Full Code of theislab/sc-pert for AI

Repository: theislab/sc-pert
Branch: main
Commit: ebaf4cb36eb0
Files: 23
Total size: 42.0 MB

Directory structure:
gitextract__e0r27pu/

├── LICENSE
├── README.md
├── data_table.csv
├── dataset_example.ipynb
├── datasets/
│   ├── Dixit_2016.ipynb
│   ├── Frangieh_2021.ipynb
│   ├── Jaitin_2014.ipynb
│   ├── McFarland_2020_curation.ipynb
│   ├── Norman_2019.ipynb
│   ├── Norman_2019_curation.ipynb
│   ├── Schraivogel_2020.ipynb
│   ├── Srivatsan_2019_sciplex2_curation.ipynb
│   ├── Srivatsan_2019_sciplex3.ipynb
│   ├── block0_load.py
│   ├── block1_init.py
│   ├── block2_process.py
│   ├── block3_standardize.py
│   ├── template.ipynb
│   └── utils.py
├── readme_body.txt
├── resources.py
├── statistics.ipynb
└── update.py

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FILE CONTENTS
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FILE: LICENSE
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MIT License

Copyright (c) 2020 Theis Lab

Permission 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:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE 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.


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FILE: README.md
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# sc-pert - Machine learning for perturbational single-cell omics

*This repository provides a community-maintained summary of models and datasets. It was initially curated for [(Cell Systems, 2021)](https://doi.org/10.1016/j.cels.2021.05.016).*

### External annotations

There are various resources for evaluation of single cell perturbation models. We discuss five tasks in the publication which can be supported by the following publicly available annotations:

- [GDSC](https://www.cancerrxgene.org/downloads/bulk_download) provides a collection of cell viability measurements for many compounds and cell lines. We provide a [code snippet](https://github.com/theislab/sc-pert/blob/main/resources.py#L4) to conveniently load GDSC-provided z-score compound response rankings per cell line.
- Additional viability data can be obtained from [DepMap's PRISM dataset](https://depmap.org/portal/download/).
- [Therapeutics Data Commons](https://github.com/mims-harvard/TDC) provides access to a number of compound databases as part of their [cheminformatics tasks](https://tdcommons.ai/benchmark/overview/). (In the same vein, [OpenProblems](https://openproblems.bio/) provides a [framework for tasks in single-cell](https://github.com/openproblems-bio/openproblems/tree/main/openproblems/tasks) which can also support perturbation modeling tasks in a more long term format than was previously seen in the [DREAM challenges](https://dreamchallenges.org/dream-7-nci-dream-drug-sensitivity-and-drug-synergy-challenge/).)
- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) contains a comprehensive record of compounds ranging from experimental entities to non-proprietary small molecules. It is queryable via [PubChemPy](https://github.com/mcs07/PubChemPy).
- [DrugBank](https://go.drugbank.com/releases/latest) provides annotations for a relatively small number of small molecules in a standardized format.

### Current modeling approaches

We [maintain a list of perturbation-related tools at scrna-tools](https://www.scrna-tools.org/tools?sort=name&cats=Perturbations). Please consider further updating and tagging tools [there](https://github.com/scRNA-tools/scRNA-tools).

For the basis of the table in the article, see this [spreadsheet of a subset of perturbation models](https://docs.google.com/spreadsheets/d/1nqNg0DW1-Om7WtvRS20q-6b28usVRv5czOcxgj83Sgg/) which includes more details.

### Datasets

Below, we curated a [table](https://raw.githubusercontent.com/theislab/sc-pert/main/data_table.csv) of perturbation datasets based on [Svensson *et al.* (2020)](https://doi.org/10.1093/database/baaa073).

We also offer some datasets in a curated `.h5ad` format via the download links in the table below. `raw h5ad` denotes a version of the dataset that has not been filtered, normalized, or standardized.

H5ads denoted as `processed` have an accompanying processing notebook, and have been similarly preprocessed. These datasets have the following standardized fields in `.obs`:
* `perturbation_name` -- Human-readable ompound names (International non-proprietary naming where possible) for small molecules and gene names for genetic perturbations.
* `perturbation_type` -- `small molecule` or `genetic`
* `perturbation_value` -- A continuous covariate quantity, such as the dosage concentration or the number of hours since treatment.
* `perturbation_unit` -- Describes `perturbation_value`, such as `'ug'` or `'hrs'`.


| Shorthand                                                                        | Title                                                                                                                                                                                                                         | .h5ad availability                                                                                                                                                                                                                                                                                                                                                                                                                                    | Treatment            | # perturbations   | # cell types   | # doses   | # timepoints   | Reported cells total   | Organism     | Tissue               | Technique             | Data location   | Panel size   | Measurement   | Cell source                                                                                                                      |   Disease | Contrasts             |   Developmental stage |   Number of reported cell types or clusters | Cell clustering   | Pseudotime   | RNA Velocity   | PCA   | tSNE   |   H5AD location | Isolation            | BC --> Cell ID _OR_ BC --> Cluster ID                              |   Number individuals |
|----------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|-------------------|----------------|-----------|----------------|------------------------|--------------|----------------------|-----------------------|-----------------|--------------|---------------|----------------------------------------------------------------------------------------------------------------------------------|-----------|-----------------------|-----------------------|---------------------------------------------|-------------------|--------------|----------------|-------|--------|-----------------|----------------------|--------------------------------------------------------------------|----------------------|
| [Jaitin *et al.* Science](https://doi.org/10.1126/science.1247651)               | Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types                                                         |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 8-22              | 1              | -         | 1              | 4,468                  | Mouse        | Spleen               | MARS-seq              | GSE54006        | nan          | RNA-seq       | CD11c+ enriched splenocytes                                                                                                      |       nan | nan                   |                   nan |                                           9 | Yes               | No           | nan            | No    | No     |             nan | Sorting (FACS)       | nan                                                                |                  nan |
| [Dixit *et al.* Cell](https://doi.org/10.1016/j.cell.2016.11.038)                | Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens                                            | [\[raw h5ad\]](https://ndownloader.figshare.com/files/34011689) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34014608) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Dixit_2016.ipynb)                                                                                                                                                                                                   | CRISPR               | 10,24             | 1              | -         | 1-2            | 200,000                | Human, Mouse | Culture              | Perturb-seq           | GSE90063        | nan          | RNA-seq       | BMDCs, K562                                                                                                                      |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | No     |             nan | Nanodroplet dilution | nan                                                                |                  nan |
| [Datlinger *et al.* NMeth](https://doi.org/10.1038/nmeth.4177)                   | Pooled CRISPR screening with single-cell transcriptome readout                                                                                          |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 29                | 1              | -         | 1              | 5,905                  | Human, Mouse | Culture              | CROP-seq              | GSE92872        | nan          | RNA-seq       | HEK293T, 3T3, Jurkat                                                                                                             |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | No     |             nan | nan                  | nan                                                                |                  nan |
| [Hill *et al.* NMethods](https://doi.org/10.1038/nmeth.4604)                     | On the design of CRISPR-based single-cell molecular screens                                                                                             |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 32                | 1              | 2         | 1              | 5,879                  | Human        | Culture              | CROP-seq              | GSE108699       | nan          | RNA-seq       | MCF10a cells                                                                                                                     |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | https://github.com/shendurelab/single-cell-ko-screens#result-files |                  nan |
| [Ursu *et al.* bioRxiv](https://doi.org/10.1101/2020.11.16.383307)               | Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 200               | 1              | -         | 1              | 162,314                | Human        | Lung                 | Perturb-seq           | nan             | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Jin *et al.* Science](https://doi.org/10.1126/science.aaz6063)                  | In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes                                                          |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 35                | -              | -         | 1              | 46,770                 | Mouse        | Brain                | Perturb-seq           | nan             | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Frangieh *et al.* NGenetics](https://doi.org/10.1038/s41588-021-00779-1)        | Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion                                                 | [\[raw h5ad\]](https://ndownloader.figshare.com/files/34012565) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34013717) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Frangieh_2021.ipynb)                                                                                                                                                                                                | CRISPR               | 248               | 1              | -         | 1              | 218,331                | Human        | Culture              | Perturb-CITE-seq      | SCP1064         | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Papalexi *et al.* NGenetics](https://doi.org/10.1038/s41588-021-00778-2)        | Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens                                            |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR               | 111 (sgRNA)       | 1              | 2         | -              | 28,295                 | Human        | Culture              | CITE-seq & ECCITE-seq | GSE153056       | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Datlinger *et al.* NMethods](https://doi.org/10.1038/s41592-021-01153-z)        | Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing                                         |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPR KO + antibody | 96                | 1              | 1         | 1              | nan                    | Human, Mouse | nan                  | scifi-RNA-seq         | nan             | nan          | nan           | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Alda-Catalinas *et al.* CSystems](https://doi.org/10.1016/j.cels.2020.06.004)   | A Single-Cell Transcriptomics CRISPR-Activation Screen Identifies Epigenetic Regulators of the Zygotic Genome Activation Program                        |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPRa              | 230               | 1              | -         | -              | 203,894                | Mouse        | Culture              | Chromium              | nan             | nan          | RNA-seq       | mESCs                                                                                                                            |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Norman *et al.* (2019)](https://doi.org/10.1126/science.aax4438)                | nan                                                                                                                                                     | [\[raw h5ad\]](https://ndownloader.figshare.com/files/34002548) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34027562) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Norman_2019_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Norman_2019.ipynb)                                                                            | CRISPRa              | 287               | 1              | -         | 1              | nan                    | nan          | nan                  | Perturb-seq           | nan             | nan          | RNA-seq       | induction of gene pair targets+single gene controls in K562 cells after screening 112 genes (2x gRNA per) and their combinations |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Adamson *et al.* Cell](https://doi.org/10.1016/j.cell.2016.11.048)              | A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response                                      |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPRi              | 9-93 (sgRNA)      | 1              | -         | 1              | 86,000                 | Human        | Culture              | Perturb-seq           | GSE90546        | nan          | RNA-seq       | K562                                                                                                                             |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | Yes    |             nan | nan                  | nan                                                                |                  nan |
| [Gasperini *et al.* Cell](https://doi.org/10.1016/j.cell.2018.11.029)            | A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens                                                                        |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPRi              | 1119, 5779        | 1              | -         | 1              | 207,324                | Human        | Culture              | CROP-seq              | GSE120861       | nan          | RNA-seq       | K562 Cells                                                                                                                       |       nan | CRISPR Screen         |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Jost *et al.* NBT](https://doi.org/10.1038/s41587-019-0387-5)                   | Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs                                                                |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPRi              | 25                | 2              | -         | 1              | 19,587                 | Human        | Culture              | Perturb-seq           | GSE132080       | nan          | RNA-seq       | K562 cells                                                                                                                       |       nan | 25 gene screen        |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Schraivogel *et al.* NMethods](https://doi.org/10.1038/s41592-020-0837-5)       | Targeted Perturb-seq enables genome-scale genetic screens in single cells                                                                               | [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Schraivogel_2020.ipynb)                                                                                                                                                                                                                                                                                                                                   | CRISPRi              | 1778 (enhancers)  | 1              | -         | 1              | 231,667                | Human, Mouse | Bone marrow, Culture | TAP-seq               | GSE135497       | 1,000        | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | Yes    |             nan | nan                  | nan                                                                |                  nan |
| [Leng *et al.* bioRxiv](https://doi.org/10.1101/2021.08.23.457400)               | CRISPRi screens in human astrocytes elucidate regulators of distinct inflammatory reactive states                                                       |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | CRISPRi              | 30                | 1              | 2         | -              | nan                    | nan          | nan                  | nan                   | nan             | nan          | nan           | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Replogle *et al.* (2020)](https://doi.org/nan)                                  | nan                                                                                                                                                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | genetic targets      | nan               | nan            | nan       | nan            | nan                    | nan          | nan                  | nan                   | nan             | nan          | nan           | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Replogle *et al.* (2021)](https://doi.org/10.1101/2021.12.16.473013v3)          | nan                                                                                                                                                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | genetic targets      | >10000            | 2              | -         | -              | nan                    | nan          | nan                  | Perturb-seq           | nan             | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Shin *et al.* SAdvances](https://doi.org/10.1126/sciadv.aav2249)                | Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations                             |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | small molecules      | 45                | 2              | 1         | 1              | 3,091                  | Mouse, Human | Culture              | Drop-seq              | PRJNA493658     | nan          | RNA-seq       | HEK293T, NIIH3T3, A375, SW480, K562                                                                                              |       nan | 45 perturbations      |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Srivatsan *et al.* Science](https://doi.org/10.1126/science.aax6234)            | Massively multiplex chemical transcriptomics at single-cell resolution                                                                                  | [\[raw h5ad\]](https://ndownloader.figshare.com/files/33979517) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_sciplex2_curation.ipynb) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_sciplex3.ipynb) | small molecules      | 188               | 3              | 4         | 2              | 650,000                | Human        | Culture              | sci-Plex              | GSE139944       | nan          | RNA-seq       | Cancer cell lines A549, K562, and MCF7                                                                                           |       nan | 5,000 drug conditions |                   nan |                                           3 | Yes               | Yes          | No             | Yes   | No     |             nan | nan                  | nan                                                                |                  nan |
| [Zhao *et al.* bioRxiv](https://doi.org/10.1101/2020.04.22.056341)               | Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq                                                       |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | small molecules      | 2,6               | 6,1            | -         | -              | 48,404                 | Human        | Brain, Tumor         | SCRB-seq (microwell)  | GSE148842       | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                    6 |
| [McFarland *et al.* NCommunications](https://doi.org/10.1038/s41467-020-17440-w) | Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action                         | [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/McFarland_2020_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/McFarland_2020.ipynb)                                                                                                                                                                                                            | small molecules      | 1-13              | 24-99          | 1         | 1-5            | nan                    | Human        | Culture              | MIX-seq               | nan             | nan          | RNA-seq       | nan                                                                                                                              |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |
| [Chen *et al.* (2020)](https://doi.org/10.1038/s41592-019-0689-z)                | nan                                                                                                                                                     |                                                                                                                                                                                                                                                                                                                                                                                                                                                       | small molecules      | 300               | 1              | 1         | 1              | nan                    | nan          | nan                  | CyTOF                 | nan             | nan          | protein       | breast  cancer  cells  undergoing  TGF-β-induced  EMT                                                                            |       nan | nan                   |                   nan |                                         nan | nan               | nan          | nan            | nan   | nan    |             nan | nan                  | nan                                                                |                  nan |

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FILE: data_table.csv
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,Shorthand,Title,.h5ad availability,Treatment,# perturbations,# cell types,# doses,# timepoints,Reported cells total,Organism,Tissue,Technique,Data location,Panel size,Measurement,Cell source,Disease,Contrasts,Developmental stage,Number of reported cell types or clusters,Cell clustering,Pseudotime,RNA Velocity,PCA,tSNE,H5AD location,Isolation,BC --> Cell ID _OR_ BC --> Cluster ID,Number individuals
0,[Jaitin *et al.* Science](https://doi.org/10.1126/science.1247651),Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types,,CRISPR,8-22,1,-,1,"4,468",Mouse,Spleen,MARS-seq,GSE54006,,RNA-seq,CD11c+ enriched splenocytes,,,,9.0,Yes,No,,No,No,,Sorting (FACS),,
2,[Dixit *et al.* Cell](https://doi.org/10.1016/j.cell.2016.11.038),Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens, [\[raw h5ad\]](https://ndownloader.figshare.com/files/34011689) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34014608) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Dixit_2016.ipynb),CRISPR,"10,24",1,-,1-2,"200,000","Human, Mouse",Culture,Perturb-seq,GSE90063,,RNA-seq,"BMDCs, K562",,,,,,,,,No,,Nanodroplet dilution,,
3,[Datlinger *et al.* NMeth](https://doi.org/10.1038/nmeth.4177),Pooled CRISPR screening with single-cell transcriptome readout,,CRISPR,29,1,-,1,"5,905","Human, Mouse",Culture,CROP-seq,GSE92872,,RNA-seq,"HEK293T, 3T3, Jurkat",,,,,,,,,No,,,,
4,[Hill *et al.* NMethods](https://doi.org/10.1038/nmeth.4604),On the design of CRISPR-based single-cell molecular screens,,CRISPR,32,1,2,1,"5,879",Human,Culture,CROP-seq,GSE108699,,RNA-seq,MCF10a cells,,,,,,,,,,,,https://github.com/shendurelab/single-cell-ko-screens#result-files,
13,[Ursu *et al.* bioRxiv](https://doi.org/10.1101/2020.11.16.383307),Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations,,CRISPR,200,1,-,1,"162,314",Human,Lung,Perturb-seq,,,RNA-seq,,,,,,,,,,,,,,
14,[Jin *et al.* Science](https://doi.org/10.1126/science.aaz6063),In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes,,CRISPR,35,-,-,1,"46,770",Mouse,Brain,Perturb-seq,,,RNA-seq,,,,,,,,,,,,,,
15,[Frangieh *et al.* NGenetics](https://doi.org/10.1038/s41588-021-00779-1),Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion, [\[raw h5ad\]](https://ndownloader.figshare.com/files/34012565) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34013717) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Frangieh_2021.ipynb),CRISPR,248,1,-,1,"218,331",Human,Culture,Perturb-CITE-seq,SCP1064,,RNA-seq,,,,,,,,,,,,,,
16,[Papalexi *et al.* NGenetics](https://doi.org/10.1038/s41588-021-00778-2),Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens,,CRISPR,111 (sgRNA),1,2,-,"28,295",Human,Culture,CITE-seq & ECCITE-seq,GSE153056,,RNA-seq,,,,,,,,,,,,,,
17,[Datlinger *et al.* NMethods](https://doi.org/10.1038/s41592-021-01153-z),Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing,,CRISPR KO + antibody,96,1,1,1,,"Human, Mouse",,scifi-RNA-seq,,,,,,,,,,,,,,,,,
11,[Alda-Catalinas *et al.* CSystems](https://doi.org/10.1016/j.cels.2020.06.004),A Single-Cell Transcriptomics CRISPR-Activation Screen Identifies Epigenetic Regulators of the Zygotic Genome Activation Program,,CRISPRa,230,1,-,-,"203,894",Mouse,Culture,Chromium,,,RNA-seq,mESCs,,,,,,,,,,,,,
19,[Norman *et al.* (2019)](https://doi.org/10.1126/science.aax4438),, [\[raw h5ad\]](https://ndownloader.figshare.com/files/34002548) [\[processed h5ad\]](https://ndownloader.figshare.com/files/34027562) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Norman_2019_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Norman_2019.ipynb),CRISPRa,287,1,-,1,,,,Perturb-seq,,,RNA-seq,induction of gene pair targets+single gene controls in K562 cells after screening 112 genes (2x gRNA per) and their combinations,,,,,,,,,,,,,
1,[Adamson *et al.* Cell](https://doi.org/10.1016/j.cell.2016.11.048),A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response,,CRISPRi,9-93 (sgRNA),1,-,1,"86,000",Human,Culture,Perturb-seq,GSE90546,,RNA-seq,K562,,,,,,,,,Yes,,,,
5,[Gasperini *et al.* Cell](https://doi.org/10.1016/j.cell.2018.11.029),A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens,,CRISPRi,"1119, 5779",1,-,1,"207,324",Human,Culture,CROP-seq,GSE120861,,RNA-seq,K562 Cells,,CRISPR Screen,,,,,,,,,,,
8,[Jost *et al.* NBT](https://doi.org/10.1038/s41587-019-0387-5),Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs,,CRISPRi,25,2,-,1,"19,587",Human,Culture,Perturb-seq,GSE132080,,RNA-seq,K562 cells,,25 gene screen,,,,,,,,,,,
10,[Schraivogel *et al.* NMethods](https://doi.org/10.1038/s41592-020-0837-5),Targeted Perturb-seq enables genome-scale genetic screens in single cells, [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Schraivogel_2020.ipynb),CRISPRi,1778 (enhancers),1,-,1,"231,667","Human, Mouse","Bone marrow, Culture",TAP-seq,GSE135497,"1,000",RNA-seq,,,,,,,,,,Yes,,,,
18,[Leng *et al.* bioRxiv](https://doi.org/10.1101/2021.08.23.457400),CRISPRi screens in human astrocytes elucidate regulators of distinct inflammatory reactive states,,CRISPRi,30,1,2,-,,,,,,,,,,,,,,,,,,,,,
20,[Replogle *et al.* (2020)](https://doi.org/nan),,,genetic targets,,,,,,,,,,,,,,,,,,,,,,,,,
21,[Replogle *et al.* (2021)](https://doi.org/10.1101/2021.12.16.473013v3),,,genetic targets,>10000,2,-,-,,,,Perturb-seq,,,RNA-seq,,,,,,,,,,,,,,
6,[Shin *et al.* SAdvances](https://doi.org/10.1126/sciadv.aav2249),Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations,,small molecules,45,2,1,1,"3,091","Mouse, Human",Culture,Drop-seq,PRJNA493658,,RNA-seq,"HEK293T, NIIH3T3, A375, SW480, K562",,45 perturbations,,,,,,,,,,,
7,[Srivatsan *et al.* Science](https://doi.org/10.1126/science.aax6234),Massively multiplex chemical transcriptomics at single-cell resolution, [\[raw h5ad\]](https://ndownloader.figshare.com/files/33979517) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_sciplex2_curation.ipynb) [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/Srivatsan_2019_sciplex3.ipynb),small molecules,188,3,4,2,"650,000",Human,Culture,sci-Plex,GSE139944,,RNA-seq,"Cancer cell lines A549, K562, and MCF7",,"5,000 drug conditions",,3.0,Yes,Yes,No,Yes,No,,,,
9,[Zhao *et al.* bioRxiv](https://doi.org/10.1101/2020.04.22.056341),Deconvolution of Cell Type-Specific Drug Responses in Human Tumor Tissue with Single-Cell RNA-seq,,small molecules,"2,6","6,1",-,-,"48,404",Human,"Brain, Tumor",SCRB-seq (microwell),GSE148842,,RNA-seq,,,,,,,,,,,,,,6.0
12,[McFarland *et al.* NCommunications](https://doi.org/10.1038/s41467-020-17440-w),Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action, [\[curation nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/McFarland_2020_curation.ipynb) [\[processing nb\]](https://nbviewer.ipython.org/github/theislab/sc-pert/blob/main/datasets/McFarland_2020.ipynb),small molecules,1-13,24-99,1,1-5,,Human,Culture,MIX-seq,,,RNA-seq,,,,,,,,,,,,,,
22,[Chen *et al.* (2020)](https://doi.org/10.1038/s41592-019-0689-z),,,small molecules,300,1,1,1,,,,CyTOF,,,protein,breast  cancer  cells  undergoing  TGF-β-induced  EMT ,,,,,,,,,,,,,


================================================
FILE: dataset_example.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "902dfc4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scanpy as sc\n",
    "import anndata as ad\n",
    "import sklearn as sk\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d9fb6e",
   "metadata": {},
   "source": [
    "Load every available dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d94b8e15",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 59s, sys: 2min 25s, total: 4min 24s\n",
      "Wall time: 4min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "adatas = []\n",
    "for i, row in pd.read_csv('personal.csv').iterrows():\n",
    "    try:\n",
    "        adata = sc.read(f'datasets/{row.Author}_{row.Year}.h5ad')\n",
    "        adata.obs['dataset'] = f'{row.Author}_{row.Year}'\n",
    "        adatas.append(adata)\n",
    "    except FileNotFoundError:\n",
    "        pass\n",
    "    \n",
    "adata = ad.concat(adatas)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b65993f",
   "metadata": {},
   "source": [
    "## Explore the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed4c66c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/icb/yuge.ji/miniconda3/envs/py37/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  IPython.display.set_matplotlib_formats(*ipython_format)\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sc.set_figure_params(dpi=100, frameon=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c74e3c66",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nth condition</th>\n",
       "      <th>condition</th>\n",
       "      <th>n_samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ELF1</td>\n",
       "      <td>11676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>control</td>\n",
       "      <td>8878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>CREB1</td>\n",
       "      <td>7587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ELK1</td>\n",
       "      <td>7366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>INTERGENIC393453</td>\n",
       "      <td>7177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nth condition         condition  n_samples\n",
       "0              0              ELF1      11676\n",
       "1              1           control       8878\n",
       "2              2             CREB1       7587\n",
       "3              3              ELK1       7366\n",
       "4              4  INTERGENIC393453       7177"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 397,
       "width": 863
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nth condition</th>\n",
       "      <th>condition</th>\n",
       "      <th>n_samples</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>11835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>KLF1</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>BAK1</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>CEBPE</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CEBPE,RUNX1T1</td>\n",
       "      <td>1215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nth condition      condition  n_samples\n",
       "0              0                     11835\n",
       "1              1           KLF1       1954\n",
       "2              2           BAK1       1451\n",
       "3              3          CEBPE       1230\n",
       "4              4  CEBPE,RUNX1T1       1215"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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