == self.header[i]) & (df[\"epoch\"] > epoch_min)\n ],\n x=\"epoch\",\n y=\"value\",\n palette=col_dict,\n hue=\"split\",\n ax=axs[i1, i2],\n )\n axs[i1, i2].set_title(self.header[i], fontweight=\"bold\")\n i += 1\n sns.despine()\n plt.tight_layout()\n if filename:\n save_to_file(fig, filename)\n\n def plot_disentanglement_metrics(self, epoch_min=0, filename=None):\n \"\"\"\n Parameters\n ----------\n epoch_min : int (default: 0)\n Epoch from which to show metrics history plot. Done for readability.\n\n filename : str (default: None)\n Name of the file to save the plot. If None, will automatically\n generate name from prefix file.\n \"\"\"\n assert self.eval_metrics == True, 'The evaluation metrics were not computed'\n \n if filename is None:\n if self.fileprefix is None:\n filename = None\n else:\n filename = f\"{self.fileprefix}_history_metrics.png\"\n\n fig, axs = plt.subplots(\n 1, \n 1+len(self.covariate_names), \n sharex=True, \n sharey=False, \n figsize=(2 + 5*(len(self.covariate_names)), 3)\n )\n\n ax = plt.gca()\n sns.lineplot(\n data=self.disent[self.disent[\"epoch\"] > epoch_min],\n x=\"epoch\",\n y=\"perturbation disentanglement\",\n legend=False,\n ax=axs[0],\n )\n axs[0].set_title(\"perturbation disent\", fontweight=\"bold\")\n\n for i, cov in enumerate(self.covariate_names):\n sns.lineplot(\n data=self.disent[self.disent['epoch'] > epoch_min],\n x=\"epoch\",\n y=f\"{cov} disentanglement\",\n legend=False,\n ax=axs[1+i]\n )\n axs[1+i].set_title(f\"{cov} disent\", fontweight=\"bold\")\n fig.tight_layout()\n sns.despine()" }, { "path": "cpa/train.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nimport argparse\nimport json\nimport os\nimport time\nfrom collections import defaultdict\n\nimport numpy as np\nimport torch\nfrom cpa.data import load_dataset_splits\nfrom cpa.model import CPA, MLP\nfrom sklearn.metrics import r2_score\nfrom torch.autograd import Variable\nfrom torch.distributions import NegativeBinomial\nfrom torch import nn\n\n\ndef pjson(s):\n \"\"\"\n Prints a string in JSON format and flushes stdout\n \"\"\"\n print(json.dumps(s), flush=True)\n\ndef _convert_mean_disp_to_counts_logits(mu, theta, eps=1e-6):\n r\"\"\"NB parameterizations conversion\n Parameters\n ----------\n mu :\n mean of the NB distribution.\n theta :\n inverse overdispersion.\n eps :\n constant used for numerical log stability. (Default value = 1e-6)\n Returns\n -------\n type\n the number of failures until the experiment is stopped\n and the success probability.\n \"\"\"\n assert (mu is None) == (\n theta is None\n ), \"If using the mu/theta NB parameterization, both parameters must be specified\"\n logits = (mu + eps).log() - (theta + eps).log()\n total_count = theta\n return total_count, logits\n\ndef evaluate_disentanglement(autoencoder, dataset):\n \"\"\"\n Given a CPA model, this function measures the correlation between\n its latent space and 1) a dataset's drug vectors 2) a datasets covariate\n vectors.\n\n \"\"\"\n with torch.no_grad():\n _, latent_basal = autoencoder.predict(\n dataset.genes,\n dataset.drugs,\n dataset.covariates,\n return_latent_basal=True,\n )\n \n mean = latent_basal.mean(dim=0, keepdim=True)\n stddev = latent_basal.std(0, unbiased=False, keepdim=True)\n normalized_basal = (latent_basal - mean) / stddev\n criterion = nn.CrossEntropyLoss()\n pert_scores, cov_scores = 0, []\n\n def compute_score(labels):\n if len(np.unique(labels)) > 1:\n unique_labels = set(labels)\n label_to_idx = {labels: idx for idx, labels in enumerate(unique_labels)}\n labels_tensor = torch.tensor(\n [label_to_idx[label] for label in labels], dtype=torch.long, device=autoencoder.device\n )\n assert normalized_basal.size(0) == len(labels_tensor)\n #might have to perform a train/test split here\n dataset = torch.utils.data.TensorDataset(normalized_basal, labels_tensor)\n data_loader = torch.utils.data.DataLoader(dataset, batch_size=256, shuffle=True)\n\n # 2 non-linear layers of size \n # followed by a linear layer.\n disentanglement_classifier = MLP(\n [normalized_basal.size(1)]\n + [normalized_basal.size(1) for _ in range(2)]\n + [len(unique_labels)]\n ).to(autoencoder.device)\n optimizer = torch.optim.Adam(disentanglement_classifier.parameters(), lr=1e-2)\n\n for epoch in range(50):\n for X, y in data_loader:\n pred = disentanglement_classifier(X)\n loss = Variable(criterion(pred, y), requires_grad=True)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n with torch.no_grad():\n pred = disentanglement_classifier(normalized_basal).argmax(dim=1)\n acc = torch.sum(pred == labels_tensor) / len(labels_tensor)\n return acc.item()\n else:\n return 0\n\n if dataset.perturbation_key is not None:\n pert_scores = compute_score(dataset.drugs_names)\n for cov in list(dataset.covariate_names):\n cov_scores = []\n if len(np.unique(dataset.covariate_names[cov])) == 0:\n cov_scores = [0]\n break\n else:\n cov_scores.append(compute_score(dataset.covariate_names[cov]))\n return [np.mean(pert_scores), *[np.mean(cov_score) for cov_score in cov_scores]]\n\n\ndef evaluate_r2(autoencoder, dataset, genes_control):\n \"\"\"\n Measures different quality metrics about an CPA `autoencoder`, when\n tasked to translate some `genes_control` into each of the drug/covariates\n combinations described in `dataset`.\n\n Considered metrics are R2 score about means and variances for all genes, as\n well as R2 score about means and variances about differentially expressed\n (_de) genes.\n \"\"\"\n\n mean_score, var_score, mean_score_de, var_score_de = [], [], [], []\n num, dim = genes_control.size(0), genes_control.size(1)\n\n total_cells = len(dataset)\n\n for pert_category in np.unique(dataset.pert_categories):\n # pert_category category contains: 'celltype_perturbation_dose' info\n de_idx = np.where(\n dataset.var_names.isin(np.array(dataset.de_genes[pert_category]))\n )[0]\n\n idx = np.where(dataset.pert_categories == pert_category)[0]\n\n if len(idx) > 30:\n emb_drugs = dataset.drugs[idx][0].view(1, -1).repeat(num, 1).clone()\n emb_covars = [\n covar[idx][0].view(1, -1).repeat(num, 1).clone()\n for covar in dataset.covariates\n ]\n\n genes_predict = (\n autoencoder.predict(genes_control, emb_drugs, emb_covars).detach().cpu()\n )\n\n mean_predict = genes_predict[:, :dim]\n var_predict = genes_predict[:, dim:]\n\n if autoencoder.loss_ae == 'nb':\n counts, logits = _convert_mean_disp_to_counts_logits(\n torch.clamp(\n torch.Tensor(mean_predict),\n min=1e-4,\n max=1e4,\n ),\n torch.clamp(\n torch.Tensor(var_predict),\n min=1e-4,\n max=1e4,\n )\n )\n dist = NegativeBinomial(\n total_count=counts,\n logits=logits\n )\n nb_sample = dist.sample().cpu().numpy()\n yp_m = nb_sample.mean(0)\n yp_v = nb_sample.var(0)\n else:\n # predicted means and variances\n yp_m = mean_predict.mean(0)\n yp_v = var_predict.mean(0)\n # estimate metrics only for reasonably-sized drug/cell-type combos\n\n y_true = dataset.genes[idx, :].numpy()\n\n # true means and variances\n yt_m = y_true.mean(axis=0)\n yt_v = y_true.var(axis=0)\n\n mean_score.append(r2_score(yt_m, yp_m))\n var_score.append(r2_score(yt_v, yp_v))\n\n mean_score_de.append(r2_score(yt_m[de_idx], yp_m[de_idx]))\n var_score_de.append(r2_score(yt_v[de_idx], yp_v[de_idx]))\n\n return [\n np.mean(s) if len(s) else -1\n for s in [mean_score, mean_score_de, var_score, var_score_de]\n ]\n\n\ndef evaluate(autoencoder, datasets):\n \"\"\"\n Measure quality metrics using `evaluate()` on the training, test, and\n out-of-distribution (ood) splits.\n \"\"\"\n\n autoencoder.eval()\n with torch.no_grad():\n stats_test = evaluate_r2(\n autoencoder, \n datasets[\"test\"].subset_condition(control=False), \n datasets[\"test\"].subset_condition(control=True).genes\n )\n\n disent_scores = evaluate_disentanglement(autoencoder, datasets[\"test\"])\n stats_disent_pert = disent_scores[0]\n stats_disent_cov = disent_scores[1:]\n\n evaluation_stats = {\n \"training\": evaluate_r2(\n autoencoder,\n datasets[\"training\"].subset_condition(control=False),\n datasets[\"training\"].subset_condition(control=True).genes,\n ),\n \"test\": stats_test,\n \"ood\": evaluate_r2(\n autoencoder, datasets[\"ood\"], datasets[\"test\"].subset_condition(control=True).genes\n ),\n \"perturbation disentanglement\": stats_disent_pert,\n \"optimal for perturbations\": 1 / datasets[\"test\"].num_drugs\n if datasets[\"test\"].num_drugs > 0\n else None,\n }\n if len(stats_disent_cov) > 0:\n for i in range(len(stats_disent_cov)):\n evaluation_stats[\n f\"{list(datasets['test'].covariate_names)[i]} disentanglement\"\n ] = stats_disent_cov[i]\n evaluation_stats[\n f\"optimal for {list(datasets['test'].covariate_names)[i]}\"\n ] = 1 / datasets[\"test\"].num_covariates[i]\n autoencoder.train()\n return evaluation_stats\n\ndef prepare_cpa(args, state_dict=None):\n \"\"\"\n Instantiates autoencoder and dataset to run an experiment.\n \"\"\"\n\n device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n datasets = load_dataset_splits(\n args[\"data\"],\n args[\"perturbation_key\"],\n args[\"dose_key\"],\n args[\"covariate_keys\"],\n args[\"split_key\"],\n args[\"control\"],\n )\n\n autoencoder = CPA(\n datasets[\"training\"].num_genes,\n datasets[\"training\"].num_drugs,\n datasets[\"training\"].num_covariates,\n device=device,\n seed=args[\"seed\"],\n loss_ae=args[\"loss_ae\"],\n doser_type=args[\"doser_type\"],\n patience=args[\"patience\"],\n hparams=args[\"hparams\"],\n decoder_activation=args[\"decoder_activation\"],\n )\n if state_dict is not None:\n autoencoder.load_state_dict(state_dict)\n\n return autoencoder, datasets\n\n\ndef train_cpa(args, return_model=False):\n \"\"\"\n Trains a CPA autoencoder\n \"\"\"\n\n autoencoder, datasets = prepare_cpa(args)\n\n datasets.update(\n {\n \"loader_tr\": torch.utils.data.DataLoader(\n datasets[\"training\"],\n batch_size=autoencoder.hparams[\"batch_size\"],\n shuffle=True,\n )\n }\n )\n\n pjson({\"training_args\": args})\n pjson({\"autoencoder_params\": autoencoder.hparams})\n args[\"hparams\"] = autoencoder.hparams\n\n start_time = time.time()\n for epoch in range(args[\"max_epochs\"]):\n epoch_training_stats = defaultdict(float)\n\n for data in datasets[\"loader_tr\"]:\n genes, drugs, covariates = data[0], data[1], data[2:]\n\n minibatch_training_stats = autoencoder.update(genes, drugs, covariates)\n\n for key, val in minibatch_training_stats.items():\n epoch_training_stats[key] += val\n\n for key, val in epoch_training_stats.items():\n epoch_training_stats[key] = val / len(datasets[\"loader_tr\"])\n if not (key in autoencoder.history.keys()):\n autoencoder.history[key] = []\n autoencoder.history[key].append(epoch_training_stats[key])\n autoencoder.history[\"epoch\"].append(epoch)\n\n ellapsed_minutes = (time.time() - start_time) / 60\n autoencoder.history[\"elapsed_time_min\"] = ellapsed_minutes\n\n # decay learning rate if necessary\n # also check stopping condition: patience ran out OR\n # time ran out OR max epochs achieved\n stop = ellapsed_minutes > args[\"max_minutes\"] or (\n epoch == args[\"max_epochs\"] - 1\n )\n\n if (epoch % args[\"checkpoint_freq\"]) == 0 or stop:\n evaluation_stats = evaluate(autoencoder, datasets)\n for key, val in evaluation_stats.items():\n if not (key in autoencoder.history.keys()):\n autoencoder.history[key] = []\n autoencoder.history[key].append(val)\n autoencoder.history[\"stats_epoch\"].append(epoch)\n\n pjson(\n {\n \"epoch\": epoch,\n \"training_stats\": epoch_training_stats,\n \"evaluation_stats\": evaluation_stats,\n \"ellapsed_minutes\": ellapsed_minutes,\n }\n )\n\n torch.save(\n (autoencoder.state_dict(), args, autoencoder.history),\n os.path.join(\n args[\"save_dir\"],\n \"model_seed={}_epoch={}.pt\".format(args[\"seed\"], epoch),\n ),\n )\n\n pjson(\n {\n \"model_saved\": \"model_seed={}_epoch={}.pt\\n\".format(\n args[\"seed\"], epoch\n )\n }\n )\n stop = stop or autoencoder.early_stopping(np.mean(evaluation_stats[\"test\"]))\n if stop:\n pjson({\"early_stop\": epoch})\n break\n\n if return_model:\n return autoencoder, datasets\n\n\ndef parse_arguments():\n \"\"\"\n Read arguments if this script is called from a terminal.\n \"\"\"\n\n parser = argparse.ArgumentParser(description=\"Drug combinations.\")\n # dataset arguments\n parser.add_argument(\"--data\", type=str, required=True)\n parser.add_argument(\"--perturbation_key\", type=str, default=\"condition\")\n parser.add_argument(\"--control\", type=str, default=None)\n parser.add_argument(\"--dose_key\", type=str, default=\"dose_val\")\n parser.add_argument(\"--covariate_keys\", nargs=\"*\", type=str, default=\"cell_type\")\n parser.add_argument(\"--split_key\", type=str, default=\"split\")\n parser.add_argument(\"--loss_ae\", type=str, default=\"gauss\")\n parser.add_argument(\"--doser_type\", type=str, default=\"sigm\")\n parser.add_argument(\"--decoder_activation\", type=str, default=\"linear\")\n\n # CPA arguments (see set_hparams_() in cpa.model.CPA)\n parser.add_argument(\"--seed\", type=int, default=0)\n parser.add_argument(\"--hparams\", type=str, default=\"\")\n\n # training arguments\n parser.add_argument(\"--max_epochs\", type=int, default=2000)\n parser.add_argument(\"--max_minutes\", type=int, default=300)\n parser.add_argument(\"--patience\", type=int, default=20)\n parser.add_argument(\"--checkpoint_freq\", type=int, default=20)\n\n # output folder\n parser.add_argument(\"--save_dir\", type=str, required=True)\n # number of trials when executing cpa.sweep\n parser.add_argument(\"--sweep_seeds\", type=int, default=200)\n return dict(vars(parser.parse_args()))\n\n\nif __name__ == \"__main__\":\n train_cpa(parse_arguments())\n" }, { "path": "datasets/.gitkeep", "content": "" }, { "path": "notebooks/demo.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# A tour of the CPA model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# some standard packages to assist this tutorial\\n\",\n \"import cpa\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import scanpy as sc\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Training your model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Init your model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = sc.read('../../cpa-reproducibility/datasets/GSM_new.h5ad')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING. Special characters ('_') were found in: 'dummy_cov'. They will be replaced with '-'. Be careful, it may lead to errors downstream.\\n\",\n \"Creating 'cov_drug_dose_name' field.\\n\",\n \"Ranking genes for DE genes.\\n\",\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'dummy_cov' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'covars_comb' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"cpa_api = cpa.api.API(\\n\",\n \" adata, \\n\",\n \" perturbation_key='condition',\\n\",\n \" doser_type='logsigm',\\n\",\n \" split_key='split',\\n\",\n \" covariate_keys=[],\\n\",\n \" only_parameters=False,\\n\",\n \" hparams={}, \\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also load a pretrained model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Loaded pretrained model from:\\t../../cpa-reproducibility/notebooks/sciplex2_other.pt\\n\"\n ]\n }\n ],\n \"source\": [\n \"cpa_api = cpa.api.API(\\n\",\n \" adata, \\n\",\n \" pretrained='../../cpa-reproducibility/notebooks/sciplex2_other.pt'\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can print parameters of the model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"CPA(\\n\",\n \" (encoder): MLP(\\n\",\n \" (network): Sequential(\\n\",\n \" (0): Linear(in_features=4999, out_features=128, bias=True)\\n\",\n \" (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (2): ReLU()\\n\",\n \" (3): Linear(in_features=128, out_features=128, bias=True)\\n\",\n \" (4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (5): ReLU()\\n\",\n \" (6): Linear(in_features=128, out_features=128, bias=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (decoder): MLP(\\n\",\n \" (network): Sequential(\\n\",\n \" (0): Linear(in_features=128, out_features=128, bias=True)\\n\",\n \" (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (2): ReLU()\\n\",\n \" (3): Linear(in_features=128, out_features=128, bias=True)\\n\",\n \" (4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (5): ReLU()\\n\",\n \" (6): Linear(in_features=128, out_features=9998, bias=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (adversary_drugs): MLP(\\n\",\n \" (network): Sequential(\\n\",\n \" (0): Linear(in_features=128, out_features=64, bias=True)\\n\",\n \" (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (2): ReLU()\\n\",\n \" (3): Linear(in_features=64, out_features=64, bias=True)\\n\",\n \" (4): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\\n\",\n \" (5): ReLU()\\n\",\n \" (6): Linear(in_features=64, out_features=5, bias=True)\\n\",\n \" )\\n\",\n \" )\\n\",\n \" (loss_adversary_drugs): BCEWithLogitsLoss()\\n\",\n \" (dosers): GeneralizedSigmoid()\\n\",\n \" (covariates_embeddings): Sequential(\\n\",\n \" (0): Embedding(1, 128)\\n\",\n \" )\\n\",\n \" (drug_embeddings): Embedding(5, 128)\\n\",\n \" (loss_autoencoder): GaussianNLLLoss()\\n\",\n \")\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cpa_api.model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Start training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Results will be saved to the folder: /tmp/\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Rec: -1.6080, AdvPert: 0.49, AdvCov: 0.00: 100%|▉| 999/1000 [12:45<00:00, 1.30i\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model saved to: /tmp/None\\n\",\n \"{'ellapsed_minutes': 12.652799173196156,\\n\",\n \" 'epoch': 999,\\n\",\n \" 'evaluation_stats': {'cell_type disentanglement': 0.0,\\n\",\n \" 'ood': [0.9116713597573521,\\n\",\n \" 0.8682491544314631,\\n\",\n \" 0.6297205495339959,\\n\",\n \" -0.2600031534834341],\\n\",\n \" 'optimal for cell_type': 1.0,\\n\",\n \" 'optimal for perturbations': 0.2,\\n\",\n \" 'perturbation disentanglement': 0.2595809996128082,\\n\",\n \" 'test': [0.9242513761083746,\\n\",\n \" 0.7737687104344115,\\n\",\n \" 0.8351889804394074,\\n\",\n \" 0.0565223552642065],\\n\",\n \" 'training': [0.9295517385365168,\\n\",\n \" 0.7780530537975899,\\n\",\n \" 0.8627033078033616,\\n\",\n \" 0.12276226755805468]},\\n\",\n \" 'training_stats': defaultdict(,\\n\",\n \" {'loss_adv_covariates': 0.0,\\n\",\n \" 'loss_adv_drugs': 0.49258487340476775,\\n\",\n \" 'loss_reconstruction': -1.6079967220624287,\\n\",\n \" 'penalty_adv_covariates': 0.0,\\n\",\n \" 'penalty_adv_drugs': 2.152925470492543e-07})}\\n\",\n \"Stop epoch: 999\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Model saved to: /tmp/None\\n\"\n ]\n }\n ],\n \"source\": [\n \"cpa_api.train(\\n\",\n \" max_epochs=1000, \\n\",\n \" run_eval=True, \\n\",\n \" checkpoint_freq=20,\\n\",\n \" filename=None, \\n\",\n \" max_minutes=2*60\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Visualize training history\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Computation time: 19 min\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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f8qtOr34ZlAWvNmjX1ToLZYDsUWBIRSyOiHbgSmFMSJoAJacLp8WRDJndUGHcbzlNmted8ZVZbzlM2XNSkgFVBUyZJOj9tf0DSIZXGNRuhprF10mXI5oOZVhLmAuBVZPMYPQh8KiK6KoxrZmZmZoOg6gJWhc2RTiCb3HImMJdsUstK45qNRCqzrrSa+q1kE1PuDhxENgP7DhXGRdJcSQskLVi9enV1qTUzMzMzoDY1WJU0R5oDXB6ZO4CJkqZWGNdsJFoOTC9a3oOspqrYR4CrU75aAvwFeGWFcYmIiyNiVkTMmjx5ck0Tb2ZmZjZS1aKAVUlzpJ7CVNyUyU/bbYS5C5gpaS9JbcDJwLySME+QTSqLpF2BVwBLK4xrZmZmZoOgFgWsSpoj9RSmoqZMUNnT9q6uYEtnV29pNRsWIqIDOAu4AVgMXBURiySdIemMFOzLwBskPQjcDHwuItb0FHfoz8LMzGzwdXYFHb7/swbSUoN9VNIcqacwbRXErdi5v1rI7xY/w13nHjfQXZg1jIiYD8wvWXdR0ecVwFsqjTsQjz2zjuO/eRvXfPwNHLznTtXuzszMrOY+9uMFrFy7ies+eWS9k2IG1KYGq5LmSPOAU9NogocDayNiZYVxK5YTRAxouHozK+OWR1YBcN0DK+ucEjMzM7PhoeoarIjokFRojpQHLi00ZUrbLyJ7kn4isATYQNY5v8e4A01LTqLL5Suzmik8r1C5xrxmZmZmtp1aNBGspClTAGdWGnegcsra4ZpZbcklLDMza2BuwGSNpCYTDTeKXE50OYeZ1Yxzk5mZNT4/BLTG0lwFLMlPMMxqqLuJYH2TYWZmZjZsNFkBy00EzWopcAnLzMwan+/+rJE0VwHLTQTNBoVcwjIzswblbsLWaJqrgOUmgmY15fxkZmZm1j9NVsCCTt8RmtWcnw6amVkj8zyo1kiaqoCVl5sImtXSlAmjAJg4prXOKTEzMyvPzwCt0TRVAUupiaCfYpjVxgF7TARg+qSx9U2ImZmZ2TDRVAWsXGrH5IEEzWqj0DTQNcPWDCTNlvSIpCWSzimzXZLOT9sfkHRIyfa8pHsl/WboUm1mZsNNUxWw8ulsfDNoVhuFZhfOUjbcScoD3wVOAPYDTpG0X0mwE4CZ6TUXuLBk+6eAxYOcVDMzG+aaqoCl7hos3w3a8FfB0/bPSrovvRZK6pQ0KW1bJunBtG3BwNOQvTtHWRM4FFgSEUsjoh24EphTEmYOcHlk7gAmSpoKIGkP4G3AJUOZaDPrmwdiskbTVAWs7iaCXXVOiFmVKnnaHhFfi4iDIuIg4PPAHyLiuaIgx6Tts6pIR+FYA92FWaOYBjxZtLw8ras0zLeAfwR6vcJImitpgaQFq1evrirBZmY2PDVVActNBK2JVPK0vdgpwM9qnQg3EbQmUu4Zd+kvu2wYSW8HVkXE3X0dJCIujohZETFr8uTJA0mnmQ2Ar1PWSJqqgJVzE0FrHpU8bQdA0lhgNvDLotUB3Cjpbklze4jX55P27hosNxK04W85ML1oeQ9gRYVhjgDeKWkZ2cOON0v6yeAl1cz6Qx6o3RpMUxWw5CaC1jwqedpe8A7gv0uaBx4REYeQNTE8U9KbtttZBU/aXYNlTeQuYKakvSS1AScD80rCzANOTaMJHg6sjYiVEfH5iNgjImakeL+PiA8NaerNzGzYqKqAJWmSpJskPZbedyoTZrqkWyQtlrRI0qeKtn1R0lNFHfVPrCY9OQ8pbc2jkqftBSdT0jwwIlak91XANWRNDvst190HayCxzRpHRHQAZwE3kI0EeFVELJJ0hqQzUrD5wFJgCfAD4ON1SayZ9ZtbWlgjaaky/jnAzRFxXhrl7BzgcyVhOoB/iIh7JE0A7pZ0U0Q8lLZ/MyL+o8p0AJDPuYmgNY3up+3AU2SFqA+UBpK0I3AU8KGideOAXESsS5/fAvzrQBLhebCsmUTEfLJCVPG6i4o+B3BmH/u4Fbh1EJJnZgPkUQSt0VTbRHAOcFn6fBnwrtIAqXnFPenzOrInh2X7klRLnmjYmkSFT9sBTgJujIiXitbtCtwu6X7gTuC6iLi+qvRUE9nMzMxsBKm2BmvXiFgJWUFK0pTeAkuaARwM/Llo9VmSTgUWkNV0Pd9D3LlkEz+y5557lt2/mwhaM+nraXta/hHwo5J1S4EDa5GG7qeCzlJmZmZmFemzBkvS79IkpqWv3oaMLref8WSjnJ0dES+m1RcC+wAHASuBr/cUv5IO+XmPImhWUzmPImhmZsOAb/2skfRZwIqI4yJi/zKva4Fnima5nwqsKrcPSa1khaufRsTVRft+JiI6I6KLrEPxgDrid5+Mmwia1dTWPlj1TYeZmdWPpNmSHpG0JPW5L90uSeen7Q9IOqSSuJI+kbYtkvTVgadvoDHNBke1fbDmAaelz6cB15YGUNYx6ofA4oj4Rsm2qUWLJwELq0lM982g7wbNaqIwt4ifDJqZjUyS8sB3yab92A84RdJ+JcFOAGam11yyFkq9xpV0DFlf/gMi4tVATQY8M2sE1RawzgOOl/QYcHxaRtLukgp9R44APkw2MWPpcOxflfSgpAeAY4C/ryYxHkXQrLYKDy3cRNDMbMQ6FFgSEUsjop1ssu3SbiJzgMsjcwcwMT1E7y3u3wHnRcRm6J5WZMB8lbJGUtUgFxHxLHBsmfUrgBPT59spP2kqEfHhao5fyk0EzWqru4DlPGVmNlJNA54sWl4OHFZBmGl9xN0XOFLSV4BNwGci4q7Sg1cyyJnK32aa1U21NVgNpXAz2OkSlllNbG0i6DxlZjZClSu9lF4UegrTW9wWYCfgcOCzwFXS9r2pKhnkzKzRVDtMe0MpNBH0zaBZbWxtImhmZiPUcmB60fIewIoKw7T1Enc5cHWa4PtOSV3ALsDqgSTS937WSJqqBstNBM1qq3saLOcpM7OR6i5gpqS9JLUBJ5MNclZsHnBqGk3wcGBtmie1t7i/At4MIGlfssLYmgGl0C0ErcE0VQ1Wzk0EzWqqex4sl7DMzEakiOiQdBZwA5AHLo2IRZLOSNsvAuaT9b1fAmwAPtJb3LTrS4FLJS0E2oHTwhcbaxJNVsDyKIJmteR5sMzMLCLmkxWiitddVPQ5gDMrjZvWtwMfqm1KzRpDUzYRdPnKrDa6B7moczrMzMx64+uUNZLmKmCls+l0CcusNrqHaXeeMjOzxuQuWNZomquA5SaCZjWV81XLzMzMrF+asoDlp+3WDCTNlvSIpCWSzimz/bOS7kuvhZI6JU2qJG4/0gD4oYWZmTU4X6asgTRlAcsd8m24k5QHvgucAOwHnCJpv+IwEfG1iDgoIg4CPg/8ISKeqyRuxenoPtbAzsPMzGywlZmf2KyumqyAlb17mHZrAocCSyJiaRpp6UpgTi/hTwF+NsC4PfJEw2ZmZmb901wFrJybM1nTmAY8WbS8PK3bjqSxwGzgl/2JK2mupAWSFqxevbpsItyv0czMhgNfpayRNFcBy8O0W/Mo196hp1/2O4D/jojn+hM3Ii6OiFkRMWvy5Mm9JsZ5yszMGpUbCFqjabICVvbuJoLWBJYD04uW9wBW9BD2ZLY2D+xv3F65WbuZmZlZ/zRXActNBK153AXMlLSXpDayQtS80kCSdgSOAq7tb9xKeGROayYVjMwpSeen7Q9IOiStny7pFkmLJS2S9KmhT72ZmQ0XVRWwJE2SdJOkx9L7Tj2EWybpwTSc9IL+xq+Umwhas4iIDuAs4AZgMXBVRCySdIakM4qCngTcGBEv9RV3IOkoVGC5UtiGuwpH1zwBmJlec4EL0/oO4B8i4lXA4cCZAx2Z08wGhx8EWiOptgbrHODmiJgJ3JyWe3JMGlJ61gDj98lNBK2ZRMT8iNg3IvaJiK+kdRdFxEVFYX4UESdXEncg5IcW1jwqGV1zDnB5ZO4AJkqaGhErI+IegIhYR/bgouygM2Y29Nyc3RpNtQWsOcBl6fNlwLuGOP42POKZWW11z4Pl8Zls+KtkdM0+w0iaARwM/LncQSoZndPMzJpbtQWsXSNiJUB6n9JDuABulHS3pLkDiN/PIaX7fR5mVkb3PFjOUzb8VTK6Zq9hJI0nmw7h7Ih4sdxB+jM6p5nVji9T1kha+gog6XfAbmU2nduP4xwRESskTQFukvRwRNzWj/hExMXAxQCzZs0qm49yqbjoGiyz2pAHubDmUcnomj2GkdRKVrj6aURcPYjpNLN+cgtBazR9FrAi4rietkl6ptA+XdJUYFUP+1iR3ldJuoasLfxtQEXxK5V3E0GzmpP8ZNCaQvfomsBTZKNrfqAkzDzgLElXAocBa9P1ScAPgcUR8Y2hTLSZmQ0/1TYRnAeclj6fxrZDRQMgaZykCYXPwFuAhZXG7w+5iaBZzQk3EbThr8KROecDS4ElwA+Aj6f1RwAfBt6cRsO9T9KJQ3sGZtYbX6eskfRZg9WH84CrJH0UeAJ4L4Ck3YFLIuJEYFfgmlT4aQGuiIjre4s/UIVRBLtcwjKrmZzkQS6sKUTEfLJCVPG64lE5AzizTLzbcSsks4YlDyNoDaaqAlZEPAscW2b9CuDE9HkpcGB/4g9U3hMNm9Wc5FphMzMzs0pV20SwoXgUQbPa29IZrHpxc72TYWZmZjYsNFUBS24iaDYofnnP8nonwczMrEduym6NpKkKWJ5o2MzMzGxkcQ8sazRNVcDa2gerzgkxMzMzM7MRqakKWN1NBF2DZWZmZjZi+NbPGklTFbDcRNDMzMxshHEbQWswTVXAyhcKWG4jaE1A0mxJj0haIumcHsIcnSY9XSTpD0Xrl0l6MG1bMHSpNjMzMxvZqp1ouKF4mHZrFpLywHeB44HlwF2S5kXEQ0VhJgLfA2ZHxBOSppTs5piIWDNUaTYzM6sXN16yRtJUNVhKZ+MmgtYEDgWWRMTSiGgHrgTmlIT5AHB1RDwBEBGrBisx++46frB2bWZmVhW5jaA1mKYqYOXdB8uaxzTgyaLl5WldsX2BnSTdKuluSacWbQvgxrR+brkDSJoraYGkBatXr+4xIQdNn8iuO4we2FmYmZmZjTBuImjWmMo9jiv9ZbcArwWOBcYAf5J0R0Q8ChwREStSs8GbJD0cEbdts7OIi4GLAWbNmtVjrsnn5KYXZmZmZhVqqhqswjDtnS5h2fC3HJhetLwHsKJMmOsj4qXU1+o24ECAiFiR3lcB15A1ORyQnJynzMzMzCrVVAWs7omGfTNow99dwExJe0lqA04G5pWEuRY4UlKLpLHAYcBiSeMkTQCQNA54C7BwoAnJSW52a2Y2gvU1qq0y56ftD0g6pB9xPyMpJO0y8PQNNKbZ4GiqJoJ5NxG0JhERHZLOAm4A8sClEbFI0hlp+0URsVjS9cADQBdwSUQslLQ3cI2y/NACXBER1w80LTmJjq6uak/JzMyGoUpGtQVOAGam12HAhcBhfcWVND1te2KozsdsKDRVASuXarA6fTNoTSAi5gPzS9ZdVLL8NeBrJeuWkpoK1kI+JzZ3+KmFmdkI1T2qLYCkwqi2xQWsOcDlERHAHZImSpoKzOgj7jeBfyRrkVGVcEsLayBN1UQQoCUnOp3JzGpm7cYtLF65rt7JMDOz+qhkVNuewvQYV9I7gaci4v7eDl7JiLduIWiNpqoClqRJkm6S9Fh636lMmFdIuq/o9aKks9O2L0p6qmjbidWkB7JarA63ETSrmQefWsvGLZ31ToaZmdVHJaPa9hSm7PrUb/hc4At9HTwiLo6IWRExa/LkyX0m1qwRVFuDdQ5wc0TMBG5Oy9uIiEci4qCIOIhsSOkNZKOaFXyzsD01iapKS04e5MLMzMysNiod1bZcmJ7W7wPsBdwvaVlaf4+k3QaaSN/5WSOptoA1B7gsfb4MeFcf4Y8FHo+Iv1Z53B7l5RosMzMzsxqpZFTbecCpaTTBw4G1EbGyp7gR8WBETImIGRExg6wgdkhEPD2QBHoUQWs01Rawdk0ZiPQ+pY/wJwM/K1l3VhrS89JyTQwLKmmDC5DPuwbLzMy2N5hDTZs1q4joAAqj2i4GriqMalsY2ZZsQKalwBLgB8DHe4s7xKdgNuT6HEVQ0u+AclW25/bnQOnJxTuBzxetvhD4MlnN7peBrwN/Uy5+RFwMXAwwa9asHktQrsEyGxwRgfyY0IapwRxq2qzZ9TWqbRo98MxK45YJM6P6VJo1jj4LWBFxXE/bJD0jaWpErEzDca7qZVcnAPdExDNF++7+LOkHwG8qS3bP8jlPimo2GCLcDMOGtcEcarpfPnXlvfgyZdWYNWMnTn39jHono6E4T1kjqXYerHnAacB56b23eQxOoaR5YKFwlhZPAhZWmR7yOdHR6VxmVmvOVTbMlRsu+rAKwvQ01HRpXCBrzg7MBdhzzz3LJuTB5Wudn6wqUyaMqncSGoo8ULs1mGoLWOcBV0n6KNks3O8FkLQ7cElEnJiWx5I1rfhYSfyvSjqI7N5tWZnt/Zb3PFhmNfWOA3fn1/evoCuCvC9iNnzVfKjpcgeppDn77z9zdI+JNDOz4a+qAlZEPEs2MmDp+hXAiUXLG4Cdy4T7cDXHLyefE53ug2VWM6/cbQK/vt/NL2zYq2ao6bYK4ppZHYXrha2BVDuKYMNxActscLhvow1zNR9qeigTb2Y9c/9gazTVNhFsOHm5gGVWSzlfuawJRESHpMJw0Xng0sJQ02n7RWQjnZ1INtT0BuAjvcWtw2mYmdkw0HwFLNdgWZOQNBv4NtkN3SURcV6ZMEcD3wJagTURcVSlcStPR/buGiwb7gZ7qGkzqx9foqyRuIBl1oAqmXdH0kTge8DsiHhC0pRK4/ZHrruANeDTMTMzMxsxmq4PVotHEbTm0D1nT0S0A4V5d4p9ALg6Ip4AiIhV/YhbsUITwXC+MjOzBuSW7NZomq6AlXMNljWHnubjKbYvsJOkWyXdLenUfsTtt/kPruw7kJmZmdkI13QFrBYXsKw5VDLvTgvwWuBtwFuBf5a0b4VxkTRX0gJJC1avXt1jQgo1WJ/75YOVpdzMzGyI+c7PGknTFbByEh0uYNnwV+mcPddHxEsRsQa4DTiwwrhExMURMSsiZk2ePLnHhLjphZmZNTZfqKyxNF0BqyUvulzAsuGvknl3rgWOlNQiaSxwGLC4wrgVc42wmZmZWeWabhRB12BZM6hkzp6IWCzpeuABoItsOPaFALWcs+cXdy+v8mzMzMwGl8dhskbSdAWslpw8X481hb7m7EnLXwO+VkncgWrv6KrFbszMzAaFm7Jbo2m6JoL5nOjodAHLrFacm8zMzMwq15QFLNdgmZmZmY0kvvezxtGUBSz3wTKrHU8wbGZmZla5Jixg5TyKoJmZmdkI4S5Y1miqKmBJeq+kRZK6JM3qJdxsSY9IWiLpnKL1kyTdJOmx9L5TNekByAvXYJnVkHOTmZmZWeWqrcFaCLybbILTsiTlge8CJwD7AadI2i9tPge4OSJmAjen5arkcznP22NWQ24haGZmjc7XKmskVRWwImJxRDzSR7BDgSURsTQi2oErgTlp2xzgsvT5MuBd1aQHIJ/zxKhmZmZmI4WHabdGMxR9sKYBTxYtL0/rAHaNiJUA6X1KTzuRNFfSAkkLVq9e3ePB8rkcnX6MYVYz4UaCZmZmZhXrs4Al6XeSFpZ5zekrbmEXZdb1+44tIi6OiFkRMWvy5Mk9hnMNllltnXviq+qdBDMzs175zs8aSUtfASLiuCqPsRyYXrS8B7AifX5G0tSIWClpKrCqymPR4j5YZjX1it12qHcSzMzMeiSPI2gNZiiaCN4FzJS0l6Q24GRgXto2DzgtfT4NuLbag+UkF7DMaijn65aZmZlZxaodpv0kScuB1wPXSbohrd9d0nyAiOgAzgJuABYDV0XEorSL84DjJT0GHJ+Wq9KSdwHLrJZy7j1sZmYNLtz/3hpIn00EexMR1wDXlFm/AjixaHk+ML9MuGeBY6tJQynXYJmZWTFJk4CfAzOAZcD7IuL5MuFmA98G8sAlEXFeWv814B1AO/A48JGIeGEo0m5mZsPPUDQRHFItOXkUQWsKPU3QXbT9aElrJd2XXl8o2rZM0oNp/YJq0pFzG0Eb/vqcc7GPORtvAvaPiAOAR4HPD0mqzawibmhhjaaqGqxGlMtlNVgRgZzjbJgqutk7nmygmLskzYuIh0qC/jEi3t7Dbo6JiDVVp6XaHZjV3xzg6PT5MuBW4HMlYbrnbASQVJiz8aGIuLEo3B3AewYzsWZmNrw1ZQ0WgFsJ2jDX2wTdQ8p9sKwJVDLnYm9zNhb7G+C3PR2o0jkbzay2fNtnjaTpClj5VMDq6Oqqc0rMqlLpzd7rJd0v6beSXl20PoAbJd0taW65A1R6I1jcQvCRp9f14xTMhs5QzNko6VygA/hpTzupdM5Gs+GkgibrknR+2v6ApEP6iivpa5IeTuGvkTRxwOkbaESzQdK0BSyXr2yYq2SC7nuAl0XEgcB3gF8VbTsiIg4h609ypqQ3bbezSm8Ei1LyxHMbKky+2dCKiOMiYv8yr2tJcy4C9DLnYm9zNiLpNODtwAfDw5XZCNJH/8SCE4CZ6TUXuLCCuO7baE2r+QpYcg2WNYVeb/YAIuLFiFifPs8HWiXtkpZXpPdVZCN9HjrQhLiJoDWBSuZc7HHOxjS64OeAd0aEnzLYSFNJk/U5wOWRuQOYmB5m9Bg3Im5MU/lA1rdxj2oS6cce1kiar4DlGixrDr1N0A2ApN2URnKRdChZfn5W0jhJE9L6ccBbgIUDTUhx8coP7m2YKjvnYj/mbLwAmADclEbmvGioT8Csjippst5TmKr7NlbSnN2DmlmjabpRBN0Hy5pBRHRIKtzs5YFLI2KRpDPS9ovIRjL7O0kdwEbg5IgISbsC16QLTgtwRURcP9C0FNdguXhlw1FPcy72Y87Glw9qAs0aWyVN1nsKU3Xfxoi4GLgYYNasWb4M2bDQdAWswpw9ngvLhrtyN3upYFX4fAHZk/XSeEuBA2uVDjcRNDMb0fpsst5LmLbe4hb1bTy22r6NbmFhjaTpmggWhmnv9DjtZrXh8pWZ2UjWZ5P1tHxqGk3wcGBtmhLBfRttRGq6AlZhkAsXsMxqo3iY9ivvfKJ+CTEzsyHXU/9ESWcUmq2TtbZYCiwBfgB8vLe4KY77NlrTaromgnnXYJnVVHHn4VseWc2SVet5+ZTxdUyRmZkNpQqarAdwZqVx03r3bbSm1Xw1WC5gmdVUrqSJ4JZODyBjZmaNxXd91khcwDKzXpUOcnHCt//IfU++UJ/EmJmZlfBYTNZomreA5dFkzAbN5X9aVu8kmJmZmTWkqgpYkt4raZGkLkmzeggzXdItkhansJ8q2vZFSU+lzo33STqx3D76o3serE4XsMxqwcO0m5lZw/NtnzWQage5WAi8G/h+L2E6gH+IiHskTQDulnRTRDyUtn8zIv6jynR0K4wi2OUaLLOaKO2DZWZm1kjk+USswVRVwIqIxbDtKGNlwqwEVqbP6yQtBqYBD/UYqQr5fKrBch8ss5roLX+bmZnV25tW/JC9eQp4a72TYgYMcR8sSTOAg4E/F60+S9IDki6VtFO1xyg0Z3px45Zqd2VmlK/B6qryAcbKtRu5+6/PV7UPMzMzgKkbHua1LK53Msy69VnAkvQ7SQvLvOb050CSxgO/BM6OiBfT6guBfYCDyGq5vt5L/LmSFkhasHr16h6P8+elzwLwhWsX9RjGzCpXrgbrV/et4O3f+SMxwKa4R33tVv7Xhf9TbdLMzMwIcsidsKyB9NlEMCKOq/YgklrJClc/jYiri/b9TFGYHwC/6SUdFwMXA8yaNavHXFQYnv3pFzdVm2wz68XCp15kQ3sn40a18NdnX2LKhNGMactXFLe9w3NpmZlZjQhyLmBZAxn0JoLKHn//EFgcEd8o2Ta1aPEkskEzqrLDmFYAWtwz32zQFaZDOOprtzL3xwsAWPjUWs695sGqmxGamZlVIsjhYQStkVQ7TPtJkpYDrweuk3RDWr+7pPkp2BHAh4E3lxmO/auSHpT0AHAM8PfVpAfgI0fMAOCjb9yr2l2Z1ZWk2ZIekbRE0jllth8taW1RvvpCpXFr5e5lz7NoxVoA/vjYGp5dv5m3f+d2fvrnJ3h89Xr+sual7rAvbGjnEz+7lyef2zBYyTEzs5FIcg2WNZRqRxG8BrimzPoVwInp8+1QfvzMiPhwNccvZ0xrnnxOeJR2G84k5YHvAscDy4G7JM0rmt6g4I8R8fYBxq3Ybz7xRt7+ndu3W/+RH921zfK///bh7s8f/uGdPP3iJpb+24nkcuL7ty3l1/ev4Nf3rxhoMrq9sKGdP//lOd766t2q3peZmQ1zcg2WNZZq58FqOJIY05pn45bOeifFrBqHAksiYimApCuBOVQ2vUE1ccuaNnFMReF+cffy7s+FfpAbtnTy3Pr27v6RxW566Bkmjm3ldTMm9Ss9Z11xL7cvWQPAhR88hBNeM3W7MOff/Bg3LHqamVPG8833H+Th5s3MmpY8yIU1lKYrYAGMdgHLhr9pwJNFy8uBw8qEe72k+4EVwGciYlGlcSXNBeYC7Lnnnr0mJldFn8ZP//w+bnzombLb/vflWb+tdx8yjX13ncCadZu55Pa/APDxo/fh/a+bzst2HtcdfsmqdSx86kX++tzWpod/99N7+P0/HMVeu4zrLkQ9sPwFvnHTowAsWvEi++2+A287YPdtCopPPLuBPzy6ipMO2YPxo1p4aXMHY9vyLoiZmQ0zmzuDtgjaO7poaxnSGYjMymrKAtb4UXnWbvA8WDaslbvLL308dw/wsohYn/o1/gqYWWHcikfmhOoGjempcFXs6nue2m7d9259nO/d+jg7jG7hxU0dvcZ/89f/AMC+u47n0WfWb7f93+Y/zL/Nf5jJE0axet1mxrbl2dCePYT552sXMaolx+Y0suG4tjxnH7cvmzuy7TuObeOQPSfy8injac3lWN/ewbI1L7F+cwcTx7Sx8/g28jmxpbOLjs7sa5w4tpW1G7ewob2TnLL5+YLsvVCTlxMUKvUKX2+Q/fGUwkUEWzoDpX3kc9qmJlDaGj6fEy2peXQQCNHe2QUELbkc+ZyQoCPtr1KdXUFrPkd7Z1f3PIOw9UdWWJWT6IrsuKX7nzSujXGjmvJyY2YN4K/PbeQAdfH7h59h9v7bt2gwG2pNecXbY6exrFi7sd7JMKvGcmB60fIeZLVU3YrmkyMi5kv6nqRdKonbX/k6jsrZV+GqWLnCVbHV6zYDdBeuCjYXDRv/UnsnX5nvCStr6fxTDuadB+5e72SYWZPqQt0Pm8waQVMWsKZMGMXV965h4VNredXUHcjJmc6GnbuAmZL2Ap4CTgY+UBxA0m7AMxERkg4lGxX0WeCFvuL2Vz0LWPU0YVQLF3zwENZu3MJfVmfNEttacuy1yzh2GNPC8y9t4YWN7Wzp6KIln8uapgQ8v6GdCaNb2WFMC11B95D1nV1BS16phqqru7anuFlLoQYqJ5GTaM1ntVJdAR1dXbTkckhbw2Xv0NHZRUdXkFe2z66A1nSsLR1dbO7oymrTcuoO05cIaMmL9o4uJBXVxkX39sJ7Z0qIyvy/PXj6xJr8PczMyhk/qpVcexcTXFNuDaIpf4kTRmenVRj17LTXv4wvzdm/nkky65eI6JB0FnADkAcujYhFks5I2y8C3gP8naQOYCNwckQEUDZuNenJ1+kBxTsP3J33v246M6eM56X2To75j1t7DHvtmUfw24VPs8v4Nv7vddvXQO21yzhu/vRRdHQFG7d0ct0DK/mnax4E4L4vHM+CZc/T1pLjgD12pK0lx6YtXUwa1zZYp2ZDSNIk4OfADGAZ8L6IeL5MuNnAt8nyzSURcV7J9s8AXwMmR8SaQU62mVXo1dMm0vkXKp7s3mywNWUBK5/btoPjZX/6K19856tdi2XDSkTMB+aXrLuo6PMFwAWVxq1GNYNcDNQrd5vAv7/7Ndv03Vl23tv48Z+W8c/XbltenDxhFAdOn8iBqabk8L133mZY+RP2340LP/RaANpyoq0lxymHTmf1us286+DdmTi2jeP223WbfY512aqZnAPcHBHnpXnhzgE+Vxygr+kNJE1P254Y0pSbWZ9yuRxdCro8R481iKYcamVsmScYa9a31yElZtYf3znlYJZ85QSWnfc2rj/7TWUHRvjw62fwsp3HbrPuhrPftM3y/tN25HsfPKR7+avvOWC7/UjiU8fN3GaUQmtac4DL0ufLgHeVCdM9vUFEtAOF6Q0Kvgn8I55sx6zhSDlEcO8TL9Q7KWZAkxawPnj4ntvN2/Mv8xbyszuf4E1fvYVf37+CC299HIDPX/0gZ15xTz2SaWbJ2cfNBGDvyeNoyff9b+n6T72JthTuq+85oGxTvhNfM5VTX/8yzjhqHyaMbq1tgm242TUiVgKk9yllwpSb3mAagKR3Ak9FxP19HUjSXEkLJC1YvXp19Sk3sz5t7uwiR5RtHm5WD03ZRHDqjmP473PezIxzruteN//Bp5n/4NMAfOJn9wIwbacx/OzOrLXHN97XyagWt901q4e/PXJv3jtresUTGo9py3PLZ4/mnr8+zzt6GZ3uX933csSQ9DtgtzKbzq10F2XWhaSxaR9vqWQn/Zn+wMxqQ7m8Jxq2htKUBayC+75wPJ+88j5ue7T8U8RPpoIWwCv+z/V855SDueXhVXzsqH3oiuBVU3cYqqSajWht+VzFhauCaRPH9DuONa+IOK6nbZKekTQ1IlZKmgqsKhOsp+kN9gH2Au5P/Xj3AO6RdGhEPF2zEzCzKoicC1jWQJq6gDVxbBuX/82hdHUFr/vK73j2pd77YRVqtq6+d+ukp996/0F8/aZH+Pvj9uXdh+wxqOk1G47OOublXHDLkorD7zd1Bx5amU3hNX5UC+s3d9Ca9wA0NqjmAacB56X3a8uEKTs1QhqBs7tJoaRlwCyPImjWOLIpzrv6Dmg2RJqyD1apXE7c/c/Hs+y8t/FfZ7wegI+9ae/u7aNbe/4azv75fTz53EY+fdX9zDjnOr7/h8e757QxG+k+dPiefOLYl/caZt5ZRzB90hjeeeDunH/KwVz3yTd2b/v08fuy7Ly3eYRPG2znAcdLeoxsJMDzACTtLmk+ZFMjAIXpDRYDV1U7vYGZDY221pbuNr5HfvX3dU2LGTR5DVY5r5sxifu/8BbGj27hH2e/kqdf3MTUHUZz57LnOPXSO2nv6CKfE1+esz9f+vUiNnds+0Tk33/7MP/+24c5cuYu7DN5PK/efQfe89o9fINoTe+nf3sYH7zkz9usy0vb9F289swj2Lilk5MvvoP9p+3Abz5xJAB//Mc3D2lazYpFxLPAsWXWrwBOLFruc3qDiJhR6/SZWXV223EMz6UarCef21jn1JiNwAIWwI5jt44oVujDcfjeO/PIl2cDdBeWPnDYnvzPkjWsXr+ZfSaP5+bFq/jNAyt4bNV6/vjYGv74WNZC5LO/eIAjZ+7C3x65Nxs2d3DoXpPYefyo7mNEBCvXbmLZmpcY1ZrjtS+bNFSnalYzuTIPEUrnxzpw+kTWbdoCwFnHzBySdJmZ2Qin3Daj1Kzf3MH4MtN8mA0V//qKlKuFesPLd+n+vP+0HflUGk76xkVP85M/P8HajVtYWlLggqzTfntnF6NactvVgs162U785G8PY3Tr1if/XV1Rl8lczapRKHR98/0H8vDKdQBMGN3KsvPeVlF8970yM7PqiVxRH6z9/+UGHv7y7G3us8yGUlUFLEnvBb4IvAo4NCIW9BBuGbAO6AQ6ImJWWj8J+DkwA1gGvC8inq8mTUPlLa/ejbe8euuIwHcte47v3rIEAbc8spr2ziyjlxauABb89XnecN7v2WV8G8e8cgodncEPb//LNmFOOXRP/uUd+/mfgzWMyRO2n2uq8EzgpIP3gIMr39erpu7A4pUvMmWH0TVKnZmZjVglNVgAtz+2huP227UuyTGrtgZrIfBu4PsVhD2mzKhL5wA3R8R5ks5Jy5+rMk118boZk/jRRw7dbn1EbFMz1tUVXHDLEv70+LP8aemzPPrM+rL7+9mdT3TP0fXGl+/Cy6eMZ979Kzhq38kcsudEjn3VruzuIaptCL18yoTt1pVrNliJX591BLc8sprjXlVuvlczM7N+kLabB+tTV97Lon+dXacE2UhXVQErIhZD+aZ1FZoDHJ0+XwbcyjAtYPWk9LvJ5cQnj53JJ4/Nmho+91I7E0a30JKqAl7c1MHo1hy/e2gV/3X3k9yx9FluX7KG25dkZdNr7n2Ka+59in++Nhvc6o0v34WZu45n9x3H8OHXv8w1XjaoLjl1Fn97+daK6oE2a23J5zjeTxbNzKwW2sYxQRu54H37cdZVDwHwUnsn3//D43zsqH3qnDgbiYaqD1YAN0oK4PtppnuAXSNiJUCaALLHx9mS5gJzAfbcc8/BTu+QmTRu22ZXO47JBuB42wFTedsBUwHo6OzijqXPkc+JfE585/eP8T+PP0tnV3DvE893F77+/beLeeurd+OVu+3A2LY8Y9ryjGnNl3xu4cVNW5g4tpW2fI5cTrTmcoxuzTGqJU9ri2jL58jn5JERbTvFzS3e+PJdmHvk3r2ENjOzZiBpNvBtIA9cEhHnlWxX2n4isAE4PSLu6S1uTbuJ7PpqAN6+yyrGnf46PvKju4Bs5GcXsKwe+ixgSfodsFuZTedGRLnJGss5IiJWpALUTZIejojb+pPQVCi7GGDWrFkjaiKqlnyON87cOtjGjz96WPfniOD5DVv4yR1/5Ye3/4WbF6/itwufrslxW/NixzFtjGrZWgAb1ZpjdEtWYFu3aQs7jxvFuFEttLWIHUa30prP5hQb05YnJzF+dAvjR+UZP6qVcaPyjB/VQk5idGueUS1ZAW9cKgDmJRfsivR1QSsK9zrgDuD9EfGLtG4ZZfo91tJ3P3hI9wMBMzNrTpLywHfJ5pBbDtwlaV5EPFQU7ARgZnodBlwIHNZH3Np1E9k5zcf45B0cc8RhXH/2kcz+1h8B+OZNj3L2cTN9b2FDqs8CVkQcV+1B0lwjRMQqSdcAhwK3Ac9Imppqr6YCq6o91kgjiUnj2rZpdtjR2cWGLZ1sau9kQ3pt3NLJxvZONrR3sKmji03tnYxpy9MVQUdnsGFLJ+0dXWzp7GJLRxfPvtRORLClK9i0pZPNHV1sTu/rNnWwduMWHlr5IjuOaWVcW57nNrTT1UX34B4DlROMa2thVGuOllyOiWNb6Ypg3KgWRrfkaWvJ0ZrPMaolR2te3cvjR7cwKp99/p/Hn6W1JceJ++9GV0BnBJ2dXTz3UjvjRrWw8/hRdEWQk9jQ3sGa9e3stctYWnI5NrR38L5Z0+v+j7jCC1oh3P8jmxy1VLl+j1UrjJCZ96iXZmYjwaHAkohYCiDpSrIuHsXXoznA5RERwB2SJqb7uhm9xK1dN5FJqTXFTV8A4JUT92T+WzfztZuW8MAt9/J3t+WYOnE0a9ZvZnNHF2+cOZnRLXnaOzoZk+45RrfkyeVEV1fQGUEOGN2aJ8geZre25GjL5dg6moYo3CoUVtXi1qGSXfR9HF+f+yuXb+E1R727Zvsb9CaCksYBuYhYlz6/BfjXtHkecBpwXnqvtEbMetGSz7FDPscOo4e2diEi6AroiuClzR0Isbmzk/WbOnhpcyfrNm9h9brNjG7NsykV6P6y5iVa8jlac6IzgvWbOmjv7OKlzZ0EwTMvbmJ0S571mzvo6Opiw8YsXntHJ1s6I/vc2dUdr9htj64e0Hm87YDdG2H+jEouaACfAH4JvG6oEvaxo/bmO79fQluqrTQzs6Y2DXiyaHk5WS1VX2Gm9RG3om4iFXURaRsHB34A7r+iu5C1H/Cfxb0w1qX3PLC0/G5s5FofY6BRCliSTgK+A0wGrpN0X0S8VdLuZE2aTgR2Ba5JNQItwBURcX3axXnAVZI+CjwBvLea9Fh9SSIvyCMmji38V2ulzOBzg6K9o4v1mzu6a9xacqIlnzU7bMnl6Ojq4sWNW2jJ5bqf/rywYQtdEUwc20Z7RxfjR7cwtjEGCunzgiZpGnAS8Ga2L2D11O+xOP6A+jV+4s0zOf0NM2hrcQHLzGwEKFcdUtpVo6cwlcTtVcVdRE66EI7/Emx6ETo3Q2c7RJlWNQFB9kBYwJbOLoJgQ3snAPlcjrxEe2cnWzoC5bLWNZu3dHU/yI2iVEQ/zqavsFHBVzOi+sgMISnHzBrur9pRBK8BrimzfgVZR0fSE/gDe4j/LHBsNWkwK2hryTGpZfu5mopNmbDtvEsv23kwU1SVSi5K3wI+FxGdZZo09tnvcaD9Gttacuw8flSlwc3MbHhbDkwvWt4DWFFhmLZe4ta+m8j4KdmrDyKryAIoXM08K6PVkh9BmzWmSi5os4Ar04AW7wG+J+ldsG2/R7KHINtP0mZmZta3u4CZkvaS1AacTNbFo9g84FRlDgfWpuZ/vcUtdBMBdxOxJlP3jiZmVlb3RQl4iuyi9IHiABGxV+GzpB8Bv4mIX/XR79HMzKxiEdEh6SyywZTywKURsUjSGWn7RcB8spZLS8iGaf9Ib3HTrt1NxJqWC1hmDajCC1pPeuv3aGZm1i8RMZ+sEFW87qKizwGcWWnctN7dRKxpuYBl1qD6uqCVrD+96HOP/R7NzMzMbHC5D5aZmZmZmVmNKPozvmSDkLQa+GsPm3cBaj65ahWcnt4Nt/SsiYjZQ5WYoTLM8hQ0Xpqcnt71lp6mzFMw7PKV09O74ZQe56nG4PT0bjilp995algWsHojaUFEzKp3Ogqcnt45PY2vEb+TRkuT09O7RktPI2i078Tp6Z3T0/ga7TtxenrX7OlxE0EzMzMzM7MacQHLzMzMzMysRpqxgHVxvRNQwunpndPT+BrxO2m0NDk9vWu09DSCRvtOnJ7eOT2Nr9G+E6end02dnqbrg2VmZmZmZlYvzViDZWZmZmZmVhcuYJmZmZmZmdVIUxWwJM2W9IikJZLOGaJjLpP0oKT7JC1I6yZJuknSY+l9p6Lwn0/pe0TSW2uUhkslrZK0sGhdv9Mg6bXpXJZIOl+SapieL0p6Kn1P90k6cSjSI2m6pFskLZa0SNKn6v39DCf1yFPpuHXNV85TfabH+WqAnKecp3pIj/PUAI3UPJX26XzVc1rqm6cioileQB54HNgbaAPuB/YbguMuA3YpWfdV4Jz0+Rzg/6XP+6V0jQL2SunN1yANbwIOARZWkwbgTuD1gIDfAifUMD1fBD5TJuygpgeYChySPk8AHk3HrNv3M1xe9cpT6dh1zVfOU85Xg/S7dp5ynnKequ3vesTmqV5+x85XUf881Uw1WIcCSyJiaUS0A1cCc+qUljnAZenzZcC7itZfGRGbI+IvwBKydFclIm4DnqsmDZKmAjtExJ8i+zVdXhSnFunpyaCmJyJWRsQ96fM6YDEwjTp+P8NII+UpGMJ85TzVZ3qcrwbGeaqK4ztPOU+VMWLzFDhf9ZGWuuapZipgTQOeLFpentYNtgBulHS3pLlp3a4RsRKyPzAwpQ5p7G8apqXPg5m2syQ9kKqQC1WyQ5YeSTOAg4E/05jfT6OpV56CxsxXjfibqWueAuerfnKe2lYj/l6cp4YX56ntNeJvZsTd/zVTAatce8gYguMeERGHACcAZ0p6Uy9h65XGStIw2Gm7ENgHOAhYCXx9KNMjaTzwS+DsiHixt6BDkZ5hop7nPJzy1YjMU+B8NQDOU5VxnnKeqpTzVOVGZL6qV55qpgLWcmB60fIewIrBPmhErEjvq4BryKp8n0lViqT3VXVIY3/TsDx9HpS0RcQzEdEZEV3AD9haNT7o6ZHUSpa5fhoRV6fVDfX9NKi65Clo2HzVUL+ZeuYpcL4aIOepbTXU78V5alhyntpeQ/1mRur9XzMVsO4CZkraS1IbcDIwbzAPKGmcpAmFz8BbgIXpuKelYKcB16bP84CTJY2StBcwk6zj3GDoVxpSNek6SYen0VFOLYpTtcKPOTmJ7Hsa9PSkuD8EFkfEN4o2NdT306CGPE9BQ+erhvrN1CtPpWM7Xw2M89S2Gur34jw1LDlPba+hfjMj9v4vhmCklaF6ASeSjRLyOHDuEBxvb7IRR+4HFhWOCewM3Aw8lt4nFcU5N6XvEWo0sg/wM7Jq1y1kJe2PDiQNwCyyH/7jwAWAapieHwMPAg+kH/HUoUgP8EayqtwHgPvS68R6fj/D6TXUeSods+75ynnK+WoQf9/OU85TzlO1/X2PyDzVy+/Y+Srqn6eUIpqZmZmZmVmVmqmJoJmZmZmZWV25gGVmZmZmZlYjLmCZmZmZmZnViAtYZmZmZmZmNeIClpmZmZmZWY24gGVmZmZmZlYjLmCZmZmZmZnVyP8HTBW2hnw9jqAAAAAASUVORK5CYII=\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from cpa.plotting import CPAHistory\\n\",\n \"pretty_history = CPAHistory(cpa_api)\\n\",\n \"pretty_history.print_time()\\n\",\n \"pretty_history.plot_losses()\\n\",\n \"pretty_history.plot_r2_metrics(epoch_min=0)\\n\",\n \"pretty_history.plot_disentanglement_metrics(epoch_min=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 5 × 128\\n\",\n \" obs: 'condition'\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"perts_anndata = cpa_api.get_drug_embeddings()\\n\",\n \"perts_anndata\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Print and plot covars embeddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 1 × 128\\n\",\n \" obs: 'cell_type'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"covars_anndata = cpa_api.get_covars_embeddings('cell_type')\\n\",\n \"covars_anndata\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(0.0, 0.0, 'A549_Nutlin_0.05', 'A549_Nutlin_1.0')\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cpa_api.compute_comb_emb(thrh=0)\\n\",\n \"cpa_api.compute_uncertainty(\\n\",\n \" cov={'cell_type': 'A549'}, \\n\",\n \" pert='Nutlin', \\n\",\n \" dose='1.0'\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Setting up a variable for automatic plotting. The plots also could be used separately.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cpa_plots = cpa.plotting.CPAVisuals(cpa_api, fileprefix=None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_latent_embeddings(cpa_api.emb_perts, kind='perturbations', show_text=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_latent_embeddings(cpa_api.emb_perts + covars_anndata.X, kind='perturbations', show_text=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If your have a lot of cell types or a lot of perturbations, you can also chose to not display their names.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_latent_embeddings(cpa_api.emb_perts, kind='perturbations', show_text=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or I can change the color scheme for the emebddings.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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dXQ6FQIDw8HG+99Rbu3btn2Gf79u0IDw9HcHAwrly5AgDYvXs3YmNjAQC3b9/GnDlzEBISgpCQEBQUFDQ67+eff46IiAgEBwfjk08+abFOs07ilZSUBI1GY85TED1RlEollEql4bVCoYBCoQAAODs7Y8iQITh+/Dj8/PyQkZGBkSNHYvPmzUhMTETHjh2xZcsWJCYmYu7cuYZ99uzZg5SUFGzduhXx8fENzhcXF4fhw4fj008/hU6nQ1VVVYPtJ06cQFFREXbt2gW9Xo/Zs2fj9OnTGD58eLOfwayho9FoMHPmTHOe4pHMmjVL6hKITOLBkGlKfRfLz88P6enpeOmll3D06FFMmzYNAKDVajF06FDD9/v7+wMABg0ahMOHDzc63qlTp/DRRx8BAORyOTp37txge15eHvLy8hAWFgYAqKqqwrVr16QLHSISy8/PD6tXr8bFixehVqvx3HPPwdvbG+vXr2/y+zt06AAAsLGxgU6na/X59Ho9ZsyYgcjIyEfeh2M6RFbEyckJI0aMwNKlSxEUFIShQ4eioKAARUVFAIDq6moUFhY+8vFGjhyJHTt2AAB0Oh3u3r3bYLuPjw9SU1MN40QlJSUoKyszekyGDpGVCQoKwvfff4+AgAB069YNq1atwvz58xEcHIypU6fi6tWrj3ysP//5z8jPz0dwcDAmTZqEH374ocF2Hx8fBAUFITIyEsHBwY0Gqpsi0+v1+jZ9sockJCQ0Gr9p6j0pyGTNr7Tw7Q+W8aCqJa0GUVPXQeoSAFjOahAvWNBqEC6DLONn0h5s6RCRUAwdIhKKoUNEQjF0iEgohg4RCcXQISKhGDpEJBRDh4iEYugQkVAMHSISiqFDREKZbGqLhxffAyxnAT6vZQea3abTywVW0jy5rPXTCphLb901qUsAADxtIc88nfL8vdQlGARqL0tdQruZLHQeXnwP4AJ8RNQYu1dEJBRDh4iEYugQkVAMHSISiqFDREIxdIhIKIYOEQnF0CEioRg6RCQUQ4eIhGLoEJFQDB0iEspkD3wSkfQGDBiAZ599FrW1tZDL5QgPD0d0dDRsbCynfcHQIbIiDg4O2LdvHwCgrKwMCxYsQGVlJd566y2JK/svy4k/IjIpFxcXrFixAikpKdDr9dDpdFizZg0iIiIQHByMnTt3AgASExOxZMkSAMDly5cRFBSE6upqs9XFlg6RFXNzc0NdXR3KysqQlZWFzp07IzU1FTU1NYiMjIS3tzeio6MRFRWFw4cPY9OmTfjwww/h6OhotprMGjpNzSYojWekLoDIJJRKJZRKpeG1QqGAQqEwuo9erwcA5OXl4fLlyzh48CAAoLKyEkVFRXBzc8Pq1asREhIChUKBYcOGme8DwMyh09RsglJIXH5Q6hKITOJRQuZB169fh1wuh4uLC/R6Pd577z2MGjWq0fddu3YNHTt2xK1bt0xZbpM4pkNkpcrLy7F8+XK88sorkMlk8PHxwT/+8Q9otVoAQGFhIaqqqlBZWYn4+Hhs374dFRUVOHCg+TnFTYFjOkRWRK1WIzQ01HDJPDQ0FL///f2J5adMmYLi4mJMmjQJer0ezs7O2LhxI1auXImXX34Zffr0QXx8PF577TUMHz4cLi4uZqlRpq/v8FmxF4x0r7a86imwkuZZ0moQ/6O1jFU8bHUaqUsAwNUgTI3dKyISiqFDREIxdIhIKIYOEQnF0CEioRg6RCQUQ4eIhGLoEJFQDB0iEoqhQ0RCMXSISCiGDhEJ9UQ88ElEloMtHSISiqFDREIxdIhIKIYOEQnF0CEioRg6RCQUQ4eIhGLoEJFQDJ120mq1uHTpEsrKyqQuBQCg0WiQmZkpdRmE+/8XDysvL5egEsvC0GmlZcuW4YcffgBwf1nW0NBQvPvuuwgLC4NKpZKkJp1Oh5ycHCxatAhjx4594kPnzJkzSE1NBXD/l/z69euS1DF58mScO3fO8PrgwYOYNm2aJLVYEj4G0UqBgYFIT08HACQlJeHrr7/Gxo0bUVpaijfeeAN79+4VVsvp06eRlpaGnJwcDBkyBAUFBThy5AgcHR2F1fCgQ4cOYe3atSgrK4Ner4der4dMJkNBQYGwGjZs2IBvv/0WhYWFOHjwIEpKSvCnP/0JO3fuFFZDvcuXL2Pp0qUYMWIEbt26hYqKCsTHx6Nnz57Ca7EkXOGzlTp06GD498mTJzFhwgQAQPfu3YXWMXr0aPzqV79CZGQkFi1ahE6dOsHX11eywAGAv/zlL9i8eTP69u0rWQ2HDx/G3r17ER4eDgBwdXXFvXv3JKmlX79+mD17Nt555x04OTkhJSXliQ8cgN2rVuvcuTOys7Nx6dIlFBQUGBajr62thVqtFlaHv78/SkpKkJmZiezsbFRVVUEmkwk7f1NcXFwkDRzg/h8FmUxm+FlUVVVJVsvSpUuRnJyM/fv3Y9WqVZg1axZSUlIkq8dSsHvVSoWFhYiLi8Pt27cRHR2NSZMmAQByc3ORl5eHxYsXC6tFr9fj1KlTSE9PR05ODu7evYv4+HiMGTMGTk5OwuqoV/9z8fPzg52dneF9f39/YTV88cUXKCoqQl5eHmbOnInU1FQEBQUhKipKWA31kpKSEB0dbQjAyspKrFq1CitXrhReiyVh6FgJrVaL48ePIyMjAydOnEB+fr7wGpYsWdLk+6tWrRJaR15eHk6cOAEA8PHxgbe3t9DzP6i4uBhFRUV48cUXoVarUVtbi06dOklWjyVg6LRSXFyc0e3vvfeeoEqap1ar4eDgIHUZT7wvv/wSSqUSv/zyC44cOYJr165h+fLlSE5Olro0SXEguZV27twJDw8PTJw4ET169IBUmR0cHGx0e1pamqBKgM8++wxvvPEGVqxY0eS4kogg9vT0hEwmM1wxqyfFFbR6KSkp+Oc//4mpU6cCAJ555hnepwOGTqvl5ubiwIEDyMjIgK2tLQICAuDv748uXboIrWPz5s0A7v9SzZw5E1u2bBF6/gfVDx4PGjRIshrOnj0r2bmbY2dn12Bsq7a2VsJqLAe7V+1QUlIClUqFxMRELFy4EGFhYZLUER4ejj179khybktz7tw5uLu7G8ZN7t27hx9//BHPP/+88Fo++ugjPPXUU9i7dy/ef/997NixA+7u7nj77beF12JJGDptdPHiRahUKpw8eRIDBw5ETEwM3N3dJalF6tCZNWuW0e31rTIRwsLCsGfPHkMXq66uDhEREZL8fOrq6rBr164Gg9pTpkyR/NYGqbF71UqffPIJjh07hl//+tcIDAzEggULYGsr/sd48eJFw7/VajUuXbrUYHxp4MCBwmqJiYkRdq6WPDymY2NjI1m3xsbGBn5+fvDz80O3bt0kqcESsaXTSv3794ebm1uzV4dEDeAau+9EJpNh27ZtQup4UHJyMqKjo1t8z5zmzp2LESNGGJ5x2rFjB/Lz87Fx40ZhNej1emzYsAHbt283vLaxscGrr76KuXPnCqvDUjF0Wqm4uNjo9t69ewuqpHlarbbB4xqiNNXNCwsLE/o8WllZGeLi4nDq1CnIZDKMHDkSS5cuhYuLi7AakpKSkJOTg9jYWLi5uQEArl+/jg8++ACjRo3C9OnThdViiRg6JlBeXg5nZ2dJ++r1dyerVCpkZ2fj5MmTws6tUqmgUqnwr3/9C8OGDTO8f+/ePcjlciQlJQmrxRKEhYVh69atjbpU5eXliImJERrClohjOq107tw5rFu3Dl26dMEf//hHLFq0CHfu3EFdXR3WrFmD0aNHC63n/PnzSEtLw5EjR/DLL79g2bJlWLRokdAaPD090b17d9y5c6fB+I6TkxP69esnpAZLuFeoXm1tbZNjON26deNlczB0Wi02Nhbz589HZWUloqOj8dlnn2Ho0KG4cuUKFixYICx0/vrXvyIzMxO9evVCUFAQ5syZg4iICMPT1SL17t0bvXv3hlKpFH7uepZwr1A9Y11bKbq9loah00o6nQ4+Pj4A7l/JGjp0KAAIf7paqVSiT58+mDZtGsaOHQt7e3vJL8XW3xUM3B9Xqq2thaOjo5C7gX19fQFAktB92Pfff4/f/OY3jd7X6/WoqamRoCLLwtBpJRub/84G8vAVLJG/9PUPNaanp2PlypXw8vKCRqNBbW2tJJfwgcZ3BR85cgQXLlwQWkNhYSG2bt2K4uLiBl0ZkVfzvvvuO2HnehxxILmVBgwYAEdHR+j1emg0GkPw1P8Ve/D+GVE0Gg2ys7ORnp6OgoICvPDCC1i3bp3wOpoydepUfPnll8LOFxISgsjISAwaNKjBHwhL6HbRfWzptJKl/BW7cOECevXqhe7du8Pe3h5qtRparRZjxoyBh4eHJDUdOnTI8O+6ujp8++23wrt8tra2ePnll4Wek1qHMwc+ppYvX24YlDx9+jTWrl2L8PBw9OjRQ7KHH7Ozsw1fJ06cgJOTk7Cb8ioqKlBRUYGxY8ciJSXFMCdx/RdZDnavHlMhISHYv38/AODDDz9Et27d8OabbwIAQkNDsW/fPinLE87X19cwtcXDZDIZsrKyJKiKmsLu1WOqrq7OMGj81VdfYcWKFYZtOp1OaC0bNmxodptMJsOcOXPMXsPRo0fNfg4yDYbOYyowMBCvvvoqnJ2d4eDggN/+9rcAgKKiIuHTYXbs2LHRe1VVVUhNTUVFRYWQ0KlXXV2NxMRE/Pzzz1ixYgWuXbuGwsJCjB07VlgNZBy7V4+xc+fOobS0FN7e3oZf/MLCQlRVVQl9yvxBd+/exbZt27Br1y5MnDgRMTExQp97mjdvHgYOHIh9+/ZBpVJBrVZDoVA8cd1NS8aWzmOs/sbEB/Xp00d8Ibg/kJuYmIi0tDTDg5+iZ1MEgJ9++gkff/yxYUFEBwcHyaaUpaYxdKjd1qxZg8OHD2Pq1KlIS0uTZPmbenZ2dlCr1YZL9T/99FODKUNJeuxeUbv1798fdnZ2kMvlkk2KHhsbi8DAQFRXV2Pz5s348ccf4e3tjbNnz2LVqlXw8vIyew30aB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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"perts_palette = {'BMS': '#999999', \\n\",\n \" 'SAHA': '#4daf4a',\\n\",\n \" 'Dex': '#377eb8',\\n\",\n \" 'Nutlin': '#e41a1c',\\n\",\n \" 'Vehicle': '#000000'\\n\",\n \" \\n\",\n \" }\\n\",\n \"\\n\",\n \"cpa_plots.perts_palette = perts_palette\\n\",\n \"cpa_plots.plot_latent_embeddings(cpa_api.emb_perts, kind='perturbations', show_text=True)\\n\",\n \"cpa_plots.plot_latent_embeddings(cpa_api.emb_perts + covars_anndata.X, kind='perturbations', show_text=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Embedding contains only 1 vectors. Not enough to plot.\\n\"\n ]\n }\n ],\n \"source\": [\n \"cpa_plots.plot_latent_embeddings(list(cpa_api.emb_covars['cell_type'].values()), kind='cell_type')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"latent_response = cpa_api.latent_dose_response(perturbations=None)\\n\",\n \"cpa_plots.plot_contvar_response(\\n\",\n \" latent_response, \\n\",\n \" postfix='latent',\\n\",\n \" var_name=cpa_api.perturbation_key,\\n\",\n \" title_name='Latent dose response')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"perturbations_pair = ['Nutlin', 'BMS']\\n\",\n \"latent_dose_2D = cpa_api.latent_dose_response2D(perturbations_pair, n_points=100)\\n\",\n \"cpa_plots.plot_contvar_response2D(latent_dose_2D, \\n\",\n \" title_name='Latent dose-response')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 6.1 s, sys: 338 ms, total: 6.44 s\\n\",\n \"Wall time: 1.65 s\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"\\n\",\n \"reconstructed_response2D = cpa_api.get_response2D(\\n\",\n \" perturbations=perturbations_pair,\\n\",\n \" covar={'cell_type': ['A549']}\\n\",\n \")\\n\",\n \"\\n\",\n \"\\n\",\n \"cpa_plots.plot_contvar_response2D(\\n\",\n \" reconstructed_response2D,\\n\",\n \" title_name='Reconstructed dose-response 2D',\\n\",\n \" logdose=False,\\n\",\n \" # xlims=(-3, 0), ylims=(-3, 0)\\n\",\n \" )\\n\",\n \"\\n\",\n \"cpa_plots.plot_contvar_response2D(\\n\",\n \" reconstructed_response2D,\\n\",\n \" title_name='Reconstructed log10-dose-response 2D',\\n\",\n \" logdose=True,\\n\",\n \" xlims=(-3, 0), ylims=(-3, 0)\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you want to plot in on a log scale, you can just log values in the data frame.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# %%time\\n\",\n \"df_reference = cpa_api.get_response_reference() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'cell_type': ['A549']}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# %%time\\n\",\n \"reconstructed_response = cpa_api.get_response(n_points=100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can plot an average response (saved under \\\"response\\\" column) among all genes, however, we don't consider it to be a good metric and strongly advise to look at the individual response among DE genes.\\n\",\n \"\\n\",\n \"Solid lines in this plot correspond to the model predictions, dashed lines -- linear interpolations between measured points. Dots represent measured points, their color is proportional to the number of cells in this condition. Black dots represent points used in training and red dots correspond to the out-of-distribution examples.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_reference = df_reference.replace('training_treated', 'train')\\n\",\n \"cpa_plots.plot_contvar_response(\\n\",\n \" reconstructed_response, \\n\",\n \" df_ref=df_reference, \\n\",\n \" postfix='reconstructed',\\n\",\n \" title_name='Reconstructed dose response')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example we can take of the top 50 DE genes for Nutlin - MDM2. MDM2 is itself transcriptionally-regulated by p53. And p53 is the target of Nutlin. Therefore, we expect our model to learn it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_contvar_response(\\n\",\n \" reconstructed_response, \\n\",\n \" df_ref=df_reference,\\n\",\n \" response_name='MDM2',\\n\",\n \" postfix='MDM2',\\n\",\n \" title_name='Reconstructed dose response of MDM2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can also look at this plot on the log10-scale. It makes sense for this dataset, because the measured doses were not evenly distributed.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_contvar_response(\\n\",\n \" reconstructed_response, \\n\",\n \" df_ref=df_reference,\\n\",\n \" response_name='MDM2',\\n\",\n \" postfix='MDM2',\\n\",\n \" logdose=True,\\n\",\n \" title_name='Reconstructed log10-dose response of MDM2')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Predictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Perturbations: ['BMS', 'Vehicle', 'Nutlin', 'SAHA', 'Dex']\\n\",\n \"Covariates: {'cell_type': ['A549']}\\n\",\n \"Datasets splits: dict_keys(['training', 'test', 'ood'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Perturbations:', cpa_api.unique_perts)\\n\",\n \"print('Covariates:', cpa_api.unique_covars)\\n\",\n \"print('Datasets splits:', cpa_api.datasets.keys())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can chose control cells from which we want to make our predictions. It is easy to chose these cells from either training or test splits.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"genes_control = cpa_api.datasets['test'].subset_condition(control=True).genes\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default, the prediction function returns means and variances of the applied perturbations. By default, the prediction function returns means and variances of the applied perturbations. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 185 ms, sys: 48.8 ms, total: 234 ms\\n\",\n \"Wall time: 71.5 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"anndata_predicted = cpa_api.predict(\\n\",\n \" genes_control,\\n\",\n \" cov={'cell_type': ['A549', 'A549']}, \\n\",\n \" pert=['BMS', 'Dex'], \\n\",\n \" dose=['1.0', '1.0'],\\n\",\n \" return_anndata=True,\\n\",\n \" sample=False\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, in some cases you want to sample from this distribution, so you can explicitly specify it in the predict function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 200 ms, sys: 29 ms, total: 229 ms\\n\",\n \"Wall time: 66.9 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"anndata_predicted_samples = cpa_api.predict(\\n\",\n \" genes_control,\\n\",\n \" cov={'cell_type': ['A549', 'A549']}, \\n\",\n \" pert=['BMS', 'Dex'], \\n\",\n \" dose=['1.0', '1.0'],\\n\",\n \" return_anndata=True,\\n\",\n \" sample=False,\\n\",\n \" n_samples=10\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"genes_control = cpa_api.datasets['training'].subset_condition(control=True).genes\\n\",\n \"df_train = cpa_api.evaluate_r2(cpa_api.datasets['training'].subset_condition(control=False), genes_control)\\n\",\n \"df_train['benchmark'] = 'CPA'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"genes_control = cpa_api.datasets['test'].subset_condition(control=True).genes\\n\",\n \"df_ood = cpa_api.evaluate_r2(cpa_api.datasets['ood'], genes_control)\\n\",\n \"df_ood['benchmark'] = 'CPA'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"genes_control = cpa_api.datasets['test'].subset_condition(control=True).genes\\n\",\n \"df_test = cpa_api.evaluate_r2(cpa_api.datasets['test'].subset_condition(control=False), genes_control)\\n\",\n \"df_test['benchmark'] = 'CPA'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_test = cpa_api.evaluate_r2(cpa_api.datasets['test'].subset_condition(control=False), genes_control)\\n\",\n \"df_test['benchmark'] = 'CPA'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df_ood['split'] = 'ood'\\n\",\n \"df_test['split'] ='test'\\n\",\n \"df_train['split'] ='train'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\\n\",\n \" \\n\",\n \"
cell_typeconditiondose_valR2_meanR2_mean_DER2_varR2_var_DEmodelnum_cellsbenchmarksplit
18A549SAHA0.0010.970.940.910.45cpa169CPAtest
18A549SAHA0.0010.970.930.920.50cpa392CPAtrain
20A549SAHA0.010.970.890.950.87cpa383CPAtrain
3A549SAHA0.50.970.850.920.65cpa604CPAood
20A549SAHA0.010.960.900.930.85cpa160CPAtest
22A549SAHA0.10.960.800.910.75cpa297CPAtrain
19A549SAHA0.0050.950.860.920.64cpa376CPAtrain
21A549SAHA0.050.950.810.910.73cpa299CPAtrain
21A549SAHA0.050.950.810.880.71cpa118CPAtest
19A549SAHA0.0050.940.850.900.64cpa143CPAtest
23A549SAHA1.00.940.800.850.59cpa282CPAtrain
23A549SAHA1.00.940.770.850.64cpa137CPAtest
22A549SAHA0.10.940.740.890.81cpa129CPAtest
16A549Nutlin0.10.990.990.980.88cpa457CPAtrain
12A549Nutlin0.0010.990.980.960.91cpa284CPAtrain
14A549Nutlin0.010.990.980.960.83cpa387CPAtrain
14A549Nutlin0.010.990.980.940.82cpa180CPAtest
15A549Nutlin0.050.990.970.980.92cpa350CPAtrain
15A549Nutlin0.050.990.970.950.89cpa136CPAtest
16A549Nutlin0.10.990.970.950.76cpa200CPAtest
12A549Nutlin0.0010.980.990.940.92cpa135CPAtest
13A549Nutlin0.0050.980.960.950.85cpa252CPAtrain
13A549Nutlin0.0050.980.960.930.78cpa107CPAtest
2A549Nutlin0.50.860.810.800.29cpa265CPAood
17A549Nutlin1.00.700.630.44-0.40cpa6CPAtrain
17A549Nutlin1.00.150.580.000.00cpa1CPAtest
1A549Dex0.51.000.980.97-0.03cpa864CPAood
9A549Dex0.051.000.970.960.01cpa484CPAtrain
10A549Dex0.11.000.970.97-0.17cpa486CPAtrain
11A549Dex1.00.990.970.960.27cpa568CPAtrain
10A549Dex0.10.990.970.960.19cpa218CPAtest
11A549Dex1.00.990.970.940.36cpa222CPAtest
9A549Dex0.050.990.960.940.17cpa210CPAtest
8A549Dex0.010.990.880.960.24cpa479CPAtrain
8A549Dex0.010.980.880.940.09cpa238CPAtest
7A549Dex0.0050.970.730.94-0.10cpa264CPAtrain
6A549Dex0.0010.950.840.91-0.11cpa204CPAtrain
7A549Dex0.0050.950.660.91-0.28cpa108CPAtest
6A549Dex0.0010.940.810.88-0.47cpa123CPAtest
0A549BMS0.0010.980.970.910.18cpa212CPAtest
0A549BMS0.0010.980.960.920.24cpa442CPAtrain
1A549BMS0.0050.970.940.870.28cpa391CPAtrain
1A549BMS0.0050.960.940.84-0.03cpa151CPAtest
4A549BMS0.10.930.870.850.70cpa103CPAtrain
4A549BMS0.10.900.860.730.52cpa50CPAtest
3A549BMS0.050.880.730.68-1.30cpa59CPAtest
3A549BMS0.050.870.730.73-0.63cpa134CPAtrain
2A549BMS0.010.87-0.270.78-0.93cpa262CPAtrain
0A549BMS0.50.860.630.58-0.27cpa34CPAood
2A549BMS0.010.84-0.390.74-1.00cpa82CPAtest
5A549BMS1.00.69-0.220.35-0.20cpa13CPAtrain
5A549BMS1.00.46-2.040.13-0.88cpa6CPAtest
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" cell_type condition dose_val R2_mean R2_mean_DE R2_var R2_var_DE model \\\\\\n\",\n \"18 A549 SAHA 0.001 0.97 0.94 0.91 0.45 cpa \\n\",\n \"18 A549 SAHA 0.001 0.97 0.93 0.92 0.50 cpa \\n\",\n \"20 A549 SAHA 0.01 0.97 0.89 0.95 0.87 cpa \\n\",\n \"3 A549 SAHA 0.5 0.97 0.85 0.92 0.65 cpa \\n\",\n \"20 A549 SAHA 0.01 0.96 0.90 0.93 0.85 cpa \\n\",\n \"22 A549 SAHA 0.1 0.96 0.80 0.91 0.75 cpa \\n\",\n \"19 A549 SAHA 0.005 0.95 0.86 0.92 0.64 cpa \\n\",\n \"21 A549 SAHA 0.05 0.95 0.81 0.91 0.73 cpa \\n\",\n \"21 A549 SAHA 0.05 0.95 0.81 0.88 0.71 cpa \\n\",\n \"19 A549 SAHA 0.005 0.94 0.85 0.90 0.64 cpa \\n\",\n \"23 A549 SAHA 1.0 0.94 0.80 0.85 0.59 cpa \\n\",\n \"23 A549 SAHA 1.0 0.94 0.77 0.85 0.64 cpa \\n\",\n \"22 A549 SAHA 0.1 0.94 0.74 0.89 0.81 cpa \\n\",\n \"16 A549 Nutlin 0.1 0.99 0.99 0.98 0.88 cpa \\n\",\n \"12 A549 Nutlin 0.001 0.99 0.98 0.96 0.91 cpa \\n\",\n \"14 A549 Nutlin 0.01 0.99 0.98 0.96 0.83 cpa \\n\",\n \"14 A549 Nutlin 0.01 0.99 0.98 0.94 0.82 cpa \\n\",\n \"15 A549 Nutlin 0.05 0.99 0.97 0.98 0.92 cpa \\n\",\n \"15 A549 Nutlin 0.05 0.99 0.97 0.95 0.89 cpa \\n\",\n \"16 A549 Nutlin 0.1 0.99 0.97 0.95 0.76 cpa \\n\",\n \"12 A549 Nutlin 0.001 0.98 0.99 0.94 0.92 cpa \\n\",\n \"13 A549 Nutlin 0.005 0.98 0.96 0.95 0.85 cpa \\n\",\n \"13 A549 Nutlin 0.005 0.98 0.96 0.93 0.78 cpa \\n\",\n \"2 A549 Nutlin 0.5 0.86 0.81 0.80 0.29 cpa \\n\",\n \"17 A549 Nutlin 1.0 0.70 0.63 0.44 -0.40 cpa \\n\",\n \"17 A549 Nutlin 1.0 0.15 0.58 0.00 0.00 cpa \\n\",\n \"1 A549 Dex 0.5 1.00 0.98 0.97 -0.03 cpa \\n\",\n \"9 A549 Dex 0.05 1.00 0.97 0.96 0.01 cpa \\n\",\n \"10 A549 Dex 0.1 1.00 0.97 0.97 -0.17 cpa \\n\",\n \"11 A549 Dex 1.0 0.99 0.97 0.96 0.27 cpa \\n\",\n \"10 A549 Dex 0.1 0.99 0.97 0.96 0.19 cpa \\n\",\n \"11 A549 Dex 1.0 0.99 0.97 0.94 0.36 cpa \\n\",\n \"9 A549 Dex 0.05 0.99 0.96 0.94 0.17 cpa \\n\",\n \"8 A549 Dex 0.01 0.99 0.88 0.96 0.24 cpa \\n\",\n \"8 A549 Dex 0.01 0.98 0.88 0.94 0.09 cpa \\n\",\n \"7 A549 Dex 0.005 0.97 0.73 0.94 -0.10 cpa \\n\",\n \"6 A549 Dex 0.001 0.95 0.84 0.91 -0.11 cpa \\n\",\n \"7 A549 Dex 0.005 0.95 0.66 0.91 -0.28 cpa \\n\",\n \"6 A549 Dex 0.001 0.94 0.81 0.88 -0.47 cpa \\n\",\n \"0 A549 BMS 0.001 0.98 0.97 0.91 0.18 cpa \\n\",\n \"0 A549 BMS 0.001 0.98 0.96 0.92 0.24 cpa \\n\",\n \"1 A549 BMS 0.005 0.97 0.94 0.87 0.28 cpa \\n\",\n \"1 A549 BMS 0.005 0.96 0.94 0.84 -0.03 cpa \\n\",\n \"4 A549 BMS 0.1 0.93 0.87 0.85 0.70 cpa \\n\",\n \"4 A549 BMS 0.1 0.90 0.86 0.73 0.52 cpa \\n\",\n \"3 A549 BMS 0.05 0.88 0.73 0.68 -1.30 cpa \\n\",\n \"3 A549 BMS 0.05 0.87 0.73 0.73 -0.63 cpa \\n\",\n \"2 A549 BMS 0.01 0.87 -0.27 0.78 -0.93 cpa \\n\",\n \"0 A549 BMS 0.5 0.86 0.63 0.58 -0.27 cpa \\n\",\n \"2 A549 BMS 0.01 0.84 -0.39 0.74 -1.00 cpa \\n\",\n \"5 A549 BMS 1.0 0.69 -0.22 0.35 -0.20 cpa \\n\",\n \"5 A549 BMS 1.0 0.46 -2.04 0.13 -0.88 cpa \\n\",\n \"\\n\",\n \" num_cells benchmark split \\n\",\n \"18 169 CPA test \\n\",\n \"18 392 CPA train \\n\",\n \"20 383 CPA train \\n\",\n \"3 604 CPA ood \\n\",\n \"20 160 CPA test \\n\",\n \"22 297 CPA train \\n\",\n \"19 376 CPA train \\n\",\n \"21 299 CPA train \\n\",\n \"21 118 CPA test \\n\",\n \"19 143 CPA test \\n\",\n \"23 282 CPA train \\n\",\n \"23 137 CPA test \\n\",\n \"22 129 CPA test \\n\",\n \"16 457 CPA train \\n\",\n \"12 284 CPA train \\n\",\n \"14 387 CPA train \\n\",\n \"14 180 CPA test \\n\",\n \"15 350 CPA train \\n\",\n \"15 136 CPA test \\n\",\n \"16 200 CPA test \\n\",\n \"12 135 CPA test \\n\",\n \"13 252 CPA train \\n\",\n \"13 107 CPA test \\n\",\n \"2 265 CPA ood \\n\",\n \"17 6 CPA train \\n\",\n \"17 1 CPA test \\n\",\n \"1 864 CPA ood \\n\",\n \"9 484 CPA train \\n\",\n \"10 486 CPA train \\n\",\n \"11 568 CPA train \\n\",\n \"10 218 CPA test \\n\",\n \"11 222 CPA test \\n\",\n \"9 210 CPA test \\n\",\n \"8 479 CPA train \\n\",\n \"8 238 CPA test \\n\",\n \"7 264 CPA train \\n\",\n \"6 204 CPA train \\n\",\n \"7 108 CPA test \\n\",\n \"6 123 CPA test \\n\",\n \"0 212 CPA test \\n\",\n \"0 442 CPA train \\n\",\n \"1 391 CPA train \\n\",\n \"1 151 CPA test \\n\",\n \"4 103 CPA train \\n\",\n \"4 50 CPA test \\n\",\n \"3 59 CPA test \\n\",\n \"3 134 CPA train \\n\",\n \"2 262 CPA train \\n\",\n \"0 34 CPA ood \\n\",\n \"2 82 CPA test \\n\",\n \"5 13 CPA train \\n\",\n \"5 6 CPA test \"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_score = pd.concat([df_train, df_test, df_ood])\\n\",\n \"df_score.round(2).sort_values(by=['condition', 'R2_mean', 'R2_mean_DE'], ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" condition dose_val R2_mean R2_mean_DE R2_var R2_var_DE split num_cells\\n\",\n \"18 SAHA 0.001 0.97 0.94 0.91 0.45 test 169\\n\",\n \"18 SAHA 0.001 0.97 0.93 0.92 0.50 train 392\\n\",\n \"20 SAHA 0.01 0.97 0.89 0.95 0.87 train 383\\n\",\n \"3 SAHA 0.5 0.97 0.85 0.92 0.65 ood 604\\n\",\n \"20 SAHA 0.01 0.96 0.90 0.93 0.85 test 160\\n\",\n \"22 SAHA 0.1 0.96 0.80 0.91 0.75 train 297\\n\",\n \"19 SAHA 0.005 0.95 0.86 0.92 0.64 train 376\\n\",\n \"21 SAHA 0.05 0.95 0.81 0.91 0.73 train 299\\n\",\n \"21 SAHA 0.05 0.95 0.81 0.88 0.71 test 118\\n\",\n \"19 SAHA 0.005 0.94 0.85 0.90 0.64 test 143\\n\",\n \"23 SAHA 1.0 0.94 0.80 0.85 0.59 train 282\\n\",\n \"23 SAHA 1.0 0.94 0.77 0.85 0.64 test 137\\n\",\n \"22 SAHA 0.1 0.94 0.74 0.89 0.81 test 129\\n\",\n \"16 Nutlin 0.1 0.99 0.99 0.98 0.88 train 457\\n\",\n \"12 Nutlin 0.001 0.99 0.98 0.96 0.91 train 284\\n\",\n \"14 Nutlin 0.01 0.99 0.98 0.96 0.83 train 387\\n\",\n \"14 Nutlin 0.01 0.99 0.98 0.94 0.82 test 180\\n\",\n \"15 Nutlin 0.05 0.99 0.97 0.98 0.92 train 350\\n\",\n \"15 Nutlin 0.05 0.99 0.97 0.95 0.89 test 136\\n\",\n \"16 Nutlin 0.1 0.99 0.97 0.95 0.76 test 200\\n\",\n \"12 Nutlin 0.001 0.98 0.99 0.94 0.92 test 135\\n\",\n \"13 Nutlin 0.005 0.98 0.96 0.95 0.85 train 252\\n\",\n \"13 Nutlin 0.005 0.98 0.96 0.93 0.78 test 107\\n\",\n \"2 Nutlin 0.5 0.86 0.81 0.80 0.29 ood 265\\n\",\n \"17 Nutlin 1.0 0.70 0.63 0.44 -0.40 train 6\\n\",\n \"17 Nutlin 1.0 0.15 0.58 0.00 0.00 test 1\\n\",\n \"1 Dex 0.5 1.00 0.98 0.97 -0.03 ood 864\\n\",\n \"9 Dex 0.05 1.00 0.97 0.96 0.01 train 484\\n\",\n \"10 Dex 0.1 1.00 0.97 0.97 -0.17 train 486\\n\",\n \"11 Dex 1.0 0.99 0.97 0.96 0.27 train 568\\n\",\n \"10 Dex 0.1 0.99 0.97 0.96 0.19 test 218\\n\",\n \"11 Dex 1.0 0.99 0.97 0.94 0.36 test 222\\n\",\n \"9 Dex 0.05 0.99 0.96 0.94 0.17 test 210\\n\",\n \"8 Dex 0.01 0.99 0.88 0.96 0.24 train 479\\n\",\n \"8 Dex 0.01 0.98 0.88 0.94 0.09 test 238\\n\",\n \"7 Dex 0.005 0.97 0.73 0.94 -0.10 train 264\\n\",\n \"6 Dex 0.001 0.95 0.84 0.91 -0.11 train 204\\n\",\n \"7 Dex 0.005 0.95 0.66 0.91 -0.28 test 108\\n\",\n \"6 Dex 0.001 0.94 0.81 0.88 -0.47 test 123\\n\",\n \"0 BMS 0.001 0.98 0.97 0.91 0.18 test 212\\n\",\n \"0 BMS 0.001 0.98 0.96 0.92 0.24 train 442\\n\",\n \"1 BMS 0.005 0.97 0.94 0.87 0.28 train 391\\n\",\n \"1 BMS 0.005 0.96 0.94 0.84 -0.03 test 151\\n\",\n \"4 BMS 0.1 0.93 0.87 0.85 0.70 train 103\\n\",\n \"4 BMS 0.1 0.90 0.86 0.73 0.52 test 50\\n\",\n \"3 BMS 0.05 0.88 0.73 0.68 -1.30 test 59\\n\",\n \"3 BMS 0.05 0.87 0.73 0.73 -0.63 train 134\\n\",\n \"2 BMS 0.01 0.87 -0.27 0.78 -0.93 train 262\\n\",\n \"0 BMS 0.5 0.86 0.63 0.58 -0.27 ood 34\\n\",\n \"2 BMS 0.01 0.84 -0.39 0.74 -1.00 test 82\\n\",\n \"5 BMS 1.0 0.69 -0.22 0.35 -0.20 train 13\\n\",\n \"5 BMS 1.0 0.46 -2.04 0.13 -0.88 test 6\\n\"\n ]\n }\n ],\n \"source\": [\n \"cols_print = ['condition', 'dose_val','R2_mean', 'R2_mean_DE', 'R2_var', 'R2_var_DE', 'split', 'num_cells']\\n\",\n \"df_score = df_score.round(2).sort_values(by=['condition', 'R2_mean', 'R2_mean_DE'], ascending=False)\\n\",\n \"print(df_score[cols_print])\\n\",\n \"# print(df_score[cols_print].to_latex(index=False))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Uncertainty\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can profile all the predictions with an uncertainty score. Low uncertainty means \\\"good/trustworthy\\\" predictions, high values mean \\\"bad/unknown quality\\\" predictions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'A549': {'BMS': [0.001, 0.005, 0.01, 0.05, 0.1, 1.0, 0.5],\\n\",\n \" 'Dex': [0.001, 0.005, 0.01, 0.05, 0.1, 1.0, 0.5],\\n\",\n \" 'Nutlin': [0.001, 0.005, 0.01, 0.05, 0.1, 1.0, 0.5],\\n\",\n \" 'SAHA': [0.001, 0.005, 0.01, 0.05, 0.1, 1.0, 0.5],\\n\",\n \" 'Vehicle': [1.0]}}\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cpa_api.measured_points['all']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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zDoEKtRZyDLY56orOh+IkSFCEsYhgGP5eqMS0xbEhbKEZFBMDXS4Tz1fxMUCYzg78fuILaZm6Oqa9va4e2w+SUAgkrOhuKfyhBEcKiSrUOtS16uwskrERCAcZHS3H+Gj8T1OXaFmz7oQj7Cqo5ef2Kzgq+BCdN8QF0NhQfUYIihEXHSy0ti+zZ/9RTWrQUF6pbeNkjrrjesjZWqeamPZC1xHy4XSS6orOh+IcSFCEs+rG4AWH+EiRHBgz5sakxQdB2mFDawL9CiZJ6yx/xCq4SlFoLgQCIDbF/XW8wXc+GIvxACYoQljAMgyNFKsxICneoFNpaKMHHaT7rCIqrBFXRoEF0kC+8xc472JHOhuIfuxJUU1MTy2EQ4nmuKFtR39qOmUnhDj0+KTIAErGQlwnKOoKqatRxMgVZ7sQKPis6G4p/7EpQd911Fx577DHk5ub2ec4TIaS3I1dVACwdyh3hJRJiXJQU56/x6w+m2cygpF4Df4kI7UYz6tvaXR5DRec5UM5mreTj47rfSGRXgjpw4ABWr16NvXv3Yv78+di6dStKS0vZjo0Qt3akSIVREf6IDnZ8nSQtWorz1c28emNY26KHzmDCzZ0jQ1dP87XqDWjQdDi1QMJqfJQUmg4TZ1OXpDu7EpRAIMCMGTOwdetWvPjii/jyyy+xatUq/OIXv8Dp06fZjpEQt9NhNON4idrh6T2rtJggtOqNvPqDWdK5/jRnbASA6yXfrmJrEjvMLuZ9sZ0NRdN8vGDXcRuNjY346quvsHfvXoSHh+NPf/oT5s6di0uXLuHxxx/H999/z3achLiVUxWN0BlMw05Qto4S15qd1tZnuIo7159mJ0dAIIDLT6K1Jms2pvhsZ0NVt2AxnQ3FObsS1Jo1a7B06VJs374dcrncdnt6ejrWrFnDWnCEuKsjV1UQCQW4cbT9/ff6kiwLgJdIgPPXWnD7hGgnRTc8JfVtCPAWIzbEF1FSH5eP7px5zEZP1rOh+NrBY6Sxa4rv8ccfx6OPPtotOX3zzTcAQAcOEtKHI0UqTIwNgtTHa1jP4y0WYaw8EBd49AezRKXBqAh/CAQCxIb6uXyzboVag1B/CQKHeW37k6EIxpnKJl6t+41UdiWot99+u9dtO3bscHowhHiCZq0BBVVNmJkc4ZTnS4sOwrlr/CmUKK5rw+gIy8bjuFA/VKp1Ln398gbnl5h3NTkuBE1aA3WS54EBp/hyc3ORl5cHpVKJF1980XZ7W1sbRCLnbZAjxJMcK1HBzACzHCwv7yk1Jggf/1yJa006xIaw94fZHtoOI6qb9RgVblkPiwv1Q22LHnqDCT5ervmbUN6gxZSEENae/4Z4y3OfLG+0JWLCjQFHUDKZDGlpafD29kZqaqrtY+7cuXj33XddFSMhbuVIkQr+EhEyFMFOeb7rHSW4ryyzVvCN7mzdZD1CpKrRNaOoDqMZNc06xLM4ghodEQCpjxinK+hEY64NOIJKSUlBSkoKbr/9dnh5sTPfS4inOXJVhRtHhcFL5JxOYilyS2XZ+WvNWJgmH/wBLLJOe42KuD6CAixNY5Mc6Dc4VFWNWpgZII7FikahUICMuBCcKm9i7TWIfeyq4isoKMC2bdtQXV0No9EIhmEgEAiQk5PDdnyEuJVKtRZlDVrcf3OC057Tx0uE5MgAXlSWFde1QSC4fsyFwpqgXFRqzmYFX1eT44Lxes5VtOgNwy50IY6zK0E9++yzePrpp5GWlgahkPrLEtKfo0Wd7Y2Guf+pp7SYIBy6Umd7c8iVEpUGsSG+tvWmiABv+HgJXbZZt7yzszv7CSoEDAOcrWzCLCcVu5ChsyvbBAYGIjMzE2FhYQgJCbF9EEK6O1ykgkzq7fTprrRoKVRtHVC2uL7vXVcl9W3dCgcEAgEUIX4u2wtVrtbCTyJCRIA3q6+TERcMgcBSKEG4Y9cIavr06XjllVdw2223QSKR2G5PTU1lLTBC3I3ZzODHIhXmpsicPspJj71+9IY8yMepz20va5PY6YndNx/Hhfqh0kVFEhWdJeZsjyKlPl4YExmIUxVNrL4OGZhdCers2bMAgPPnz9tuEwgEeP/999mJihA3dLGmBY1aA2YmD697RF/GRUkhFFhaHs0bL3P689vD2iTWWiBhpQj1w/FStUumH8vVWluJO9smxwdjX0ENzGbGofO8yPDZlaA++OADtuMgxO0d7jxeY4aT158AwE8ixuiIAE47Slh78PXcG6QI9UNbuxGNWgNC/SV9PdQpzGYGFWotbhnrmjWhSXEh2HWiEsX1bUiWBbrkNUl3AyaovXv3YtmyZdi5c2ef3//lL3/JSlCEuKOjRSqMlQUiMpCdKbi0mCAcK25g5bntYdsD1WMEZS01r1BrWU1QylY9OoxmVkvMu7Ju2D1V0UgJiiMDFknodJZ5ZY1G0+cHIcRCbzDhRJna4cMJ7ZEaLUVtix71rdwUShTXtyHQW4yIwO4FCl33QrHp+jEbrummMSrcH8F+XlQowaEBR1DWTuW//e1vXRIMIe7q5zI1OoxmVhOUraNEdTNuGRvJ2uv0p6T+epPYrmJDLN0k2K7kq3DRHigrgUCASYpgKpTgkF1rUO3t7di9ezeuXr2K9vbr797++te/shYYIe7kSJEKXiIBpieGsvYa1sP0zldxlaDacOOo3gUg/t5ihAdIWB9BlTVoIBYKEDOME4qHanJcCH64Uo9mrQFBfrRh19Xs2gf15JNPor6+HkeOHMG0adOgVCrh78+Pw9MI4YMjV1WYHBcCP4ld7/kcEujjhVHh3JxVZGsSG9H3770i1I/1bhLlai1iQnwhdlILKXtM7lyHOl1J03xcsOtfuqKiAhs2bICvry/uvPNO/Pvf/0ZhYSHbsRHiFtSaDlyobnFa9/KBpMYEcdI09nqBRN8bkONC2d+sW8HyMRt9magIhlAAnKJ1KE7YlaDEYsu7QqlUisLCQrS2tuLatWusBkaIu8gtrAMAp53/NJC0aCmuNenQqOlg/bW6spaYj+onQSlC/FDdpIfRZGYthvIGjcvWn6wCvMUYK5fSOhRH7EpQq1evRnNzMzZs2IBf//rXyMrKwsMPP8x2bIS4hYOX6hAR6I0JnUUMbOpaKOFKJfUaCAT9FyjEhfrBZGZQ06xn5fWbtB1o0RsRH+r6pYXJcZYTdk1mfhwYOZLYlaBuvPFGBAUFYerUqcjJycGxY8cwY8YMtmMjhPc6jGbkXanH3LGRLuk2kBrNzdlQxfVtUIT49XsooaLLXig2WEvM41w8ggIshRJt7UZcrWt1+WuPdHYlqMcee6zXbY8//vigj8vLy8OCBQswf/78Po+I/+qrr7BkyRIsWbIEa9asweXLl+0JhxDeyC9To7XdiFvHuaaqLsjPCzHBvrhS69oEZS0x74/14ELWEpTatSXmXdk27NL5UC43YMlRcXExioqK0Nraim+//dZ2e1tbW7dy876YTCZs3rwZO3fuhEwmw8qVKzF37lwkJSXZ7hMbG4sPP/wQQUFByM3NxZ/+9Cd89tlnw/yRCHGdg5fqIBELWd3/1FOKPBCXa133bt5sZlCq0uCm0f33GIwK8oVYKGAtQVV0HrPh6iIJwJIUQ/0lOFneiLunx7n89UeyARNUaWkpDh06hNbWVvzwww+22/39/fHCCy8M+MQFBQWIj4+HQqEAAGRlZSEnJ6dbgpo8ebLt84yMDNTW1jr0QxDCBYZhkHNZiRmjw1gtL+8pJSoQuYX16DCaIRGzX3Jd00+T2K5EQgFiQ3xZ2wtV3qBFRKC3S6+zlUAgwOS4YDoCngMD/mvPmzcPt9xyC95++2088sgjQ3pipVIJufz68dQymQwFBQX93n/37t2YPXv2kF6DEC4V17ehvEGLdbNGufR1U+RSGM0MiuvbMC5KyvrrlfTTJLYnRagfqwkqgYPpPatJcSE4eKkOjZoOhLDYb5B0N+jbL5FIhKNHjw75iRmmd8VLf634f/rpJ+zevRtPPPHEkF+HEK4cvGQpL781xbVdHcZFWRqXXnbROlRxnbXEfOAKOgWLe6HK1RrEcVDBZzU5jjbscsGu+YHJkydj8+bNyM/Px4ULF2wfA5HL5d2m7JRKJSIje/8iX758Gc899xy2b99Op/QSt5JzSYnxUVJEu7D1DgAkhPlDIhbico1r1qFKVBoE+ogHPcU2LtQPjVoDWvUGp76+3mCCsqWdk/Unq4mKIIiEAiqUcDG7JnRPnToFAHj99ddttw12YGF6ejrKyspQWVkJmUyG7Oxs/OMf/+h2n+rqavzud7/Dq6++isTEREfiJ4QTjZoOnCxvxG9vSRr8zk4mFgkxRhaASy4qlLBU8AUMehjh9a7mOoyPdl7fuqrO03rjwlz7RqArP4kY46ICqbO5i7F2YKFYLMamTZuwbt06mEwmrFixAsnJydi1axcAYO3atXjzzTfR1NSEv/zlLwAs04lffPHFkF+LEFc7VFgHMwPcOo6b021T5FLkFta75LWK69twUx9NYntShFzfC2VtbOsM1nUtLkdQgGWab/fJKhhNZpf2AxzJ7EpQKpUKW7duRV1dHd555x0UFRXh9OnTWLVq1YCPy8zMRGZmZrfb1q5da/t8y5Yt2LJliwNhE8Ita/eIdBd0j+hLijwQu09WQdXWjvBBpt6GQ9NuRE2zHqMjBy6QANg7F8rahNaaALkyOS4E7x8rxxVlq23DNGGXXW8DnnrqKcycORN1dZZF4YSEhAGn9wjxZK7uHtEXa/XeFZan+UpVlv1Ho8IHL1AI8vOC1Efs9K7mFQ1aeIuFvQ5KdDVroQQ1jnUduxJUY2MjFi9eDKHQcnexWGz7nJCR5mcXd4/oS4rcUsl3qYbdSj5rk1h7RlAAO5V8FWpLF/PB1sDYpgj1RVSQD44WNXAax0hiV5bx8/NDY2Oj7X+QM2fOIDAwkNXACOGrg5eULu8e0VNYgDfCA7xZ7yhRXK+BcIAmsT2xcexGZaPO1uuPSwKBALOTI3C0WMVq13Zynd1TfL/+9a9RUVGBNWvW4I9//COee+45tmMjhHcYhkHOpTqXd4/oy7ioQNb3QpXUt0ER6gdvcd9NYnuKC/VDVaMOZid1/mYYBpVq158D1Z/ZYyLQqjfiTGUT16GMCHb9hqWmpuLDDz9EaWkpGIZBYmIivLzo+GMy8hTVtaFCrcX62a7tHtGXFHkg3jtWzmpVWUm9xq71J6vYUD90GM2oa22HPMhn2K/fpDWgrd2I2BDuSsy7mpkUDqEAyCusx5SEUK7D8Xh2/V/90UcfQavVIjk5GWPGjIFWq8VHH33EdmyE8E7O5c7uERyuP1mlyKXoMJpR1tlI1dkYxtIktr9DCvsS5+RjNyp4UmJuFeTnhYmKYOReVXEdyohgV4L69NNPIZVe39cQFBREXcfJiGTtHhEVxP07+pQoa6EEO+tQypZ26AwmJAxhBMVaguKwD19Ps5MjUFDV5PJTjUciuxKU2Wzu1lvPZDLBYHBuOxNC+M7aPWIeD0ZPAJAUGQCRUMDaOpS1xDwxzP4EFRPsC4HAeXuh+LIHqqvZYyLAMMCRIhpFsc2uNaiZM2fi8ccft22y/fjjjzFr1ixWAyOEb364wm33iJ68xSKMjvBnrSdfeefU4VAOCZSIhYiS+jgvQam1CPOXwN+b24KUribGBkHqI0ZeYT2WTIzmOhyPZte/+pNPPomPP/4Yu3btAsMwmDFjxqBdJAjxNDkcd4/oS4pcylp/uNIGDSQi4ZCb4TpzL1SlWodYnqw/WYlFli0GeVfrwTAM5/uzPJldCUooFOLuu+/G3XffzXY8hPBSu9GEvMJ6ZE2I4qx7RF9SogLx1dlqNOsMCPJ1bmVtmUqDuDA/iIb488aF+iHvqnP6BFaotZioCHbKcznT7OQIfH2uFoXKNoyV055QttiVoE6ePIlt27ahuroaRqPR9q4hJyeH7fgI4YUjV1VobTdiQap88Du70Dj59ZZH0xKdW/ZcptIiYQjrT1aKUD8oW9qhN5jg42Xf/qm+GE1mXGvSYcnEKIefgy2zx0QAAHIL6yhBsciuBPXss8/i6aefRlpaGrU4IiNSdkENgny9MCOJu+4RfUnpcnihMxOU2cygrEGD2WOG/vNaK/mqGrVIinT8j3dNsx4mM8OrAgmr6GBfJEUGIK9QhfWzR3MdjseyK0EFBgb26kpOyEihN5jw7UUlFqfLIRHz6w2aXOqDIF8vp5ea17bo0W40D6nE3EoRalmzqlTrhpWg+HLMRn8yx0Tgg5/KoeswwVfi+EiR9M+u37bp06fjlVdewenTp+0+UZcQT5FXWI+2diOyJvCvYksgECBF7vyWR2UOlJhbWUc8w+1qbisx52mCmj0mAh1GM34qpeaxbLFrBHX27FkAsCUl6xoUHblBRoLsczUI8fPCzaMHP7SPC+OipPg0vxJmM+O0Ao6yBktyiHdgBBUR6A1vsXDYpeYVai1EQgGinNAyiQ3TE0PhLRYir7Aet4zlx944TzNggtq5cycA4JZbbgFgebcWEhKCG264AQqFgv3oCOGY3mDCwYtKLM2IhhdPT1FNkQdC22FCZaMW8Q6MePpS1qCBd+eepqESCASIDfFFpVo3rBgq1DrEBPvy9vRaHy8RpiWGIs9FJxuPRAP+y2s0Gmg0Gmi1Wmi1Wmg0Gpw/fx4PP/wwsrOzXRUjIZw5dKUOmg4TstL5N71nldJ5eKEz16FKVRrEh/k5PCJThPoNf4pPrbWtZ/FV5pgIFNdrcK1peMmY9G3AEdRvf/vbPm9vamrCL3/5S2RlZbESFCF8sa+gBmH+Etw4ir+dq8fIAiAQWCr5FqY5pwy+TKVBogPTe1aKEL9hnzxbqdbitlR+dO3oz+wxEUD2JeQV1mPttDiuw/E4Do2dg4ODu/XmI8QT6TpMyLlUh4Vpct5OMwGAn0SMhDDntTwymxmUq7XDS1ChvmjRG9Gsc6xnp6bdiAZNB2J5WGLeVXJkAORSH5rmY4lDv3XHjh3r1t2cEE/0w5U66AwmZE3g30bRnpxZyVfdrEOHgyXmVrZKPgcLJazTg3wtMbcSCASYPSYcR4rolF02DDjFt2TJkl63NTc3IzIyEq+88gprQRHCB9kFNQgPkGB6Ij+r97pKkUux/0ItNO3GYTdWLVN1VvAN44gLRZfNumkO9C60FljwPUEBlmm+T/OrcLaqCTfE83cq2B0N+H/yv/71r25fCwQCBAcHw8+P///TEDIcmnYjci4rseoGxZB70XEhJSoQDAMUKlsxKS5kWM9lPQBxuGtQAByu5LM2m+XrHqiurKfs5haqKEE52YAJKiYmxlVxEMIr31+ug95gxu1uML0HXO/Jd7nWCQlKpYGPlxCyQMf3HwX5eSHQR+xwJV+lWosAbzFC/JzbAJcNwX4STIgNRl5hPTbOH8N1OB6Fvyu/hHAou6AGkYHemJLgHu+IY0N84S8R4XLN8Nehyho0SAjzH/amX0WIn+NrUGotYkN83eYoi9ljLKfsNmnplF1nogRFSA9t7Ub8cKUOi9Oj3GJ6DwCEQgHGygNxqXb4lXylKo1DXcx7UoT6orLR8Sk+d1h/spozNgJmxnJmGHEeSlCE9JBzSYl2o9ktqve6SomS4nJNy7C2gJjMDCrVumFV8FkpQvxQ1agdcjwMw6Cy0b0SVEZsMGJDfLHnzDWuQ/EolKAI6WFfQQ3kUh/cMMy1HFcbJw9Ei96Imma9w89R3aRDh8mMxPDhJwdFqB/0BjPq29qH9Lj6tnboDWa3KJCwEgoFuCMjBkeLVKhrcfz6k+4oQRHSRavegNwr9Viczq+Tc+0xtsvhhY4q7exi7oyefl2P3RgKvh+z0Z87JsXAzABfna3mOhSPQQmKkC4OXlKiw+R+03uApasBAFytczxBlTuhxNzKWmpeNcRKPmtC43sfvp6SIgMwITYIX56maT5noQRFSBf7ztYgOsgHkxTBXIcyZCH+EoQHeOOqss3h5yhVaeEnESEy0HvY8cQ62E3CugeK722O+nJHRgwuVLegUOncAyRHKkpQhHSqb23HocJ6LMmIdrvpPavkyABcrXM8QZU1aBAf5u+U8m5fiQjhAd4OTfFFBnrDx8v9TqldMjEaIqEAe2gU5RSUoAjptOf0NZjMDFbd4L5nnY2RBaCors3hSj5LF3PnjVwspeZDH0G52/qTVUSgN2Ylh2PvmWqYzdRQe7goQRECS2nzp/mVmBQXjKTOtRx3lCQLRFu7Y5V8RpMZFWqtU/ZAWSlChn4uVKUbJygAuHNSDK416XCiTM11KG6PEhQhAM5WNeNqXRvumuK+oyega6HE0Kf5rjXpYDQzTtkDZaUI9UV1k97uTt8dRjNqWvSIdeMENX+8DH4SEU3zOQGrCSovLw8LFizA/PnzsWPHjl7fLy4uxurVq5GWloZ3332XzVAIGdBn+ZXw8RK6Te+9/oyRBQIArjqwSG8tMXf2CMpkZuwe0V1r0oFh3K/EvCs/iRgLU+XIPlcDvcHEdThujbUEZTKZsHnzZrzzzjvIzs7Gvn37UFRU1O0+wcHBePbZZ/HQQw+xFQYhg9IbTPjqbDUWpUUh0If/zUkHEuovQZi/xKFKvvIGy1RcglPXoDor+eyc5rNW/ClC3KvEvKc7J8egVW/ED5ep9dFwsJagCgoKEB8fD4VCAYlEgqysLOTk5HS7T1hYGCZMmACxeHjn1xAyHAcu1KJVb8SqG2K5DsUpkiIDHNoLVarSwF8iQkTA8EvMrWx7oeys5LOWmMcN4ywqPrh5dDgiAr3xBU3zDQtrCUqpVEIul9u+lslkUCqVbL0cIQ77LL8KsSG+uHEU/w8mtMcYWSCuKodeyVfWoEFCuHNKzK2ign0gFAxtBCURDe+oDz4QCQVYNjEah67UoVFDHc4dxVqC6uuXw11a55OR41qTDkeLVVgxOdZt9z71lCwLQGu7EcqWofXAK1NpnFogAQBeIiGignzt3qxb2Wg5ZsMT/i3umBQDg4lB9rkarkNxW6wlKLlcjtraWtvXSqUSkZGRbL0cIQ75/GQVGAZY6SHTewBsZfJDmeYzmMyobNQh0YkFElZDOXajQq11qyaxA0mNlmKMLICq+YaBtQSVnp6OsrIyVFZWoqOjA9nZ2Zg7dy5bL0fIkJnNDHafrMLNo8M85o8icL2Sr3AIhRJVjTqYnFxibjWUgwsr1Tq368HXH4FAgDsmxSC/vBEVDY4d3DjSsZagxGIxNm3ahHXr1mHx4sVYtGgRkpOTsWvXLuzatQsAUF9fj9mzZ2Pnzp146623MHv2bLS1Od6mhZChOFGmRoVai1VTPGf0BABh/hKE+HmhaAgjqLIGa4m58xO1ItQPda3tg5ZcN2sNaNYZ3LrEvKdlGTEAQOdEOYjV8rnMzExkZmZ2u23t2rW2zyMiIpCXl8dmCIT069P8SgR6i7Ew1b33PvUkEAiQHBk4pFLzMuseKDZGUJ0joqpG3YBdOqyFFJ6UoGKCfTE9MRR7Tl/D7+Ym0Tr8EFEnCTIitbUb8c25Wtw+MQq+EvdrSjqYZFkACpWtdlfylak0CPQWI8xf4vRYrKXmg1XyVbpxF/OBrLwhFiUqDXIL67kOxe1QgiIjUnZBNXQGE1a5eWuj/iRHBqBFb0R9q32VfKUNWqeXmFtZ1/eqBlmH8pQ9UD0ty4hBdJAP/ifnqsNNfEcqSlBkRPosvwqjI/zd8twneyRbWx7Z2ZOPjRJzq4gAb0jEwkEr+SobtQjy9YLUzbt59CQRC/HrOaNxqqIJPxY3cB2OW6EERUackvo25Jc3YtUUhceuCSTLLGs99hyc12E0o6pRi0SWRi5CoQCxIYPvhSqp13jU+lNXq6YoEBnojf/Jucp1KG6FEhQZcT7Nr4JIKMDySTFch8KaiABvBPl62TWCqmzUwsywUyBhNdixG3UtehwvVWNGUjhrMXDJx0uERzJH43ipGsdLaBRlL0pQZETRG0z45OcK3JoSiUipe7fTGYilki8ARXZU8pV3lpjHs7BJ10oR6jvgybqfn7IcFnmXh5X8d7V2WhzCAyR44/uiwe9MAFCCIiPMV2er0ag14IEZCVyHwrpkWSAK6wav5CtVWUY2iSyPoJp1BrToDb2+Zz0sclpCKEZFuO9hkYPxlYiwfvYoHClS4WR5I9fhuAVKUGTEYBgG/3e0DGNlgbjJQxrDDiQ5MgBNWgNUbQM3Ky1TaSD1ESPEj73iBNuxG32sQ50oVaNUpcHqqZ5ZUdnVPdPjEeLnhTe+p7Uoe1CCIiPGz2WNuFjTggdmJHhscURX1kKJgXryMQyDn8vUGCMLZPWa2PZC9THN90nnhunF6Z61Ybov/t5irJs1Coeu1KOgqonrcHiPEhQZMf7vx1IE+XrhjgzPLY7oKjnSUmpeNEChxOnKJlyubcWdk9m9Jte7SXQfQbXoDfj6XA2WZER75Ibpvtx3UzyCfL3wPzm0FjUYSlBkRKhu0uHABSXWTFWMmD+EMqk3An3EA5aa/+d4BfwlIlvPOLYE+Xoh0Fvca4rvqzPV0BvMWDMCpvesAn288OCMRBy8pMSF6mauw+E1SlBkRPjwp3IwDINf3BjPdSguY63k668nX7POgH0F1ViaEYMAb3ZPtRYIBIgN9eu1WffT/EqkyAORHhPE6uvzzQMzEhDoLcY2qugbECUo4vH0BhN2najA/PEyjzpWwx7JkYH9TvHtOX0NeoMZd0+Lc0ksih6bdS9Wt6Cgqhmrp3ruhun+BPl64YEZCfjmfC2u1NrfdX6koQRFPJ61tPz+mxO4DsXlkmUBaNB0oKGte08+hmHwn+MVSI8JQnqsa0YvilA/VDXqbGXvn+ZXQiISjpg1wZ4enJEIf4mIuksMgBIU8WgjrbS8p/568p2qaMIVZSvunu6a0RNgGUHpDCao2jqgN5jw5elrWJAmRwgLHdTdQYi/BOtmjUL2uRocuFA7+ANGIEpQxKONtNLynpJtx793T1DW4oglE6NdFottL1SjFgcu1KJZZ8BqD+0mb69Hb0lCWowUT31egLoWPdfh8A4lKOLR3vuxbESVlvcUFeSDAG8xrnap5GvWWoojlk1ivziiq66bdT/Nr0RsiC9uHj3yRrVdScRC/HN1BrQdJjy5u4CO4+iBEhTxWNVNOuy/UDuiSst7EggESOpRyffl6Sq0G11XHGEVG2LZC3WsuAFHixpw1xQFhMKRN6rtKSkyEM9mjUNuYT0++Kmc63B4hRIU8VgjsbS8L8mRAbYpPoZh8J8TFZgQG4Q0F5d2+0nECA+QYPfJKggElpNmicW9N8Yjc0wEtmRfQtEAnT9GGkpQHqCt3Yh3DpfggZ0n8N1FJdfhDIhhGDS0taOgqgnfnKvBO4dL8OevLuDh9/PxzuESp73OSC4t72mMLBCqtnY0ajpwqqIRhco2l4+erGJD/GA0M5idHIHoYF9OYuAjgUCAv62cAD+JCBs+OYMOo5nrkHjBdRPQxOlqmnX4v6Nl+M+JCrTqjQj1l+DQlXwsTpfjz0tSeXecxLtHSvH3A1egM5i63e4vEcHfW4yDl5SYmhCKiU445XbP6WsjtrS8pyTZ9UKJj3+uQIC32KXFEV0pQv1wprJpRDSGHapIqQ/+unwCHvnwJP55sBD/b2EK1yFxjhKUG7pY3YJ3Dpfgq7PVMDMMFqdH4eFZozA+WoodeSV4PecqDl9V4elF47BmKj/m+XMuKfFi9kXMTArHLWMjERPii5hgX8SG+CLI1wtt7UbM25qLp784h69+OwNikeODe6PJjO2HijEhNmhElpb3ZK3kyy9XI7ugBitviIW/C4sjupoSH4Lz15oxb5yMk9fnu4Vpctw1JRZv5RZjzthITEsM5TokTgkYNysbWb58Ob744guuw3A5hmHwU4ka2w8V4fBVFfwkIqyeqsCDMxJ7TWGVqjR45otzOFbSgGmJoXjpznQkRXJ3zk5RXSvuePNHJIT7YfcjN8PHq++Chf3na/DIh6fwXNY4rJs1yuHX+/J0FX7/yVnsuPcG3JYqd/h5PAXDMEh7/gDEIiGadQZkPzYTqdHctRZiGGZElvzbq63diMWvH4bJzOCbDbMg9WHvGBS+ozUonmMYBrmF9bjr38ew9u2fcKmmFf9v4Vgce+pWPL8ktc/1lcRwf/zn4el4dcUEXKltxeLXD+ONnKswmlw/r92sM+Dh90/Cx0uIf987pd/kBAALUuWYNy4S//i2sFfXa3uZzQy2fV+EFHkgvUvvZK3ka9YZMDE2iNPkZI2H9C/AW4zXVmegplmH3398BvoeU+IjCSUonmIYBt9dVOKON4/i/v89gapGHf6yNBVH/ngLfjMnCUGDHC4nEAhw11QFDm7MxG2pMvzju0Ks2fGTw3/4HWEyM3j849OoVGux/Z4bEDPIorhAIMBflqVBIACe33vBoT0h+y/Uorheg0dvSeLF1CZfJHUeveHKzhHEcTfEh+Avy9KQc7kO697Lh7bDyHVInKAExTMGkxl7z1zDotcP4+H389GoNeDl5enIffIW3H9zwoAjkL5EBHpj292T8c/VGbjcOZr65lwNS9F39/dvr+DQlXr8eWmq3XPpMcG+2Dh/DHIu1w25/QvDMHjj+yKMivAfEYffDcXM5DDEh/lxVhxBhu7eG+Px91UT8WOxCve+ewLNOgPXIbkcrUHxRF2rHruOV+Kj4+Woa23HqAh//PaWJCydGD2sgoGuyhs0eGzXaZytasbaaXHYdPt41jaw/vdsNX636zTWTovDX5enD+mxRpMZS7cdRYOmHQc3ZiLQzjn4gxeVWPd+Pv6xaiJW0B4b4iG+OVeDxz4+jeTIQLz/0DSEB3hzHZLLUILiEMMwOF3ZhPd+LMPX52pgMDHIHBOB+2+Ox5wxkaxMUXUYzdj6XSH+lVuM5MgAvHH3JKTIpU59jfPXmrHyXz8iLToI/3n4RkjEQ0+wZyubcMf2o7j/pgT8eWnqoPdnGAZ3bP8Rak07vv/DHHg5KakTwgeHrtThkQ9PIjrYFx+tm46ooJGxh4x+iznQojfg4xMVWLrtKJZv/xHfX6rDL26Mx/d/yMR7D07D3BQZa+snErEQTy1KwQcPTUOj1oCl247io+POa6+iamvHrz44iRA/Cd76xQ0OJScAmKgIxv03JeC9Y2U4W9k06P2PFKlwtrIJv85MouREPM6csZF4/8HpqGtpx8q3jqG8QcN1SC5BIygXMZkZHL5aj89PXcO3F2rRbjQjKTIA998Ujzsnx7q0aaeVqq0dGz89i7zCevzixjg8vyR1WH/cW/UGrH37JxTVteHTX92ECbHBw4qvVW/AvK25CPP3HnRv1F3/PoaKBi1y/98ceItHZt894vnOVTXjvv89Di+REG/9YjJuiPfsfVKUoFh2pbYVX5yqwpenr6GutR1Bvl5YOjEaK26IxcTYIM5Lbk1mBq8euIx/55ZgRlIY3rx7MoL9hn4+j95gwgM7TyC/rBFv3zcFt6REOiU+696oOzKi8UzWOEQG9u6OcbykAat3/ITnl4zHL2ckOuV1CeGrq8pW3P+/J1DdrMfyyTF4alFKn78XnoASlJMxDIPLta345nwt9p+vQaGyDWKhAHPGRmLF5BjMHRfJy3f4u09W4ZkvziE62Afv3D91SBt7jSYzfv3RKXx3UYnX12RgmROPtmAYBn87cAU78krgLRbikczRWDdrVLfijnvfPY5LNS048se5Q65yJMQdadqN2PZDEd45XAJvsQgb5iXj/psTPG56mxKUEzAMg7NVzfjmfA0OnK9FWYMWAgEwNSEUi9PkuH1itFtU3pwsV+NXH5xEu9GMN++ejNljIgZ9DMMweHJ3AXafrMJflqay1vuuVKXBK99cxv4LtZBJvfHEbWOxfHIsCqqacOf2H/H0ohT8KnM0K69NCF+VqjT4y38v4NCVeiRFBuAvS1MxIymc67CchhKUg1r1BhwtakBuYT0OXalDTbMeYqEAN40Ow6K0KMwfL0NEIP+TUk9VjVqsey8fhcpW/On28Xjg5v5PomUYBi99fQlvHy7FhnnJ2DBvDOvx/VymxovZl3C2sgnjoqTw8RKiVKXBkT/O5WQdjxCuMQyDnEt12LzvIirUWixMleP+mxMwPTHU7TerU4Kyk9nM4GJNC3IL65FbWI9T5Y0wmhkEeIsxIykMt42XY9442aAdHtyBpt2I339yBt9eVCJDEYyZSeG4aXQYbogP6TaFtv1QEV7dfwX33xSPPy9Nddl6GsMw2FdQg1f2X0ZVow4b54/BY7cmu+S1CeErvcGEt/NK8K/cYmg6TJBLfbBkYhSWToxBWoyU8/VuR1CC6ofZzOBqXRuOlzbgeIkax0sboGrrAACMj5Iic2wE5oyJwOT4EI+b9wUsP///Hi1F9rkaFFQ1w2RmIBEJMSkuGDeNDoNQIMDW7wqxLCMar92Vwck7tXajCUeuqjArOcLhcnZCPI22w4icS3XYe6YauYV1MJgYjAr3x9KMaCxKi0JyZIDbjKxYTVB5eXnYsmULzGYzVq1ahfXr13f7PsMw2LJlC3Jzc+Hj44OXX34ZqakDb8pkK0EZTGZcrmnFiTI1jpc04OcyNRq1ltYiUUE+mJ4YipnJEZidHM67c5bY1qo3IL+sET8Wq3CspAEXqlvAMMCcsRF4+74pHpmgCfEETdoOfHO+Fl+dqcZPpQ1gGMv5a+OjpUiNtpyqnBYjRVJEgNM61jgTawnKZDJhwYIF2LlzJ2QyGVauXImtW7ciKSnJdp/c3Fx88MEHePvtt3H27Fls2bIFn3322YDP64wExTAMqhp1OFPZZPs4f60Z7Z2nWCpCfTE9MQzTE0Nx46gwxIb4uuXwmC3NWgOu1rUiPTaIlxWJhJDeapv1OHy1HheqW3D+WjMu1rRA22HplO4tFiIx3B8yqQ9kUm/IpD6IlPpA3vl1sK8E/t6Wg0W9xUKX/T1kbVW5oKAA8fHxUCgsJ2dmZWUhJyenW4LKycnBHXfcAYFAgIyMDLS0tKCurg6Rkc7ZQ9OXHy7X4cndZ23Tdd5iIdJigvCLG+ORoQjGDfEhdBT1IIL8vDAlwbM3CBLiaeRBPlg1RYFVnV+bzAxKVRpcqG7GuapmlDVoUdeqx+XaFtS3tsPcz9BFLBTATyJCgLcYYQHeeH1NBkZFsHPeHGsJSqlUQi6/flicTCZDQUHBgPeRy+VQKpWsJqgQfwnmj5djfFQgJsWFYKw8kKaoCCEjjkhoOScsKTKg195Fk5mBqq0dyhY9apv1aNEboWk3oq3d8l/L5yYwYFhrOA2wmKD6mjnsOSy05z7OlqEIRoYimNXXIIQQdyYSCjqn+3wwgcODAVgbOsjlctTWXj/Pp6+RUc/71NbWsjp6IoQQ4j5YS1Dp6ekoKytDZWUlOjo6kJ2djblz53a7z9y5c7Fnzx4wDIMzZ84gMDCQEhQhhBAALE7xicVibNq0CevWrYPJZMKKFSuQnJyMXbt2AQDWrl2LzMxM5ObmYv78+fD19cVLL73EVjiEEELcDG3UJYQQwktUvkYIIYSXKEERQgjhJUpQhBBCeMnt1qCmT5+OmBjnHYhHCCGEWyEhIXj33Xd73e52CYoQQsjIQFN8hBBCeIkSFCGEEF6iBEUIIYSXKEERQgjhJUpQhBBCeIkSFCGEEF6iBEUIIYSXKEERQgjhJUpQxK19/vnnXIfgVvLz87Fz504cOXKE61BsNBoN1yG4naamJq5DcIkRnaDWrVvHdQi8lpeXZ/u8tbUVzzzzDJYsWYI//OEPUKlUHEZ23RtvvMF1CLy2cuVK2+effvopXnjhBWg0Gmzbtg07duzgMLLrsrKyuA6B17Zv3277vKioCAsWLMDy5csxd+5cnD17lpOYVCoVLly4gIsXL7L6t4C1Awv54sKFC33ezjAMLl++7OJo3Mtrr72G2bNnAwBefvllRERE4F//+he+++47bNq0qdsvDpuWLFnS7/f4kij5ymg02j7/5JNPsHPnToSGhuLBBx/E6tWrsX79epfEsXPnzj5vZxgGWq3WJTG4q++++w6/+c1vAACvvvoqnnnmGWRmZqKgoAAvvfQSPv74Y5fFcunSJTz//PNobW2FTCYDANTW1kIqleL5559HamqqU1/P4xPUypUrMXXqVPTVcrClpYWDiNzT+fPnsXfvXgDAAw88gC+//NJlr93Q0IB3330XUqm02+0Mw2DNmjUui8Mdmc1mNDc3w2w2g2EYhIaGAgD8/PwgEolcFsfWrVvx0EMPQSzu/SfHbDa7LA53V1dXh8zMTADAhAkToNfrXfr6Tz31FDZv3oyJEyd2u/3MmTN4+umn8dVXXzn19Tw+QY0ePRqbN29GQkJCr+9Z/6FJ3xoaGrBz504wDIO2tjYwDAOBQADAtX9U5syZA41Gg3HjxvX63vTp010Whztqa2vD8uXLbf929fX1iIiIgEaj6fNNG1tSU1Mxb948pKWl9freZ5995rI43FFlZSUeeeQRAJbRik6ng6+vL4DuI2RX0Ol0vZITAGRkZECn0zn99Ty+m/n+/fsxZswYjBo1qtf3Dh48iHnz5nEQlXvYtm1bt6/vvvtuhIaGor6+Hn/729/w6quvchQZGS6dTgeVSgWFQuGS1yspKUFwcLBtBNeVSqVCeHi4S+JwRydOnOj2dWpqKvz9/aFSqXDgwAHcc889LovlxRdfREVFBe644w7I5XIAlqS5Z88exMbGYtOmTU59PY9PUMRzqFQqKJVKCAQCREZG0h+1IaBrR5wlNzcXOTk5qKurA8MwkMlkuPXWW1mZkRoRCaq4uNh2QQEgMjISt956K0aPHs1xZPzHh2t38eJF/PnPf3bZwqwncYdr98knn2D16tVch+GWPP3aefwa1I4dO5CdnY2srCykp6cDAJRKJTZu3IisrCyXVTG5I75cu6efftqlC7OexB2u3Qh4j8waPl07NpKlxyeozz//HPv27YOXl1e32x944AHcfvvtlKAGwJdr5+qFWU/Cp2vX32icKjEH5w7Xjo1k6fEJSiAQoK6uDjExMd1ur6+vt1Wkkb7x5drNnj0b69ev73NhdtasWS6Lwx3x5drxZTTujtzl2vV8I+sMHr8GlZeXhxdeeAHx8fGIiooCAFRXV6OiogJ/+tOfbBtRSW98unauXJj1NHy4dgsWLOhzNN7R0YHbb78d3377rcticTfucu3mzJmDQ4cOOfU5PX4ENXv2bBw4cAAFBQVQKpVgGAZyuRzp6eku3ajojvh07TIzMykZOYgP144vo3F3xKdr5+quLh6foABAKBQiNjYWXl5etjJbSk724fu18/QqJja58to988wzeOCBB/odjZP+8enaubqri8cnqK69o+RyORiG4V2ZLV+5w7Xz8BlqVrny2vFpNO5u+HTtXN3VxePXoJYtW9Zvme2mTZt4UWbLV3y6dnzYj+Wu6NoRd+Xxx23wqczW3fDl2u3YsQMbN24EAKSnp9sqmTZu3MibIyP4iq4dcWceP4Jyde8oT8KXa+cuVUx8RNeOuDOPX4N67rnn+iyzveeeezivbOI7vlw7PlUxuRu6dsSdefwIirg/Pu3Hcjd07Yg7G9EJikqUHefqa2c2m3lRxeSO6NoRd+XxU3wDGcG5edhcfe2EQiEyMjJc+pqegq4dcVcjYgRFZbaOo2tHCOGKx5eZU5mt4+jaEUK45PEjKCqzdRxdO0IIlzx+BGUts+2JymwHR9eOEMIljy+S4FOjRXdD144QwiWPn+IDqMx2OOjaEUK4MiISFCGEEPfj8WtQhBBC3BMlKEIIIbxECYoQFxs3bhyWLVuGpUuX4s4778SpU6cAAFVVVRg7diz++c9/2u6rVquRmpqKzZs3AwBKSkpw7733YtmyZVi0aBEVqxCP5vFVfITwjY+PD/bu3QsAOHz4MLZu3YoPP/wQAKBQKHDo0CFs2LABALB//34kJSXZHrtlyxbcf//9mDdvHgDgypUrrg2eEBeiERQhHGpra4NUKrV97ePjg9GjR+PcuXMAgG+++QaLFi2yfb+urs52NhcAjB071nXBEuJiNIIixMX0ej2WLVuG9vZ21NfX47333uv2/cWLF+Prr79GREQEhEIhIiMjbRumH3jgAdx///2YNGkSZs6cieXLl3dLcIR4EhpBEeJi1im+/fv345133sEf//jHbt3hZ82ahaNHj2Lfvn1YvHhxt8euWLECX3/9NRYuXIjjx4/jrrvuQkdHh6t/BEJcghIUIRyaNGkSGhsboVarbbdJJBKkpqZi586duO2223o9RiaTYeXKlXjrrbcgFotRWFjoypAJcRlKUIRwqLi4GCaTCcHBwd1uf/DBB/HEE08gJCSk2+15eXkwGAwALD0Rm5qaIJPJXBUuIS5Fa1CEuJh1DQqwHPz4yiuv9GodlZycjOTk5F6PPXr0KLZs2QJvb28AwJNPPomIiAj2gyaEA9TqiBBCCC/RFB8hhBBeogRFCCGElyhBEUII4SVKUIQQQniJEhQhhBBeogRFCCGElyhBEUII4aX/DzoUEoz6siC3AAAAAElFTkSuQmCC\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import cpa.plotting as pl\\n\",\n \"\\n\",\n \"for drug in ['Nutlin', 'BMS', 'Dex', 'SAHA']:\\n\",\n \" df_pred = pl.plot_uncertainty_dose(\\n\",\n \" cpa_api,\\n\",\n \" cov={'cell_type': 'A549'},\\n\",\n \" pert=drug,\\n\",\n \" N=51,\\n\",\n \" measured_points=cpa_api.measured_points['all'],\\n\",\n \" cond_key='condition',\\n\",\n \" log=True,\\n\",\n \" metric='eucl'\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Previously, we demonstrated CPA predictions for drugs combinations. But our training data didn't contain any combinations examples. How much can we trust these examples? We can try to asses by running model uncertainty predictions:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df_pred2D = pl.plot_uncertainty_comb_dose(\\n\",\n \" cpa_api=cpa_api,\\n\",\n \" cov={'cell_type': 'A549'},\\n\",\n \" pert='Nutlin+BMS',\\n\",\n \" N=51,\\n\",\n \" cond_key='treatment',\\n\",\n \" metric='eucl',\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"And here is the predicted response we plotted before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cpa_plots.plot_contvar_response2D(\\n\",\n \" reconstructed_response2D, \\n\",\n \" title_name='Reconstructed dose-response',\\n\",\n \" logdose=False)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "preprocessing/GSM.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import scanpy as sc\\n\",\n \"import anndata as ad\\n\",\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.rcParams['figure.figsize']=(5, 5)\\n\",\n \"sc.settings.verbosity = 3\\n\",\n \"sc.logging.print_versions()\\n\",\n \"os.chdir('./../')\\n\",\n \"\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = sc.read('./datasets/GSM_raw.h5ad')\\n\",\n \"adata.var_names_make_unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['drug'] = [i.split('_')[0] for i in adata.obs['top_oligo']]\\n\",\n \"adata = adata[adata.obs['drug'] != 'NA']\\n\",\n \"adata.obs['dose'] = [i.split('_')[1] for i in adata.obs['top_oligo']]\\n\",\n \"adata.obs['dose'] = adata.obs['dose'].apply(pd.to_numeric, args=('coerce',))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"print(adata.obs['drug'].value_counts())\\n\",\n \"print('')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['vehicle'] = (adata.obs['dose'] == 0.0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['vehicle'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['vehicle'] = adata.obs['vehicle'].astype('category')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['drug'][adata.obs['vehicle'] == True] = 'Vehicle'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# QC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Quality control - calculate QC covariates\\n\",\n \"adata.obs['n_counts'] = adata.X.sum(1)\\n\",\n \"adata.obs['log_counts'] = np.log(adata.obs['n_counts'])\\n\",\n \"adata.obs['n_genes'] = (adata.X > 0).sum(1)\\n\",\n \"mt_gene_mask = [gene.startswith('MT-') for gene in adata.var_names]\\n\",\n \"adata.obs['mt_frac'] = np.asarray(adata.X[:, np.flatnonzero(mt_gene_mask)].sum(1)).flatten()/adata.obs['n_counts']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Data quality summary plots\\n\",\n \"#plt.rcParams['figure.figsize'] = (10, 10)\\n\",\n \"p1 = sc.pl.scatter(adata, 'n_counts', 'n_genes', size=2, color='drug', alpha=0.6)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Quality control - plot QC metrics\\n\",\n \"#Sample quality plots\\n\",\n \"t1 = sc.pl.violin(adata, 'n_counts', groupby='drug', size=0.5, log=True, cut=0, rotation=90)\\n\",\n \"t1 = sc.pl.violin(adata, 'mt_frac', groupby='drug', size=0.5, log=False, cut=0, rotation=90)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Thresholding decision: counts\\n\",\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['n_counts'], kde=False)\\n\",\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['n_counts'][adata.obs['n_counts']<4000], kde=False, bins=30)\\n\",\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['n_counts'][adata.obs['n_counts']>10000], kde=False, bins=30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Thresholding decision: genes\\n\",\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['n_genes'], kde=False, bins=60)\\n\",\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['n_genes'][adata.obs['n_genes']<2000], kde=False, bins=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"plt.figure()\\n\",\n \"sns.distplot(adata.obs['mt_frac'], kde=False, bins=60)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total number of cells: 23733\\n\",\n \"Number of cells after min count filter: 23733\\n\",\n \"filtered out 434 cells that have less than 750 genes expressed\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Filter cells according to identified QC thresholds:\\n\",\n \"print('Total number of cells: {:d}'.format(adata.n_obs))\\n\",\n \"\\n\",\n \"sc.pp.filter_cells(adata, min_counts = 500)\\n\",\n \"print('Number of cells after min count filter: {:d}'.format(adata.n_obs))\\n\",\n \"sc.pp.filter_cells(adata, min_genes = 750)\\n\",\n \"adata = adata[adata.obs.mt_frac<0.1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total number of genes: 58347\\n\",\n \"filtered out 40656 genes that are detected in less than 100 cells\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Trying to set attribute `.var` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Number of genes after cell filter: 17691\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Total number of genes: {:d}'.format(adata.n_vars))\\n\",\n \"\\n\",\n \"# Min 20 cells - filters out 0 count genes\\n\",\n \"sc.pp.filter_genes(adata, min_cells=100)\\n\",\n \"print('Number of genes after cell filter: {:d}'.format(adata.n_vars))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"#Data quality summary plots\\n\",\n \"p1 = sc.pl.scatter(adata, 'n_counts', 'n_genes', size=10, color='drug', alpha=0.6)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Normalization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Keep the count data in a counts layer\\n\",\n \"adata.layers[\\\"counts\\\"] = adata.X.copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Normalize adata \\n\",\n \"adata.X /= adata.obs['size_factor'].values[:,None]\\n\",\n \"sc.pp.log1p(adata)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# HVG\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"extracting highly variable genes\\n\",\n \" finished (0:00:02)\\n\",\n \"--> added\\n\",\n \" 'highly_variable', boolean vector (adata.var)\\n\",\n \" 'means', float vector (adata.var)\\n\",\n \" 'dispersions', float vector (adata.var)\\n\",\n \" 'dispersions_norm', float vector (adata.var)\\n\",\n \"\\n\",\n \" Number of highly variable genes: 4999\\n\"\n ]\n }\n ],\n \"source\": [\n \"import scipy\\n\",\n \"adata.X = scipy.sparse.csr_matrix(adata.X)\\n\",\n \"sc.pp.highly_variable_genes(adata, n_top_genes=5000, subset=True)\\n\",\n \"print('\\\\n','Number of highly variable genes: {:d}'.format(np.sum(adata.var['highly_variable'])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Visualization\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"computing PCA\\n\",\n \" on highly variable genes\\n\",\n \" with n_comps=50\\n\",\n \" finished (0:00:10)\\n\",\n \"computing neighbors\\n\",\n \" using 'X_pca' with n_pcs = 50\\n\",\n \" finished: added to `.uns['neighbors']`\\n\",\n \" `.obsp['distances']`, distances for each pair of neighbors\\n\",\n \" `.obsp['connectivities']`, weighted adjacency matrix (0:00:14)\\n\",\n \"computing UMAP\\n\",\n \" finished: added\\n\",\n \" 'X_umap', UMAP coordinates (adata.obsm) (0:00:18)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Calculate the visualizations\\n\",\n \"sc.pp.pca(adata, n_comps=50, use_highly_variable=True, svd_solver='arpack')\\n\",\n \"sc.pp.neighbors(adata)\\n\",\n \"\\n\",\n \"sc.tl.umap(adata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sc.pl.pca_scatter(adata, color='n_counts')\\n\",\n \"sc.pl.umap(adata, color=['n_counts', 'n_genes', 'mt_frac'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['dose'] = adata.obs['dose'].astype(str)\\n\",\n \"adata.obs['product_dose'] = adata.obs[['drug', 'dose']].apply(lambda x: ' '.join(x), axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'dose' as categorical\\n\",\n \"... storing 'product_dose' as categorical\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.rcParams['figure.figsize'] = (10, 10)\\n\",\n \"sc.pl.umap(adata, color='product_dose', color_map=sc.pl.palettes.default_20, size=20)\\n\",\n \"sc.pl.umap(adata, color='drug', color_map=sc.pl.palettes.default_20, size=20)\\n\",\n \"sc.pl.umap(adata, color='dose', size=20, palette='Reds')\\n\",\n \"sc.pl.umap(adata, color='vehicle', size=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"running Louvain clustering\\n\",\n \" using the \\\"louvain\\\" package of Traag (2017)\\n\",\n \" finished: found 11 clusters and added\\n\",\n \" 'louvain', the cluster labels (adata.obs, categorical) (0:00:02)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.tl.louvain(adata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sc.pl.umap(adata, color='louvain')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = adata[adata.obs.louvain != '2'] #remove weird cluster\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Prepare for the model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":2: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs['dose_val'][adata.obs['drug'].str.contains('Vehicle')] = 1.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.obs['dose_val'] = adata.obs.dose.astype(float) / np.max(adata.obs.dose.astype(float))\\n\",\n \"adata.obs['dose_val'][adata.obs['drug'].str.contains('Vehicle')] = 1.0\\n\",\n \"\\n\",\n \"adata.obs['cell_type'] = 'A549'\\n\",\n \"adata.obs['drug_dose_name'] = adata.obs.drug.astype(str) + '_' + adata.obs.dose_val.astype(str)\\n\",\n \"adata.obs['cov_drug_dose_name'] = adata.obs.cell_type.astype(str) + '_' + adata.obs.drug_dose_name.astype(str)\\n\",\n \"adata.obs['condition'] = adata.obs.drug.copy()\\n\",\n \"adata.obs['control'] = [1 if x == 'Vehicle_1.0' else 0 for x in adata.obs.drug_dose_name.values]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from cpa.helper import rank_genes_groups_by_cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A549\\n\",\n \"ranking genes\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi/lib/python3.8/site-packages/anndata/_core/anndata.py:1209: ImplicitModificationWarning: Initializing view as actual.\\n\",\n \" warnings.warn(\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cell_type' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" finished: added to `.uns['rank_genes_groups']`\\n\",\n \" 'names', sorted np.recarray to be indexed by group ids\\n\",\n \" 'scores', sorted np.recarray to be indexed by group ids\\n\",\n \" 'logfoldchanges', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rank_genes_groups_by_cov(adata, groupby='cov_drug_dose_name', covariate='cell_type', control_group='Vehicle_1.0')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Splits\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi/lib/python3.8/site-packages/pandas/core/indexing.py:671: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_with_indexer(indexer, value)\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.obs['split'] = 'nan'\\n\",\n \"adata.obs['split'].loc[\\n\",\n \" adata.obs['cov_drug_dose_name'].isin([\\n\",\n \" 'A549_Dex_0.5', \\n\",\n \" 'A549_Nutlin_0.5',\\n\",\n \" 'A549_SAHA_0.5',\\n\",\n \" 'A549_BMS_0.5'\\n\",\n \" ])\\n\",\n \"] = 'ood'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_idx = adata.obs_names[adata.obs.split!='ood']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.3, random_state=42)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['split'].loc[adata_idx_train] = 'train'\\n\",\n \"adata.obs['split'].loc[adata_idx_test] = 'test'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"split\\n\",\n \"ood 1767\\n\",\n \"test 3914\\n\",\n \"train 9130\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata.obs.groupby('split').size()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Cleanup\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"del adata.raw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'cell_type' as categorical\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \"... storing 'split' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.write('./datasets/GSM_new.h5ad')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "preprocessing/Norman19.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"scanpy==1.7.1 anndata==0.7.5 umap==0.5.1 numpy==1.19.2 scipy==1.6.1 pandas==1.2.3 scikit-learn==0.24.1 statsmodels==0.12.2 python-igraph==0.8.3 leidenalg==0.8.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import scanpy as sc\\n\",\n \"import anndata as ad\\n\",\n \"import pandas as pd\\n\",\n \"import re\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"plt.rcParams['figure.figsize']=(5, 5)\\n\",\n \"sc.settings.verbosity = 3\\n\",\n \"sc.logging.print_header()\\n\",\n \"os.chdir('./../')\\n\",\n \"\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sc.set_figure_params(dpi=100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## loading the raw data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/home/mohammad'\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = sc.read(\\\"./Desktop/test_AdvAE/datasets/Norman2019_raw.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"needed_obs = adata.obs[[\\\"guide_identity\\\",\\\"read_count\\\", \\\"UMI_count\\\",\\\"gemgroup\\\",\\\"good_coverage\\\",\\\"number_of_cells\\\",\\\"guide_ids\\\"]].copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new = sc.AnnData(adata.X.copy(), obs=needed_obs, var=adata.var.copy())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 111445 × 33694\\n\",\n \" obs: 'guide_identity', 'read_count', 'UMI_count', 'gemgroup', 'good_coverage', 'number_of_cells', 'guide_ids'\\n\",\n \" var: 'gene_symbols'\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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guide_identityread_countUMI_countgemgroupgood_coveragenumber_of_cellsguide_ids
index
AAACCTGAGAAGAAGC-1NegCtrl0_NegCtrl0__NegCtrl0_NegCtrl01252671True2
AAACCTGAGGCATGTG-1TSC22D1_NegCtrl0__TSC22D1_NegCtrl021511041True1TSC22D1
AAACCTGAGGCCCTTG-1KLF1_MAP2K6__KLF1_MAP2K61037591True1KLF1,MAP2K6
AAACCTGCACGAAGCA-1NegCtrl10_NegCtrl0__NegCtrl10_NegCtrl0958391True1
AAACCTGCAGACGTAG-1CEBPE_RUNX1T1__CEBPE_RUNX1T1244141True1CEBPE,RUNX1T1
........................
TTTGTCATCAGTACGT-8FOXA3_NegCtrl0__FOXA3_NegCtrl02068958True1FOXA3
TTTGTCATCCACTCCA-8CELF2_NegCtrl0__CELF2_NegCtrl0829338True1CELF2
TTTGTCATCCCAACGG-8BCORL1_NegCtrl0__BCORL1_NegCtrl013698True1BCORL1
TTTGTCATCCTCCTAG-8ZBTB10_PTPN12__ZBTB10_PTPN121254598True3PTPN12,ZBTB10
TTTGTCATCTGGCGAC-8MAP4K3_NegCtrl0__MAP4K3_NegCtrl01226598True1MAP4K3
\\n\",\n \"

111445 rows × 7 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" guide_identity read_count \\\\\\n\",\n \"index \\n\",\n \"AAACCTGAGAAGAAGC-1 NegCtrl0_NegCtrl0__NegCtrl0_NegCtrl0 1252 \\n\",\n \"AAACCTGAGGCATGTG-1 TSC22D1_NegCtrl0__TSC22D1_NegCtrl0 2151 \\n\",\n \"AAACCTGAGGCCCTTG-1 KLF1_MAP2K6__KLF1_MAP2K6 1037 \\n\",\n \"AAACCTGCACGAAGCA-1 NegCtrl10_NegCtrl0__NegCtrl10_NegCtrl0 958 \\n\",\n \"AAACCTGCAGACGTAG-1 CEBPE_RUNX1T1__CEBPE_RUNX1T1 244 \\n\",\n \"... ... ... \\n\",\n \"TTTGTCATCAGTACGT-8 FOXA3_NegCtrl0__FOXA3_NegCtrl0 2068 \\n\",\n \"TTTGTCATCCACTCCA-8 CELF2_NegCtrl0__CELF2_NegCtrl0 829 \\n\",\n \"TTTGTCATCCCAACGG-8 BCORL1_NegCtrl0__BCORL1_NegCtrl0 136 \\n\",\n \"TTTGTCATCCTCCTAG-8 ZBTB10_PTPN12__ZBTB10_PTPN12 1254 \\n\",\n \"TTTGTCATCTGGCGAC-8 MAP4K3_NegCtrl0__MAP4K3_NegCtrl0 1226 \\n\",\n \"\\n\",\n \" UMI_count gemgroup good_coverage number_of_cells \\\\\\n\",\n \"index \\n\",\n \"AAACCTGAGAAGAAGC-1 67 1 True 2 \\n\",\n \"AAACCTGAGGCATGTG-1 104 1 True 1 \\n\",\n \"AAACCTGAGGCCCTTG-1 59 1 True 1 \\n\",\n \"AAACCTGCACGAAGCA-1 39 1 True 1 \\n\",\n \"AAACCTGCAGACGTAG-1 14 1 True 1 \\n\",\n \"... ... ... ... ... \\n\",\n \"TTTGTCATCAGTACGT-8 95 8 True 1 \\n\",\n \"TTTGTCATCCACTCCA-8 33 8 True 1 \\n\",\n \"TTTGTCATCCCAACGG-8 9 8 True 1 \\n\",\n \"TTTGTCATCCTCCTAG-8 59 8 True 3 \\n\",\n \"TTTGTCATCTGGCGAC-8 59 8 True 1 \\n\",\n \"\\n\",\n \" guide_ids \\n\",\n \"index \\n\",\n \"AAACCTGAGAAGAAGC-1 \\n\",\n \"AAACCTGAGGCATGTG-1 TSC22D1 \\n\",\n \"AAACCTGAGGCCCTTG-1 KLF1,MAP2K6 \\n\",\n \"AAACCTGCACGAAGCA-1 \\n\",\n \"AAACCTGCAGACGTAG-1 CEBPE,RUNX1T1 \\n\",\n \"... ... \\n\",\n \"TTTGTCATCAGTACGT-8 FOXA3 \\n\",\n \"TTTGTCATCCACTCCA-8 CELF2 \\n\",\n \"TTTGTCATCCCAACGG-8 BCORL1 \\n\",\n \"TTTGTCATCCTCCTAG-8 PTPN12,ZBTB10 \\n\",\n \"TTTGTCATCTGGCGAC-8 MAP4K3 \\n\",\n \"\\n\",\n \"[111445 rows x 7 columns]\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.obs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#check all ctrl guides\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"list_control = []\\n\",\n \"for i in np.unique(adata_new.obs[\\\"guide_identity\\\"]):\\n\",\n \" m = re.match(r\\\"NegCtrl(.*)_NegCtrl(.*)+NegCtrl(.*)_NegCtrl(.*)\\\", i)\\n\",\n \" if m :\\n\",\n \" list_control.append(m.group())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"remove \\\"NegCtrl1_NegCtrl0__NegCtrl1_NegCtrl0\\\" suggested by authors\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new = adata_new[adata_new.obs[\\\"guide_identity\\\"] != \\\"NegCtrl1_NegCtrl0__NegCtrl1_NegCtrl0\\\"] \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"View of AnnData object with n_obs × n_vars = 108497 × 33694\\n\",\n \" obs: 'guide_identity', 'read_count', 'UMI_count', 'gemgroup', 'good_coverage', 'number_of_cells', 'guide_ids'\\n\",\n \" var: 'gene_symbols'\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"merge all controls \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Trying to set attribute `.obs` of view, copying.\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata_new.obs[\\\"guide_merged\\\"] = adata_new.obs[\\\"guide_identity\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for i in np.unique(adata_new.obs[\\\"guide_merged\\\"]):\\n\",\n \" m = re.match(r\\\"NegCtrl(.*)_NegCtrl(.*)+NegCtrl(.*)_NegCtrl(.*)\\\", i)\\n\",\n \" if m :\\n\",\n \" adata_new.obs[\\\"guide_merged\\\"].replace(i,\\\"ctrl\\\",inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"relabeling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"old: HOXC13_NegCtrl0__HOXC13_NegCtrl0_2 new: HOXC13+ctrl\\n\",\n \"old: TGFBR2_IGDCC3__TGFBR2_IGDCC3_2 new: TGFBR2+IGDCC3\\n\",\n \"old: ZBTB10_NegCtrl0__ZBTB10_NegCtrl0_2 new: ZBTB10+ctrl\\n\"\n ]\n }\n ],\n \"source\": [\n \"old_pool = []\\n\",\n \"for i in np.unique(adata_new.obs[\\\"guide_merged\\\"]):\\n\",\n \" if i == \\\"ctrl\\\":\\n\",\n \" old_pool.append(i)\\n\",\n \" continue\\n\",\n \" split = i.split(\\\"__\\\")[1]\\n\",\n \" split = split.split(\\\"_\\\")\\n\",\n \" for j, string in enumerate(split):\\n\",\n \" if \\\"NegCtrl\\\" in split[j]:\\n\",\n \" split[j] = \\\"ctrl\\\"\\n\",\n \" if len(split) == 1:\\n\",\n \" if split[0] in old_pool:\\n\",\n \" print(\\\"old:\\\",i, \\\"new:\\\",split[0])\\n\",\n \" adata_new.obs[\\\"guide_merged\\\"].replace(i,split[0],inplace=True)\\n\",\n \" old_pool.append(split[0])\\n\",\n \" else:\\n\",\n \" if f\\\"{split[0]}+{split[1]}\\\" in old_pool:\\n\",\n \" print(\\\"old:\\\",i, \\\"new:\\\",f\\\"{split[0]}+{split[1]}\\\")\\n\",\n \" adata_new.obs[\\\"guide_merged\\\"].replace(i, f\\\"{split[0]}+{split[1]}\\\",inplace=True)\\n\",\n \" old_pool.append(f\\\"{split[0]}+{split[1]}\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"index\\n\",\n \"AAACCTGAGAAGAAGC-1 ctrl\\n\",\n \"AAACCTGAGGCATGTG-1 TSC22D1+ctrl\\n\",\n \"AAACCTGAGGCCCTTG-1 KLF1+MAP2K6\\n\",\n \"AAACCTGCACGAAGCA-1 ctrl\\n\",\n \"AAACCTGCAGACGTAG-1 CEBPE+RUNX1T1\\n\",\n \" ... \\n\",\n \"TTTGTCATCAGTACGT-8 FOXA3+ctrl\\n\",\n \"TTTGTCATCCACTCCA-8 CELF2+ctrl\\n\",\n \"TTTGTCATCCCAACGG-8 BCORL1+ctrl\\n\",\n \"TTTGTCATCCTCCTAG-8 ZBTB10+PTPN12\\n\",\n \"TTTGTCATCTGGCGAC-8 MAP4K3+ctrl\\n\",\n \"Name: guide_merged, Length: 108497, dtype: category\\n\",\n \"Categories (284, object): ['AHR+FEV', 'AHR+KLF1', 'AHR+ctrl', 'ARID1A+ctrl', ..., 'ZC3HAV1+HOXC13', 'ZC3HAV1+ctrl', 'ZNF318+FOXL2', 'ZNF318+ctrl']\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.obs[\\\"guide_merged\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# preprocessing \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Keep the count data in a counts layer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.layers[\\\"counts\\\"] = adata_new.X.copy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalization and HVG selection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"normalizing counts per cell\\n\",\n \" finished (0:00:01)\\n\",\n \"If you pass `n_top_genes`, all cutoffs are ignored.\\n\",\n \"extracting highly variable genes\\n\",\n \" finished (0:00:05)\\n\",\n \"--> added\\n\",\n \" 'highly_variable', boolean vector (adata.var)\\n\",\n \" 'means', float vector (adata.var)\\n\",\n \" 'dispersions', float vector (adata.var)\\n\",\n \" 'dispersions_norm', float vector (adata.var)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.pp.normalize_total(adata_new)\\n\",\n \"sc.pp.log1p(adata_new)\\n\",\n \"sc.pp.highly_variable_genes(adata_new,n_top_genes=5000, subset=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 108497 × 5000\\n\",\n \" obs: 'guide_identity', 'read_count', 'UMI_count', 'gemgroup', 'good_coverage', 'number_of_cells', 'guide_ids', 'guide_merged'\\n\",\n \" var: 'gene_symbols', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\\n\",\n \" uns: 'log1p', 'hvg'\\n\",\n \" layers: 'counts'\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Prepare for the model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.obs['dose_val'] = 'nan'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.obs['dose_val'].loc[\\n\",\n \" adata_new.obs['guide_merged']==\\\"ctrl\\\"\\n\",\n \"] = '1'\\n\",\n \"\\n\",\n \"adata_new.obs['dose_val'].loc[\\n\",\n \" adata_new.obs['guide_merged']!=\\\"ctrl\\\"\\n\",\n \"] = \\\"1+1\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.obs[\\\"condition\\\"] = adata_new.obs[\\\"guide_merged\\\"]\\n\",\n \"adata_new.obs['cell_type'] = 'A549'\\n\",\n \"adata_new.obs['control'] = [1 if x == 'ctrl' else 0 for x in adata_new.obs.condition.values]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.obs['drug_dose_name'] = adata_new.obs.condition.astype(str) + '_' + adata_new.obs.dose_val.astype(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.obs['cov_drug_dose_name'] = adata_new.obs.cell_type.astype(str) + '_' + adata_new.obs.drug_dose_name.astype(str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"DE test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/home/mohammad'\"\n ]\n },\n \"execution_count\": 90,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.chdir('./Desktop/test_AdvAE/')\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from cpa.helper import rank_genes_groups_by_cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\",\n \"ranking genes\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A549\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'split' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'dose_val' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cell_type' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \" finished: added to `.uns['rank_genes_groups']`\\n\",\n \" 'names', sorted np.recarray to be indexed by group ids\\n\",\n \" 'scores', sorted np.recarray to be indexed by group ids\\n\",\n \" 'logfoldchanges', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:02)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rank_genes_groups_by_cov(adata_new, groupby='cov_drug_dose_name', covariate='cell_type', control_group='ctrl_1', n_genes=20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"saving to new object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 126,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"to_pick = adata_new.obs[[\\\"cov_drug_dose_name\\\",\\\"dose_val\\\",\\\"control\\\",\\\"condition\\\",\\\"split\\\",\\\"guide_identity\\\",\\\"drug_dose_name\\\",\\\"cell_type\\\"]]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new_small = sc.AnnData(adata_new.X, obs=to_pick,\\n\",\n \" var=adata_new.var, uns=adata_new.uns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new_small.layers = adata_new.layers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# splits\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"visualization split \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 311,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mohammad/anaconda3/envs/scvi-env/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" iloc._setitem_with_indexer(indexer, value)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"split\\n\",\n \"ood 2\\n\",\n \"test 10848\\n\",\n \"train 97647\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 311,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = []\\n\",\n \"adata_new_small.obs['split'] = 'nan'\\n\",\n \"adata_idx = adata_new_small.obs_names\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.1, random_state=42)\\n\",\n \"adata_idx_test, adata_idx_ood = train_test_split(adata_idx_test, test_size=0.0001, random_state=42)\\n\",\n \"adata_idx = adata_new_small.obs_names[adata_new_small.obs.split!='ood']\\n\",\n \"adata_new_small.obs['split'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new_small.obs['split'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new_small.obs['split'].loc[adata_idx_ood] = 'ood'\\n\",\n \"adata_new_small.obs.groupby('split').size()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First ood set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 270,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"split1\\n\",\n \"ood 1884\\n\",\n \"test 21323\\n\",\n \"train 85290\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 270,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = ['DUSP9+MAPK1',\\n\",\n \" 'ETS2+MAPK1',\\n\",\n \" 'DUSP9+ETS2',\\n\",\n \" 'CBL+CNN1',\\n\",\n \" 'MAPK1+DUSP9',\\n\",\n \" 'MAPK1+ETS2',\\n\",\n \" 'ETS2+DUSP9',\\n\",\n \" 'CNN1+CBL']\\n\",\n \"adata_new_small.obs['split1'] = 'nan'\\n\",\n \"adata_new_small.obs['split1'].loc[\\n\",\n \" adata_new_small.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new_small.obs_names[adata_new_small.obs.split1!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_new_small.obs['split1'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new_small.obs['split1'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new_small.obs.groupby('split1').size()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 271,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"condition_key = \\\"condition\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"split for leave one 10 out and predict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 272,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"double_ko_list = [i for i in adata_new_small.obs[condition_key].unique() if \\\"ctrl\\\" not in i]\\n\",\n \"single = [i for i in adata_new_small.obs[condition_key].unique() if \\\"ctrl\\\" in i]\\n\",\n \"np.random.shuffle(double_ko_list)\\n\",\n \"\\n\",\n \"#drug split into splits of 10\\n\",\n \"ood_list = []\\n\",\n \"i = 0\\n\",\n \"while(i10):\\n\",\n \" ood_list.append(double_ko_list[0:min(i,len(double_ko_list))])\\n\",\n \" print(i)\\n\",\n \" i-= 20\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 276,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1', 'FOXF1+HOXB9', 'IGDCC3+MAPK1', 'MAP2K6+IKZF3', 'MAPK1+PRTG', 'FOXA3+FOXF1', 'RHOXF2+SET', 'SAMD1+PTPN12', 'MAPK1+IKZF3', 'PTPN12+ZBTB25', 'FEV+CBFA2T3', 'MAP2K6+SPI1', 'SNAI1+UBASH3B', 'CEBPE+PTPN12', 'BCL2L11+TGFBR2', 'ETS2+CEBPE', 'FEV+ISL2', 'JUN+CEBPB', 'ZNF318+FOXL2', 'TGFBR2+ETS2', 'LHX1+ELMSAN1', 'MAP2K3+ELMSAN1', 'FOSB+UBASH3B', 'SET+KLF1', 'FOSB+OSR2', 'PTPN12+SNAI1', 'CEBPE+CEBPA', 'ZBTB10+DLX2', 'DUSP9+SNAI1', 'ETS2+MAPK1', 'CEBPE+KLF1', 'CNN1+UBASH3A', 'ZBTB10+PTPN12', 'ZC3HAV1+CEBPE', 'MAPK1+TGFBR2', 'PRDM1+CBFA2T3', 'KLF1+BAK1', 'UBASH3B+PTPN12', 'ETS2+IKZF3', 'FOSB+PTPN12', 'UBASH3B+ZBTB25', 'TGFBR2+PRTG', 'FOXA3+FOXL2', 'ETS2+IGDCC3', 'KLF1+TGFBR2', 'CEBPB+OSR2', 'ZC3HAV1+HOXC13', 'ETS2+CNN1', 'UBASH3B+PTPN9', 'PTPN12+UBASH3A', 'ZC3HAV1+CEBPA', 'ETS2+MAP7D1', 'FOXA1+FOXL2', 'FEV+MAP7D1', 'FOXA1+FOXF1', 'KLF1+COL2A1', 'KIF18B+KIF2C', 'BPGM+ZBTB1', 'IGDCC3+PRTG', 'CBL+UBASH3A', 'KLF1+FOXA1', 'MAP2K3+SLC38A2', 'FOSB+CEBPE', 'POU3F2+CBFA2T3', 'UBASH3B+UBASH3A', 'DUSP9+MAPK1', 'CBL+CNN1', 'UBASH3B+OSR2', 'BCL2L11+BAK1', 'CEBPE+SPI1', 'CEBPE+CEBPB', 'SGK1+TBX2', 'FOXA1+HOXB9', 'CEBPB+MAPK1', 'SET+CEBPE', 'MAP2K3+MAP2K6', 'DUSP9+KLF1', 'AHR+FEV', 'MAP2K3+IKZF3', 'FOSB+IKZF3', 'CDKN1B+CDKN1A', 'CNN1+MAPK1', 'FOXL2+HOXB9', 'TGFBR2+C19orf26', 'AHR+KLF1', 'CEBPE+RUNX1T1', 'IRF1+SET', 'DUSP9+PRTG', 'FOXA3+FOXA1', 'POU3F2+FOXL2', 'TGFBR2+IGDCC3', 'KLF1+CLDN6', 'CDKN1C+CDKN1A', 'CBL+PTPN9', 'C3orf72+FOXL2', 'ETS2+PRTG', 'JUN+CEBPA', 'KLF1+CEBPA', 'SNAI1+DLX2', 'SGK1+S1PR2', 'IGDCC3+ZBTB25'] 0\\n\",\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1', 'FOXF1+HOXB9', 'IGDCC3+MAPK1', 'MAP2K6+IKZF3', 'MAPK1+PRTG', 'FOXA3+FOXF1', 'RHOXF2+SET', 'SAMD1+PTPN12', 'MAPK1+IKZF3', 'PTPN12+ZBTB25', 'FEV+CBFA2T3', 'MAP2K6+SPI1', 'SNAI1+UBASH3B', 'CEBPE+PTPN12', 'BCL2L11+TGFBR2', 'ETS2+CEBPE', 'FEV+ISL2', 'JUN+CEBPB', 'ZNF318+FOXL2', 'TGFBR2+ETS2', 'LHX1+ELMSAN1', 'MAP2K3+ELMSAN1', 'FOSB+UBASH3B', 'SET+KLF1', 'FOSB+OSR2', 'PTPN12+SNAI1', 'CEBPE+CEBPA', 'ZBTB10+DLX2', 'DUSP9+SNAI1', 'ETS2+MAPK1', 'CEBPE+KLF1', 'CNN1+UBASH3A', 'ZBTB10+PTPN12', 'ZC3HAV1+CEBPE', 'MAPK1+TGFBR2', 'PRDM1+CBFA2T3', 'KLF1+BAK1', 'UBASH3B+PTPN12', 'ETS2+IKZF3', 'FOSB+PTPN12', 'UBASH3B+ZBTB25', 'TGFBR2+PRTG', 'FOXA3+FOXL2', 'ETS2+IGDCC3', 'KLF1+TGFBR2', 'CEBPB+OSR2', 'ZC3HAV1+HOXC13', 'ETS2+CNN1', 'UBASH3B+PTPN9', 'PTPN12+UBASH3A', 'ZC3HAV1+CEBPA', 'ETS2+MAP7D1', 'FOXA1+FOXL2', 'FEV+MAP7D1', 'FOXA1+FOXF1', 'KLF1+COL2A1', 'KIF18B+KIF2C', 'BPGM+ZBTB1', 'IGDCC3+PRTG', 'CBL+UBASH3A', 'KLF1+FOXA1', 'MAP2K3+SLC38A2', 'FOSB+CEBPE', 'POU3F2+CBFA2T3', 'UBASH3B+UBASH3A', 'DUSP9+MAPK1', 'CBL+CNN1', 'UBASH3B+OSR2', 'BCL2L11+BAK1', 'CEBPE+SPI1', 'CEBPE+CEBPB', 'SGK1+TBX2', 'FOXA1+HOXB9', 'CEBPB+MAPK1', 'SET+CEBPE', 'MAP2K3+MAP2K6', 'DUSP9+KLF1', 'AHR+FEV', 'MAP2K3+IKZF3', 'FOSB+IKZF3', 'CDKN1B+CDKN1A'] 1\\n\",\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1', 'FOXF1+HOXB9', 'IGDCC3+MAPK1', 'MAP2K6+IKZF3', 'MAPK1+PRTG', 'FOXA3+FOXF1', 'RHOXF2+SET', 'SAMD1+PTPN12', 'MAPK1+IKZF3', 'PTPN12+ZBTB25', 'FEV+CBFA2T3', 'MAP2K6+SPI1', 'SNAI1+UBASH3B', 'CEBPE+PTPN12', 'BCL2L11+TGFBR2', 'ETS2+CEBPE', 'FEV+ISL2', 'JUN+CEBPB', 'ZNF318+FOXL2', 'TGFBR2+ETS2', 'LHX1+ELMSAN1', 'MAP2K3+ELMSAN1', 'FOSB+UBASH3B', 'SET+KLF1', 'FOSB+OSR2', 'PTPN12+SNAI1', 'CEBPE+CEBPA', 'ZBTB10+DLX2', 'DUSP9+SNAI1', 'ETS2+MAPK1', 'CEBPE+KLF1', 'CNN1+UBASH3A', 'ZBTB10+PTPN12', 'ZC3HAV1+CEBPE', 'MAPK1+TGFBR2', 'PRDM1+CBFA2T3', 'KLF1+BAK1', 'UBASH3B+PTPN12', 'ETS2+IKZF3', 'FOSB+PTPN12', 'UBASH3B+ZBTB25', 'TGFBR2+PRTG', 'FOXA3+FOXL2', 'ETS2+IGDCC3', 'KLF1+TGFBR2', 'CEBPB+OSR2', 'ZC3HAV1+HOXC13', 'ETS2+CNN1', 'UBASH3B+PTPN9', 'PTPN12+UBASH3A', 'ZC3HAV1+CEBPA', 'ETS2+MAP7D1', 'FOXA1+FOXL2', 'FEV+MAP7D1', 'FOXA1+FOXF1', 'KLF1+COL2A1', 'KIF18B+KIF2C', 'BPGM+ZBTB1', 'IGDCC3+PRTG', 'CBL+UBASH3A', 'KLF1+FOXA1'] 2\\n\",\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1', 'FOXF1+HOXB9', 'IGDCC3+MAPK1', 'MAP2K6+IKZF3', 'MAPK1+PRTG', 'FOXA3+FOXF1', 'RHOXF2+SET', 'SAMD1+PTPN12', 'MAPK1+IKZF3', 'PTPN12+ZBTB25', 'FEV+CBFA2T3', 'MAP2K6+SPI1', 'SNAI1+UBASH3B', 'CEBPE+PTPN12', 'BCL2L11+TGFBR2', 'ETS2+CEBPE', 'FEV+ISL2', 'JUN+CEBPB', 'ZNF318+FOXL2', 'TGFBR2+ETS2', 'LHX1+ELMSAN1', 'MAP2K3+ELMSAN1', 'FOSB+UBASH3B', 'SET+KLF1', 'FOSB+OSR2', 'PTPN12+SNAI1', 'CEBPE+CEBPA', 'ZBTB10+DLX2', 'DUSP9+SNAI1', 'ETS2+MAPK1', 'CEBPE+KLF1', 'CNN1+UBASH3A', 'ZBTB10+PTPN12', 'ZC3HAV1+CEBPE', 'MAPK1+TGFBR2', 'PRDM1+CBFA2T3', 'KLF1+BAK1', 'UBASH3B+PTPN12', 'ETS2+IKZF3', 'FOSB+PTPN12', 'UBASH3B+ZBTB25'] 3\\n\",\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1', 'FOXF1+HOXB9', 'IGDCC3+MAPK1', 'MAP2K6+IKZF3', 'MAPK1+PRTG', 'FOXA3+FOXF1', 'RHOXF2+SET', 'SAMD1+PTPN12', 'MAPK1+IKZF3', 'PTPN12+ZBTB25', 'FEV+CBFA2T3', 'MAP2K6+SPI1', 'SNAI1+UBASH3B', 'CEBPE+PTPN12', 'BCL2L11+TGFBR2', 'ETS2+CEBPE', 'FEV+ISL2', 'JUN+CEBPB', 'ZNF318+FOXL2', 'TGFBR2+ETS2', 'LHX1+ELMSAN1'] 4\\n\",\n \"['TMSB4X+BAK1', 'BPGM+SAMD1', 'TBX3+TBX2', 'CDKN1C+CDKN1B', 'SAMD1+UBASH3B', 'CEBPB+CEBPA', 'CBL+PTPN12', 'DUSP9+ETS2', 'CEBPE+CNN1', 'PTPN12+OSR2', 'KLF1+MAP2K6', 'FOXA3+HOXB9', 'UBASH3B+CNN1', 'FOSB+CEBPB', 'MAP2K6+ELMSAN1', 'CEBPB+PTPN12', 'DUSP9+IGDCC3', 'SAMD1+ZBTB1', 'LYL1+IER5L', 'ZBTB10+SNAI1'] 5\\n\"\n ]\n }\n ],\n \"source\": [\n \"for idx, splits in enumerate(ood_list):\\n\",\n \" ood_set = splits\\n\",\n \" print(splits, idx)\\n\",\n \" adata_new_small.obs[f'split{idx+15}'] = 'nan'\\n\",\n \" adata_new_small.obs[f'split{idx+15}'].loc[\\n\",\n \" adata_new_small.obs['condition'].isin(ood_set)\\n\",\n \" ] = 'ood'\\n\",\n \" adata_idx = adata_new_small.obs_names[adata_new_small.obs[f'split{idx+15}']!='ood']\\n\",\n \" adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \" adata_new_small.obs[f'split{idx+15}'].loc[adata_idx_train] = 'train'\\n\",\n \" adata_new_small.obs[f'split{idx+15}'].loc[adata_idx_test] = 'test'\\n\",\n \" adata_new_small.obs.groupby(f'split{idx+15}').size()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"epistasis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 329,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mohammad/anaconda3/envs/scvi-env/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" iloc._setitem_with_indexer(indexer, value)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"split21\\n\",\n \"ood 3824\\n\",\n \"test 20935\\n\",\n \"train 83738\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 329,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"epistasis = [\\\"AHR+KLF1\\\",\\\"MAPK1+TGFBR2\\\",\\\"TGFBR2+IGDCC3\\\",\\\"TGFBR2+PRTG\\\",\\n\",\n \" \\\"UBASH3B+OSR2\\\",\\\"DUSP9+ETS2\\\",\\\"KLF1+CEBPA\\\",\\\"MAP2K6+IKZF3\\\",\\\"ZC3HAV1+CEBPA\\\"]\\n\",\n \"ood_set = epistasis\\n\",\n \"adata_new_small.obs['split21'] = 'nan'\\n\",\n \"adata_new_small.obs['split21'].loc[\\n\",\n \" adata_new_small.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new_small.obs_names[adata_new_small.obs.split21!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_new_small.obs['split21'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new_small.obs['split21'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new_small.obs.groupby('split21').size()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 339,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"check = adata_new_small[adata_new_small.obs[\\\"split21\\\"] == \\\"ood\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 341,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"UBASH3B+OSR2 796\\n\",\n \"DUSP9+ETS2 787\\n\",\n \"MAPK1+TGFBR2 497\\n\",\n \"AHR+KLF1 481\\n\",\n \"KLF1+CEBPA 311\\n\",\n \"TGFBR2+IGDCC3 301\\n\",\n \"MAP2K6+IKZF3 300\\n\",\n \"TGFBR2+PRTG 265\\n\",\n \"ZC3HAV1+CEBPA 86\\n\",\n \"Name: condition, dtype: int64\"\n ]\n },\n \"execution_count\": 341,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"check.obs[condition_key].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"neomorphic interactions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 344,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"nemorphic = [\\\"CBL+TGFBR2\\\",\\\"KLF1+TGFBR2\\\",\\\"MAP2K6+SPI1\\\",\\n\",\n \" \\\"SAMD1+TGFBR2\\\",\\\"TGFBR2+ETS2\\\",\\\"CBL+UBASH3A\\\",\\n\",\n \" \\\"CEBPE+KLF1\\\",\\\"PTPN12+OSR2\\\",\\\"ZC3HAV1+CEBPE\\\",\\\"PLK4+STIL\\\",\\\"FOSB+PTPN12\\\",\\\"FEV+CBFA2T3\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 345,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mohammad/anaconda3/envs/scvi-env/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" iloc._setitem_with_indexer(indexer, value)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"split22\\n\",\n \"ood 3083\\n\",\n \"test 21083\\n\",\n \"train 84331\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 345,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = nemorphic\\n\",\n \"adata_new_small.obs['split22'] = 'nan'\\n\",\n \"adata_new_small.obs['split22'].loc[\\n\",\n \" adata_new_small.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new_small.obs_names[adata_new_small.obs.split22!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_new_small.obs['split22'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new_small.obs['split22'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new_small.obs.groupby('split22').size()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 347,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"check = adata_new_small[adata_new_small.obs[\\\"split22\\\"] == \\\"ood\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 348,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"CEBPE+KLF1 468\\n\",\n \"ZC3HAV1+CEBPE 410\\n\",\n \"FOSB+PTPN12 345\\n\",\n \"PTPN12+OSR2 339\\n\",\n \"KLF1+TGFBR2 337\\n\",\n \"TGFBR2+ETS2 318\\n\",\n \"MAP2K6+SPI1 302\\n\",\n \"CBL+TGFBR2 188\\n\",\n \"FEV+CBFA2T3 159\\n\",\n \"PLK4+STIL 81\\n\",\n \"SAMD1+TGFBR2 72\\n\",\n \"CBL+UBASH3A 64\\n\",\n \"Name: condition, dtype: int64\"\n ]\n },\n \"execution_count\": 348,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"check.obs[condition_key].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 356,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"set()\"\n ]\n },\n \"execution_count\": 356,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"set(set_orig)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 359,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"set_orig = []\\n\",\n \"for i in adata_new_small.obs[condition_key].unique():\\n\",\n \" for j in nemorphic:\\n\",\n \" split = j.split(\\\"+\\\")\\n\",\n \" if (split[0] in i) and (split[1] in i):\\n\",\n \" set_orig.append(i)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 361,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 361,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"set(set_orig) == set(nemorphic)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"leave all doubles out out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 364,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"double_ko_list = [i for i in adata_new_small.obs[condition_key].unique() if \\\"ctrl\\\" not in i]\\n\",\n \"single = [i for i in adata_new_small.obs[condition_key].unique() if \\\"ctrl\\\" in i]\\n\",\n \"np.random.shuffle(double_ko_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 368,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"split23\\n\",\n \"ood 41759\\n\",\n \"test 13348\\n\",\n \"train 53390\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 368,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = double_ko_list\\n\",\n \"adata_new_small.obs['split23'] = 'nan'\\n\",\n \"adata_new_small.obs['split23'].loc[\\n\",\n \" adata_new_small.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new_small.obs_names[adata_new_small.obs.split23!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_new_small.obs['split23'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new_small.obs['split23'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new_small.obs.groupby('split23').size()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"saving final object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 369,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 108497 × 5000\\n\",\n \" obs: 'cov_drug_dose_name', 'dose_val', 'control', 'condition', 'guide_identity', 'drug_dose_name', 'cell_type', 'split', 'split1', 'split2', 'split3', 'split4', 'split5', 'split6', 'split7', 'split8', 'split9', 'split10', 'split11', 'split12', 'split13', 'split14', 'split15', 'split16', 'split17', 'split18', 'split19', 'split20', 'split21', 'split22', 'split23'\\n\",\n \" var: 'gene_symbols', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\\n\",\n \" uns: 'log1p', 'hvg', 'rank_genes_groups_cov'\\n\",\n \" layers: 'counts'\"\n ]\n },\n \"execution_count\": 369,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new_small\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 370,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'split23' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata_new_small.write(\\\"./datasets/Norman2019_prep_new.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"add two splits \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"split DUSP9+MAPK1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new = sc.read(\\\"./datasets/Norman2019_prep_new.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mo/miniconda3/envs/pytorch/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\",\n \"/home/mo/miniconda3/envs/pytorch/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"split24\\n\",\n \"ood 290\\n\",\n \"test 21642\\n\",\n \"train 86565\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = ['DUSP9+MAPK1']\\n\",\n \"adata_new.obs['split24'] = 'nan'\\n\",\n \"adata_new.obs['split24'].loc[\\n\",\n \" adata_new.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new.obs_names[adata_new.obs.split24!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_idx = adata_new.obs_names[adata_new.obs.split24!='ood']\\n\",\n \"train_test = adata_new[~adata_new.obs[\\\"condition\\\"].isin(ood_set)].copy()\\n\",\n \"adata_new.obs['split24'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new.obs['split24'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new.obs.groupby('split24').size()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"split DUSP9+ETS2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mo/miniconda3/envs/pytorch/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\",\n \"/home/mo/miniconda3/envs/pytorch/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"split25\\n\",\n \"ood 787\\n\",\n \"test 21542\\n\",\n \"train 86168\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ood_set = [\\\"DUSP9+ETS2\\\"]\\n\",\n \"adata_new.obs['split25'] = 'nan'\\n\",\n \"adata_new.obs['split25'].loc[\\n\",\n \" adata_new.obs['condition'].isin(ood_set)\\n\",\n \"] = 'ood'\\n\",\n \"adata_idx = adata_new.obs_names[adata_new.obs.split25!='ood']\\n\",\n \"adata_idx_train, adata_idx_test = train_test_split(adata_idx, test_size=0.2, random_state=42)\\n\",\n \"adata_idx = adata_new.obs_names[adata_new.obs.split25!='ood']\\n\",\n \"train_test = adata_new[~adata_new.obs[\\\"condition\\\"].isin(ood_set)].copy()\\n\",\n \"adata_new.obs['split25'].loc[adata_idx_train] = 'train'\\n\",\n \"adata_new.obs['split25'].loc[adata_idx_test] = 'test'\\n\",\n \"adata_new.obs.groupby('split25').size()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'split24' as categorical\\n\",\n \"... storing 'split25' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata_new.write(\\\"./datasets/Norman2019_prep_new.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "preprocessing/lincs.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\\n\",\n \" and should_run_async(code)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"scanpy==1.6.0 anndata==0.7.4 umap==0.4.6 numpy==1.19.2 scipy==1.6.1 pandas==1.2.3 scikit-learn==0.23.2 statsmodels==0.11.1 python-igraph==0.8.3 leidenalg==0.8.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import scanpy as sc\\n\",\n \"sc.set_figure_params(dpi=100, frameon=False)\\n\",\n \"sc.logging.print_header()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\\n\",\n \" and should_run_async(code)\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"os.chdir('./../')\\n\",\n \"from cpa.helper import rank_genes_groups_by_cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"warnings.filterwarnings('ignore')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = sc.read('datasets/lincs.h5ad')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['condition'] = adata.obs['pert_iname']\\n\",\n \"adata.obs['cell_type'] = adata.obs['cell_id']\\n\",\n \"adata.obs['dose_val'] = adata.obs['pert_dose']\\n\",\n \"adata.obs['cov_drug_dose_name'] = adata.obs.cell_type.astype(str) + '_' + adata.obs.condition.astype(str) + '_' + adata.obs.dose_val.astype(str)\\n\",\n \"adata.obs['control'] = (adata.obs['condition'] == 'DMSO').astype(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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condition
1B Parent8000000000...0000000000
2-methoxyestradiol18000000000...00000000017
3,6-dimethoxyflavone5300300000...0000000000
3-amino-benzamide306122000022...44222029218
5-methoxy-alpha-methyltryptamine0000000000...0000000000
..................................................................
xanthoxyline3500400000...0000000700
yohimbine351600400000...000000026018
zacopride91100000000...00000001900
zaprinast21600400000...000000012018
zileuton321322000011...342120219218
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1001 rows × 82 columns

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\"\n ],\n \"text/plain\": [\n \"cell_type A375 A549 A673 AGS ASC ASC.C BT20 \\\\\\n\",\n \"condition \\n\",\n \"1B Parent 8 0 0 0 0 0 0 \\n\",\n \"2-methoxyestradiol 18 0 0 0 0 0 0 \\n\",\n \"3,6-dimethoxyflavone 5 3 0 0 3 0 0 \\n\",\n \"3-amino-benzamide 30 61 2 2 0 0 0 \\n\",\n \"5-methoxy-alpha-methyltryptamine 0 0 0 0 0 0 0 \\n\",\n \"... ... ... ... ... ... ... ... \\n\",\n \"xanthoxyline 3 5 0 0 4 0 0 \\n\",\n \"yohimbine 35 16 0 0 4 0 0 \\n\",\n \"zacopride 9 11 0 0 0 0 0 \\n\",\n \"zaprinast 21 6 0 0 4 0 0 \\n\",\n \"zileuton 32 13 2 2 0 0 0 \\n\",\n \"\\n\",\n \"cell_type CD34 CL34 CORL23 ... SW620 SW948 \\\\\\n\",\n \"condition ... \\n\",\n \"1B Parent 0 0 0 ... 0 0 \\n\",\n \"2-methoxyestradiol 0 0 0 ... 0 0 \\n\",\n \"3,6-dimethoxyflavone 0 0 0 ... 0 0 \\n\",\n \"3-amino-benzamide 0 2 2 ... 4 4 \\n\",\n \"5-methoxy-alpha-methyltryptamine 0 0 0 ... 0 0 \\n\",\n \"... ... ... ... ... ... ... \\n\",\n \"xanthoxyline 0 0 0 ... 0 0 \\n\",\n \"yohimbine 0 0 0 ... 0 0 \\n\",\n \"zacopride 0 0 0 ... 0 0 \\n\",\n \"zaprinast 0 0 0 ... 0 0 \\n\",\n \"zileuton 0 1 1 ... 3 4 \\n\",\n \"\\n\",\n \"cell_type T3M10 THP1 TYKNU U266 U937 VCAP \\\\\\n\",\n \"condition \\n\",\n \"1B Parent 0 0 0 0 0 0 \\n\",\n \"2-methoxyestradiol 0 0 0 0 0 0 \\n\",\n \"3,6-dimethoxyflavone 0 0 0 0 0 0 \\n\",\n \"3-amino-benzamide 2 2 2 0 2 9 \\n\",\n \"5-methoxy-alpha-methyltryptamine 0 0 0 0 0 0 \\n\",\n \"... ... ... ... ... ... ... \\n\",\n \"xanthoxyline 0 0 0 0 0 7 \\n\",\n \"yohimbine 0 0 0 0 0 26 \\n\",\n \"zacopride 0 0 0 0 0 19 \\n\",\n \"zaprinast 0 0 0 0 0 12 \\n\",\n \"zileuton 2 1 2 0 2 19 \\n\",\n \"\\n\",\n \"cell_type WSUDLCL2 YAPC \\n\",\n \"condition \\n\",\n \"1B Parent 0 0 \\n\",\n \"2-methoxyestradiol 0 17 \\n\",\n \"3,6-dimethoxyflavone 0 0 \\n\",\n \"3-amino-benzamide 2 18 \\n\",\n \"5-methoxy-alpha-methyltryptamine 0 0 \\n\",\n \"... ... ... \\n\",\n \"xanthoxyline 0 0 \\n\",\n \"yohimbine 0 18 \\n\",\n \"zacopride 0 0 \\n\",\n \"zaprinast 0 18 \\n\",\n \"zileuton 2 18 \\n\",\n \"\\n\",\n \"[1001 rows x 82 columns]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.crosstab(adata.obs.condition, adata.obs.cell_type)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Calculate differential genes manually, such that the genes are the same per condition.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'cov_drug_dose_name' as categorical\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 4min 4s, sys: 412 ms, total: 4min 4s\\n\",\n \"Wall time: 4min 5s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"sc.tl.rank_genes_groups(\\n\",\n \" adata,\\n\",\n \" groupby='condition', \\n\",\n \" reference='DMSO',\\n\",\n \" rankby_abs=True,\\n\",\n \" n_genes=50\\n\",\n \")\\n\",\n \"\\n\",\n \"de_genes = {}\\n\",\n \"for cond in adata.obs['condition']:\\n\",\n \" if cond != 'DMSO':\\n\",\n \" df = sc.get.rank_genes_groups_df(adata, group=cond) # this takes a while\\n\",\n \" de_genes[cond] = df['names'][:50].values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.uns['rank_genes_groups_cov'] = {cond: de_genes[cond.split('_')[1]] for cond in adata.obs['cov_drug_dose_name'].unique() if cond.split('_')[1] != 'DMSO'}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"adata.obs['split'] = 'train'\\n\",\n \"\\n\",\n \"# take ood from top occurring perturbations to avoid losing data on low occ ones\\n\",\n \"ood_idx = sc.pp.subsample(\\n\",\n \" adata[adata.obs.condition.isin(list(adata.obs.condition.value_counts().index[1:50]))],\\n\",\n \" .1,\\n\",\n \" copy=True\\n\",\n \").obs.index\\n\",\n \"adata.obs['split'].loc[ood_idx] = 'ood'\\n\",\n \"\\n\",\n \"# take test from a random subsampling of the rest\\n\",\n \"test_idx = sc.pp.subsample(\\n\",\n \" adata[adata.obs.split != 'ood'],\\n\",\n \" .16,\\n\",\n \" copy=True\\n\",\n \").obs.index\\n\",\n \"adata.obs['split'].loc[test_idx] = 'test'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
condition1B Parent2-methoxyestradiol3,6-dimethoxyflavone3-amino-benzamide5-methoxy-alpha-methyltryptamineABT-737AG-490AG-14361AICA-ribonucleotideALW-II-38-3...veliparibvinburninevoglibosewiskostatinxanthohumolxanthoxylineyohimbinezacopridezaprinastzileuton
split
ood00000640000...135000000000
test217459412523665089...188242079960263351
train201003034314583130272249509...93615010558585326681175279
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3 rows × 1001 columns

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\"\n ],\n \"text/plain\": [\n \"condition 1B Parent 2-methoxyestradiol 3,6-dimethoxyflavone \\\\\\n\",\n \"split \\n\",\n \"ood 0 0 0 \\n\",\n \"test 2 17 4 \\n\",\n \"train 20 100 30 \\n\",\n \"\\n\",\n \"condition 3-amino-benzamide 5-methoxy-alpha-methyltryptamine ABT-737 \\\\\\n\",\n \"split \\n\",\n \"ood 0 0 64 \\n\",\n \"test 59 4 125 \\n\",\n \"train 343 14 583 \\n\",\n \"\\n\",\n \"condition AG-490 AG-14361 AICA-ribonucleotide ALW-II-38-3 ... veliparib \\\\\\n\",\n \"split ... \\n\",\n \"ood 0 0 0 0 ... 135 \\n\",\n \"test 23 66 50 89 ... 188 \\n\",\n \"train 130 272 249 509 ... 936 \\n\",\n \"\\n\",\n \"condition vinburnine voglibose wiskostatin xanthohumol xanthoxyline \\\\\\n\",\n \"split \\n\",\n \"ood 0 0 0 0 0 \\n\",\n \"test 24 20 7 9 9 \\n\",\n \"train 150 105 58 58 53 \\n\",\n \"\\n\",\n \"condition yohimbine zacopride zaprinast zileuton \\n\",\n \"split \\n\",\n \"ood 0 0 0 0 \\n\",\n \"test 60 26 33 51 \\n\",\n \"train 266 81 175 279 \\n\",\n \"\\n\",\n \"[3 rows x 1001 columns]\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.crosstab(adata.obs['split'], adata.obs['condition'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"del(adata.uns['rank_genes_groups']) # too large\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# code compatibility\\n\",\n \"from scipy import sparse\\n\",\n \"adata.X = sparse.csr_matrix(adata.X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'split' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.write('datasets/lincs.h5ad', adata)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "preprocessing/pachter.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"scanpy==1.7.1 anndata==0.7.5 umap==0.4.6 numpy==1.19.4 scipy==1.5.4 pandas==1.1.4 scikit-learn==0.23.2 statsmodels==0.12.1 python-igraph==0.8.3 leidenalg==0.8.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import scanpy as sc\\n\",\n \"import anndata as ad\\n\",\n \"import pandas as pd\\n\",\n \"import re\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"plt.rcParams['figure.figsize']=(5, 5)\\n\",\n \"sc.settings.verbosity = 3\\n\",\n \"sc.logging.print_header()\\n\",\n \"os.chdir('./../')\\n\",\n \"\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sc.set_figure_params(dpi=100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## loading the raw data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'/home/mohammad/Desktop/test_AdvAE'\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/mohammad/anaconda3/envs/scvi-env/lib/python3.7/site-packages/anndata/compat/__init__.py:182: FutureWarning: Moving element from .uns['neighbors']['distances'] to .obsp['distances'].\\n\",\n \"\\n\",\n \"This is where adjacency matrices should go now.\\n\",\n \" FutureWarning,\\n\",\n \"/home/mohammad/anaconda3/envs/scvi-env/lib/python3.7/site-packages/anndata/compat/__init__.py:182: FutureWarning: Moving element from .uns['neighbors']['connectivities'] to .obsp['connectivities'].\\n\",\n \"\\n\",\n \"This is where adjacency matrices should go now.\\n\",\n \" FutureWarning,\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata = sc.read(\\\"./datasets/pachter_raw.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_anna = sc.read(\\\"./datasets/pachter.h5ad\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"undoing some reindexing done by scanpy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_index = []\\n\",\n \"for obs in adata_anna.obs.index:\\n\",\n \" split = obs.split(\\\"-\\\")\\n\",\n \" new_index.append(split[0]+\\\"-\\\"+split[1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_anna.obs.index = new_index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(set(adata_anna.obs.index).intersection(adata.obs.index)) == len(adata_anna)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata = adata[new_index,:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"raw = adata.raw.to_adata()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"the original data is already normalized \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"8.53352\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"raw.X.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new = sc.AnnData(X=raw.X, obs=adata_anna.obs, uns=adata_anna.uns, var=raw.var)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['Xkr4', 'Gm1992', 'Rp1', 'Mrpl15', 'Lypla1', 'Gm37988', 'Tcea1',\\n\",\n \" 'Rgs20', 'Atp6v1h', 'Rb1cc1',\\n\",\n \" ...\\n\",\n \" 'Vamp7', 'Spry3', 'Tmlhe', 'AC132444.1', 'Csprs', 'AC125149.3',\\n\",\n \" 'AC168977.1', 'PISD', 'DHRSX', 'CAAA01147332.1'],\\n\",\n \" dtype='object', name='index', length=16950)\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.var_names\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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BMPEGFPlatePosRAScripDecbatchcell_typecell_typescombconditioncontroldose_vallouvainn_countsn_countslogsplitcov_drug_dose_name
CTGATAGCAATGGACG-20.820.08000unknown110R,0.8B,20.0E,0SBMP+EGF00.04+1.0112054.03.312600trainunknown_BMP+EGF_0.04+1.0
CATCCACGTAGTGAAT-10.820.018500unknown105R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.0103832.03.583426trainunknown_BMP+EGF+RA_0.04+1.0+1.0
AGTAGTCAGTGGAGTC-20.00.859000unknown140R,0.0B,0.8E,0SEGF10.04146045.03.781396trainunknown_EGF_0.04
TGGGCGTAGAATTCCC-10.820.018500unknown65R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.067974.03.901676trainunknown_BMP+EGF+RA_0.04+1.0+1.0
TGGCCAGCACATGACT-24.00.863500unknown135R,4.0B,0.8E,0SBMP+EGF+RA00.2+0.04+1.0132576.03.410946trainunknown_BMP+EGF+RA_0.2+0.04+1.0
......................................................
TGTATTCTCAGGTTCA-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251730.03.238046oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTAGTTCCACAGATTC-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251287.03.109579oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTCTCCTCACAGATTC-44.04.028012unknown50R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.255099.03.707485oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGCGCCAAGACACG-44.04.028012unknown30R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.236342.03.802226oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGGTTGTCCAGTAT-44.04.040102unknown51R,4.0B,4.0E,0SBMP+EGF+RA00.2+0.2+0.258049.03.905742oodunknown_BMP+EGF+RA_0.2+0.2+0.2
\\n\",\n \"

19637 rows × 17 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" BMP EGF PlatePos RA ScripDec batch cell_type \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.8 20.0 8 0 0 0 unknown \\n\",\n \"CATCCACGTAGTGAAT-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.0 0.8 59 0 0 0 unknown \\n\",\n \"TGGGCGTAGAATTCCC-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"TGGCCAGCACATGACT-2 4.0 0.8 63 5 0 0 unknown \\n\",\n \"... ... ... ... .. ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTAGTTCCACAGATTC-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTCTCCTCACAGATTC-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGCGCCAAGACACG-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGGTTGTCCAGTAT-4 4.0 4.0 40 1 0 2 unknown \\n\",\n \"\\n\",\n \" cell_types comb condition control \\\\\\n\",\n \"CTGATAGCAATGGACG-2 11 0R,0.8B,20.0E,0S BMP+EGF 0 \\n\",\n \"CATCCACGTAGTGAAT-1 10 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 14 0R,0.0B,0.8E,0S EGF 1 \\n\",\n \"TGGGCGTAGAATTCCC-1 6 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TGGCCAGCACATGACT-2 13 5R,4.0B,0.8E,0S BMP+EGF+RA 0 \\n\",\n \"... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTAGTTCCACAGATTC-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTCTCCTCACAGATTC-4 5 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGCGCCAAGACACG-4 3 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGGTTGTCCAGTAT-4 5 1R,4.0B,4.0E,0S BMP+EGF+RA 0 \\n\",\n \"\\n\",\n \" dose_val louvain n_counts n_countslog split \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.04+1.0 11 2054.0 3.312600 train \\n\",\n \"CATCCACGTAGTGAAT-1 0.04+1.0+1.0 10 3832.0 3.583426 train \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.04 14 6045.0 3.781396 train \\n\",\n \"TGGGCGTAGAATTCCC-1 0.04+1.0+1.0 6 7974.0 3.901676 train \\n\",\n \"TGGCCAGCACATGACT-2 0.2+0.04+1.0 13 2576.0 3.410946 train \\n\",\n \"... ... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 0.2+1.0+0.2 5 1730.0 3.238046 ood \\n\",\n \"TTAGTTCCACAGATTC-4 0.2+1.0+0.2 5 1287.0 3.109579 ood \\n\",\n \"TTCTCCTCACAGATTC-4 0.2+0.2+0.2 5 5099.0 3.707485 ood \\n\",\n \"TTTGCGCCAAGACACG-4 0.2+0.2+0.2 3 6342.0 3.802226 ood \\n\",\n \"TTTGGTTGTCCAGTAT-4 0.2+0.2+0.2 5 8049.0 3.905742 ood \\n\",\n \"\\n\",\n \" cov_drug_dose_name \\n\",\n \"CTGATAGCAATGGACG-2 unknown_BMP+EGF_0.04+1.0 \\n\",\n \"CATCCACGTAGTGAAT-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 unknown_EGF_0.04 \\n\",\n \"TGGGCGTAGAATTCCC-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"TGGCCAGCACATGACT-2 unknown_BMP+EGF+RA_0.2+0.04+1.0 \\n\",\n \"... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTAGTTCCACAGATTC-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTCTCCTCACAGATTC-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGCGCCAAGACACG-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGGTTGTCCAGTAT-4 unknown_BMP+EGF+RA_0.2+0.2+0.2 \\n\",\n \"\\n\",\n \"[19637 rows x 17 columns]\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.obs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# preprocessing \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Keep the all genes in adata .raw\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.raw = adata_new\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Normalization and HVG selection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"If you pass `n_top_genes`, all cutoffs are ignored.\\n\",\n \"extracting highly variable genes\\n\",\n \" finished (0:00:00)\\n\",\n \"--> added\\n\",\n \" 'highly_variable', boolean vector (adata.var)\\n\",\n \" 'means', float vector (adata.var)\\n\",\n \" 'dispersions', float vector (adata.var)\\n\",\n \" 'dispersions_norm', float vector (adata.var)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.pp.highly_variable_genes(adata_new,n_top_genes=5000, subset=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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BMPEGFPlatePosRAScripDecbatchcell_typecell_typescombconditioncontroldose_vallouvainn_countsn_countslogsplitcov_drug_dose_name
CTGATAGCAATGGACG-20.820.08000unknown110R,0.8B,20.0E,0SBMP+EGF00.04+1.0112054.03.312600trainunknown_BMP+EGF_0.04+1.0
CATCCACGTAGTGAAT-10.820.018500unknown105R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.0103832.03.583426trainunknown_BMP+EGF+RA_0.04+1.0+1.0
AGTAGTCAGTGGAGTC-20.00.859000unknown140R,0.0B,0.8E,0SEGF10.04146045.03.781396trainunknown_EGF_0.04
TGGGCGTAGAATTCCC-10.820.018500unknown65R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.067974.03.901676trainunknown_BMP+EGF+RA_0.04+1.0+1.0
TGGCCAGCACATGACT-24.00.863500unknown135R,4.0B,0.8E,0SBMP+EGF+RA00.2+0.04+1.0132576.03.410946trainunknown_BMP+EGF+RA_0.2+0.04+1.0
......................................................
TGTATTCTCAGGTTCA-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251730.03.238046oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTAGTTCCACAGATTC-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251287.03.109579oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTCTCCTCACAGATTC-44.04.028012unknown50R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.255099.03.707485oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGCGCCAAGACACG-44.04.028012unknown30R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.236342.03.802226oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGGTTGTCCAGTAT-44.04.040102unknown51R,4.0B,4.0E,0SBMP+EGF+RA00.2+0.2+0.258049.03.905742oodunknown_BMP+EGF+RA_0.2+0.2+0.2
\\n\",\n \"

19637 rows × 17 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" BMP EGF PlatePos RA ScripDec batch cell_type \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.8 20.0 8 0 0 0 unknown \\n\",\n \"CATCCACGTAGTGAAT-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.0 0.8 59 0 0 0 unknown \\n\",\n \"TGGGCGTAGAATTCCC-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"TGGCCAGCACATGACT-2 4.0 0.8 63 5 0 0 unknown \\n\",\n \"... ... ... ... .. ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTAGTTCCACAGATTC-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTCTCCTCACAGATTC-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGCGCCAAGACACG-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGGTTGTCCAGTAT-4 4.0 4.0 40 1 0 2 unknown \\n\",\n \"\\n\",\n \" cell_types comb condition control \\\\\\n\",\n \"CTGATAGCAATGGACG-2 11 0R,0.8B,20.0E,0S BMP+EGF 0 \\n\",\n \"CATCCACGTAGTGAAT-1 10 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 14 0R,0.0B,0.8E,0S EGF 1 \\n\",\n \"TGGGCGTAGAATTCCC-1 6 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TGGCCAGCACATGACT-2 13 5R,4.0B,0.8E,0S BMP+EGF+RA 0 \\n\",\n \"... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTAGTTCCACAGATTC-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTCTCCTCACAGATTC-4 5 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGCGCCAAGACACG-4 3 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGGTTGTCCAGTAT-4 5 1R,4.0B,4.0E,0S BMP+EGF+RA 0 \\n\",\n \"\\n\",\n \" dose_val louvain n_counts n_countslog split \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.04+1.0 11 2054.0 3.312600 train \\n\",\n \"CATCCACGTAGTGAAT-1 0.04+1.0+1.0 10 3832.0 3.583426 train \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.04 14 6045.0 3.781396 train \\n\",\n \"TGGGCGTAGAATTCCC-1 0.04+1.0+1.0 6 7974.0 3.901676 train \\n\",\n \"TGGCCAGCACATGACT-2 0.2+0.04+1.0 13 2576.0 3.410946 train \\n\",\n \"... ... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 0.2+1.0+0.2 5 1730.0 3.238046 ood \\n\",\n \"TTAGTTCCACAGATTC-4 0.2+1.0+0.2 5 1287.0 3.109579 ood \\n\",\n \"TTCTCCTCACAGATTC-4 0.2+0.2+0.2 5 5099.0 3.707485 ood \\n\",\n \"TTTGCGCCAAGACACG-4 0.2+0.2+0.2 3 6342.0 3.802226 ood \\n\",\n \"TTTGGTTGTCCAGTAT-4 0.2+0.2+0.2 5 8049.0 3.905742 ood \\n\",\n \"\\n\",\n \" cov_drug_dose_name \\n\",\n \"CTGATAGCAATGGACG-2 unknown_BMP+EGF_0.04+1.0 \\n\",\n \"CATCCACGTAGTGAAT-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 unknown_EGF_0.04 \\n\",\n \"TGGGCGTAGAATTCCC-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"TGGCCAGCACATGACT-2 unknown_BMP+EGF+RA_0.2+0.04+1.0 \\n\",\n \"... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTAGTTCCACAGATTC-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTCTCCTCACAGATTC-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGCGCCAAGACACG-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGGTTGTCCAGTAT-4 unknown_BMP+EGF+RA_0.2+0.2+0.2 \\n\",\n \"\\n\",\n \"[19637 rows x 17 columns]\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.obs\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"DE test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from cpa.helper import rank_genes_groups_by_cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\",\n \"ranking genes\\n\",\n \"Trying to set attribute `.uns` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"unknown\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" finished: added to `.uns['rank_genes_groups']`\\n\",\n \" 'names', sorted np.recarray to be indexed by group ids\\n\",\n \" 'scores', sorted np.recarray to be indexed by group ids\\n\",\n \" 'logfoldchanges', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals', sorted np.recarray to be indexed by group ids\\n\",\n \" 'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rank_genes_groups_by_cov(adata_new, groupby='cov_drug_dose_name', covariate='cell_type', control_group='EGF_0.04', n_genes=50)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"saving the object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"AnnData object with n_obs × n_vars = 19637 × 5000\\n\",\n \" obs: 'BMP', 'EGF', 'PlatePos', 'RA', 'ScripDec', 'batch', 'cell_type', 'cell_types', 'comb', 'condition', 'control', 'dose_val', 'louvain', 'n_counts', 'n_countslog', 'split', 'cov_drug_dose_name'\\n\",\n \" var: 'gene_ids', 'feature_types', 'genome', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\\n\",\n \" uns: 'rank_genes_groups', 'rank_genes_groups_cov', 'hvg'\"\n ]\n },\n \"execution_count\": 75,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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BMPEGFPlatePosRAScripDecbatchcell_typecell_typescombconditioncontroldose_vallouvainn_countsn_countslogsplitcov_drug_dose_name
CTGATAGCAATGGACG-20.820.08000unknown110R,0.8B,20.0E,0SBMP+EGF00.04+1.0112054.03.312600trainunknown_BMP+EGF_0.04+1.0
CATCCACGTAGTGAAT-10.820.018500unknown105R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.0103832.03.583426trainunknown_BMP+EGF+RA_0.04+1.0+1.0
AGTAGTCAGTGGAGTC-20.00.859000unknown140R,0.0B,0.8E,0SEGF10.04146045.03.781396trainunknown_EGF_0.04
TGGGCGTAGAATTCCC-10.820.018500unknown65R,0.8B,20.0E,0SBMP+EGF+RA00.04+1.0+1.067974.03.901676trainunknown_BMP+EGF+RA_0.04+1.0+1.0
TGGCCAGCACATGACT-24.00.863500unknown135R,4.0B,0.8E,0SBMP+EGF+RA00.2+0.04+1.0132576.03.410946trainunknown_BMP+EGF+RA_0.2+0.04+1.0
......................................................
TGTATTCTCAGGTTCA-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251730.03.238046oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTAGTTCCACAGATTC-44.020.016102unknown51R,4.0B,20.0E,0SBMP+EGF+RA00.2+1.0+0.251287.03.109579oodunknown_BMP+EGF+RA_0.2+1.0+0.2
TTCTCCTCACAGATTC-44.04.028012unknown50R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.255099.03.707485oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGCGCCAAGACACG-44.04.028012unknown30R,4.0B,4.0E,1SBMP+EGF+ScripDec00.2+0.2+0.236342.03.802226oodunknown_BMP+EGF+ScripDec_0.2+0.2+0.2
TTTGGTTGTCCAGTAT-44.04.040102unknown51R,4.0B,4.0E,0SBMP+EGF+RA00.2+0.2+0.258049.03.905742oodunknown_BMP+EGF+RA_0.2+0.2+0.2
\\n\",\n \"

19637 rows × 17 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" BMP EGF PlatePos RA ScripDec batch cell_type \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.8 20.0 8 0 0 0 unknown \\n\",\n \"CATCCACGTAGTGAAT-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.0 0.8 59 0 0 0 unknown \\n\",\n \"TGGGCGTAGAATTCCC-1 0.8 20.0 18 5 0 0 unknown \\n\",\n \"TGGCCAGCACATGACT-2 4.0 0.8 63 5 0 0 unknown \\n\",\n \"... ... ... ... .. ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTAGTTCCACAGATTC-4 4.0 20.0 16 1 0 2 unknown \\n\",\n \"TTCTCCTCACAGATTC-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGCGCCAAGACACG-4 4.0 4.0 28 0 1 2 unknown \\n\",\n \"TTTGGTTGTCCAGTAT-4 4.0 4.0 40 1 0 2 unknown \\n\",\n \"\\n\",\n \" cell_types comb condition control \\\\\\n\",\n \"CTGATAGCAATGGACG-2 11 0R,0.8B,20.0E,0S BMP+EGF 0 \\n\",\n \"CATCCACGTAGTGAAT-1 10 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 14 0R,0.0B,0.8E,0S EGF 1 \\n\",\n \"TGGGCGTAGAATTCCC-1 6 5R,0.8B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TGGCCAGCACATGACT-2 13 5R,4.0B,0.8E,0S BMP+EGF+RA 0 \\n\",\n \"... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTAGTTCCACAGATTC-4 5 1R,4.0B,20.0E,0S BMP+EGF+RA 0 \\n\",\n \"TTCTCCTCACAGATTC-4 5 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGCGCCAAGACACG-4 3 0R,4.0B,4.0E,1S BMP+EGF+ScripDec 0 \\n\",\n \"TTTGGTTGTCCAGTAT-4 5 1R,4.0B,4.0E,0S BMP+EGF+RA 0 \\n\",\n \"\\n\",\n \" dose_val louvain n_counts n_countslog split \\\\\\n\",\n \"CTGATAGCAATGGACG-2 0.04+1.0 11 2054.0 3.312600 train \\n\",\n \"CATCCACGTAGTGAAT-1 0.04+1.0+1.0 10 3832.0 3.583426 train \\n\",\n \"AGTAGTCAGTGGAGTC-2 0.04 14 6045.0 3.781396 train \\n\",\n \"TGGGCGTAGAATTCCC-1 0.04+1.0+1.0 6 7974.0 3.901676 train \\n\",\n \"TGGCCAGCACATGACT-2 0.2+0.04+1.0 13 2576.0 3.410946 train \\n\",\n \"... ... ... ... ... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 0.2+1.0+0.2 5 1730.0 3.238046 ood \\n\",\n \"TTAGTTCCACAGATTC-4 0.2+1.0+0.2 5 1287.0 3.109579 ood \\n\",\n \"TTCTCCTCACAGATTC-4 0.2+0.2+0.2 5 5099.0 3.707485 ood \\n\",\n \"TTTGCGCCAAGACACG-4 0.2+0.2+0.2 3 6342.0 3.802226 ood \\n\",\n \"TTTGGTTGTCCAGTAT-4 0.2+0.2+0.2 5 8049.0 3.905742 ood \\n\",\n \"\\n\",\n \" cov_drug_dose_name \\n\",\n \"CTGATAGCAATGGACG-2 unknown_BMP+EGF_0.04+1.0 \\n\",\n \"CATCCACGTAGTGAAT-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"AGTAGTCAGTGGAGTC-2 unknown_EGF_0.04 \\n\",\n \"TGGGCGTAGAATTCCC-1 unknown_BMP+EGF+RA_0.04+1.0+1.0 \\n\",\n \"TGGCCAGCACATGACT-2 unknown_BMP+EGF+RA_0.2+0.04+1.0 \\n\",\n \"... ... \\n\",\n \"TGTATTCTCAGGTTCA-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTAGTTCCACAGATTC-4 unknown_BMP+EGF+RA_0.2+1.0+0.2 \\n\",\n \"TTCTCCTCACAGATTC-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGCGCCAAGACACG-4 unknown_BMP+EGF+ScripDec_0.2+0.2+0.2 \\n\",\n \"TTTGGTTGTCCAGTAT-4 unknown_BMP+EGF+RA_0.2+0.2+0.2 \\n\",\n \"\\n\",\n \"[19637 rows x 17 columns]\"\n ]\n },\n \"execution_count\": 74,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata_new.obs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata_new.write(\\\"./datasets/pachter_new.h5ad\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "preprocessing/sciplex3.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"satellite-immigration\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import scanpy as sc\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"distinguished-personal\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"os.chdir('./../')\\n\",\n \"from cpa.helper import rank_genes_groups_by_cov\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"previous-steal\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adatas = []\\n\",\n \"for i in range(5):\\n\",\n \" adatas.append(sc.read(f'./datasets/sciplex_raw_chunk_{i}.h5ad'))\\n\",\n \"adata = adatas[0].concatenate(adatas[1:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"supposed-clinton\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.pp.subsample(adata, fraction=0.5)\\n\",\n \"sc.pp.normalize_per_cell(adata)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"verified-reverse\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.pp.log1p(adata)\\n\",\n \"sc.pp.highly_variable_genes(adata, n_top_genes=5000, subset=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"sorted-proof\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":2: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs['dose_val'][adata.obs['product_name'].str.contains('Vehicle')] = 1.0\\n\",\n \":4: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs['product_name'][adata.obs['product_name'].str.contains('Vehicle')] = 'control'\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.obs['dose_val'] = adata.obs.dose.astype(float) / np.max(adata.obs.dose.astype(float))\\n\",\n \"adata.obs['dose_val'][adata.obs['product_name'].str.contains('Vehicle')] = 1.0\\n\",\n \"adata.obs['product_name'] = [x.split(' ')[0] for x in adata.obs['product_name']]\\n\",\n \"adata.obs['product_name'][adata.obs['product_name'].str.contains('Vehicle')] = 'control'\\n\",\n \"adata.obs['drug_dose_name'] = adata.obs.product_name.astype(str) + '_' + adata.obs.dose_val.astype(str)\\n\",\n \"adata.obs['cov_drug_dose_name'] = adata.obs.cell_type.astype(str) + '_' + adata.obs.drug_dose_name.astype(str)\\n\",\n \"adata.obs['condition'] = adata.obs.product_name.copy()\\n\",\n \"adata.obs['control'] = [1 if x == 'Vehicle_1.0' else 0 for x in adata.obs.drug_dose_name.values]\\n\",\n \"adata.obs['cov_drug'] = adata.obs.cell_type.astype(str) + '_' + adata.obs.condition.astype(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"renewable-insertion\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/anndata/_core/anndata.py:1207: ImplicitModificationWarning: Initializing view as actual.\\n\",\n \" warnings.warn(\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A549\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'cell_type' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'pathway' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'product_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'target' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'condition' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug' as categorical\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/anndata/_core/anndata.py:1207: ImplicitModificationWarning: Initializing view as actual.\\n\",\n \" warnings.warn(\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MCF7\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'cell_type' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'pathway' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'product_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'target' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'condition' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug' as categorical\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"WARNING: Default of the method has been changed to 't-test' from 't-test_overestim_var'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/anndata/_core/anndata.py:1207: ImplicitModificationWarning: Initializing view as actual.\\n\",\n \" warnings.warn(\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cell_type' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"K562\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'pathway' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'product_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'target' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug_dose_name' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'condition' as categorical\\n\",\n \"Trying to set attribute `.obs` of view, copying.\\n\",\n \"... storing 'cov_drug' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"from cpa.helper import rank_genes_groups_by_cov\\n\",\n \"rank_genes_groups_by_cov(adata, groupby='cov_drug', covariate='cell_type', control_group='control')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"latter-designer\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"new_genes_dict = {}\\n\",\n \"for cat in adata.obs.cov_drug_dose_name.unique():\\n\",\n \" if 'control' not in cat:\\n\",\n \" rank_keys = np.array(list(adata.uns['rank_genes_groups_cov'].keys()))\\n\",\n \" bool_idx = [x in cat for x in rank_keys]\\n\",\n \" genes = adata.uns['rank_genes_groups_cov'][rank_keys[bool_idx][0]]\\n\",\n \" new_genes_dict[cat] = genes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"private-candy\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.uns['rank_genes_groups_cov'] = new_genes_dict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"advised-speed\",\n \"metadata\": {},\n \"source\": [\n \"# Split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"australian-looking\",\n \"metadata\": {\n \"tags\": []\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"19\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata.obs['split'] = 'train' # reset\\n\",\n \"ho_drugs = [\\n\",\n \" # selection of drugs from various pathways\\n\",\n \" \\\"Azacitidine\\\",\\n\",\n \" \\\"Carmofur\\\",\\n\",\n \" \\\"Pracinostat\\\",\\n\",\n \" \\\"Cediranib\\\",\\n\",\n \" \\\"Luminespib\\\",\\n\",\n \" \\\"Crizotinib\\\",\\n\",\n \" \\\"SNS-314\\\",\\n\",\n \" \\\"Obatoclax\\\",\\n\",\n \" \\\"Momelotinib\\\",\\n\",\n \" \\\"AG-14361\\\",\\n\",\n \" \\\"Entacapone\\\",\\n\",\n \" \\\"Fulvestrant\\\",\\n\",\n \" \\\"Mesna\\\",\\n\",\n \" \\\"Zileuton\\\",\\n\",\n \" \\\"Enzastaurin\\\",\\n\",\n \" \\\"IOX2\\\",\\n\",\n \" \\\"Alvespimycin\\\",\\n\",\n \" \\\"XAV-939\\\",\\n\",\n \" \\\"Fasudil\\\"\\n\",\n \"]\\n\",\n \"ood = adata.obs['condition'].isin(ho_drugs)\\n\",\n \"len(ho_drugs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"gentle-cliff\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":1: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs['split'][ood & (adata.obs['dose_val'] == 1.0)] = 'ood'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.obs['split'][ood & (adata.obs['dose_val'] == 1.0)] = 'ood'\\n\",\n \"test_idx = sc.pp.subsample(adata[adata.obs['split'] != 'ood'], .10, copy=True).obs.index\\n\",\n \"adata.obs['split'].loc[test_idx] = 'test'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"ruled-mechanism\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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condition(+)-JQ12-MethoxyestradiolA-366ABT-737AC480AG-14361AG-490AICARAMG-900AR-42...ValproicVandetanibVeliparibWHI-P154WP1066XAV-939YM155ZMZileutoncontrol
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\"\n ],\n \"text/plain\": [\n \"condition (+)-JQ1 2-Methoxyestradiol A-366 ABT-737 AC480 AG-14361 \\\\\\n\",\n \"split \\n\",\n \"ood 0 0 0 0 0 355 \\n\",\n \"test 168 141 170 133 165 137 \\n\",\n \"train 1338 1346 1511 1261 1490 1162 \\n\",\n \"\\n\",\n \"condition AG-490 AICAR AMG-900 AR-42 ... Valproic Vandetanib \\\\\\n\",\n \"split ... \\n\",\n \"ood 0 0 0 0 ... 0 0 \\n\",\n \"test 189 173 149 156 ... 192 142 \\n\",\n \"train 1559 1650 1187 1250 ... 1612 1225 \\n\",\n \"\\n\",\n \"condition Veliparib WHI-P154 WP1066 XAV-939 YM155 ZM Zileuton \\\\\\n\",\n \"split \\n\",\n \"ood 0 0 0 249 0 0 403 \\n\",\n \"test 152 180 190 120 40 134 127 \\n\",\n \"train 1445 1584 1661 1057 354 1260 1198 \\n\",\n \"\\n\",\n \"condition control \\n\",\n \"split \\n\",\n \"ood 0 \\n\",\n \"test 664 \\n\",\n \"train 5800 \\n\",\n \"\\n\",\n \"[3 rows x 188 columns]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.crosstab(adata.obs['split'], adata.obs['condition'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"brown-marking\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"train 256214\\n\",\n \"test 28468\\n\",\n \"ood 6206\\n\",\n \"Name: split, dtype: int64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata.obs['split'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"outstanding-newport\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"Fasudil 474\\n\",\n \"Mesna 464\\n\",\n \"IOX2 444\\n\",\n \"Entacapone 433\\n\",\n \"Fulvestrant 417\\n\",\n \"Zileuton 403\\n\",\n \"Azacitidine 385\\n\",\n \"Carmofur 379\\n\",\n \"Enzastaurin 366\\n\",\n \"AG-14361 355\\n\",\n \"Pracinostat 318\\n\",\n \"SNS-314 280\\n\",\n \"Crizotinib 256\\n\",\n \"Momelotinib 249\\n\",\n \"XAV-939 249\\n\",\n \"Cediranib 248\\n\",\n \"Obatoclax 195\\n\",\n \"Luminespib 194\\n\",\n \"Alvespimycin 97\\n\",\n \"Name: condition, dtype: int64\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata[adata.obs.split == 'ood'].obs.condition.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"after-family\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"control 664\\n\",\n \"ENMD-2076 280\\n\",\n \"MK-0752 202\\n\",\n \"RG108 196\\n\",\n \"Ramelteon 195\\n\",\n \" ... \\n\",\n \"Luminespib 68\\n\",\n \"Flavopiridol 68\\n\",\n \"Patupilone 65\\n\",\n \"Epothilone 56\\n\",\n \"YM155 40\\n\",\n \"Name: condition, Length: 188, dtype: int64\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"adata[adata.obs.split == 'test'].obs.condition.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"committed-cherry\",\n \"metadata\": {},\n \"source\": [\n \"Also a split which sees all data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"future-librarian\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata.obs['split_all'] = 'train'\\n\",\n \"test_idx = sc.pp.subsample(adata, .10, copy=True).obs.index\\n\",\n \"adata.obs['split_all'].loc[test_idx] = 'test'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"unable-cheese\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['ct_dose'] = adata.obs.cell_type.astype('str') + '_' + adata.obs.dose_val.astype('str')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"understood-battle\",\n \"metadata\": {},\n \"source\": [\n \"Round robin splits: dose and cell line combinations will be held out in turn.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"prospective-mainland\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"i = 0\\n\",\n \"split_dict = {}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"crazy-nylon\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":9: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{ct}_{dose}'] = 'ood'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229595\\n\",\n \"test 43732\\n\",\n \"ood 17561\\n\",\n \"Name: split1, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 228515\\n\",\n \"test 43526\\n\",\n \"ood 18847\\n\",\n \"Name: split2, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229387\\n\",\n \"test 43692\\n\",\n \"ood 17809\\n\",\n \"Name: split3, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229731\\n\",\n \"test 43758\\n\",\n \"ood 17399\\n\",\n \"Name: split4, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214593\\n\",\n \"test 40874\\n\",\n \"ood 35421\\n\",\n \"Name: split5, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 212368\\n\",\n \"test 40450\\n\",\n \"ood 38070\\n\",\n \"Name: split6, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 213013\\n\",\n \"test 40573\\n\",\n \"ood 37302\\n\",\n \"Name: split7, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214820\\n\",\n \"test 40917\\n\",\n \"ood 35151\\n\",\n \"Name: split8, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229287\\n\",\n \"test 43673\\n\",\n \"ood 17928\\n\",\n \"Name: split9, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 227678\\n\",\n \"test 43367\\n\",\n \"ood 19843\\n\",\n \"Name: split10, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 228686\\n\",\n \"test 43559\\n\",\n \"ood 18643\\n\",\n \"Name: split11, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 230139\\n\",\n \"test 43835\\n\",\n \"ood 16914\\n\",\n \"Name: split12, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# single ct holdout\\n\",\n \"for ct in adata.obs.cell_type.unique():\\n\",\n \" for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \" split_dict[split_name] = f'{ct}_{dose}'\\n\",\n \" \\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{ct}_{dose}'] = 'ood'\\n\",\n \" \\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \" \\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"hundred-queue\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":9: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[0]}_{dose}'] = 'ood'\\n\",\n \":10: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[1]}_{dose}'] = 'ood'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 199842\\n\",\n \"ood 52982\\n\",\n \"test 38064\\n\",\n \"Name: split13, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 196536\\n\",\n \"ood 56917\\n\",\n \"test 37435\\n\",\n \"Name: split14, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 198053\\n\",\n \"ood 55111\\n\",\n \"test 37724\\n\",\n \"Name: split15, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 200204\\n\",\n \"ood 52550\\n\",\n \"test 38134\\n\",\n \"Name: split16, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214536\\n\",\n \"test 40863\\n\",\n \"ood 35489\\n\",\n \"Name: split17, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 211847\\n\",\n \"test 40351\\n\",\n \"ood 38690\\n\",\n \"Name: split18, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 213727\\n\",\n \"test 40709\\n\",\n \"ood 36452\\n\",\n \"Name: split19, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 215523\\n\",\n \"test 41052\\n\",\n \"ood 34313\\n\",\n \"Name: split20, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 199533\\n\",\n \"ood 53349\\n\",\n \"test 38006\\n\",\n \"Name: split21, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 195699\\n\",\n \"ood 57913\\n\",\n \"test 37276\\n\",\n \"Name: split22, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 197353\\n\",\n \"ood 55945\\n\",\n \"test 37590\\n\",\n \"Name: split23, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 200612\\n\",\n \"ood 52065\\n\",\n \"test 38211\\n\",\n \"Name: split24, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# double ct holdout\\n\",\n \"for cts in [('A549', 'MCF7'), ('A549', 'K562'), ('MCF7', 'K562')]:\\n\",\n \" for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \" split_dict[split_name] = f'{cts[0]}+{cts[1]}_{dose}'\\n\",\n \" \\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[0]}_{dose}'] = 'ood'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[1]}_{dose}'] = 'ood'\\n\",\n \" \\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \" \\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"canadian-banking\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 184782\\n\",\n \"ood 70910\\n\",\n \"test 35196\\n\",\n \"Name: split25, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 179868\\n\",\n \"ood 76760\\n\",\n \"test 34260\\n\",\n \"Name: split26, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 182393\\n\",\n \"ood 73754\\n\",\n \"test 34741\\n\",\n \"Name: split27, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \":8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/carlo.dedonno/anaconda3/envs/cpi2/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 185997\\n\",\n \"ood 69464\\n\",\n \"test 35427\\n\",\n \"Name: split28, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# triple ct holdout\\n\",\n \"for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \"\\n\",\n \" split_dict[split_name] = f'all_{dose}'\\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"\\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \"\\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"executive-little\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.uns['splits'] = split_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"arranged-trauma\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sc.write('./datasets/sciplex3_new.h5ad', adata)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "preprocessing/sciplex3_round_robin.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\\n\",\n \" and should_run_async(code)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"scanpy==1.6.0 anndata==0.7.4 umap==0.4.6 numpy==1.19.2 scipy==1.6.1 pandas==1.2.3 scikit-learn==0.23.2 statsmodels==0.11.1 python-igraph==0.8.3 leidenalg==0.8.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import scanpy as sc\\n\",\n \"sc.set_figure_params(dpi=100, frameon=False)\\n\",\n \"sc.logging.print_header()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\\n\",\n \" and should_run_async(code)\\n\"\n ]\n }\n ],\n \"source\": [\n \"adata = sc.read('../datasets/sciplex3_old_reproduced.h5ad')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.obs['ct_dose'] = adata.obs.cell_type.astype('str') + '_' + adata.obs.dose_val.astype('str')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Round robin splits: dose and cell line combinations will be held out in turn.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"i = 0\\n\",\n \"split_dict = {}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" if __name__ == '__main__':\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\\n\",\n \" if not is_categorical(df_full[k]):\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229595\\n\",\n \"test 43732\\n\",\n \"ood 17561\\n\",\n \"Name: split1, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 228515\\n\",\n \"test 43526\\n\",\n \"ood 18847\\n\",\n \"Name: split2, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229387\\n\",\n \"test 43692\\n\",\n \"ood 17809\\n\",\n \"Name: split3, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229731\\n\",\n \"test 43758\\n\",\n \"ood 17399\\n\",\n \"Name: split4, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214593\\n\",\n \"test 40874\\n\",\n \"ood 35421\\n\",\n \"Name: split5, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 212368\\n\",\n \"test 40450\\n\",\n \"ood 38070\\n\",\n \"Name: split6, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 213013\\n\",\n \"test 40573\\n\",\n \"ood 37302\\n\",\n \"Name: split7, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214820\\n\",\n \"test 40917\\n\",\n \"ood 35151\\n\",\n \"Name: split8, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 229287\\n\",\n \"test 43673\\n\",\n \"ood 17928\\n\",\n \"Name: split9, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 227678\\n\",\n \"test 43367\\n\",\n \"ood 19843\\n\",\n \"Name: split10, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 228686\\n\",\n \"test 43559\\n\",\n \"ood 18643\\n\",\n \"Name: split11, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 230139\\n\",\n \"test 43835\\n\",\n \"ood 16914\\n\",\n \"Name: split12, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# single ct holdout\\n\",\n \"for ct in adata.obs.cell_type.unique():\\n\",\n \" for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \" split_dict[split_name] = f'{ct}_{dose}'\\n\",\n \" \\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{ct}_{dose}'] = 'ood'\\n\",\n \" \\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \" \\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" if __name__ == '__main__':\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 199842\\n\",\n \"ood 52982\\n\",\n \"test 38064\\n\",\n \"Name: split13, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 196536\\n\",\n \"ood 56917\\n\",\n \"test 37435\\n\",\n \"Name: split14, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 198053\\n\",\n \"ood 55111\\n\",\n \"test 37724\\n\",\n \"Name: split15, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 200204\\n\",\n \"ood 52550\\n\",\n \"test 38134\\n\",\n \"Name: split16, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 214536\\n\",\n \"test 40863\\n\",\n \"ood 35489\\n\",\n \"Name: split17, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 211847\\n\",\n \"test 40351\\n\",\n \"ood 38690\\n\",\n \"Name: split18, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 213727\\n\",\n \"test 40709\\n\",\n \"ood 36452\\n\",\n \"Name: split19, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 215523\\n\",\n \"test 41052\\n\",\n \"ood 34313\\n\",\n \"Name: split20, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 199533\\n\",\n \"ood 53349\\n\",\n \"test 38006\\n\",\n \"Name: split21, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 195699\\n\",\n \"ood 57913\\n\",\n \"test 37276\\n\",\n \"Name: split22, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 197353\\n\",\n \"ood 55945\\n\",\n \"test 37590\\n\",\n \"Name: split23, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 200612\\n\",\n \"ood 52065\\n\",\n \"test 38211\\n\",\n \"Name: split24, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# double ct holdout\\n\",\n \"for cts in [('A549', 'MCF7'), ('A549', 'K562'), ('MCF7', 'K562')]:\\n\",\n \" for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \" split_dict[split_name] = f'{cts[0]}+{cts[1]}_{dose}'\\n\",\n \" \\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[0]}_{dose}'] = 'ood'\\n\",\n \" adata.obs[split_name][adata.obs.ct_dose == f'{cts[1]}_{dose}'] = 'ood'\\n\",\n \" \\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \" \\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" \\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\\n\",\n \" if not is_categorical(df_full[k]):\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 184782\\n\",\n \"ood 70910\\n\",\n \"test 35196\\n\",\n \"Name: split25, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" \\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\\n\",\n \" if not is_categorical(df_full[k]):\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 179868\\n\",\n \"ood 76760\\n\",\n \"test 34260\\n\",\n \"Name: split26, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" \\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\\n\",\n \" if not is_categorical(df_full[k]):\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 182393\\n\",\n \"ood 73754\\n\",\n \"test 34741\\n\",\n \"Name: split27, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" \\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/anndata/_core/anndata.py:1094: FutureWarning: is_categorical is deprecated and will be removed in a future version. Use is_categorical_dtype instead\\n\",\n \" if not is_categorical(df_full[k]):\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/arrays/categorical.py:2487: FutureWarning: The `inplace` parameter in pandas.Categorical.remove_unused_categories is deprecated and will be removed in a future version.\\n\",\n \" res = method(*args, **kwargs)\\n\",\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: \\n\",\n \"A value is trying to be set on a copy of a slice from a DataFrame\\n\",\n \"\\n\",\n \"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\\n\",\n \" self._setitem_single_block(indexer, value, name)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"train 185997\\n\",\n \"ood 69464\\n\",\n \"test 35427\\n\",\n \"Name: split28, dtype: int64\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# triple ct holdout\\n\",\n \"for dose in adata.obs.dose_val.unique():\\n\",\n \" i += 1\\n\",\n \" split_name = f'split{i}'\\n\",\n \"\\n\",\n \" split_dict[split_name] = f'all_{dose}'\\n\",\n \" adata.obs[split_name] = 'train'\\n\",\n \" adata.obs[split_name][adata.obs.dose_val == dose] = 'ood'\\n\",\n \"\\n\",\n \" test_idx = sc.pp.subsample(adata[adata.obs[split_name] != 'ood'], .16, copy=True).obs.index\\n\",\n \" adata.obs[split_name].loc[test_idx] = 'test'\\n\",\n \"\\n\",\n \" display(adata.obs[split_name].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\\n\",\n \" and should_run_async(code)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'split1': 'A549_0.1',\\n\",\n \" 'split2': 'A549_0.001',\\n\",\n \" 'split3': 'A549_0.01',\\n\",\n \" 'split4': 'A549_1.0',\\n\",\n \" 'split5': 'MCF7_0.1',\\n\",\n \" 'split6': 'MCF7_0.001',\\n\",\n \" 'split7': 'MCF7_0.01',\\n\",\n \" 'split8': 'MCF7_1.0',\\n\",\n \" 'split9': 'K562_0.1',\\n\",\n \" 'split10': 'K562_0.001',\\n\",\n \" 'split11': 'K562_0.01',\\n\",\n \" 'split12': 'K562_1.0',\\n\",\n \" 'split13': 'A549+MCF7_0.1',\\n\",\n \" 'split14': 'A549+MCF7_0.001',\\n\",\n \" 'split15': 'A549+MCF7_0.01',\\n\",\n \" 'split16': 'A549+MCF7_1.0',\\n\",\n \" 'split17': 'A549+K562_0.1',\\n\",\n \" 'split18': 'A549+K562_0.001',\\n\",\n \" 'split19': 'A549+K562_0.01',\\n\",\n \" 'split20': 'A549+K562_1.0',\\n\",\n \" 'split21': 'MCF7+K562_0.1',\\n\",\n \" 'split22': 'MCF7+K562_0.001',\\n\",\n \" 'split23': 'MCF7+K562_0.01',\\n\",\n \" 'split24': 'MCF7+K562_1.0',\\n\",\n \" 'split25': 'all_0.1',\\n\",\n \" 'split26': 'all_0.001',\\n\",\n \" 'split27': 'all_0.01',\\n\",\n \" 'split28': 'all_1.0'}\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"split_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"adata.uns['splits'] = split_dict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"... storing 'ct_dose' as categorical\\n\",\n \"... storing 'split1' as categorical\\n\",\n \"... storing 'split2' as categorical\\n\",\n \"... storing 'split3' as categorical\\n\",\n \"... storing 'split4' as categorical\\n\",\n \"... storing 'split5' as categorical\\n\",\n \"... storing 'split6' as categorical\\n\",\n \"... storing 'split7' as categorical\\n\",\n \"... storing 'split8' as categorical\\n\",\n \"... storing 'split9' as categorical\\n\",\n \"... storing 'split10' as categorical\\n\",\n \"... storing 'split11' as categorical\\n\",\n \"... storing 'split12' as categorical\\n\",\n \"... storing 'split13' as categorical\\n\",\n \"... storing 'split14' as categorical\\n\",\n \"... storing 'split15' as categorical\\n\",\n \"... storing 'split16' as categorical\\n\",\n \"... storing 'split17' as categorical\\n\",\n \"... storing 'split18' as categorical\\n\",\n \"... storing 'split19' as categorical\\n\",\n \"... storing 'split20' as categorical\\n\",\n \"... storing 'split21' as categorical\\n\",\n \"... storing 'split22' as categorical\\n\",\n \"... storing 'split23' as categorical\\n\",\n \"... storing 'split24' as categorical\\n\",\n \"... storing 'split25' as categorical\\n\",\n \"... storing 'split26' as categorical\\n\",\n \"... storing 'split27' as categorical\\n\",\n \"... storing 'split28' as categorical\\n\"\n ]\n }\n ],\n \"source\": [\n \"sc.write('../datasets/sciplex3_old_reproduced.h5ad', adata)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "pretrained_models/.gitattributes", "content": "Norman2010_prep_new_deg_collect/ filter=lfs diff=lfs merge=lfs -text\n" }, { "path": "pretrained_models/.gitkeep", "content": "" }, { "path": "requirements.txt", "content": "adjustText\nargparse\nmatplotlib\nnumpy\npandas\nscanpy \nscipy \nseaborn \nsklearn\nsubmitit\ntorch\n" }, { "path": "scripts/.gitkeep", "content": "" }, { "path": "scripts/run_collect_results.sh", "content": "#!/bin/bash\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_GSM_new_logsigm\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_pachter_new\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_cross_species_new\nfor i in {1..4}\ndo\n python -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_cross_species_new_split$i \ndone\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_new_logsigm\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_new\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_split1\n\nfor i in 1 21 22\ndo\n python -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split$i\ndone\n\nfor i in {1..28}\ndo\n python -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_split$i\ndone\n\nfor i in {2..23}\ndo\n python -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_split$i\ndone\n\n\n\nfor i in {2..20}\ndo\n python -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split$i\ndone\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split23\n\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_new\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_GSM_new\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_pachter_new_logsigm\n\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_logsigm\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_sigm\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_lincs_logsigm\npython -m cpa.collect_results --save_dir /checkpoint/$USER/sweep_lincs_sigm\n\npython -m cpa.collect_results --save_dir /checkpoint/$USER/kang_split\n\n\n\n\n" }, { "path": "scripts/run_one_epoch.sh", "content": "#!/bin/bash\n\n# change the path vairable to your path to the datasets folder\npath='../cpa_binaries'\n\npython -m cpa.train --data $path/datasets/GSM_new.h5ad --save_dir /tmp --max_epochs 100 --doser_type sigm\npython -m cpa.train --data $path/datasets/pachter_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type logsigm\npython -m cpa.train --data $path/datasets/cross_species_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type mlp --split_key split4\n\npython -m cpa.train --data $path/datasets/sciplex3_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm\npython -m cpa.train --data $path/datasets/sciplex3_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm --split_key split1\n\npython -m cpa.train --data $path/datasets/Norman2019_prep_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type linear\n\n" }, { "path": "scripts/run_sweeps.sh", "content": "#!/bin/bash\n\n# rm -rf /checkpoint/$USER/sweep_GSM_2k_hvg\n# rm -rf /checkpoint/$USER/sweep_GSM_4k_hvg\n# rm -rf /checkpoint/$USER/sweep_pachter\n# rm -rf /checkpoint/$USER/sweep_cross_species\n# rm -rf /checkpoint/$USER/sweep_Norman2019\n# rm -rf /checkpoint/$USER/sweep_sciplex3_prepared\n# rm -rf /checkpoint/$USER/sweep_sciplex3_prepared_logsigm\n\n\n\n\npython -m cpa.sweep --data datasets/GSM_new.h5ad --save_dir /checkpoint/$USER/sweep_GSM_new_logsigm --doser_type logsigm --max_minutes 120\npython -m cpa.sweep --data datasets/pachter_new.h5ad --save_dir /checkpoint/$USER/sweep_pachter_new_logsigm --doser_type logsigm --max_minutes 120\npython -m cpa.sweep --data datasets/cross_species_new.h5ad --save_dir /checkpoint/$USER/sweep_cross_species_new --doser_type mlp --max_minutes 120\nfor i in {1..4}\ndo\n python -m cpa.sweep --data datasets/cross_species_new.h5ad --save_dir /checkpoint/$USER/sweep_cross_species_new_split$i --doser_type mlp --split_key split$i --max_minutes 120\ndone\n\n\npython -m cpa.sweep --data datasets/Norman2019_prep_new.h5ad --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu --decoder_activation ReLU --doser_type linear --max_minutes 300\n\nfor i in 1 21 22\ndo\n python -m cpa.sweep --data datasets/Norman2019_prep_new.h5ad --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split$i --doser_type linear --split_key split$i --decoder_activation ReLU --max_minutes 300\ndone\n\nfor i in 12 16 20 24 28 4 8\ndo\n python -m cpa.sweep --data datasets/sciplex3_old_reproduced.h5ad --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_split$i --doser_type logsigm --split_key split$i --max_minutes 300\ndone\n\n\nfor i in {1..28}\ndo\n python -m cpa.sweep --data datasets/sciplex3_old_reproduced.h5ad --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_split$i --doser_type logsigm --split_key split$i --max_minutes 300\ndone\n\n\nfor i in {2..20}\ndo\n python -m cpa.sweep --data datasets/Norman2019_prep_new.h5ad --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split$i --doser_type linear --decoder_activation ReLU --split_key split$i --max_minutes 300\ndone\n\npython -m cpa.sweep --data datasets/Norman2019_prep_new.h5ad --save_dir /checkpoint/$USER/sweep_Norman2019_prep_new_relu_split23 --split_key 23 --decoder_activation ReLU --doser_type linear --max_minutes 300\n\n\n\n\npython -m cpa.sweep --data datasets/sciplex3_old_reproduced.h5ad --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_logsigm --doser_type logsigm --max_minutes 3000\npython -m cpa.sweep --data datasets/sciplex3_old_reproduced.h5ad --save_dir /checkpoint/$USER/sweep_sciplex3_old_reproduced_sigm --doser_type sigm --max_minutes 3000\n\npython -m cpa.sweep --data datasets/pachter_new.h5ad --save_dir /checkpoint/$USER/sweep_pachter_new --doser_type sigm --max_minutes 120\npython -m cpa.sweep --data datasets/GSM_new.h5ad --save_dir /checkpoint/$USER/sweep_GSM_new --doser_type sigm --max_minutes 120\npython -m cpa.sweep --data datasets/sciplex3_new.h5ad --save_dir /checkpoint/$USER/sweep_sciplex3_new --doser_type sigm --max_minutes 300\n\npython -m cpa.sweep --data datasets/lincs.h5ad --save_dir /checkpoint/$USER/sweep_lincs_logsigm --doser_type logsigm --max_minutes 3000\npython -m cpa.sweep --data datasets/lincs.h5ad --save_dir /checkpoint/$USER/sweep_lincs_sigm --doser_type sigm --max_minutes 3000\n\n" }, { "path": "setup.py", "content": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved\n\nfrom distutils.core import setup\n\next_modules = []\ncmdclass = {}\n\nwith open(\"requirements.txt\", \"r\") as handle:\n install_requires = handle.read().splitlines()\n\nsetup(\n name=\"cpa\",\n version=\"1.0.0\",\n description=\"\",\n url=\"http://github.com/facebookresearch/CPA\",\n author=\"See README.md\",\n author_email=\"dlp@fb.com\",\n license=\"MIT\",\n packages=[\"cpa\"],\n cmdclass=cmdclass,\n ext_modules=ext_modules,\n install_requires=install_requires,\n)\n" }, { "path": "tests/test.py", "content": "import sys\n\nsys.path.append(\"../\")\nimport cpa\nimport scanpy as sc\nimport scvi\nfrom cpa.helper import rank_genes_groups_by_cov\n\n\ndef sim_adata():\n adata = scvi.data.synthetic_iid(run_setup_anndata=False)\n sc.pp.filter_cells(adata, min_counts=0)\n sc.pp.log1p(adata)\n\n adata.obs[\"condition\"] = \"drugA\"\n adata.obs[\"condition\"].values[:100] = \"control\"\n adata.obs[\"condition\"].values[350:400] = \"control\"\n adata.obs[\"condition\"].values[100:200] = \"drugB\"\n adata.obs[\"split\"] = \"train\"\n\n return adata\n\n\nif __name__ == \"__main__\":\n adata = sim_adata()\n\n cpa_api = cpa.api.API(\n adata,\n pretrained=None,\n perturbation_key=\"condition\",\n dose_key=\"dose_val\",\n covariate_keys=[\"batch\"],\n hparams={},\n device=\"cuda:0\",\n control=\"control\",\n )\n\n print(\"\\nStart training\")\n cpa_api.train(max_epochs=1)\n" } ]