[ { "path": ".Rbuildignore", "content": "^.*\\.Rproj$\n^\\.Rproj\\.user$\n^data-raw$\n^data-raw$\n^dev$\n^CODE_OF_CONDUCT\\.md$\n^LICENSE\\.md$\n^\\.github$\n^\\.RDataTmp$\n" }, { "path": ".github/.gitignore", "content": "*.html\n" }, { "path": ".github/workflows/R-CMD-check.yaml", "content": "name: R-CMD-check\n\non:\n push:\n branches: [main, master]\n pull_request:\n branches: [main, master]\n\njobs:\n R-CMD-check:\n runs-on: ${{ matrix.config.os }}\n\n name: ${{ matrix.config.os }} (${{ matrix.config.r }})\n\n strategy:\n fail-fast: false\n matrix:\n config:\n - {os: macos-latest, r: 'release'}\n - {os: windows-latest, r: 'release'}\n - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'}\n - {os: ubuntu-latest, r: 'release'}\n - {os: ubuntu-latest, r: 'oldrel-1'}\n\n env:\n R_KEEP_PKG_SOURCE: yes\n\n steps:\n # Checkout repository\n - uses: actions/checkout@v3\n\n # Set up pandoc\n - uses: r-lib/actions/setup-pandoc@v2\n\n # Set up R environment\n - uses: r-lib/actions/setup-r@v2\n with:\n r-version: ${{ matrix.config.r }}\n http-user-agent: ${{ matrix.config.http-user-agent }}\n use-public-rspm: true\n\n # Automatically install dependencies from DESCRIPTION\n - uses: r-lib/actions/setup-r-dependencies@v2\n with:\n extra-packages: >-\n any::rcmdcheck\n\n # Manually install Bioconductor and other missing dependencies\n - name: Install dependencies\n run: |\n Rscript -e \"if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager')\"\n Rscript -e \"BiocManager::install()\"\n Rscript -e \"if (!requireNamespace('remotes', quietly = TRUE)) install.packages('remotes')\"\n Rscript -e \"remotes::install_deps(dependencies = TRUE)\"\n\n # Run R CMD check\n - uses: r-lib/actions/check-r-package@v2\n with:\n upload-snapshots: true\n" }, { "path": ".gitignore", "content": ".Rproj.user\n.Rhistory\n.RData\n.Ruserdata\n.Rdata\n.httr-oauth\n.DS_Store\n.quarto\n.RDataTmp\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nWe as members, contributors, and leaders pledge to make participation in our\ncommunity a harassment-free experience for everyone, regardless of age, body\nsize, visible or invisible disability, ethnicity, sex characteristics, gender\nidentity and expression, level of experience, education, socio-economic status,\nnationality, personal appearance, race, caste, color, religion, or sexual\nidentity and orientation.\n\nWe pledge to act and interact in ways that contribute to an open, welcoming,\ndiverse, inclusive, and healthy community.\n\n## Our Standards\n\nExamples of behavior that contributes to a positive environment for our\ncommunity include:\n\n* Demonstrating empathy and kindness toward other people\n* Being respectful of differing opinions, viewpoints, and experiences\n* Giving and gracefully accepting constructive feedback\n* Accepting responsibility and apologizing to those affected by our mistakes,\n and learning from the experience\n* Focusing on what is best not just for us as individuals, but for the overall\n community\n\nExamples of unacceptable behavior include:\n\n* The use of sexualized language or imagery, and sexual attention or advances of\n any kind\n* Trolling, insulting or derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or email address,\n without their explicit permission\n* Other conduct which could reasonably be considered inappropriate in a\n professional setting\n\n## Enforcement Responsibilities\n\nCommunity leaders are responsible for clarifying and enforcing our standards of\nacceptable behavior and will take appropriate and fair corrective action in\nresponse to any behavior that they deem inappropriate, threatening, offensive,\nor harmful.\n\nCommunity leaders have the right and responsibility to remove, edit, or reject\ncomments, commits, code, wiki edits, issues, and other contributions that are\nnot aligned to this Code of Conduct, and will communicate reasons for moderation\ndecisions when appropriate.\n\n## Scope\n\nThis Code of Conduct applies within all community spaces, and also applies when\nan individual is officially representing the community in public spaces.\nExamples of representing our community include using an official e-mail address,\nposting via an official social media account, or acting as an appointed\nrepresentative at an online or offline event.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be\nreported to the community leaders responsible for enforcement at yudongyue@mail.nankai.edu.cn. \nAll complaints will be reviewed and investigated promptly and fairly.\n\nAll community leaders are obligated to respect the privacy and security of the\nreporter of any incident.\n\n## Enforcement Guidelines\n\nCommunity leaders will follow these Community Impact Guidelines in determining\nthe consequences for any action they deem in violation of this Code of Conduct:\n\n### 1. Correction\n\n**Community Impact**: Use of inappropriate language or other behavior deemed\nunprofessional or unwelcome in the community.\n\n**Consequence**: A private, written warning from community leaders, providing\nclarity around the nature of the violation and an explanation of why the\nbehavior was inappropriate. A public apology may be requested.\n\n### 2. Warning\n\n**Community Impact**: A violation through a single incident or series of\nactions.\n\n**Consequence**: A warning with consequences for continued behavior. No\ninteraction with the people involved, including unsolicited interaction with\nthose enforcing the Code of Conduct, for a specified period of time. This\nincludes avoiding interactions in community spaces as well as external channels\nlike social media. Violating these terms may lead to a temporary or permanent\nban.\n\n### 3. Temporary Ban\n\n**Community Impact**: A serious violation of community standards, including\nsustained inappropriate behavior.\n\n**Consequence**: A temporary ban from any sort of interaction or public\ncommunication with the community for a specified period of time. No public or\nprivate interaction with the people involved, including unsolicited interaction\nwith those enforcing the Code of Conduct, is allowed during this period.\nViolating these terms may lead to a permanent ban.\n\n### 4. Permanent Ban\n\n**Community Impact**: Demonstrating a pattern of violation of community\nstandards, including sustained inappropriate behavior, harassment of an\nindividual, or aggression toward or disparagement of classes of individuals.\n\n**Consequence**: A permanent ban from any sort of public interaction within the\ncommunity.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage],\nversion 2.1, available at\n.\n\nCommunity Impact Guidelines were inspired by\n[Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion].\n\nFor answers to common questions about this code of conduct, see the FAQ at\n. Translations are available at .\n\n[homepage]: https://www.contributor-covenant.org\n" }, { "path": "DESCRIPTION", "content": "Package: TransProR\nType: Package\nTitle: Analysis and Visualization of Multi-Omics Data\nVersion: 1.0.4\nAuthors@R: person(\"Dongyue\", \"Yu\", email = \"yudongyue@mail.nankai.edu.cn\", role = c(\"aut\", \"cre\", \"cph\"),\n comment = c(ORCID = \"0000-0002-7041-2073\"))\nMaintainer: Dongyue Yu \nDescription: A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, ; Smyth, 2005, ), 'edgeR' (Robinson et al., 2010, ), 'DESeq2' (Love et al., 2014, ), and Wilcoxon tests (Li et al., 2022, ), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.\nImports: \n magrittr,\n stats,\n dplyr,\n rlang,\n tibble,\n sva,\n DESeq2,\n utils,\n edgeR,\n limma,\n ggplot2,\n ggVennDiagram,\n ggdensity,\n ggpubr,\n ggtree,\n hrbrthemes,\n grid,\n ggraph,\n tidygraph,\n tidyr,\n stringr,\n geomtextpath,\n ggalt,\n ggnewscale,\n Hmisc,\n circlize,\n graphics,\n spiralize,\n ComplexHeatmap,\n grDevices\nSuggests:\n prettydoc,\n knitr,\n ggtreeExtra,\n rmarkdown,\n systemfonts\nVignetteBuilder: knitr\nLicense: MIT + file LICENSE\nEncoding: UTF-8\nURL: https://github.com/SSSYDYSSS/TransProRBook\nBugReports: https://github.com/SSSYDYSSS/TransProR/issues\nLazyData: true\nRoxygenNote: 7.3.2\nDepends: \n R (>= 4.3.0)\n" }, { "path": "LICENSE", "content": "YEAR: 2023\nCOPYRIGHT HOLDER: Dongyue Yu\n" }, { "path": "LICENSE.md", "content": "# MIT License\n\nCopyright (c) 2023 Dongyue Yu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "NAMESPACE", "content": "# Generated by roxygen2: do not edit by hand\n\nexport(\"%>%\")\nexport(Combat_Normal)\nexport(Contrast_Venn)\nexport(DESeq2_analyze)\nexport(Wilcoxon_analyze)\nexport(add_boxplot)\nexport(add_new_tile_layer)\nexport(adjust_alpha_scale)\nexport(adjust_color_tone)\nexport(adjust_export_pathway)\nexport(circos_fruits)\nexport(combat_tumor)\nexport(compare_merge)\nexport(create_base_plot)\nexport(deg_filter)\nexport(drawLegends)\nexport(edgeR_analyze)\nexport(enrich_circo_bar)\nexport(enrich_polar_bubble)\nexport(enrichment_circlize)\nexport(enrichment_spiral_plots)\nexport(extract_descriptions_counts)\nexport(extract_ntop_pathways)\nexport(extract_positive_pathways)\nexport(facet_density_foldchange)\nexport(filter_diff_genes)\nexport(four_degs_venn)\nexport(gather_graph_edge)\nexport(gather_graph_node)\nexport(gene_color)\nexport(gene_highlights)\nexport(gene_map_pathway)\nexport(get_gtex_exp)\nexport(get_tcga_exp)\nexport(highlight_by_node)\nexport(highlight_genes)\nexport(limma_analyze)\nexport(log_transform)\nexport(merge_density_foldchange)\nexport(merge_gtex_tcga)\nexport(merge_id_position)\nexport(merge_method_color)\nexport(new_ggraph)\nexport(pathway_count)\nexport(pathway_description)\nexport(prep_deseq2)\nexport(prep_edgeR)\nexport(prep_limma)\nexport(prep_wilcoxon)\nexport(process_heatdata)\nexport(seek_gtex_organ)\nexport(selectPathways)\nexport(spiral_newrle)\nimportFrom(ComplexHeatmap,Legend)\nimportFrom(ComplexHeatmap,draw)\nimportFrom(ComplexHeatmap,packLegend)\nimportFrom(DESeq2,DESeq)\nimportFrom(DESeq2,DESeqDataSetFromMatrix)\nimportFrom(DESeq2,results)\nimportFrom(Hmisc,capitalize)\nimportFrom(circlize,chordDiagram)\nimportFrom(dplyr,across)\nimportFrom(dplyr,all_of)\nimportFrom(dplyr,arrange)\nimportFrom(dplyr,bind_rows)\nimportFrom(dplyr,desc)\nimportFrom(dplyr,distinct)\nimportFrom(dplyr,filter)\nimportFrom(dplyr,group_by)\nimportFrom(dplyr,inner_join)\nimportFrom(dplyr,mutate)\nimportFrom(dplyr,n)\nimportFrom(dplyr,pull)\nimportFrom(dplyr,row_number)\nimportFrom(dplyr,select)\nimportFrom(dplyr,summarise)\nimportFrom(edgeR,DGEList)\nimportFrom(edgeR,calcNormFactors)\nimportFrom(edgeR,cpm)\nimportFrom(edgeR,estimateGLMCommonDisp)\nimportFrom(edgeR,estimateGLMTagwiseDisp)\nimportFrom(edgeR,estimateGLMTrendedDisp)\nimportFrom(edgeR,filterByExpr)\nimportFrom(edgeR,glmFit)\nimportFrom(edgeR,glmLRT)\nimportFrom(edgeR,topTags)\nimportFrom(geomtextpath,geom_textpath)\nimportFrom(ggVennDiagram,ggVennDiagram)\nimportFrom(ggalt,stat_xspline)\nimportFrom(ggdensity,geom_hdr)\nimportFrom(ggdensity,geom_hdr_rug)\nimportFrom(ggnewscale,new_scale)\nimportFrom(ggnewscale,new_scale_fill)\nimportFrom(ggplot2,aes)\nimportFrom(ggplot2,aes_string)\nimportFrom(ggplot2,alpha)\nimportFrom(ggplot2,annotate)\nimportFrom(ggplot2,coord_cartesian)\nimportFrom(ggplot2,coord_polar)\nimportFrom(ggplot2,element_blank)\nimportFrom(ggplot2,element_rect)\nimportFrom(ggplot2,expansion)\nimportFrom(ggplot2,facet_wrap)\nimportFrom(ggplot2,geom_bar)\nimportFrom(ggplot2,geom_hline)\nimportFrom(ggplot2,geom_point)\nimportFrom(ggplot2,geom_segment)\nimportFrom(ggplot2,geom_smooth)\nimportFrom(ggplot2,geom_text)\nimportFrom(ggplot2,ggplot)\nimportFrom(ggplot2,guide_legend)\nimportFrom(ggplot2,labs)\nimportFrom(ggplot2,margin)\nimportFrom(ggplot2,scale_alpha_continuous)\nimportFrom(ggplot2,scale_color_brewer)\nimportFrom(ggplot2,scale_color_manual)\nimportFrom(ggplot2,scale_fill_gradient)\nimportFrom(ggplot2,scale_fill_identity)\nimportFrom(ggplot2,scale_fill_manual)\nimportFrom(ggplot2,scale_size)\nimportFrom(ggplot2,scale_x_continuous)\nimportFrom(ggplot2,scale_y_continuous)\nimportFrom(ggplot2,theme)\nimportFrom(ggplot2,theme_minimal)\nimportFrom(ggplot2,theme_void)\nimportFrom(ggplot2,unit)\nimportFrom(ggpubr,stat_cor)\nimportFrom(ggraph,geom_edge_diagonal)\nimportFrom(ggraph,geom_node_point)\nimportFrom(ggraph,geom_node_text)\nimportFrom(ggraph,ggraph)\nimportFrom(ggraph,node_angle)\nimportFrom(ggraph,scale_edge_colour_brewer)\nimportFrom(ggtree,aes)\nimportFrom(ggtree,geom_hilight)\nimportFrom(ggtree,geom_point2)\nimportFrom(grDevices,col2rgb)\nimportFrom(grDevices,convertColor)\nimportFrom(grDevices,rgb)\nimportFrom(graphics,strwidth)\nimportFrom(grid,gpar)\nimportFrom(grid,grid.roundrect)\nimportFrom(grid,grid.text)\nimportFrom(grid,pushViewport)\nimportFrom(grid,unit)\nimportFrom(grid,upViewport)\nimportFrom(grid,viewport)\nimportFrom(hrbrthemes,theme_ipsum)\nimportFrom(limma,contrasts.fit)\nimportFrom(limma,eBayes)\nimportFrom(limma,lmFit)\nimportFrom(limma,topTable)\nimportFrom(magrittr,\"%>%\")\nimportFrom(rlang,.data)\nimportFrom(rlang,sym)\nimportFrom(spiralize,spiral_initialize)\nimportFrom(spiralize,spiral_rect)\nimportFrom(spiralize,spiral_text)\nimportFrom(spiralize,spiral_track)\nimportFrom(stats,as.formula)\nimportFrom(stats,model.matrix)\nimportFrom(stats,na.omit)\nimportFrom(stats,p.adjust)\nimportFrom(stats,quantile)\nimportFrom(stats,wilcox.test)\nimportFrom(stringr,str_c)\nimportFrom(sva,ComBat_seq)\nimportFrom(tibble,as_tibble)\nimportFrom(tibble,column_to_rownames)\nimportFrom(tibble,tibble)\nimportFrom(tidygraph,tbl_graph)\nimportFrom(tidyr,unite)\nimportFrom(utils,head)\nimportFrom(utils,read.table)\nimportFrom(utils,tail)\n" }, { "path": "R/AdjustAlphaScale.R", "content": "#' Adjust Alpha Scale for Data Visualization\n#'\n#' This function dynamically adjusts the transparency scale for visualizations,\n#' especially useful when the range of data values varies significantly across different sources.\n#' It modifies the transparency scale based on the range of values present in the data,\n#' ensuring that the visualization accurately reflects variations within the data.\n#'\n#' @importFrom ggplot2 scale_alpha_continuous guide_legend ggplot geom_point\n#' @param data A data frame containing the values for which the alpha scale is to be adjusted.\n#' @param name Character string that will be used as the title of the legend in the plot.\n#' @param range Numeric vector of length 2 specifying the range of alpha values, defaults to c(0.2, 0.8).\n#' @return A ggplot2 alpha scale adjustment layer.\n#' @export\n#'\n#' @examples\n#' # Assuming 'data' is a DataFrame with a 'value' column\n#' plot_data <- data.frame(value = c(10, 20, 30, 40, 50))\n#' ggplot2::ggplot(plot_data, ggplot2::aes(x = 1:nrow(plot_data), y = value)) +\n#' ggplot2::geom_point(ggplot2::aes(alpha = value)) +\n#' adjust_alpha_scale(plot_data, \"Transparency Scale\")\n#'\nadjust_alpha_scale <- function(data, name, range = c(0.2, 0.8)) {\n min_val <- min(data$value, na.rm = TRUE) # Calculate minimum value, excluding NA\n max_val <- max(data$value, na.rm = TRUE) # Calculate maximum value, excluding NA\n\n # Apply scale_alpha_continuous from 'ggplot2' to adjust transparency\n scale_alpha_continuous(\n name = name, # Legend title\n limits = c(min_val, max_val), # Set the data range for alpha scaling\n range = range, # Set the alpha transparency range\n guide = guide_legend(keywidth = 0.65, keyheight = 0.35, order = 2) # Customize legend appearance\n )\n}\n" }, { "path": "R/CircosFruits.R", "content": "#' Create a base plot with gene expression data on a phylogenetic tree\n#'\n#' This function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression\n#' data as colored tiles to the plot. It allows for dynamic coloring of the genes and includes\n#' adjustments for alpha transparency based on the expression value.\n#'\n#' @importFrom ggplot2 aes scale_fill_manual scale_alpha_continuous guide_legend\n#' @param p A ggtree plot object to which the data will be added.\n#' @param data A data frame containing gene expression data with columns for Samples, Genes, and Values.\n#' @param gene_colors A named vector of colors for genes.\n#' @param gene_label A character string used as a label in the legend for the genes. Default is \"Gene\".\n#' @return A `ggtree` plot object with the gene expression data added.\n#' @export\n#'\n#' @examples\n#' \\donttest{\n#' # Check and load required packages\n#' if (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n#' requireNamespace(\"ggplot2\", quietly = TRUE)) {\n#' library(ggtreeExtra)\n#' library(ggplot2)\n#'\n#' file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n#' p <- readRDS(file_path)\n#'\n#' # Create gene expression data frame\n#' expression_data <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 5),\n#' Gene = rep(paste0(\"Gene\", 1:5), times = 4),\n#' Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n#' )\n#'\n#' # Define gene colors (named vector)\n#' gene_colors <- c(\n#' Gene1 = \"#491588\",\n#' Gene2 = \"#301b8d\",\n#' Gene3 = \"#1a237a\",\n#' Gene4 = \"#11479c\",\n#' Gene5 = \"#0a5797\"\n#' )\n#'\n#' # Call create_base_plot function to add gene expression data\n#' p <- create_base_plot(p, expression_data, gene_colors)\n#' } else {\n#' message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n#' }\n#' }\n#'\ncreate_base_plot <- function(p, data, gene_colors, gene_label=\"Gene\") {\n # Define local variables\n Sample <- data$Sample\n value <- data$value\n Gene <- data$Gene\n if (!requireNamespace(\"ggtreeExtra\", quietly = TRUE)) {\n stop(\"ggtreeExtra is required for using create_base_plot. Please install it.\", call. = FALSE)\n }\n\n if (!requireNamespace(\"ggplot2\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use geom_tile. Please install it.\", call. = FALSE)\n }\n\n p <- p +\n ggtreeExtra::geom_fruit(\n data=data,\n geom=\"geom_tile\",\n mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene),\n offset=0.001,\n pwidth=2\n ) +\n ggplot2::scale_fill_manual(\n name=gene_label,\n values=gene_colors,\n guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1)\n ) +\n # Assuming the function 'adjust_alpha_scale' is defined elsewhere to adjust alpha scale based on the expression values\n adjust_alpha_scale(data, gene_label)\n return(p)\n}\n\n\n\n#' Add a boxplot layer to a `ggtree` plot\n#'\n#' This function adds a boxplot layer to an existing `ggtree` plot object using ggtreeExtra's geom_fruit for boxplots.\n#' It is primarily used to display statistical summaries of the data related to gene expressions or other metrics.\n#'\n#' @importFrom ggplot2 aes\n#' @param p An existing ggtree plot object.\n#' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample' and 'value'.\n#' @param fill_color A character string specifying the fill color for the boxplots. Default is \"#f28131\".\n#' @param alpha Numeric value for the transparency of the boxplots. Default is 0.6.\n#' @param offset Numeric value, the position of the boxplot on the x-axis relative to its gene name. Default is 0.22.\n#' @param pwidth Numeric value, the width of the boxplot. Default is 0.5.\n#' @return A `ggtree` plot object with the added boxplot layer.\n#' @export\n#'\n#' @examples\n#' \\donttest{\n#' # Check and load required packages\n#' if (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n#' requireNamespace(\"ggplot2\", quietly = TRUE)) {\n#' library(ggtreeExtra)\n#' library(ggplot2)\n#'\n#' file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n#' p <- readRDS(file_path)\n#'\n#' # Create boxplot data frame\n#' boxplot_data <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 30),\n#' value = c(\n#' rnorm(30, mean = 5, sd = 1), # Data for Species_A\n#' rnorm(30, mean = 7, sd = 1.5), # Data for Species_B\n#' rnorm(30, mean = 6, sd = 1.2), # Data for Species_C\n#' rnorm(30, mean = 8, sd = 1.3) # Data for Species_D\n#' )\n#' )\n#'\n#' # Call add_boxplot function to add boxplot layer\n#' p_with_boxplot <- add_boxplot(p, boxplot_data)\n#' } else {\n#' message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n#' }\n#' }\n#'\nadd_boxplot <- function(p, data, fill_color=\"#f28131\", alpha=0.6, offset=0.22, pwidth=0.5) {\n # Define local variables\n Sample <- data$Sample\n value <- data$value\n if (!requireNamespace(\"ggtreeExtra\", quietly = TRUE)) {\n stop(\"ggtreeExtra is required for using create_base_plot. Please install it.\", call. = FALSE)\n }\n\n if (!requireNamespace(\"ggplot2\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use geom_boxplot. Please install it.\", call. = FALSE)\n }\n\n p + ggtreeExtra::geom_fruit(\n data=data,\n geom=\"geom_boxplot\",\n mapping=ggplot2::aes(y=Sample, x=value),\n fill=fill_color,\n alpha=alpha,\n offset=offset,\n pwidth=pwidth,\n size=0.05,\n outlier.size=0.3,\n outlier.stroke=0.06,\n outlier.shape=21,\n show.legend=FALSE\n )\n}\n\n\n\n\n#' Add a new tile layer with dynamic scales to a `ggtree` plot\n#'\n#' This function adds a new tile layer to an existing `ggtree` plot object, allowing for separate scales for fill\n#' and alpha transparency. This is useful when you want to add additional data layers without interfering with\n#' the existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers.\n#'\n#' @importFrom ggplot2 aes scale_fill_manual guide_legend\n#' @importFrom ggnewscale new_scale\n#' @param p An existing ggtree plot object.\n#' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample', 'Gene', and 'value'.\n#' @param gene_colors A named vector of colors for genes.\n#' @param gene_label A character string used as a label in the legend for the genes.\n#' @param alpha_value A numeric or named vector for setting the alpha scale based on values.\n#' @param offset Numeric value, the position of the tile on the x-axis relative to its gene name. Default is 0.02.\n#' @param pwidth Numeric value, the width of the tile. Default is 2.\n#' @return A `ggtree` plot object with the added tile layer and new scales.\n#' @export\n#'\n#' @examples\n#' \\donttest{\n#' # Check and load required packages\n#' if (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n#' requireNamespace(\"ggplot2\", quietly = TRUE) &&\n#' requireNamespace(\"ggnewscale\", quietly = TRUE)) {\n#' library(ggtreeExtra)\n#' library(ggplot2)\n#' library(ggnewscale)\n#'\n#' file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n#' p <- readRDS(file_path)\n#'\n#' # Create new expression data\n#' new_expression_data <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 3),\n#' Gene = rep(c(\"Gene6\", \"Gene7\", \"Gene8\"), times = 4),\n#' Value = runif(12, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n#' )\n#'\n#' # Define new gene colors\n#' new_gene_colors <- c(\n#' Gene6 = \"#0b5f63\",\n#' Gene7 = \"#074d41\",\n#' Gene8 = \"#1f5e27\"\n#' )\n#'\n#' # Define gene label and alpha values\n#' gene_label <- \"New Genes\"\n#' alpha_value <- c(0.3, 0.9)\n#'\n#' # Add new tile layer\n#' p_with_new_layer <- add_new_tile_layer(\n#' p,\n#' new_expression_data,\n#' new_gene_colors,\n#' gene_label,\n#' alpha_value,\n#' offset = 0.02,\n#' pwidth = 2\n#' )\n#' } else {\n#' message(\"Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.\")\n#' }\n#' }\n#'\nadd_new_tile_layer <- function(p, data, gene_colors, gene_label, alpha_value=c(0.3, 0.9), offset=0.02, pwidth=2) {\n # Define local variables\n Sample <- data$Sample\n value <- data$value\n Gene <- data$Gene\n if (!requireNamespace(\"ggtreeExtra\", quietly = TRUE)) {\n stop(\"ggtreeExtra is required for using create_base_plot. Please install it.\", call. = FALSE)\n }\n\n if (!requireNamespace(\"ggplot2\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use geom_tile. Please install it.\", call. = FALSE)\n }\n\n p + ggnewscale::new_scale(\"alpha\") + ggnewscale::new_scale(\"fill\") +\n ggtreeExtra::geom_fruit(\n data=data,\n geom=\"geom_tile\",\n mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene),\n offset=offset,\n pwidth=pwidth\n ) +\n ggplot2::scale_fill_manual(\n name=gene_label,\n values=gene_colors,\n guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1)\n ) +\n adjust_alpha_scale(data, gene_label, alpha_value) # Assuming function signature and usage\n}\n\n\n\n\n#' Add multiple layers to a `ggtree` plot for visualizing gene expression and enrichment data\n#'\n#' This function sequentially adds multiple layers to a `ggtree` plot, including gene expression data, boxplots for statistical\n#' summaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate\n#' functions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics\n#' for each layer. The function is designed to work with specific data structures and assumes all functions for adding layers\n#' are defined and available.\n#'\n#' @param p A `ggtree` plot object to which the data and layers will be added.\n#' @param long_format_HeatdataDeseq A data frame containing gene expression data with columns for `Samples`, `Genes`, and `Values`.\n#' @param ssgsea_kegg_HeatdataDeseq A data frame containing SSGSEA analysis results with columns for `Samples`, `Genes`, and `Values`.\n#' @param gsva_kegg_HeatdataDeseq A data frame containing GSVA analysis results with columns for `Samples`, `Genes`, and `Values`.\n#' @param gene_colors A named vector of colors for genes, used for coloring tiles in different layers.\n#' @return A `ggtree` plot object with multiple layers added for comprehensive visualization.\n#' @export\n#'\n#' @examples\n#' \\donttest{\n#' # Check and load required packages\n#' if (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n#' requireNamespace(\"ggplot2\", quietly = TRUE)) {\n#' library(ggtreeExtra)\n#' library(ggplot2)\n#'\n#' # Example data for gene expression, SSGSEA, and GSVA\n#' file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n#' p <- readRDS(file_path)\n#'\n#' # Create gene expression data frame (long_format_HeatdataDeseq)\n#' long_format_HeatdataDeseq <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 5),\n#' Genes = rep(paste0(\"Gene\", 1:5), times = 4),\n#' Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n#' )\n#'\n#' # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq)\n#' ssgsea_kegg_HeatdataDeseq <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 3),\n#' Genes = rep(c(\"Pathway1\", \"Pathway2\", \"Pathway3\"), times = 4),\n#' Value = runif(12, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1\n#' )\n#'\n#' # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq)\n#' gsva_kegg_HeatdataDeseq <- data.frame(\n#' Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 4),\n#' Genes = rep(c(\"PathwayA\", \"PathwayB\", \"PathwayC\", \"PathwayD\"), times = 4),\n#' Value = runif(16, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1\n#' )\n#'\n#' # Define gene and pathway colors (named vector), including all genes and pathways\n#' gene_colors <- c(\n#' # Genes for gene expression\n#' Gene1 = \"#491588\",\n#' Gene2 = \"#301b8d\",\n#' Gene3 = \"#1a237a\",\n#' Gene4 = \"#11479c\",\n#' Gene5 = \"#0a5797\",\n#' # Pathways for SSGSEA\n#' Pathway1 = \"#0b5f63\",\n#' Pathway2 = \"#074d41\",\n#' Pathway3 = \"#1f5e27\",\n#' # Pathways for GSVA\n#' PathwayA = \"#366928\",\n#' PathwayB = \"#827729\",\n#' PathwayC = \"#a1d99b\",\n#' PathwayD = \"#c7e9c0\"\n#' )\n#'\n#' # Call circos_fruits function to add multiple layers\n#' final_plot <- circos_fruits(\n#' p,\n#' long_format_HeatdataDeseq,\n#' ssgsea_kegg_HeatdataDeseq,\n#' gsva_kegg_HeatdataDeseq,\n#' gene_colors\n#' )\n#' } else {\n#' message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n#' }\n#' }\n#'\ncircos_fruits <- function(p, long_format_HeatdataDeseq, ssgsea_kegg_HeatdataDeseq, gsva_kegg_HeatdataDeseq, gene_colors) {\n if (!requireNamespace(\"ggtreeExtra\", quietly = TRUE)) {\n stop(\"ggtreeExtra is required for using create_base_plot. Please install it.\", call. = FALSE)\n }\n\n if (!requireNamespace(\"ggplot2\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use geom_tile. Please install it.\", call. = FALSE)\n }\n # Create the base plot with gene expression data\n p1 <- create_base_plot(p, long_format_HeatdataDeseq, gene_colors)\n\n # Add a boxplot layer to the base plot\n p2 <- add_boxplot(p1, long_format_HeatdataDeseq)\n\n # Add a new tile layer for SSGSEA data\n p3 <- add_new_tile_layer(p2, ssgsea_kegg_HeatdataDeseq, gene_colors, \"Ssgsea Term\")\n\n # Add another boxplot layer with specific aesthetic adjustments\n p4 <- add_boxplot(p3, ssgsea_kegg_HeatdataDeseq, fill_color=\"#f28131\", alpha=0.65, offset=0.32)\n\n # Add a new tile layer for GSVA data with specific alpha and offset adjustments\n p5 <- add_new_tile_layer(p4, gsva_kegg_HeatdataDeseq, gene_colors, \"Gsva Term\", alpha_value=c(0.3, 0.9), offset=0.02)\n\n # Return the final plot\n return(p5)\n}\n" }, { "path": "R/CombatNormal.R", "content": "#' Process and Correct Batch Effects in TCGA's normal tissue and GTEX Data\n#'\n#' This function takes a TCGA's normal tissue data set and a pre-saved GTEX data set, asks the user\n#' for specific TCGA's normal tissue types to retain, then merges the two datasets. The merged dataset\n#' is then corrected for batch effects using the ComBat_seq function from the 'sva' package.\n#'\n#' @description\n#' The function first extracts histological types from the provided TCGA's normal tissue data set.\n#' After displaying these types, the user is prompted to input specific types to retain.\n#' The data is then filtered based on this input.\n#' The GTEX and TCGA's normal tissue datasets are then combined and batch corrected.\n#'\n#' Note: This function assumes that TCGA's normal samples and GTEX samples represent different batches.\n#'\n#' @param TCGA_normal_data_path The path to the tumor data stored in an RDS file.\n#' @param gtex_data_path The path to the GTEX data stored in an RDS file.\n#' @param CombatNormal_output_path A character string specifying the path where the output RDS file will be saved.\n#' @param auto_mode Logical. If set to TRUE, the function will not prompt the user for input and\n#' will instead use the values provided in default_input. Default is FALSE.\n#' @param default_input Character string. When auto_mode is TRUE, this parameter specifies the default\n#' TGCA's normal tissue types to be retained. It should be provided as a comma-separated string (e.g., \"11,12\").\n#'\n#' @return A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the\n#' combat_count_df data as an RDS file at the specified output path.\n#'\n#' @details\n#' The ComBat_seq function from the 'sva' package is used to correct batch effects.\n#' The function requires the 'sva' package to be installed and loaded externally.\n#'\n#' The example code uses `tempfile()` to generate temporary paths dynamically during execution.\n#' These paths are valid during the `R CMD check` process, even if no actual files exist,\n#' because `tempfile()` generates a unique file path that does not depend on the user's file system.\n#' Using `tempfile()` ensures that the example code does not rely on specific external files and\n#' avoids errors during `R CMD check`. CRAN review checks for documentation correctness\n#' and syntax parsing, not the existence of actual files, as long as the example code is syntactically valid.\n#'\n#' @examples\n#' TCGA_normal_file <- system.file(\"extdata\",\n#' \"SKCM_Skin_TCGA_exp_normal_test.rds\",\n#' package = \"TransProR\")\n#' gtex_file <- system.file(\"extdata\", \"Skin_SKCM_Gtex_test.rds\", package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"SKCM_Skin_Combat_Normal_TCGA_GTEX_count.rds\")\n#'\n#' SKCM_Skin_Combat_Normal_TCGA_GTEX_count <- Combat_Normal(\n#' TCGA_normal_data_path = TCGA_normal_file,\n#' gtex_data_path = gtex_file,\n#' CombatNormal_output_path = output_file,\n#' auto_mode = TRUE,\n#' default_input = \"skip\"\n#' )\n#' head(SKCM_Skin_Combat_Normal_TCGA_GTEX_count)[1:5, 1:5]\n#'\n#' @seealso \\code{\\link[sva]{ComBat_seq}}\n#' @importFrom sva ComBat_seq\n#' @importFrom tibble column_to_rownames\n#' @export\nCombat_Normal <- function(TCGA_normal_data_path, gtex_data_path, CombatNormal_output_path, auto_mode = FALSE, default_input = \"11,12\") {\n\n # Load the TGCA's normal tissue data and GTEX data\n TCGA_normal_data <- readRDS(TCGA_normal_data_path)\n gtex_data <- readRDS(gtex_data_path)\n\n # Extract histological types for the TGCA's normal tissue data's samples\n NormalHistologicalTypes <- substring(colnames(TCGA_normal_data), 14, 15)\n\n # Filter only the normal samples\n normal_data <- TCGA_normal_data[, as.numeric(NormalHistologicalTypes) > 10]\n\n # Display the table to the user\n normal_hist_table <- table(NormalHistologicalTypes)\n #print(normal_hist_table)\n\n message(\" \")\n message(\"NormalHistologicalTypes:\")\n message(paste(names(normal_hist_table), normal_hist_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Ask the user for input or use default input in auto_mode\n if(auto_mode) {\n selected_types <- strsplit(default_input, \",\")[[1]]\n } else {\n message(\"Please input the normal tissue types you wish to retain or 'skip' to only use GTEX data: \")\n selected_types <- strsplit(readline(), \",\")[[1]]\n }\n\n # If the user didn't select 'skip'\n if (!is.element(\"skip\", selected_types)) {\n # Filter the tumor data based on user's input\n normal <- normal_data[, NormalHistologicalTypes %in% selected_types]\n\n # Combine GTEX and selected TCGA data\n # Merge the datasets, ensuring both have genes as row names\n combined_data <- merge(normal, gtex_data, by = \"row.names\")\n combined_data <- tibble::column_to_rownames(combined_data, var = \"Row.names\") # Set the row names\n\n # Create group vector (All samples as same group)\n combined_group <- rep(\"all_group\", ncol(combined_data))\n\n # Create batch vector for selected normal TCGA samples\n tcga_batches <- match(NormalHistologicalTypes[NormalHistologicalTypes %in% selected_types], selected_types)\n\n # Combine the batch vector for TCGA samples with GTEX batch\n combined_batch <- c(tcga_batches, rep(\"GTEX\", ncol(gtex_data)))\n\n # Modify the tumor values\n combined_data_count <- 2^(combined_data) - 1\n combined_data_count <- apply(combined_data_count, 2, as.integer)\n rownames(combined_data_count) <- rownames(combined_data)\n\n # Correct for batch effects using ComBat_seq\n combat_count <- sva::ComBat_seq(as.matrix(combined_data_count),\n batch = combined_batch,\n group = combined_group)\n\n # Convert matrix to data frame\n combat_count_df <- as.data.frame(combat_count)\n\n saveRDS(combat_count_df, file = CombatNormal_output_path)\n return(combat_count_df)\n } else {\n combat_count_matrix <- 2^(gtex_data) - 1\n combat_count_matrix <- apply(combat_count_matrix, 2, as.integer)\n rownames(combat_count_matrix) <- rownames(gtex_data)\n combat_count_df <- as.data.frame(combat_count_matrix)\n saveRDS(combat_count_df, file = CombatNormal_output_path)\n return(combat_count_df)\n }\n}\n" }, { "path": "R/CombatTumor.R", "content": "#' Process and Correct Batch Effects in Tumor Data\n#'\n#' This function takes a tumor data set, asks the user for specific tumor types to retain,\n#' and then corrects for batch effects using the ComBat_seq function from the 'sva' package.\n#'\n#' @description\n#' The function first extracts histological types from the provided tumor data set.\n#' After displaying these types, the user is prompted to input specific types to retain.\n#' The data is then filtered based on this input.\n#'\n#' Note: This example assumes that different tumor types represent different batches in a general sense.\n#' Users need to adjust the batch and group vectors based on real-life scenarios.\n#'\n#' @param tumor_data_path The path to the tumor data stored in an RDS file.\n#' @param CombatTumor_output_path A character string specifying the path where the output RDS file will be saved.\n#' @param auto_mode Logical. If set to TRUE, the function will not prompt the user for input and\n#' will instead use the values provided in default_input. Default is FALSE.\n#' @param default_input Character string. When auto_mode is TRUE, this parameter specifies the default\n#' tumor types to be retained. It should be provided as a comma-separated string (e.g., \"01,06\").\n#'\n#' @return A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the\n#' combat_count_df data as an RDS file at the specified output path.\n#'\n#' @details\n#' The ComBat_seq function from the sva package is used to correct batch effects.\n#' The function requires the 'sva' package to be installed and loaded externally.\n#'\n#' @examples\n#' tumor_file <- system.file(\"extdata\",\n#' \"SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"SKCM_combat_count.rds\")\n#'\n#' SKCM_combat_count <- combat_tumor(\n#' tumor_data_path = tumor_file,\n#' CombatTumor_output_path = output_file,\n#' auto_mode = TRUE,\n#' default_input = \"01,06\"\n#' )\n#'\n#' head(SKCM_combat_count)[1:5, 1:5]\n#'\n#' @seealso \\code{\\link[sva]{ComBat_seq}}\n#' @importFrom sva ComBat_seq\n#' @export\ncombat_tumor <- function(tumor_data_path, CombatTumor_output_path, auto_mode = FALSE, default_input = \"01,06\") {\n\n # Load the tumor data\n tumor_data <- readRDS(tumor_data_path)\n\n # Extract histological types\n TumorHistologicalTypes <- substring(colnames(tumor_data), 14, 15)\n tumor_hist_table <- table(TumorHistologicalTypes)\n\n # Display the table to the user\n message(\" \")\n message(\"TumorHistologicalTypes:\")\n message(paste(names(tumor_hist_table), tumor_hist_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Ask the user for input or use default input in auto_mode\n if(auto_mode) {\n selected_types <- strsplit(default_input, \",\")[[1]]\n } else {\n message(\"Please input the tumor types you wish to retain, separated by commas (e.g., 01,06): \")\n selected_types <- strsplit(readline(), \",\")[[1]]\n }\n\n # Filter the tumor data based on user's input\n tumor <- tumor_data[, TumorHistologicalTypes %in% selected_types]\n\n # Modify the tumor values\n tumor1 <- 2^(tumor) - 1\n tumor1 <- apply(tumor1, 2, as.integer)\n rownames(tumor1) <- rownames(tumor)\n\n # If only one sample type is chosen, skip batch correction and return modified tumor data\n if(length(selected_types) == 1) {\n combat_count_df <- as.data.frame(tumor1)\n } else {\n # Create group vector\n selected_group = rep(\"all_group\", length(which(TumorHistologicalTypes %in% selected_types)))\n\n # Create batch vector based on group vector\n selected_batch = match(TumorHistologicalTypes[TumorHistologicalTypes %in% selected_types], selected_types)\n\n # Correct for batch effects using ComBat_seq\n combat_count <- sva::ComBat_seq(as.matrix(tumor1),\n batch = selected_batch,\n group = selected_group)\n\n # Convert matrix to data frame\n combat_count_df <- as.data.frame(combat_count)\n }\n\n saveRDS(combat_count_df, file = CombatTumor_output_path)\n\n return(combat_count_df)\n}\n" }, { "path": "R/CompareMerge.R", "content": "#' Compare and merge specific columns from two DEG data frames\n#'\n#' This function takes two DEG data frames, inner joins them by a specified gene column,\n#' checks if a specified column is identical across both data frames, and merges them if they are.\n#' The resulting data frame will have a merged column named after the compared column.\n#'\n#' @importFrom dplyr inner_join\n#' @param df1 First data frame.\n#' @param df2 Second data frame.\n#' @param by_gene Column name by which to join the data frames, typically \"Gene\".\n#' @param compare_col Column to compare for identity, which will also be the name of the merged column.\n#' @param suffixes Suffixes to use for non-identical column names in the joined data frame.\n#' @param df_name Name to assign to the resulting data frame for identification.\n#' @return A data frame with processed columns.\n#' @examples\n#' # Create simulated DESeq2 data\n#' DEG_deseq2 <- data.frame(\n#' Gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\"),\n#' change = c(\"up\", \"down\", \"no_change\", \"up\", \"down\"),\n#' log2FoldChange = c(2.5, -3.2, 0.1, 1.8, -2.5),\n#' pvalue = c(0.01, 0.05, 0.9, 0.02, 0.03)\n#' )\n#'\n#' # Display the first 5 rows of the DESeq2 data\n#' head(DEG_deseq2, 5)\n#'\n#' # Create simulated edgeR data\n#' DEG_edgeR <- data.frame(\n#' Gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\"),\n#' change = c(\"up\", \"down\", \"no_change\", \"no_change\", \"up\"),\n#' log2FoldChange = c(2.3, -3.1, 0.2, 0.1, 2.7),\n#' pvalue = c(0.02, 0.04, 0.8, 0.6, 0.01)\n#' )\n#'\n#' # Display the first 5 rows of the edgeR data\n#' head(DEG_edgeR, 5)\n#'\n#' # Merge the DESeq2 and edgeR data\n#' deseq2_edgeR <- compare_merge(\n#' df1 = DEG_deseq2,\n#' df2 = DEG_edgeR,\n#' by_gene = \"Gene\",\n#' compare_col = \"change\",\n#' suffixes = c(\"_1\", \"_2\"),\n#' df_name = \"deseq2_edgeR\"\n#' )\n#'\n#' @export\ncompare_merge <- function(df1, df2, by_gene, compare_col, suffixes, df_name) {\n # Perform an inner join on the 'Gene' column\n merged_df <- dplyr::inner_join(df1, df2, by = by_gene, suffix = suffixes)\n\n # Generate column names for comparison\n col1 <- paste0(compare_col, suffixes[1])\n col2 <- paste0(compare_col, suffixes[2])\n\n # Check if the specified columns are identical\n if (all(merged_df[[col1]] == merged_df[[col2]])) {\n # If completely identical, merge these columns into one and rename\n merged_df[[compare_col]] <- merged_df[[col1]]\n # Remove original compared columns\n merged_df[[col1]] <- NULL\n merged_df[[col2]] <- NULL\n } else {\n # Handle non-identical case\n message(\"The columns\", col1, \"and\", col2, \"are not identical.\\n\")\n }\n\n # Assign the specified name for identification\n merged_df$name <- df_name\n\n # Return the modified data frame\n return(merged_df)\n}\n" }, { "path": "R/Contrast_Venn.R", "content": "#' Function to Create a Venn Diagram of DEGs with Custom Colors\n#'\n#' This function creates a Venn Diagram using the 'ggVennDiagram' package.\n#' It allows customization of various aesthetic elements of the diagram, including colors.\n#'\n#' @importFrom ggplot2 scale_fill_gradient scale_color_manual scale_x_continuous expansion\n#' @importFrom ggVennDiagram ggVennDiagram\n#' @param all_degs_venn A list of DEG sets for Venn Diagram creation.\n#' @param edge_colors A vector of colors for the edges of the Venn Diagram sets.\n#' @param name_color A vector of colors for the names of the sets in the Venn Diagram.\n#' @param fill_colors A vector of two colors for the gradient fill of the Venn Diagram.\n#' @param label_size The size of the labels showing the number of elements in each set (default is 4).\n#' @param edge_size The size of the edges of the Venn Diagram sets (default is 3).\n#' @return A `ggplot` object representing the Venn Diagram.\n#' @examples\n#'\n#' data(\"all_degs_venn\", package = \"TransProR\")\n#'\n#' edge_colors <- c(\"#1b62bb\",\"#13822e\",\"#332c3a\",\"#9e2d39\")\n#' name_color <- c(\"#1b64bb\",\"#13828e\",\"#337c3a\",\"#9e9d39\")\n#' fill_colors <- c(\"#e3f2fa\", \"#0288d1\")\n#'\n#' Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)\n#' @export\n#'\nContrast_Venn <- function(all_degs_venn,\n edge_colors,\n name_color,\n fill_colors,\n label_size = 4,\n edge_size = 3) {\n # Draw Venn Diagram\n ggVennDiagram::ggVennDiagram(all_degs_venn,\n set_size = 5, # Font size for set names\n set_color = name_color,\n label_alpha = 0, # Transparency of the background box for counts/percentages, default is white\n label_size = label_size, # Font size for counts/percentages\n edge_size = edge_size # Thickness of the edges\n ) +\n ggplot2::scale_fill_gradient(low = fill_colors[1], high = fill_colors[2]) + # Gradient fill color based on value size\n ggplot2::scale_color_manual(values = edge_colors) + # Manually set edge colors\n ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = .3))\n}\n" }, { "path": "R/DESeq2Analyze.R", "content": "#' Differential Gene Expression Analysis using 'DESeq2'\n#'\n#' 'DESeq2': Differential gene expression analysis based on the negative binomial distribution.\n#' This function utilizes the 'DESeq2' package to conduct differential gene expression analysis.\n#' It processes tumor and normal expression data, applies DESeq2 analysis,\n#' and outputs the results along with information on gene expression changes.\n#'\n#' The DESeq2 methodology is based on modeling count data using a negative binomial distribution,\n#' which allows for handling the variability observed in gene expression data, especially in\n#' small sample sizes. This approach is well-suited for RNA-Seq data analysis.\n#'\n#' @importFrom DESeq2 DESeqDataSetFromMatrix DESeq results\n#' @importFrom dplyr mutate\n#' @importFrom tibble column_to_rownames\n#' @importFrom stats na.omit\n#' @param tumor_file Path to the tumor data file (RDS format).\n#' @param normal_file Path to the normal data file (RDS format).\n#' @param output_file Path to save the output DEG data (RDS format).\n#' @param logFC Threshold for log fold change.\n#' @param p_value Threshold for p-value.\n#' @return A data frame of differential expression results.\n#' @examples\n#' # Define file paths for tumor and normal data from the data folder\n#' tumor_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' normal_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n#' package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"DEG_DESeq2.rds\")\n#'\n#' DEG_DESeq2 <- DESeq2_analyze(\n#' tumor_file = tumor_file,\n#' normal_file = normal_file,\n#' output_file = output_file,\n#' 2.5,\n#' 0.01\n#' )\n#'\n#' # View the top 5 rows of the result\n#' head(DEG_DESeq2, 5)\n#'\n#' @references\n#' DESeq2:Differential gene expression analysis based on the negative binomial distribution.\n#' For more information, visit the page:\n#' https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/\n#' @export\nDESeq2_analyze <- function(tumor_file, normal_file, output_file, logFC = 2.5, p_value = 0.01) {\n # Read and merge data\n tumor <- readRDS(tumor_file)\n normal <- readRDS(normal_file)\n all_count_exp <- merge(tumor, normal, by = \"row.names\") # Merge the datasets, ensuring both have genes as row names\n all_count_exp <- tibble::column_to_rownames(all_count_exp, var = \"Row.names\") # Set the row names\n\n # Create group factor\n group <- factor(c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal))), levels = c(\"normal\", \"tumor\"))\n group_table <- table(group)\n\n message(\"Group Table:\")\n message(paste(names(group_table), group_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Prepare DESeq2 dataset\n colData <- data.frame(row.names = colnames(all_count_exp), group = group) # Create a dataframe to store the grouping information of samples, with the row names as sample names and the column names as group information.\n dds <- DESeq2::DESeqDataSetFromMatrix(countData = all_count_exp, # Expression matrix, with rows as genes and columns as samples, containing integers derived from the calculation of reads or fragments.\n colData = colData, # Sample information matrix (dataframe), showing the correspondence between the column names of the expression matrix and the grouping information, with row names as sample names. The first column indicates the treatment status of the sample (control or treatment, tumor or normal, etc.), referred to as the group.\n design = ~ group) # Differential comparison matrix, which informs the differential analysis function about the variables between which differences are to be analyzed. Simply put, it specifies which are the controls and which are the treatments. The group refers to the group in colData, which is the grouping information.\n\n # Perform differential expression analysis.\n dds <- DESeq2::DESeq(dds)\n\n # Perform DESeq2 analysis\n # Extract the results of differential expression and perform a comparison. Here, the 'contrast' parameter specifies the groups to be compared.\n # The 'contrast' parameter must be written in a vector format with three elements, and the order cannot be reversed.\n res <- DESeq2::results(dds, contrast = c(\"group\", \"tumor\", \"normal\"))\n\n # Sort the differential results according to 'padj' (adjusted p-value). This step is necessary only for DESeq2, as limma and edgeR will automatically sort the results.\n resOrdered <- res[order(res$padj), ]\n DEG <- as.data.frame(stats::na.omit(resOrdered)) # Remove missing values. If this step is not taken, some genes with very low expression levels will result in NA values after calculation, causing difficulties in subsequent analysis and plotting.\n\n # Add a 'change' column to mark up- or down-regulated genes, with the threshold set according to requirements.\n DEG <- dplyr::mutate(DEG, change = ifelse(DEG$pvalue < p_value & DEG$log2FoldChange < -logFC, \"down\",\n ifelse(DEG$pvalue < p_value & DEG$log2FoldChange > logFC, \"up\", \"stable\")))\n\n # Output table of gene expression changes\n change_table <- table(DEG$change)\n\n message(\" \")\n message(\"Change Table:\")\n message(paste(names(change_table), change_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Save results\n #save(DEG, file = output_file)\n saveRDS(DEG, file = output_file)\n # Return results\n return(DEG)\n}\n" }, { "path": "R/EdgeRAnalyze.R", "content": "#' Differential Gene Expression Analysis using 'edgeR'\n#'\n#' This function performs differential gene expression analysis using the 'edgeR' package.\n#' It reads tumor and normal expression data, merges them, filters low-expressed genes,\n#' normalizes the data, performs edgeR analysis, and outputs the results along with information\n#' on gene expression changes.\n#'\n#' @importFrom tibble column_to_rownames\n#' @importFrom dplyr mutate\n#' @importFrom edgeR DGEList cpm calcNormFactors estimateGLMCommonDisp estimateGLMTrendedDisp estimateGLMTagwiseDisp glmFit glmLRT topTags\n#' @importFrom stats model.matrix\n#' @param tumor_file Path to the tumor data file (RDS format).\n#' @param normal_file Path to the normal data file (RDS format).\n#' @param output_file Path to save the output DEG data (RDS format).\n#' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes.\n#' @param p_value_threshold Threshold for p-value for filtering significant genes.\n#' @return A data frame of differential expression results.\n#' @references\n#' edgeR: Differential analysis of sequence read count data.\n#' For more information, visit the edgeR Bioconductor page:\n#' https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf\n#' @export\n#'\n#' @examples\n#' # Define file paths for tumor and normal data from the data folder\n#' tumor_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' normal_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n#' package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"DEG_edgeR.rds\")\n#'\n#' DEG_edgeR <- edgeR_analyze(\n#' tumor_file = tumor_file,\n#' normal_file = normal_file,\n#' output_file = output_file,\n#' 2.5,\n#' 0.01\n#' )\n#'\n#' # View the top 5 rows of the result\n#' head(DEG_edgeR, 5)\n#'\nedgeR_analyze <- function(tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01) {\n tumor <- readRDS(tumor_file)\n normal <- readRDS(normal_file)\n\n # Merge the datasets, ensuring both have genes as row names\n all_count_exp <- merge(tumor, normal, by = \"row.names\")\n all_count_exp <- tibble::column_to_rownames(all_count_exp, var = \"Row.names\")\n\n # Define groups for tumor and normal samples\n group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal)))\n group <- factor(group, levels = c(\"normal\", \"tumor\"))\n group_table <- table(group)\n\n message(\"Group Table:\")\n message(paste(names(group_table), group_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Create DGEList object for gene expression data and group information\n d <- edgeR::DGEList(counts = all_count_exp, group = group)\n\n # Filter lowly expressed genes based on CPM values\n keep <- rowSums(edgeR::cpm(d) > 1) >= 2\n d <- d[keep, , keep.lib.sizes = FALSE]\n\n # Update library size information in the samples\n d$samples$lib.size <- colSums(d$counts)\n\n # Normalize the data using the TMM method\n d <- edgeR::calcNormFactors(d)\n\n # Create design matrix for differential analysis model\n design <- stats::model.matrix(~0 + factor(group))\n rownames(design) <- colnames(d)\n colnames(design) <- levels(factor(group))\n\n # Estimate dispersions - common dispersion, trended dispersion, tagwise dispersion\n d <- edgeR::estimateGLMCommonDisp(d, design)\n d <- edgeR::estimateGLMTrendedDisp(d, design)\n d <- edgeR::estimateGLMTagwiseDisp(d, design)\n\n # Fit the model using Generalized Linear Model (GLM)\n fit <- edgeR::glmFit(d, design)\n\n # Perform differential expression analysis using Likelihood Ratio Test (LRT)\n lrt <- edgeR::glmLRT(fit, contrast = c(-1, 1)) # Note that the 'contrast' here is different from 'DESeq2'. Here, we only need to input c(-1, 1): -1 corresponds to normal, 1 corresponds to tumor.\n\n # Retrieve top differentially expressed genes\n nrDEG <- edgeR::topTags(lrt, n = nrow(d))\n DEG_edgeR <- as.data.frame(nrDEG)\n\n # Add 'change' column to mark up/down-regulated genes\n k1 <- (DEG_edgeR$PValue < p_value_threshold) & (DEG_edgeR$logFC < -logFC_threshold)\n k2 <- (DEG_edgeR$PValue < p_value_threshold) & (DEG_edgeR$logFC > logFC_threshold)\n DEG_edgeR <- dplyr::mutate(DEG_edgeR, change = ifelse(k1, \"down\", ifelse(k2, \"up\", \"stable\")))\n\n change_table <- table(DEG_edgeR$change)\n\n message(\"Change Table:\")\n message(paste(names(change_table), change_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Save results to the specified output file\n saveRDS(DEG_edgeR, file = output_file)\n\n return(DEG_edgeR)\n}\n" }, { "path": "R/EnrichCirclize.R", "content": "#' Adjust and Export Pathway Analysis Results\n#'\n#' This function processes a dataframe containing fgsea results. It adjusts pathway names\n#' by removing underscores, converting to lowercase, then capitalizing the first letter,\n#' and joining the components with spaces. It selects and merges the top upregulated\n#' and downregulated pathways based on enrichment score (ES) and p-value.\n#'\n#' @param fgseaRes Dataframe containing fgsea results with columns 'pathway', 'ES', and 'pval'.\n#' @param nTop Integer, number of top pathways to select based on the p-value.\n#' @return A vector containing combined top upregulated and downregulated pathways.\n#' @importFrom Hmisc capitalize\n#' @export\n#' @examples\n#' # Create a synthetic fgseaRes dataframe\n#'fgseaRes <- data.frame(\n#' pathway = c(\"KEGG_APOPTOSIS\",\n#' \"GO_CELL_CYCLE\",\n#' \"REACTOME_DNA_REPAIR\",\n#' \"KEGG_METABOLISM\",\n#' \"GO_TRANSPORT\"),\n#' ES = c(0.45, -0.22, 0.56, -0.35, 0.33),\n#' pval = c(0.001, 0.02, 0.0003, 0.05, 0.01)\n#')\n#'\n#' # Run the function to get top pathways\n#'result <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2)\n#'\nadjust_export_pathway <- function(fgseaRes, nTop = 10) {\n # Adjust pathway names\n fgseaRes$pathway <- as.character(fgseaRes$pathway)\n for(i in 1:nrow(fgseaRes)){\n message(\"Processing row \", i)\n term = fgseaRes$pathway[i]\n ### 1. Split the string\n term = unlist(strsplit(term, split=\"_\", fixed=TRUE))[-1]\n ### 2. Convert to lowercase, then capitalize the first letter\n term = Hmisc::capitalize(tolower(term))\n ### 3. Concatenate with spaces\n term = paste(term, collapse=\" \")\n ### 4. Data export\n fgseaRes$pathway[i] = term\n }\n\n # Select top upregulated pathways\n topPathwaysUp <- fgseaRes[fgseaRes$ES > 0,][order(fgseaRes$pval[fgseaRes$ES > 0]), 'pathway'][1:nTop]\n\n # Select top downregulated pathways\n topPathwaysDown <- fgseaRes[fgseaRes$ES < 0,][order(fgseaRes$pval[fgseaRes$ES < 0]), 'pathway'][1:nTop]\n\n # Combine top pathways and convert any potential list to a vector\n combinedPathways <- unlist(c(topPathwaysUp, rev(topPathwaysDown)), use.names = FALSE)\n\n return(list(combinedPathways = combinedPathways, fgseaRes = fgseaRes))\n}\n\n\n\n\n\n\n#' Randomly Select Pathways with Limited Word Count\n#'\n#' This function randomly selects a specified number of pathways from a given list, ensuring that each selected pathway name does not exceed a specified number of words. It filters out pathways with names longer than the specified word limit before making the selection.\n#'\n#' @param pathways Character vector of pathways.\n#' @param max_words Integer, maximum number of words allowed in the pathway name.\n#' @param num_select Integer, number of pathways to randomly select.\n#' @return A character vector of selected pathways.\n#' @export\n#' @examples\n#' pathway_list <- c(\"pathway_one response to stimulus\",\n#' \"pathway_two cell growth and death\",\n#' \"pathway_three regulation of cellular process\",\n#' \"pathway_four metabolic process\")\n#' selected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2)\n#'\nselectPathways <- function(pathways, max_words = 10, num_select = 10) {\n # Check input\n if (!is.character(pathways)) {\n stop(\"Please provide a character vector of pathways.\")\n }\n\n # Filter pathways with word count not exceeding max_words\n filtered_pathways <- pathways[sapply(pathways, function(x) length(strsplit(x, \" \")[[1]]) <= max_words)]\n\n # Randomly select num_select pathways from the filtered list\n if (length(filtered_pathways) >= num_select) {\n selected_pathways <- sample(filtered_pathways, num_select)\n } else {\n warning(\"Not enough pathways with <= \", max_words, \" words. Returning as many as possible.\")\n selected_pathways <- sample(filtered_pathways, length(filtered_pathways))\n }\n\n return(selected_pathways)\n}\n\n\n\n\n#' Draw Dual-Sided Legends on a Plot\n#'\n#' This function creates two sets of legends, one on the left and one on the right side of a plot.\n#' It displays color-coded legends with labels corresponding to different data categories.\n#' Each legend entry consists of a colored rectangle and a text label. The left side legend has\n#' text aligned to the right of the color block, while the right side legend has text aligned\n#' to the left of the color block.\n#'\n#' @param labels Vector of labels for the legends.\n#' @param colors Vector of colors corresponding to the labels.\n#' @param legend_width The width of each legend viewport expressed in grid units.\n#' @param x_positions Numeric vector of length 2 specifying the x-positions of the left and right legends.\n#' @param y_position The y-position common for both legends, expressed as a fraction of the plot height.\n#' @param just_positions List of two vectors, each specifying the horizontal and vertical justification for the legends.\n#' @param text_alignments List of two character strings specifying text alignments for the legends ('left' or 'right').\n#' @param font_size Numeric value specifying the font size for the legend labels.\n#' @return Invisible. This function is called for its side effects of drawing legends on a plot.\n#' @importFrom grid pushViewport viewport grid.roundrect grid.text upViewport unit\n#' @export\n#' @examples\n#' labels <- c(\"Label1\", \"Label2\", \"Label3\", \"Label4\", \"Label5\", \"Label6\")\n#' colors <- c(\"#ff0000\", \"#00ff00\", \"#0000ff\", \"#ffff00\", \"#ff00ff\", \"#00ffff\")\n#'\n#' # Convert to 'unit' objects for grid\n#' grid::grid.roundrect(\n#' x = grid::unit(0.5, \"npc\"), # \"npc\" stands for normalized parent coordinates\n#' y = grid::unit(0.5, \"npc\"),\n#' width = grid::unit(0.1, \"npc\"),\n#' height = grid::unit(0.05, \"npc\"),\n#' gp = grid::gpar(fill = \"red\"),\n#' r = grid::unit(0.1, \"npc\") # rounding radius\n#' )\n#'\n#' # Example of drawing legends with specific labels and colors\n#' drawLegends(labels, colors, grid::unit(2, \"cm\"), c(0.225, 0.75), 0.5,\n#' list(c(\"left\", \"center\"), c(\"right\", \"center\")),\n#' list(\"right\", \"left\"), 10)\n#'\ndrawLegends <- function(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size) {\n half_length <- length(labels) / 2\n legend_height <- grid::unit(1, \"lines\") * half_length\n\n # Draw left-side legend\n grid::pushViewport(grid::viewport(\n width = legend_width,\n height = legend_height,\n x = x_positions[1],\n y = y_position,\n just = just_positions[[1]]\n ))\n for (i in seq_len(half_length)) {\n grid::grid.roundrect(\n x = grid::unit(1, \"npc\") - grid::unit(0.5, \"cm\"),\n y = grid::unit(1, \"npc\") - grid::unit(i / half_length, \"npc\") + grid::unit(0.5 / half_length, \"npc\"),\n width = grid::unit(0.7, \"cm\"),\n height = grid::unit(0.9 / half_length, \"npc\"),\n gp = grid::gpar(fill = colors[i], col = NA),\n r = grid::unit(0.3, \"snpc\")\n )\n grid::grid.text(\n labels[i],\n x = grid::unit(1, \"npc\") - grid::unit(1, \"cm\"),\n y = grid::unit(1, \"npc\") - grid::unit(i / half_length, \"npc\") + grid::unit(0.5 / half_length, \"npc\"),\n gp = grid::gpar(col = colors[i], fontsize = font_size),\n just = text_alignments[[1]]\n )\n }\n grid::upViewport()\n\n # Draw right-side legend\n grid::pushViewport(grid::viewport(\n width = legend_width,\n height = legend_height,\n x = x_positions[2],\n y = y_position,\n just = just_positions[[2]]\n ))\n for (i in (half_length + 1):length(labels)) {\n grid::grid.roundrect(\n x = grid::unit(1, \"npc\") - grid::unit(0.6, \"cm\"),\n y = grid::unit(1, \"npc\") - grid::unit((i - half_length) / half_length, \"npc\") + grid::unit(0.5 / half_length, \"npc\"),\n width = grid::unit(0.7, \"cm\"),\n height = grid::unit(0.9 / half_length, \"npc\"),\n gp = grid::gpar(fill = colors[i], col = NA),\n r = grid::unit(0.3, \"snpc\")\n )\n grid::grid.text(\n labels[i],\n x = grid::unit(1, \"npc\") - grid::unit(0.1, \"cm\"),\n y = grid::unit(1, \"npc\") - grid::unit((i - half_length) / half_length, \"npc\") + grid::unit(0.5 / half_length, \"npc\"),\n gp = grid::gpar(col = colors[i], fontsize = font_size),\n just = text_alignments[[2]]\n )\n }\n grid::upViewport()\n}\n\n\n#' Draw Chord Diagram with Legends\n#'\n#' This function creates a chord diagram from a specified dataframe and draws two sets of legends for it.\n#' It adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid.\n#' Legends are drawn at specified positions with configurable text alignments and font sizes.\n#'\n#' @param all_combined_df A dataframe containing the matrix for the chord diagram.\n#' @param original_colors A vector of colors for the grid columns of the chord diagram.\n#' @param labels A vector of labels for the first legend.\n#' @param colors A vector of colors corresponding to the first legend's labels.\n#' @param labels2 A vector of labels for the second legend.\n#' @param colors2 A vector of colors corresponding to the second legend's labels.\n#' @param font_size The font size used for legend texts, defaults to 10.\n#' @return Invisible, primarily used for its side effects of drawing on a graphics device.\n#' @importFrom circlize chordDiagram\n#' @importFrom grid unit\n#' @importFrom graphics strwidth\n#' @export\n#' @examples\n#' # Sample Chord Diagram Matrix\n#' all_combined_df <- data.frame(\n#' A = c(10, 20, 30),\n#' B = c(15, 25, 35),\n#' C = c(5, 10, 15)\n#' )\n#' rownames(all_combined_df) <- c(\"A\", \"B\", \"C\")\n#'\n#' # Colors for the grid of the chord diagram (corresponding to columns of the matrix)\n#' original_colors <- c(\"red\", \"green\", \"blue\")\n#'\n#' # Name the colors according to the sectors (A, B, C)\n#' names(original_colors) <- colnames(all_combined_df)\n#'\n#' # Labels and Colors for the First Legend\n#' labels <- c(\"Label 1\", \"Label 2\", \"Label 3\")\n#' colors <- c(\"yellow\", \"purple\", \"cyan\")\n#'\n#' # Labels and Colors for the Second Legend\n#' labels2 <- c(\"Label A\", \"Label B\", \"Label C\")\n#' colors2 <- c(\"orange\", \"pink\", \"brown\")\n#'\n#' # Font size for the legend texts (optional, default is 10)\n#' font_size <- 10\n#'\n#' # Call the enrichment_circlize function with the sample data\n#' # This is just an example; the plot will be rendered in an appropriate graphics context\n#' # such as RStudio's plot pane or an external plotting window.\n#' plot1 <- enrichment_circlize(all_combined_df,\n#' original_colors,\n#' labels,\n#' colors,\n#' labels2,\n#' colors2,\n#' font_size\n#' )\n#'\nenrichment_circlize <- function(all_combined_df, original_colors, labels, colors,\n labels2, colors2, font_size = 10) {\n\n # Calculate adjusted height for the chord diagram\n max_height <- max(graphics::strwidth(unlist(dimnames(all_combined_df)), \"inches\")) * 1.2\n\n # Draw the chord diagram\n circlize::chordDiagram(all_combined_df, grid.col = original_colors, annotationTrack = \"grid\",\n directional = -1, direction.type = c(\"diffHeight\", \"arrows\"),\n link.arr.type = \"big.arrow\", preAllocateTracks = list(track.height = max_height))\n\n # Draw the first set of legends\n legend_width <- grid::unit(2, \"cm\")\n x_positions <- c(0.225, 0.75)\n y_position <- 0.5\n just_positions <- list(c(\"left\", \"center\"), c(\"right\", \"center\"))\n text_alignments <- list(\"right\", \"left\")\n\n drawLegends(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size)\n\n # Draw the second set of legends\n x_positions2 <- c(0.3, 0.68)\n y_position2 <- 0.7\n\n drawLegends(labels2, colors2, legend_width, x_positions2, y_position2, just_positions, text_alignments, font_size)\n}\n" }, { "path": "R/EnrichCircoBar.R", "content": "#' Extract and Count Descriptions with Specified Color\n#'\n#' This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns,\n#' and adds a new column with a specified color.\n#'\n#' @param df A data frame containing at least 'Description' and 'Count' columns.\n#' @param descriptions A vector of descriptions to filter in the data frame.\n#' @param color A character string specifying the color to be added as a new column.\n#' @return A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column.\n#' @export\n#'\n#' @examples\n#' # Generate Sample Input Data for extract_descriptions_counts Function\n#'\n#' # Create a sample data frame with 'Description' and 'Count' columns\n#' data <- data.frame(\n#' Description = c(\n#' \"immunoglobulin production\",\n#' \"B cell mediated immunity\",\n#' \"T cell activation\",\n#' \"antigen processing and presentation\",\n#' \"cytokine signaling\",\n#' \"natural killer cell activity\",\n#' \"phagocytosis\",\n#' \"complement activation\",\n#' \"antibody-dependent cellular cytotoxicity\",\n#' \"regulatory T cell function\"\n#' ),\n#' Count = c(\n#' 150, # immunoglobulin production\n#' 200, # B cell mediated immunity\n#' 175, # T cell activation\n#' 125, # antigen processing and presentation\n#' 190, # cytokine signaling\n#' 160, # natural killer cell activity\n#' 140, # phagocytosis\n#' 180, # complement activation\n#' 130, # antibody-dependent cellular cytotoxicity\n#' 170 # regulatory T cell function\n#' ),\n#' stringsAsFactors = FALSE # Ensure that strings are not converted to factors\n#' )\n#'\n#'\n#'\n#' descriptions_to_filter <- c(\"immunoglobulin production\", \"B cell mediated immunity\")\n#' specified_color <- \"red\" # You can specify any color you desire\n#' filtered_data_with_color <- extract_descriptions_counts(\n#' data, descriptions_to_filter,\n#' specified_color)\n#' print(filtered_data_with_color)\n#'\nextract_descriptions_counts <- function(df, descriptions, color) {\n # Filter rows where the Description column values are in the descriptions vector\n result_df <- df[df$Description %in% descriptions, ]\n\n # Select Description and Count columns\n result_df <- result_df[, c(\"Description\", \"Count\")]\n\n # Add a new column 'color' with the specified color value\n result_df$color <- color\n\n return(result_df)\n}\n\n\n\n\n\n\n\n\n#' Combine and Visualize Data with Circular Bar Chart\n#'\n#' This function combines multiple data frames, arranges them, and visualizes the combined data\n#' in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.\n#'\n#' @importFrom dplyr bind_rows arrange desc row_number mutate\n#' @importFrom ggplot2 ggplot geom_bar geom_text scale_fill_manual scale_y_continuous scale_x_continuous coord_polar labs theme_minimal theme element_rect element_blank\n#' @importFrom rlang .data\n#' @param data_list A list of data frames to be combined.\n#' @return A `ggplot` object representing the Circular Bar Chart.\n#' @export\n#'\n#' @examples\n#' # Create sample data frames for each enrichment category\n#'\n#' # 1. Biological Process (BP)\n#' filtered_data_BP <- data.frame(\n#' Description = c(\n#' \"immune response\",\n#' \"cell proliferation\",\n#' \"signal transduction\",\n#' \"apoptotic process\",\n#' \"metabolic process\"\n#' ),\n#' Count = c(120, 85, 150, 60, 95),\n#' color = c(\n#' \"#1f77b4\", # blue\n#' \"#ff7f0e\", # orange\n#' \"#2ca02c\", # green\n#' \"#d62728\", # red\n#' \"#9467bd\" # purple\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # 2. Cellular Component (CC)\n#' filtered_data_CC <- data.frame(\n#' Description = c(\n#' \"nucleus\",\n#' \"cytoplasm\",\n#' \"membrane\",\n#' \"mitochondrion\",\n#' \"extracellular space\"\n#' ),\n#' Count = c(90, 110, 75, 65, 80),\n#' color = c(\n#' \"#1f77b4\",\n#' \"#ff7f0e\",\n#' \"#2ca02c\",\n#' \"#d62728\",\n#' \"#9467bd\"\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # 3. Molecular Function (MF)\n#' filtered_data_MF <- data.frame(\n#' Description = c(\n#' \"protein binding\",\n#' \"DNA binding\",\n#' \"enzyme activity\",\n#' \"transporter activity\",\n#' \"receptor activity\"\n#' ),\n#' Count = c(140, 130, 100, 70, 90),\n#' color = c(\n#' \"#1f77b4\",\n#' \"#ff7f0e\",\n#' \"#2ca02c\",\n#' \"#d62728\",\n#' \"#9467bd\"\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # 4. Disease Ontology (DO)\n#' filtered_data_DO <- data.frame(\n#' Description = c(\n#' \"cancer\",\n#' \"cardiovascular disease\",\n#' \"neurological disorder\",\n#' \"metabolic disease\",\n#' \"infectious disease\"\n#' ),\n#' Count = c(200, 150, 120, 90, 160),\n#' color = c(\n#' \"#1f77b4\",\n#' \"#ff7f0e\",\n#' \"#2ca02c\",\n#' \"#d62728\",\n#' \"#9467bd\"\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # 5. Reactome Pathways\n#' filtered_data_Reactome <- data.frame(\n#' Description = c(\n#' \"Cell Cycle\",\n#' \"Apoptosis\",\n#' \"DNA Repair\",\n#' \"Signal Transduction\",\n#' \"Metabolism of Proteins\"\n#' ),\n#' Count = c(110, 95, 80, 130, 85),\n#' color = c(\n#' \"#1f77b4\",\n#' \"#ff7f0e\",\n#' \"#2ca02c\",\n#' \"#d62728\",\n#' \"#9467bd\"\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # 6. KEGG Pathways\n#' filtered_data_kegg <- data.frame(\n#' Description = c(\n#' \"PI3K-Akt signaling pathway\",\n#' \"MAPK signaling pathway\",\n#' \"NF-kappa B signaling pathway\",\n#' \"JAK-STAT signaling pathway\",\n#' \"Toll-like receptor signaling pathway\"\n#' ),\n#' Count = c(175, 160, 145, 130, 155),\n#' color = c(\n#' \"#1f77b4\",\n#' \"#ff7f0e\",\n#' \"#2ca02c\",\n#' \"#d62728\",\n#' \"#9467bd\"\n#' ),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # Combine all filtered data frames into a list\n#' data_list <- list(\n#' BP = filtered_data_BP,\n#' CC = filtered_data_CC,\n#' MF = filtered_data_MF,\n#' DO = filtered_data_DO,\n#' Reactome = filtered_data_Reactome,\n#' KEGG = filtered_data_kegg\n#' )\n#'\n#' # Create the Circular Bar Chart\n#' combined_and_visualized_data <- enrich_circo_bar(data_list)\n#'\nenrich_circo_bar <- function(data_list) {\n # Combine data frames\n combined_data <- dplyr::bind_rows(data_list)\n\n # Sort by 'Count' column in descending order and add an 'id' column\n combined_data <- combined_data %>%\n dplyr::arrange(.data$Count) %>%\n dplyr::mutate(id = dplyr::row_number())\n\n # Ensure 'Description' is a factor with correct level order\n combined_data <- combined_data %>%\n dplyr::mutate(Description = factor(.data$Description, levels = unique(.data$Description)))\n\n # Extract fill colors, ensuring colors match 'Description' levels\n fill_colors <- combined_data$color[match(levels(combined_data$Description), combined_data$Description)]\n\n # Calculate the expanded max values for Count and id\n max_count <- max(combined_data$Count) + (max(combined_data$Count) / 5)\n max_id <- max(combined_data$id) + 1.5\n\n # Create the plot\n p <- ggplot2::ggplot(combined_data, ggplot2::aes(x = .data$id, y = .data$Count, fill = .data$Description)) +\n ggplot2::geom_bar(stat = 'identity', width = 0.7) +\n ggplot2::geom_text(ggplot2::aes(x = .data$id, y = 0, label = .data$Description), hjust = 1.03, size = 3.5, color = fill_colors) +\n ggplot2::scale_fill_manual(values = fill_colors, guide = \"none\") +\n ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0, max_count), position = 'right') +\n ggplot2::scale_x_continuous(expand = c(0, 0), limits = c(-1, max_id)) +\n ggplot2::coord_polar(theta = 'y') +\n ggplot2::labs(title = 'Enrichment CircularBar Chart', subtitle = \"Including: BP/MF/CC/DO/KEGG/Reactome\") +\n ggplot2::theme_minimal() +\n ggplot2::theme(plot.background = ggplot2::element_rect(fill = 'white', color = 'white'),\n axis.title = ggplot2::element_blank(),\n axis.text = ggplot2::element_blank())\n\n return(p)\n}\n" }, { "path": "R/EnrichPolarBubble.R", "content": "#' Enrichment Polar Bubble Plot\n#'\n#' This function creates a polar bubble plot using 'ggplot2'. It is designed to visually represent data with methods and positional metrics integrated, highlighting specific IDs if necessary.\n#'\n#' @param final_combined_df_with_id_and_position A data frame containing 'id', 'Count', 'method', 'Description', 'point_position', 'test_color'.\n#' @param pal A named vector of colors corresponding to the 'method' values.\n#' @param highlight_ids A vector of IDs to highlight.\n#' @importFrom ggplot2 ggplot aes geom_point geom_hline geom_segment scale_fill_manual scale_size coord_polar theme_void scale_color_manual scale_x_continuous scale_y_continuous annotate theme scale_fill_identity\n#' @importFrom dplyr filter\n#' @importFrom stringr str_c\n#' @importFrom tibble tibble\n#' @importFrom geomtextpath geom_textpath\n#' @importFrom ggalt stat_xspline\n#' @importFrom ggnewscale new_scale_fill\n#' @return A `ggplot` object representing the enriched polar bubble plot.\n#' @examples\n#' final_df <- data.frame(id = 1:10, Count = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100),\n#' method = rep(\"Method1\", 10),\n#' Description = LETTERS[1:10],\n#' point_position = seq(10, 100, 10),\n#' test_color = sample(c(\"red\", \"blue\"), 10, replace = TRUE))\n#' pal <- c(\"Method1\" = \"blue\")\n#' highlight_ids <- c(1, 5, 9)\n#' enrich_polar_bubble(final_df, pal, highlight_ids)\n#' @export\nenrich_polar_bubble <- function(final_combined_df_with_id_and_position, pal, highlight_ids) {\n # Extract unique levels of point_position and prepend 0\n levels <- c(0, sort(unique(final_combined_df_with_id_and_position$point_position)))\n max_level_plus_five <- max(levels) + 5\n max_id_plus_one <- max(final_combined_df_with_id_and_position$id) + 1\n\n gg <- ggplot2::ggplot(data = final_combined_df_with_id_and_position,\n ggplot2::aes(x = .data$id, y = .data$Count, group = .data$method, fill = .data$method, color = .data$method)) +\n ggplot2::geom_hline(yintercept = levels, color = \"grey85\") +\n ggplot2::geom_hline(yintercept = max_level_plus_five, color = \"grey15\") +\n ggplot2::geom_segment(data = tibble::tibble(x = 1:max_id_plus_one, y = 0, yend = max_level_plus_five),\n ggplot2::aes(x = .data$x, xend = .data$x, y = .data$y, yend = .data$yend),\n inherit.aes = FALSE, color = \"grey85\") +\n ggplot2::geom_segment(data = tibble::tibble(x = 1:max_id_plus_one, y = max(levels), yend = max_level_plus_five),\n ggplot2::aes(x = .data$x, xend = .data$x, y = .data$y, yend = .data$yend),\n inherit.aes = FALSE, color = \"grey15\") +\n geomtextpath::geom_textpath(ggplot2::aes(x = final_combined_df_with_id_and_position$id, y = max_level_plus_five + 5, label = final_combined_df_with_id_and_position$Description, angle = 55),\n inherit.aes = FALSE, hjust = 0, size = 5.5, color = final_combined_df_with_id_and_position$test_color) +\n ggplot2::geom_point(ggplot2::aes(x = .data$id, y = .data$point_position, size = .data$Count),\n shape = 21, alpha = 0.6) +\n ggalt::stat_xspline(geom = \"line\", spline_shape = 0.25, linewidth = 0.75, alpha = 0.4) +\n ggalt::stat_xspline(geom = \"area\", alpha = 0.25, spline_shape = 0.25, outline.type = \"upper\") +\n ggplot2::scale_size(range = c(0, 14), breaks = levels,\n guide = ggplot2::guide_legend(title = \"Count\")) +\n ggplot2::scale_fill_manual(values = pal) +\n ggplot2::scale_color_manual(values = pal) +\n ggplot2::scale_x_continuous(expand = c(0, 0)) +\n ggplot2::scale_y_continuous(limits = c(-50, max_level_plus_five + 15), expand = c(0, 0)) +\n ggplot2::coord_polar() +\n ggplot2::annotate(\"text\", x = 1, y = levels, label = \"-\", hjust = 1, size = 4) +\n ggplot2::annotate(\"text\", x = 1.1, y = levels, label = levels, hjust = 0, size = 4) +\n ggplot2::annotate(\"text\", x = 1, y = -50, label = stringr::str_c(\"BP/CC/MF/KEGG/DO/REACTOME\"), size = 4) +\n ggplot2::theme_void() +\n ggplot2::theme(plot.background = ggplot2::element_rect(fill = \"white\", color = NA)) +\n ggnewscale::new_scale_fill() +\n ggplot2::geom_rect(data = dplyr::filter(final_combined_df_with_id_and_position, .data$id %in% highlight_ids),\n ggplot2::aes(xmin = .data$id - 0.5, xmax = .data$id + 0.5, ymin = 0, ymax = max_level_plus_five, fill = .data$test_color),\n alpha = 0.1, inherit.aes = FALSE) +\n ggplot2::scale_fill_identity() # Use colors specified in 'test_color'\n return(gg)\n}\n" }, { "path": "R/EnrichmentSpiralize.R", "content": "#' Extract and Store Top Pathways for Each Sample\n#'\n#' This function processes a dataframe containing SSGSEA KEGG results. It allows specifying the number\n#' of top pathways to extract for each sample based on their scores, and stores these in a new dataframe\n#' with sample names and pathway scores.\n#'\n#' @param ssgsea_kegg Dataframe containing SSGSEA KEGG results with samples as columns and pathways as rows.\n#' @param nTop Integer, number of top pathways to select for each sample.\n#' @return A dataframe with columns 'Pathway', 'Sample', and 'Value' representing the top pathways for each sample.\n#' @importFrom utils head\n#' @export\n#' @examples\n#' # Example: Generating input data for the extract_ntop_pathways function\n#'\n#' # Define example pathways\n#' pathways <- c(\"Pathway_A\", \"Pathway_B\", \"Pathway_C\", \"Pathway_D\", \"Pathway_E\",\n#' \"Pathway_F\", \"Pathway_G\", \"Pathway_H\", \"Pathway_I\", \"Pathway_J\")\n#'\n#' # Define example samples\n#' samples <- c(\"Sample_1\", \"Sample_2\", \"Sample_3\")\n#'\n#' # Generate random SSGSEA KEGG scores between 0 and 1\n#' set.seed(123) # For reproducibility\n#' ssgsea_scores <- matrix(runif(length(pathways) * length(samples), min = 0, max = 1),\n#' nrow = length(pathways), ncol = length(samples),\n#' dimnames = list(pathways, samples))\n#'\n#' # Convert to a data frame\n#' ssgsea_kegg <- as.data.frame(ssgsea_scores)\n#'\n#' # Extract the top 3 pathways for each sample\n#' top_pathways <- extract_ntop_pathways(ssgsea_kegg, nTop = 3)\n#'\nextract_ntop_pathways <- function(ssgsea_kegg, nTop = 5) {\n # Initialize an empty data frame to store the results\n results <- data.frame(Pathway = character(), Sample = character(), Value = numeric(), stringsAsFactors = FALSE)\n\n # Iterate through each sample, starting from the first column\n for (i in 1:ncol(ssgsea_kegg)) {\n sample_name <- colnames(ssgsea_kegg)[i]\n # To avoid factor type errors, ensure the data is numeric\n column_data <- as.numeric(ssgsea_kegg[[i]])\n # Create a new data frame with numeric data for sorting and extracting\n pathway_data <- data.frame(Pathway = rownames(ssgsea_kegg), Value = column_data, stringsAsFactors = FALSE)\n # Sort by value in descending order and take the top nTop entries\n top_paths <- utils::head(pathway_data[order(-pathway_data$Value),], nTop)\n # Bind to the results data frame\n results <- rbind(results, data.frame(Pathway = top_paths$Pathway, Sample = sample_name, Value = top_paths$Value))\n }\n\n return(results)\n}\n\n\n\n\n\n\n\n\n#' Extract Positive Pathways from SSGSEA Results and Select Random Samples\n#'\n#' This function processes the results of SSGSEA, specifically focusing on KEGG pathways.\n#' It extracts pathways with positive values from each sample and randomly selects a subset of them.\n#'\n#' @param ssgsea_kegg A matrix or data frame with pathways as rows and samples as columns.\n#' @param max_paths_per_sample Integer, maximum number of pathways to select per sample.\n#' @return A data frame with selected pathways, samples, and their corresponding values.\n#' @export\n#' @examples\n#' # Example: Generating input data for the extract_positive_pathways function\n#'\n#' # Define example pathways\n#' pathways <- c(\"Pathway_1\", \"Pathway_2\", \"Pathway_3\", \"Pathway_4\", \"Pathway_5\",\n#' \"Pathway_6\", \"Pathway_7\", \"Pathway_8\", \"Pathway_9\", \"Pathway_10\")\n#'\n#' # Define example samples\n#' samples <- c(\"Sample_A\", \"Sample_B\", \"Sample_C\")\n#'\n#' # Generate random SSGSEA KEGG scores including both positive and negative values\n#' set.seed(456) # For reproducibility\n#' ssgsea_scores <- matrix(rnorm(length(pathways) * length(samples), mean = 0, sd = 1),\n#' nrow = length(pathways), ncol = length(samples),\n#' dimnames = list(pathways, samples))\n#'\n#' # Convert to a data frame\n#' ssgsea_kegg <- as.data.frame(ssgsea_scores)\n#'\n#' # Use the extract_positive_pathways function to extract up to 3 positive pathways per sample\n#' selected_positive_pathways <- extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 3)\n#'\nextract_positive_pathways <- function(ssgsea_kegg, max_paths_per_sample = 5) {\n # Initialize an empty data frame to store the results\n results <- data.frame(Pathway = character(), Sample = character(), Value = numeric(), stringsAsFactors = FALSE)\n\n # Iterate over each sample\n for (i in 1:ncol(ssgsea_kegg)) {\n sample_name <- colnames(ssgsea_kegg)[i]\n # Ensure the data is numeric\n column_data <- as.numeric(ssgsea_kegg[[i]])\n # Create a new data frame with pathway names and values\n pathway_data <- data.frame(Pathway = rownames(ssgsea_kegg), Value = column_data, stringsAsFactors = FALSE)\n # Filter for positive values\n positive_paths <- pathway_data[pathway_data$Value > 0,]\n # If there are positive values, randomly select a few pathways\n if (nrow(positive_paths) > 0) {\n selected_paths <- positive_paths[sample(nrow(positive_paths), min(max_paths_per_sample, nrow(positive_paths))),]\n # Bind to the results data frame\n results <- rbind(results, data.frame(Pathway = selected_paths$Pathway, Sample = sample_name, Value = selected_paths$Value))\n }\n }\n return(results)\n}\n\n\n\n\n\n\n\n\n\n#' Adjust Color Tone by Modifying Saturation and Luminance\n#'\n#' This function adjusts the saturation and luminance of a given color. It works by converting\n#' the color from RGB to Luv color space, applying the scaling factors to the saturation and luminance,\n#' and then converting it back to RGB.\n#'\n#' @param color A color in hexadecimal format (e.g., \"#FF0000\") or a valid R color name.\n#' @param saturation_scale Numeric, the scaling factor for saturation (values < 1 decrease saturation, values > 1 increase saturation).\n#' @param luminance_scale Numeric, the scaling factor for luminance (values < 1 darken the color, values > 1 lighten the color).\n#' @return Returns a color in hexadecimal format adjusted according to the provided scales.\n#' @importFrom grDevices convertColor col2rgb rgb\n#' @export\n#' @examples\n#' adjusted_color <- adjust_color_tone(\"#FF0000\", saturation_scale = 0.8, luminance_scale = 1.2)\n#' print(adjusted_color)\n#'\nadjust_color_tone <- function(color, saturation_scale, luminance_scale) {\n # Convert the input color to RGB, then to Luv color space\n rgb <- t(grDevices::col2rgb(color) / 255)\n luv <- grDevices::convertColor(rgb, from = \"sRGB\", to = \"Luv\")\n\n # Apply scaling factors to saturation and luminance\n luv[, 2:3] <- luv[, 2:3] * saturation_scale # Adjust saturation\n luv[, 1] <- luv[, 1] * luminance_scale # Adjust luminance\n\n # Convert back to RGB and correct color values to stay within the valid range\n rgb_new <- grDevices::convertColor(luv, from = \"Luv\", to = \"sRGB\")\n rgb_new <- rgb_new * 255\n rgb_new[rgb_new > 255] <- 255 # Prevent color values from exceeding the maximum\n\n # Convert adjusted RGB values back to hexadecimal format\n apply(rgb_new, 1, function(x) grDevices::rgb(x[1], x[2], x[3], maxColorValue = 255))\n}\n\n\n\n\n\n\n\n\n\n#' Render a Spiral Plot Using Run-Length Encoding\n#'\n#' This function creates a spiral plot for visualizing sequential data in a compact and visually appealing way.\n#' It uses run-length encoding to represent the lengths and colors of sequences in the spiral.\n#'\n#' @param x A vector representing categories or segments.\n#' @param samples A vector indicating the sample each segment belongs to.\n#' @param values Numeric vector indicating the lengths of each segment.\n#' @param colors Character vector specifying the colors for each segment.\n#' @param labels Logical, whether to add labels to each segment.\n#' @importFrom grid gpar unit\n#' @importFrom spiralize spiral_rect spiral_text spiral_initialize spiral_track\n#' @export\n#' @return No return value, called for side effects. This function generates a spiral plot and optionally adds labels.\n#' @examples\n#' # Example: Creating a spiral plot using the spiral_newrle function\n#'\n#' # Define example data\n#' x <- c(\"A\", \"A\", \"B\", \"C\")\n#' samples <- c(\"Sample1\", \"Sample1\", \"Sample2\", \"Sample2\")\n#' values <- c(20, 30, 15, 35)\n#' colors <- c(\"red\", \"blue\", \"green\", \"purple\")\n#' labels <- TRUE\n#'\n#' # Initialize the spiral plot, setting the x-axis range and scaling\n#' spiralize::spiral_initialize(xlim = c(0, sum(values)), scale_by = \"curve_length\",\n#' vp_param = list(x = grid::unit(0, \"npc\"), just = \"left\"))\n#'\n#' # Create a track for the spiral plot\n#' spiralize::spiral_track(height = 0.5)\n#'\n#' # Add segments to the spiral plot using run-length encoding\n#' spiral_newrle(x, samples, values, colors, labels)\n#'\nspiral_newrle <- function(x, samples, values, colors, labels = FALSE) {\n x <- as.vector(x) # Ensure x is a vector\n samples <- as.vector(samples) # Ensure samples is a vector\n values <- as.numeric(values) # Ensure values are numeric\n position_start <- 0 # Initialize starting position\n current_sample <- samples[1] # Start with the first sample\n cumulative_start <- position_start # Initialize cumulative start for labels\n\n # Loop through each value\n for (i in seq_along(values)) {\n position_end <- position_start + values[i] # Calculate end position\n\n # Use the specified color, defaulting to red if missing\n color <- if (!is.na(colors[i])) colors[i] else \"red\"\n\n # Draw the segment in the spiral\n spiralize::spiral_rect(position_start, 0, position_end, 1, gp = grid::gpar(fill = color, col = NA))\n\n # Check for sample change or last element\n if (i == length(values) || samples[i + 1] != current_sample) {\n if (labels) {\n label_position <- (cumulative_start + position_end) / 2\n spiralize::spiral_text(label_position, 0.5, current_sample, facing = \"curved_inside\", nice_facing = TRUE)\n }\n cumulative_start <- position_end # Reset for next sample\n if (i < length(values)) {\n current_sample <- samples[i + 1]\n }\n }\n\n position_start <- position_end # Move to next start position\n }\n}\n\n\n\n\n\n\n#' Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap'\n#'\n#' This function initializes a spiral plot, adds tracks for pathways and samples,\n#' and generates legends based on the sample and pathway information in the provided data frame.\n#' It uses 'spiralize' for the spiral plot and 'ComplexHeatmap' for handling legends.\n#'\n#' @param results A data frame containing 'Pathway', 'Sample', 'Value', 'PathwayColor', and 'SampleColor' columns.\n#' @importFrom grid gpar\n#' @importFrom spiralize spiral_initialize spiral_track\n#' @importFrom ComplexHeatmap packLegend Legend draw\n#' @importFrom ggplot2 unit\n#' @export\n#' @return No return value, called for side effects. This function generates spiral plots and adds legends based on sample and pathway information.\n#' @examples\n#' # Example: Creating enrichment spiral plots with legends\n#'\n#' # Define the results data frame\n#' results <- data.frame(\n#' Pathway = c(\"Pathway1\", \"Pathway1\", \"Pathway2\", \"Pathway2\", \"Pathway3\"),\n#' Sample = c(\"Sample1\", \"Sample1\", \"Sample2\", \"Sample2\", \"Sample3\"),\n#' Value = c(20, 30, 15, 35, 25),\n#' PathwayColor = c(\"red\", \"red\", \"blue\", \"blue\", \"orange\"),\n#' SampleColor = c(\"green\", \"green\", \"purple\", \"purple\", \"cyan\"),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # Create the enrichment spiral plots with legends\n#' enrichment_spiral_plots(results)\n#'\nenrichment_spiral_plots <- function(results) {\n \n if (!requireNamespace(\"systemfonts\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use the function. Please install it.\", call. = FALSE)\n }\n\n # Calculate the total value for setting the x-axis range\n n <- sum(results$Value)\n\n # Initialize the spiral plot\n spiralize::spiral_initialize(xlim = c(0, n), scale_by = \"curve_length\",\n vp_param = list(x = ggplot2::unit(0, \"npc\"), just = \"left\"))\n\n # Add a track for pathways\n spiralize::spiral_track(height = 0.4)\n spiral_newrle(results$Pathway, results$Sample, results$Value, results$PathwayColor, labels = FALSE)\n\n # Add a track for samples\n spiralize::spiral_track(height = 0.4)\n spiral_newrle(results$Sample, results$Sample, results$Value, results$SampleColor, labels = TRUE)\n\n # Generate legends based on sample, using unique pathway and color information\n lgd_list <- tapply(1:nrow(results), results$Sample, function(ind) {\n ComplexHeatmap::Legend(title = results$Sample[ind][1], at = unique(results$Pathway[ind]),\n legend_gp = grid::gpar(fill = unique(results$PathwayColor[ind])))\n })\n\n # Set the maximum height for the legends and draw them\n lgd <- ComplexHeatmap::packLegend(list = lgd_list, max_height = ggplot2::unit(7, \"inch\"))\n ComplexHeatmap::draw(lgd, x = ggplot2::unit(1, \"npc\") + ggplot2::unit(1, \"mm\"), just = \"left\")\n}\n\n\n\n\n" }, { "path": "R/FacetDensityFoldchange.R", "content": "#' Create faceted high-density region plots with optional points and density contours\n#'\n#' This function creates faceted high-density region plots using ggdensity for\n#' adding optional density rug and density contours, and scatter points. It also adds a regression line\n#' and Pearson correlation label. The plot is faceted by a grouping variable.\n#'\n#' @param data Data frame containing variables for plotting.\n#' @param x_var Name of the x-axis variable as a string.\n#' @param y_var Name of the y-axis variable as a string.\n#' @param group_var Name of the grouping variable for color mapping as a string.\n#' @param facet_var Name of the faceting variable.\n#' @param palette Color palette for the plot as a character vector.\n#' @param show_points Logical, if TRUE adds scatter points to the plot.\n#' @param show_density Logical, if TRUE adds filled density contours to the plot.\n#' @param point_size Size of the points, relevant if show_points is TRUE.\n#' @param point_alpha Transparency level of the points, relevant if show_points is TRUE.\n#' @param line_size Size of the regression line.\n#' @param cor_method Method to calculate correlation (\"pearson\" or \"spearman\").\n#' @param cor_label_pos Vector of length 2 indicating the position of the correlation label (x and y).\n#' @param cor_vjust Vertical justification for correlation label, default is NULL.\n#' @return A `ggplot` object representing the high-density region plot.\n#' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth scale_fill_manual scale_color_manual facet_wrap theme margin\n#' @importFrom ggdensity geom_hdr geom_hdr_rug\n#' @importFrom ggpubr stat_cor\n#' @importFrom hrbrthemes theme_ipsum\n#' @importFrom grid unit\n#' @importFrom stats as.formula\n#' @examples\n#' combined_df_file <- system.file(\"extdata\", \"combined_df.rds\", package = \"TransProR\")\n#' combined_df <- readRDS(combined_df_file)\n#' pal2 = c(\"#2787e0\",\"#1a9ae0\",\"#1dabbf\",\"#00897b\",\"#43a047\",\"#7cb342\")\n#' all_facet_density_foldchange_name1 <- facet_density_foldchange(\n#' data = combined_df,\n#' x_var = \"log2FoldChange_1\",\n#' y_var = \"log2FoldChange_2\",\n#' group_var = \"name\",\n#' facet_var = \"name\",\n#' palette = pal2,\n#' show_points = TRUE,\n#' show_density = FALSE,\n#' point_size = 2,\n#' point_alpha = 0.1,\n#' line_size = 1.6,\n#' cor_method = \"pearson\",\n#' cor_label_pos = c(\"left\", \"top\"),\n#' cor_vjust = 1\n#' )\n#' @export\nfacet_density_foldchange <- function(data,\n x_var,\n y_var,\n group_var,\n facet_var,\n palette,\n show_points = FALSE,\n show_density = TRUE,\n point_size = 2.5,\n point_alpha = 0.1,\n line_size = 1.6,\n cor_method = \"pearson\",\n cor_label_pos = c(\"left\", 0.97),\n cor_vjust = NULL) {\n # Begin constructing the ggplot\n plot <- ggplot2::ggplot(data, ggplot2::aes_string(x = x_var, y = y_var, fill = group_var))\n\n # Optionally add density rug\n plot <- plot + ggdensity::geom_hdr_rug()\n\n # Optionally add density contours\n if (show_density) {\n plot <- plot + ggdensity::geom_hdr()\n }\n\n # Optionally add points\n if (show_points) {\n plot <- plot + ggplot2::geom_point(ggplot2::aes_string(color = group_var), shape = 21,\n size = point_size, alpha = point_alpha)\n }\n\n # Add regression line and correlation label\n plot <- plot +\n ggplot2::geom_smooth(ggplot2::aes_string(x = x_var, y = y_var, color = group_var),\n method = 'lm', level = 0.95, size = line_size)\n\n # Add regression line and correlation label\n if (is.null(cor_vjust)) {\n plot <- plot + ggpubr::stat_cor(ggplot2::aes_string(color = group_var), method = cor_method, label.x.npc = cor_label_pos[1], label.y.npc = cor_label_pos[2])\n } else {\n plot <- plot + ggpubr::stat_cor(ggplot2::aes_string(color = group_var), method = cor_method, label.x.npc = cor_label_pos[1], label.y.npc = cor_label_pos[2], vjust = cor_vjust)\n }\n\n\n # Customize scales and facet wrapping\n plot <- plot +\n ggplot2::scale_fill_manual(values = palette) +\n ggplot2::scale_color_manual(values = palette) +\n ggplot2::facet_wrap(stats::as.formula(paste0(\"~ \", facet_var)), scales = \"free_x\") +\n hrbrthemes::theme_ipsum() +\n ggplot2::theme(plot.margin = ggplot2::margin(10, 10, 10, 10),\n plot.background = ggplot2::element_rect(fill = \"white\", color = \"white\"),\n panel.spacing = grid::unit(2, \"mm\"))\n\n # Return the `ggplot` object\n return(plot)\n}\n" }, { "path": "R/FilterDiffGenes.R", "content": "#' Filter Differentially Expressed Genes\n#'\n#' This function filters a data frame to identify genes with significant differential expression\n#' based on specified thresholds for p-values and log fold change. It allows for flexible\n#' input of column names for p-values and log fold change.\n#'\n#' @importFrom dplyr filter\n#' @importFrom rlang sym\n#' @param data A data frame containing gene expression data.\n#' @param p_val_col Character string indicating the column name for p-values. Default is \"adj.P.Val\".\n#' @param log_fc_col Character string indicating the column name for log fold change. Default is \"logFC\".\n#' @param p_val_threshold Numeric threshold for filtering p-values. Default is 0.05.\n#' @param log_fc_threshold Numeric threshold for filtering log fold change. Default is 1.0.\n#' @return A data frame with genes filtered by the specified criteria.\n#' @export\n#'\n#' @examples\n#' # Create a sample data frame with p-values and log fold changes\n#' sample_data <- data.frame(\n#' adj.P.Val = c(0.03, 0.06, 0.02, 0.07),\n#' logFC = c(1.5, 0.8, -1.2, 1.1),\n#' gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\")\n#' )\n#'\n#' # Use the filter_diff_genes function to filter significant genes\n#' filtered_genes <- filter_diff_genes(sample_data)\n#' print(filtered_genes)\nfilter_diff_genes <- function(data, p_val_col = \"adj.P.Val\", log_fc_col = \"logFC\",\n p_val_threshold = 0.05, log_fc_threshold = 1.0) {\n\n # Check for the specified columns in the data frame\n if (!(p_val_col %in% names(data))) {\n stop(paste(\"Column\", p_val_col, \"not found in the data frame.\"))\n }\n\n if (!(log_fc_col %in% names(data))) {\n stop(paste(\"Column\", log_fc_col, \"not found in the data frame.\"))\n }\n\n # Filter the data based on the specified thresholds for p-values and log fold change\n filtered_data <- data %>%\n dplyr::filter(!!rlang::sym(p_val_col) < p_val_threshold) %>%\n dplyr::filter(abs(!!rlang::sym(log_fc_col)) > log_fc_threshold)\n\n # Return the filtered data frame\n return(filtered_data)\n}\n\n" }, { "path": "R/FourDegsVenn.R", "content": "#' Function to Filter Differentially Expressed Genes (DEGs)\n#'\n#' This function filters out genes based on their expression change status.\n#' It returns the names of genes which are not \"stable\".\n#'\n#' @param df A data frame containing gene expression data.\n#' @return A vector of gene names that are differentially expressed.\n#' @examples\n#' DEG_deseq2_file <- system.file(\"extdata\", \"DEG_deseq2.rds\", package = \"TransProR\")\n#' DEG_deseq2 <- readRDS(DEG_deseq2_file)\n#' DEG_deseq2_test <- deg_filter(DEG_deseq2)\n#' @export\n\ndeg_filter <- function(df){\n # Selecting gene names where change is not \"stable\"\n rownames(df)[df$change != \"stable\"]\n}\n\n#' Function to Create a Venn Diagram of DEGs\n#'\n#' This function creates a Venn Diagram using the ggVennDiagram package.\n#' It allows customization of various aesthetic elements of the diagram.\n#' @importFrom ggplot2 alpha scale_fill_gradient scale_color_manual scale_x_continuous expansion\n#' @importFrom ggVennDiagram ggVennDiagram\n#' @param degs_list A list of DEG sets for Venn Diagram creation.\n#' @return A ggplot object representing the Venn Diagram.\n#' @examples\n#' data(\"all_degs_venn\", package = \"TransProR\")\n#' four_degs_venn <- four_degs_venn(all_degs_venn)\n#' @export\n\nfour_degs_venn <- function(degs_list){\n # Defining edge colors and alpha transparency for the Venn Diagram\n edge_colors <- c(ggplot2::alpha(\"#1b64bb\", 0.5), ggplot2::alpha(\"#13828e\", 0.5),\n ggplot2::alpha(\"#337c3a\", 0.5), ggplot2::alpha(\"#9e9d39\", 0.5))\n edge_color <- ggplot2::alpha(c(\"#1b64bb\",\"#13828e\",\"#337c3a\",\"#9e9d39\"), 0.8)\n\n # Creating a Venn Diagram using 'ggVennDiagram'\n ggVennDiagram::ggVennDiagram(\n degs_list,\n set_size = 5, # Font size for group names\n set_color = edge_color, # Color for group names\n label_alpha= 0, # Transparency of background box for labels\n label_size = 4, # Font size for labels\n edge_size = 3 # Thickness of edges\n ) +\n ggplot2::scale_fill_gradient(low=\"#e1f2f1\", high = \"#11786b\") + # Gradient fill based on values\n ggplot2::scale_color_manual(values = edge_colors) + # Manually set edge colors\n ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = .3)) # Adjusting x-axis scaling\n}\n\n" }, { "path": "R/GatherGraphEdge.R", "content": "#' Gather graph edge from data frame\n#' Please note that this function is from the 'ggraph' package and has not been altered in functionality,\n#' but it has been optimized and iterated.\n#' It is not original content of 'TransProR'.\n#' However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\n#' the author directly referenced the involved functions in 'TransProR'.\n#' This is not the author's original creation. All users please be aware!\n#' @param df A data frame\n#' @param index A vector of column names to group by\n#' @param root Root name for the root node connections, optional\n#' @return A tibble of graph edges\n#' @export\n#' @importFrom dplyr mutate select group_by summarise bind_rows all_of across\n#' @importFrom tidyr unite\n#' @importFrom tibble as_tibble\n#' @name gather_graph_edge\ngather_graph_edge <- function(df, index = NULL, root = NULL) {\n if (length(index) < 2) {\n stop(\"Please specify at least two index columns.\")\n }\n\n prepare_edge <- function(data, from, to, sep = \"/\") {\n data %>%\n tidyr::unite(\"from\", dplyr::all_of(from), sep = sep, remove = FALSE) %>%\n tidyr::unite(\"to\", dplyr::all_of(to), sep = sep) %>%\n dplyr::select(.data$from, .data$to) %>%\n dplyr::mutate(dplyr::across(c(\"from\", \"to\"), as.character))\n }\n\n edges <- lapply(seq_along(index)[-1], function(i) {\n prepare_edge(df, index[1:(i - 1)], index[1:i])\n })\n\n edges <- dplyr::bind_rows(edges)\n edges <- tibble::as_tibble(edges)\n\n if (!is.null(root)) {\n root_edges <- df %>%\n dplyr::group_by(.data[[index[1]]]) %>%\n dplyr::summarise(count = dplyr::n(), .groups = 'drop') %>%\n dplyr::mutate(from = root, to = as.character(.data[[index[1]]])) %>%\n dplyr::select(.data$from, .data$to)\n\n edges <- dplyr::bind_rows(root_edges, edges)\n }\n\n return(edges)\n}\n" }, { "path": "R/GatherGraphNode.R", "content": "#' Gather graph nodes from a data frame\n#' Please note that this function is from the 'ggraph' package and has not been altered in functionality,\n#' but it has been optimized and iterated.\n#' It is not original content of 'TransProR'.\n#' However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\n#' the author directly referenced the involved functions in 'TransProR'.\n#' This is not the author's original creation. All users please be aware!\n#' @inheritParams gather_graph_edge\n#' @param value Column name used for summarizing node size, defaults to the last column\n#' @return a tibble of graph nodes\n#' @export\n#' @importFrom dplyr group_by summarise mutate bind_rows n all_of across\n#' @importFrom tidyr unite\n#' @importFrom tibble as_tibble\n#' @importFrom utils tail\ngather_graph_node <- function(df, index = NULL, value = utils::tail(colnames(df), 1), root = NULL) {\n if (length(index) < 2) {\n stop(\"Please specify at least two index columns.\")\n } else {\n nodes_list <- lapply(seq_along(index), function(i) {\n dots <- index[1:i]\n df %>%\n dplyr::group_by(dplyr::across(dplyr::all_of(dots))) %>%\n dplyr::summarise(node.size = sum(.data[[value]], na.rm = TRUE),\n node.level = index[i],\n node.count = dplyr::n(), .groups = 'drop') %>%\n dplyr::mutate(node.short_name = as.character(.data[[dots[length(dots)]]]),\n node.branch = as.character(.data[[dots[1]]])) %>%\n tidyr::unite(col = \"node.name\", all_of(dots), sep = \"/\")\n })\n\n nodes <- dplyr::bind_rows(nodes_list) %>%\n tibble::as_tibble()\n\n nodes$node.level <- factor(nodes$node.level, levels = index)\n\n if (!is.null(root)) {\n root_data <- data.frame(\n node.name = root,\n node.size = sum(df[[value]], na.rm = TRUE),\n node.level = root,\n node.count = 1,\n node.short_name = root,\n node.branch = root,\n stringsAsFactors = FALSE\n )\n\n nodes <- dplyr::bind_rows(root_data, nodes)\n nodes$node.level <- factor(nodes$node.level, levels = c(root, index))\n }\n\n return(nodes)\n }\n}\n" }, { "path": "R/GeneColor.R", "content": "#' Merge Genes with Color Information Based on Up/Down Regulation\n#'\n#' This function merges selected genes with differential expression data and adds a color column based on up/down regulation.\n#'\n#' @param selected_genes A data frame containing selected genes with a column named \"Symble\".\n#' @param DEG_deseq2 A data frame containing differential expression data with a column named \"Symble\" and a column named \"change\" indicating up/down regulation.\n#' @param up_color The color to assign to genes with up-regulated expression.\n#' @param down_color The color to assign to genes with down-regulated expression.\n#' @return A data frame containing merged genes with an additional color column.\n#' @export\n#'\n#' @examples\n#' selected_genes_deseq2_file <- system.file(\"extdata\",\n#' \"selected_genes_deseq2.rds\",\n#' package = \"TransProR\")\n#' selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)\n#' Diff_deseq2_file <- system.file(\"extdata\", \"Diff_deseq2.rds\", package = \"TransProR\")\n#' Diff_deseq2 <- readRDS(Diff_deseq2_file)\n#'\n#' result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, \"#0000EE\", \"#fc4746\")\ngene_color <- function(selected_genes, DEG_deseq2, up_color, down_color) {\n # Ensure that selected_genes has a column named \"Symble\"\n if (!\"Symble\" %in% colnames(selected_genes)) {\n stop(\"selected_genes data frame must have a 'Symble' column.\")\n }\n\n # Ensure that the row names column of DEG_deseq2 is correctly set to \"Symble\"\n DEG_deseq2$Symble <- rownames(DEG_deseq2)\n\n # Merge data frames: add the 'change' column from DEG_deseq2 to the selected_genes data frame\n merged_genes <- merge(selected_genes, DEG_deseq2[, c(\"Symble\", \"change\")], by = \"Symble\", all.x = TRUE)\n\n # Add color column based on the 'change' column values\n merged_genes$color <- ifelse(merged_genes$change == \"up\", up_color,\n ifelse(merged_genes$change == \"down\", down_color, NA))\n\n # Ensure that the length of the color column matches the number of rows in merged_genes\n if (length(merged_genes$color) != nrow(merged_genes)) {\n stop(\"Color assignment failed due to unexpected values in 'change' column.\")\n }\n\n return(merged_genes)\n}\n" }, { "path": "R/GeneHighlights.R", "content": "#' Add gene highlights to a ggtree object\n#'\n#' This function enhances a `ggtree` plot by adding highlights for specific genes. It adds both a semi-transparent fan-shaped\n#' highlight and a point at the node corresponding to each gene. Colors for each gene can be customized.\n#'\n#' @param ggtree_obj A ggtree object to which the highlights will be added.\n#' @param genes_to_highlight A data frame containing genes and their corresponding colors.\n#' @param hilight_extend Integer, the extension of the highlight fan in degrees.\n#' @return A ggtree object with added gene highlights.\n#' @importFrom ggtree geom_hilight geom_point2\n#' @importFrom dplyr filter select pull\n#' @examples\n#' data(\"gtree\", package = \"TransProR\")\n#'\n#' # Define genes and their colors\n#' genes_df <- data.frame(Symble = c(\"t5\", \"t9\"),\n#' color = c(\"#FF0000\", \"#0000FF\"))\n#'\n#' # Add highlights\n#' gtree <- gene_highlights(gtree, genes_to_highlight = genes_df)\n#'\n#' @export\ngene_highlights <- function(ggtree_obj, genes_to_highlight, hilight_extend = 18) {\n # Ensure the input is a `ggtree` object\n if (!inherits(ggtree_obj, \"ggtree\")) {\n stop(\"The first argument must be a ggtree object.\")\n }\n\n # Ensure the second argument is a data frame\n if (!(\"data.frame\" %in% class(genes_to_highlight))) {\n stop(\"The second argument must be a data frame.\")\n }\n\n if (!requireNamespace(\"systemfonts\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use the function. Please install it.\", call. = FALSE)\n }\n\n # Extract tree data and ensure it is in a data frame format\n tree_data <- as.data.frame(ggtree_obj$data)\n\n # Map gene names and colors to the tree nodes and create geom_hilight and geom_point2 layers for each node\n highlight_commands <- lapply(1:nrow(genes_to_highlight), function(i) {\n gene <- genes_to_highlight$Symble[i]\n color <- genes_to_highlight$color[i]\n node <- dplyr::filter(tree_data, .data$label == gene) %>%\n dplyr::select(node) %>%\n dplyr::pull()\n if (!is.na(node)) {\n list(\n ggtree::geom_hilight(node=node, fill=color, alpha = .3, extend = hilight_extend),\n ggtree::geom_point2(ggtree::aes(subset = (.data$label == gene)), color=color, size=2, alpha=0.6)\n )\n } else {\n warning(paste(\"Gene\", gene, \"not found in the ggtree object.\"))\n NULL\n }\n })\n\n # Remove NULL elements from the list of commands\n highlight_commands <- Filter(Negate(is.null), highlight_commands)\n\n # Flatten the list of commands and apply them to the `ggtree` object\n ggtree_obj <- ggtree_obj + do.call(c, highlight_commands)\n\n return(ggtree_obj)\n}\n" }, { "path": "R/GeneMapPathway.R", "content": "#' Create Pathway-Gene Mapping Data Frame\n#'\n#' This function takes multiple data frames and pathway IDs, merging them into a new data frame.\n#' Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.).\n#' @param BP_dataframe Data frame for Biological Process analysis\n#' @param BP_ids Selected pathway IDs for Biological Process analysis\n#' @param KEGG_dataframe Data frame for KEGG analysis\n#' @param KEGG_ids Selected pathway IDs for KEGG analysis\n#' @param MF_dataframe Data frame for Molecular Function analysis\n#' @param MF_ids Selected pathway IDs for Molecular Function analysis\n#' @param REACTOME_dataframe Data frame for REACTOME analysis\n#' @param REACTOME_ids Selected pathway IDs for REACTOME analysis\n#' @param CC_dataframe Data frame for Cellular Component analysis\n#' @param CC_ids Selected pathway IDs for Cellular Component analysis\n#' @param DO_dataframe Data frame for Disease Ontology analysis\n#' @param DO_ids Selected pathway IDs for Disease Ontology analysis\n#' @return A new data frame that includes pathways, gene, type, and value columns\n#' @export\n#' @examples\n#' # Simulating data for different analysis types\n#'\n#' # Simulate Biological Process (BP) data frame\n#' BP_df <- data.frame(\n#' ID = c(\"GO:0002376\", \"GO:0019724\"),\n#' geneID = c(\"GENE1/GENE2\", \"GENE3/GENE4\"),\n#' Description = c(\"Immune response\", \"Glycosylation process\")\n#' )\n#'\n#' # Simulate KEGG data frame\n#' KEGG_df <- data.frame(\n#' ID = c(\"12345\", \"67890\"),\n#' geneID = c(\"GENE5/GENE6\", \"GENE7/GENE8\"),\n#' Description = c(\"Pathway 1\", \"Pathway 2\")\n#' )\n#'\n#' # Simulate Molecular Function (MF) data frame\n#' MF_df <- data.frame(\n#' ID = c(\"ABC123\", \"DEF456\"),\n#' geneID = c(\"GENE9/GENE10\", \"GENE11/GENE12\"),\n#' Description = c(\"Molecular function A\", \"Molecular function B\")\n#' )\n#'\n#' # Simulate REACTOME data frame\n#' REACTOME_df <- data.frame(\n#' ID = c(\"R-HSA-12345\", \"R-HSA-67890\"),\n#' geneID = c(\"GENE13/GENE14\", \"GENE15/GENE16\"),\n#' Description = c(\"Pathway in Reactome 1\", \"Pathway in Reactome 2\")\n#' )\n#'\n#' # Simulate Cellular Component (CC) data frame\n#' CC_df <- data.frame(\n#' ID = c(\"GO:0005575\", \"GO:0005634\"),\n#' geneID = c(\"GENE17/GENE18\", \"GENE19/GENE20\"),\n#' Description = c(\"Cellular component A\", \"Cellular component B\")\n#' )\n#'\n#' # Simulate Disease Ontology (DO) data frame\n#' DO_df <- data.frame(\n#' ID = c(\"DOID:123\", \"DOID:456\"),\n#' geneID = c(\"GENE21/GENE22\", \"GENE23/GENE24\"),\n#' Description = c(\"Disease A\", \"Disease B\")\n#' )\n#'\n#' # Example pathway IDs for each analysis\n#' BP_ids <- c(\"GO:0002376\", \"GO:0019724\")\n#' KEGG_ids <- c(\"12345\", \"67890\")\n#' MF_ids <- c(\"ABC123\", \"DEF456\")\n#' REACTOME_ids <- c(\"R-HSA-12345\", \"R-HSA-67890\")\n#' CC_ids <- c(\"GO:0005575\", \"GO:0005634\")\n#' DO_ids <- c(\"DOID:123\", \"DOID:456\")\n#'\n#' # Generate the pathway-gene map using the gene_map_pathway function\n#' pathway_gene_map <- gene_map_pathway(\n#' BP_dataframe = BP_df, BP_ids = BP_ids,\n#' KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids,\n#' MF_dataframe = MF_df, MF_ids = MF_ids,\n#' REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids,\n#' CC_dataframe = CC_df, CC_ids = CC_ids,\n#' DO_dataframe = DO_df, DO_ids = DO_ids\n#' )\n#'\n#' # Display the resulting pathway-gene mapping data frame\n#' print(pathway_gene_map)\n#'\ngene_map_pathway <- function(BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids, MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids, CC_dataframe, CC_ids, DO_dataframe, DO_ids) {\n\n # Create an empty data frame\n pathway_gene_map <- data.frame(\n pathway_description = character(),\n gene4 = character(),\n type = character(),\n value = integer()\n )\n\n # Helper function to extract information from a data frame and add it to the new data frame\n add_to_map <- function(df, ids, type) {\n if (is.null(df) || is.null(ids)) return()\n selected_rows <- df[df$ID %in% ids, ]\n for (row in seq(nrow(selected_rows))) {\n genes <- strsplit(as.character(selected_rows$geneID[row]), \"/\")[[1]]\n # Here, instead of the ID, we use the Description column\n description <- as.character(selected_rows$Description[row])\n for (gene in genes) {\n pathway_gene_map <<- rbind(pathway_gene_map, data.frame(\n pathway = description,\n gene = gene,\n type = type,\n value = 1\n ))\n }\n }\n }\n\n # Apply the helper function to add data for each type of analysis\n add_to_map(BP_dataframe, BP_ids, \"BP\")\n add_to_map(KEGG_dataframe, KEGG_ids, \"KEGG\")\n add_to_map(MF_dataframe, MF_ids, \"MF\")\n add_to_map(REACTOME_dataframe, REACTOME_ids, \"REACTOME\")\n add_to_map(CC_dataframe, CC_ids, \"CC\")\n add_to_map(DO_dataframe, DO_ids, \"DO\")\n\n return(pathway_gene_map)\n}\n" }, { "path": "R/GetGtexExp.R", "content": "#' Get GTEx Expression Data for Specific Organ\n#'\n#' This function retrieves gene expression data from the GTEx project that is specific to a certain organ.\n#' It performs various checks and processing steps to ensure that the data is consistent and relevant to the\n#' specified organ. The filtered and cleaned data is saved as an RDS file for further analysis.\n#'\n#' @param organ_specific A character string specifying the organ to filter the gene expression data by.\n#' @param file_path A character string specifying the path to the GTEx gene expression data file.\n#' @param probe_map_path A character string specifying the path to the gtex_probeMap_gencode data file.\n#' @param pheno_path A character string specifying the path to the GTEx phenotype data file.\n#' @param output_path A character string specifying the path where the output RDS file will be saved.\n#'\n#' @details The function begins by checking if the gene expression and phenotype data files exist at\n#' the specified paths. It then loads these data files and processes them by setting appropriate row names,\n#' modifying column names for clarity, and filtering samples based on the specified organ. The function ensures\n#' that only samples present in both datasets are retained for consistency. It also removes any duplicate gene\n#' entries to prevent redundancy. Finally, the processed data is saved as an RDS file.\n#'\n#' @return A data frame containing gene expression data for the specified organ.\n#' Rows represent genes, and columns represent samples. Note that this function also saves the\n#' organ-specific GTEx data as an RDS file at the specified output path.\n#'\n#' @note The function will stop and throw an error if the input files do not exist, or if no samples are found\n#' for the specified organ.\n#'\n#' @note CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\n#' that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/your_sample_type.gtex.rds'.\n#'\n#' @importFrom utils read.table\n#' @importFrom dplyr distinct filter\n#' @importFrom rlang .data\n#' @export\nget_gtex_exp <- function(organ_specific,\n file_path,\n probe_map_path,\n pheno_path,\n output_path) {\n\n # Check for the existence of the file paths\n if (!file.exists(file_path) | !file.exists(pheno_path) | !file.exists(probe_map_path)) {\n stop(\"One or more of the input files do not exist.\")\n }\n\n # Load the gene expression, probe map, and phenotype data files from the provided paths\n # gtex.exp <- data.table::fread(file_path, header = TRUE, sep = '\\t', data.table = FALSE)\n # gtex.pro <- data.table::fread(probe_map_path, header = TRUE, sep = '\\t', data.table = FALSE)\n # gtex.phe <- data.table::fread(pheno_path, header = TRUE, sep = '\\t', data.table = FALSE)\n\n # Load the gene expression, probe map, and phenotype data files\n gtex.exp <- utils::read.table(file_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n gtex.pro <- utils::read.table(probe_map_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n gtex.phe <- utils::read.table(pheno_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n # Merge the probe map with the expression data\n gtex.pro <- gtex.pro[, c(1,2)] # Assuming the columns of interest are the first two\n gtex.count.pro <- merge(gtex.pro, gtex.exp, by.x = \"id\", by.y = \"sample\")\n\n # Set the row names for the samples, facilitating subsequent operations\n rownames(gtex.phe) <- gtex.phe$Sample\n\n # Modify column names to be more intuitive\n colnames(gtex.phe) <- c(\"Sample\", \"body_site_detail (SMTSD)\", \"primary_site\", \"gender\", \"patient\", \"cohort\")\n\n # Filter samples based on the specified organ\n specific_samples <- dplyr::filter(gtex.phe, .data$primary_site == organ_specific)\n\n # If no corresponding samples are found, halt the function with an error message\n if (nrow(specific_samples) == 0) {\n stop(\"No samples found for the specified organ.\")\n }\n\n # Print the number of samples found for the specified organ\n message(\"Number of samples for\", organ_specific, \":\", nrow(specific_samples), \"\\n\")\n\n # Ensure processing only for samples present in both expression and phenotype data through intersection\n valid_sample_names <- intersect(rownames(specific_samples), colnames(gtex.count.pro)) # merge_phe_count_gtex\n gtex_data <- gtex.count.pro[, c(\"gene\", valid_sample_names)] # Extract data for relevant samples\n\n # Remove duplicate gene entries and set row names as gene names\n gtex_data <- dplyr::distinct(gtex_data, .data$gene, .keep_all = TRUE)\n rownames(gtex_data) <- gtex_data$gene\n gtex_data <- gtex_data[, -1] # Remove the 'gene' column, keeping only expression data\n\n # Save the results as an RDS file for future data analysis tasks\n saveRDS(gtex_data, output_path)\n\n return(gtex_data)\n}\n" }, { "path": "R/GetTcgaExp.R", "content": "#' TCGA Expression Data Processing\n#'\n#' This function processes expression data and phenotype information, separates tumor and normal samples,\n#' and saves the results into different files. It's specifically designed for data obtained from TCGA.\n#'\n#' @param counts_file_path File path to the counts data (usually in the form of a large matrix with gene expression data).\n#' @param gene_probes_file_path File path containing the gene probes data.\n#' @param phenotype_file_path File path to the phenotype data, which includes various sample attributes.\n#' @param output_file_path Path where the output files, distinguished between tumor and normal, will be saved.\n#'\n#' @return A list containing matrices for tumor and normal expression data.\n#'\n#' @note IMPORTANT: This function assumes that the input files follow a specific format and structure, typically found in TCGA data releases.\n#' Users should verify their data's compatibility. Additionally, the function does not perform error checking on the data's content,\n#' which users should handle through proper preprocessing.\n#'\n#' @note CRITICAL: The 'output_file_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\n#' that the path includes specific identifiers related to the target samples, as the function will create further subdivisions in the specified\n#' path for tumor or normal tissues. Please structure the 'output_file_path' following this pattern: './your_directory/your_sample_type.exp.rds'.\n#'\n#' @importFrom dplyr distinct filter\n#' @importFrom utils read.table\n#' @importFrom rlang .data\n#' @export\n#' @author Dongyue Yu\n#'\n#' @examples\n#' counts_file <- system.file(\"extdata\", \"TCGA-SKCM.htseq_counts_test.tsv\", package = \"TransProR\")\n#' gene_probes_file <- system.file(\"extdata\",\n#' \"TCGA_gencode.v22.annotation.gene.probeMap_test\",\n#' package = \"TransProR\")\n#' phenotype_file <- system.file(\"extdata\", \"TCGA-SKCM.GDC_phenotype_test.tsv\", package = \"TransProR\")\n#' ouput_file <- file.path(tempdir(), \"SKCM_Skin_TCGA_exp_test.rds\")\n#'\n#' SKCM_exp <- get_tcga_exp(\n#' counts_file_path = counts_file,\n#' gene_probes_file_path = gene_probes_file,\n#' phenotype_file_path = phenotype_file,\n#' output_file_path = ouput_file\n#' )\n#' head(SKCM_exp[[\"tumor_tcga_data\"]])[1:5, 1:5]\n#' head(SKCM_exp[[\"normal_tcga_data\"]], n = 10) # Because there is only one column.\nget_tcga_exp <- function(counts_file_path,\n gene_probes_file_path,\n phenotype_file_path,\n output_file_path) {\n # Load expression matrix\n # count_data <- data.table::fread(counts_file_path, header = TRUE, sep = '\\t', data.table = FALSE)\n count_data <- utils::read.table(counts_file_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n # Load gene ID conversion information\n # gene_probes <- data.table::fread(gene_probes_file_path, header = TRUE, sep = '\\t', data.table = FALSE)\n gene_probes <- utils::read.table(gene_probes_file_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n # Keep only necessary columns\n gene_probes <- gene_probes[, c(1, 2)]\n\n # Merge gene ID information with expression matrix\n count_probes_merged <- merge(gene_probes, count_data, by.x = \"id\", by.y = \"Ensembl_ID\")\n\n # Remove duplicates\n count_data_unique <- dplyr::distinct(count_probes_merged, .data$gene, .keep_all = TRUE)\n\n # Set gene names as row names\n rownames(count_data_unique) <- count_data_unique$gene\n count_data_final <- count_data_unique[, -c(1,2)] # Remove extra columns\n\n # Load clinical information\n # phenotype_data <- data.table::fread(phenotype_file_path, header = TRUE, sep = '\\t', data.table = FALSE)\n # Load clinical information with proper column types\n phenotype_data <- utils::read.table(phenotype_file_path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE,\n colClasses = list(\n withdrawn = \"logical\",\n releasable.project = \"logical\",\n is_ffpe.samples = \"logical\",\n oct_embedded.samples = \"logical\"\n ))\n rownames(phenotype_data) <- phenotype_data$submitter_id.samples # Set sample names as row names\n\n\n # Check first if there are any \"Metastatic\", \"Primary Tumor\", or \"normal\" samples in your data\n table(phenotype_data$sample_type.samples)\n\n # Create a data frame for tumor samples\n tumor_samples <- phenotype_data %>%\n dplyr::filter(grepl(\"Metastatic|Primary Tumor\", .data$sample_type.samples, ignore.case = TRUE))\n\n # Check for 'Primary Tumor' or 'Metastatic' samples\n if (nrow(tumor_samples) == 0) {\n message(\"No 'Primary Tumor' or 'Metastatic' samples found.\\n\")\n } else {\n message(\"Number of 'Primary Tumor' or 'Metastatic' samples: \", nrow(tumor_samples), \"\\n\")\n }\n\n # Create a data frame for normal samples\n normal_samples <- phenotype_data %>%\n dplyr::filter(grepl(\"normal\", .data$sample_type.samples, ignore.case = TRUE))\n\n if (nrow(normal_samples) == 0) {\n message(\"No 'normal' samples found.\\n\")\n } else {\n message(\"Number of 'normal' samples:\", nrow(normal_samples), \"\\n\")\n }\n\n # Get the intersection of clinical information and expression matrix\n tumor_common_samples <- intersect(rownames(tumor_samples), colnames(count_data_final))\n\n # Extract corresponding expression matrix\n tumor_expression_data <- count_data_final[, tumor_common_samples]\n\n # Get the intersection of clinical information and expression matrix\n normal_common_samples <- intersect(rownames(normal_samples), colnames(count_data_final))\n\n # Extract corresponding expression matrix\n normal_expression_data <- count_data_final[, normal_common_samples, drop = FALSE]\n\n # Save results\n tumor_output_path <- gsub(\"\\\\.rds$\", \"_tumor.rds\", output_file_path)\n normal_output_path <- gsub(\"\\\\.rds$\", \"_normal.rds\", output_file_path)\n saveRDS(tumor_expression_data, file = tumor_output_path)\n saveRDS(normal_expression_data, file = normal_output_path)\n\n\n result <- list(\n tumor_tcga_data = tumor_expression_data,\n normal_tcga_data = normal_expression_data\n )\n\n return(result)\n}\n" }, { "path": "R/HighlightByNode.R", "content": "#' Highlight Nodes in a Phylogenetic Tree with Custom Fill Colors and Transparency\n#'\n#' This function adds highlights to specific nodes in a phylogenetic tree represented by a `ggtree` object.\n#' Users can specify the nodes to highlight along with custom fill colors, transparency, and extension options.\n#'\n#' @importFrom ggtree geom_hilight\n#' @param ggtree_object A `ggtree` object representing the phylogenetic tree.\n#' @param nodes A character vector specifying the nodes to highlight.\n#' @param fill_colors A character vector specifying the fill colors for the highlighted nodes.\n#' @param alpha_values A numeric vector specifying the transparency values for the highlighted nodes (between 0 and 1).\n#' @param extend_values A logical vector specifying whether to extend the highlight to the whole clade below each node.\n#' @return A modified `ggtree` object with the specified nodes highlighted.\n#' @export\n#'\n#' @examples\n#' plot_file <- system.file(\"extdata\", \"tree_plot.rds\", package = \"TransProR\")\n#' p2_plot <- readRDS(plot_file)\n#'\n#' # Please replace the following vectors with your specific values\n#' nodes <- c(117, 129, 125, 127, 119,\n#' 123, 139, 166, 124, 131, 217) # x-values of the nodes you want to highlight\n#' fill_colors <- c(\"#CD6600\", \"#CD6600\", \"#CD6600\",\n#' \"#CD6600\", \"#009933\", \"#009933\",\n#' \"#009933\", \"#009933\", \"#9B30FF\",\n#' \"#9B30FF\", \"#9B30FF\") # Fill colors\n#' alpha_values <- c(0.3, 0.3, 0.3, 0.3, 0.2, 0.3,\n#' 0.3, 0.3, 0.3, 0.3, 0.3) # Transparency values\n#' extend_values <- c(25, 24, 24, 25, 25, 25,\n#' 24, 24, 25, 24, 24) # Values for the 'extend' parameter\n#'\n#' p2 <- highlight_by_node(\n#' p2_plot,\n#' nodes,\n#' fill_colors,\n#' alpha_values,\n#' extend_values\n#' )\nhighlight_by_node <- function(ggtree_object,\n nodes,\n fill_colors,\n alpha_values,\n extend_values) {\n # Ensure that the lengths of `nodes`, `fill_colors`, `alpha_values`, and `extend_values` are consistent\n if (!(length(nodes) == length(fill_colors) && length(nodes) == length(alpha_values) && length(nodes) == length(extend_values))) {\n stop(\"Length of nodes, fill_colors, alpha_values, and extend_values must be the same.\")\n }\n\n if (!requireNamespace(\"systemfonts\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use the function. Please install it.\", call. = FALSE)\n }\n\n # For each node, add the corresponding geom_hilight layer\n layers <- lapply(seq_along(nodes), function(i) {\n node <- nodes[i]\n fill_color <- fill_colors[i]\n alpha_val <- alpha_values[i]\n extend_val <- extend_values[i]\n\n # Create a geom_hilight layer\n ggtree::geom_hilight(node = node, fill = fill_color, alpha = alpha_val, extend = extend_val)\n })\n\n # Add all layers to the `ggtree` object\n for (layer in layers) {\n ggtree_object <- ggtree_object + layer\n }\n\n return(ggtree_object)\n}\n" }, { "path": "R/HighlightGenes.R", "content": "#' Add Highlights for Genes on a Phylogenetic Tree\n#'\n#' This function adds highlights for specified genes on a phylogenetic tree object.\n#'\n#' @importFrom ggtree geom_point2 geom_hilight aes\n#' @importFrom dplyr filter select pull\n#' @importFrom rlang .data\n#' @param ggtree_obj A ggtree object representing the phylogenetic tree.\n#' @param genes_to_highlight A data frame containing gene names and corresponding colors to highlight.\n#' @param hilight_extend Numeric value indicating the extension length for highlights.\n#' @return A `ggtree` object with added highlights for specified genes.\n#' @export\n#'\n#' @examples\n#' plot_file <- system.file(\"extdata\", \"tree_plot.rds\", package = \"TransProR\")\n#' p2_plot <- readRDS(plot_file)\n#'\n#' selected_genes_deseq2_file <- system.file(\"extdata\",\n#' \"selected_genes_deseq2.rds\",\n#' package = \"TransProR\")\n#' selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)\n#'\n#' Diff_deseq2_file <- system.file(\"extdata\", \"Diff_deseq2.rds\", package = \"TransProR\")\n#' Diff_deseq2 <- readRDS(Diff_deseq2_file)\n#'\n#' result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, \"#0000EE\", \"#fc4746\")\n#'\n#' add_gene_highlights_p3 <- highlight_genes(p2_plot, result_deseq2, hilight_extend = 26)\nhighlight_genes <- function(ggtree_obj, genes_to_highlight, hilight_extend = 18) {\n # Ensure that the first argument is a `ggtree` object\n if (!inherits(ggtree_obj, \"ggtree\")) {\n stop(\"The first argument must be a ggtree object.\")\n }\n\n # Ensure that the second argument is a data frame\n if (!(\"data.frame\" %in% class(genes_to_highlight))) {\n stop(\"The second argument must be a data frame.\")\n }\n\n if (!requireNamespace(\"systemfonts\", quietly = TRUE)) {\n stop(\"ggplot2 is required to use the function. Please install it.\", call. = FALSE)\n }\n\n # Extract the data from the tree object and ensure it is a data frame\n tree_data <- as.data.frame(ggtree_obj$data)\n\n # Map gene names and colors to nodes in the tree and create geom_hilight and geom_point2 layers for each node\n highlight_commands <- lapply(1:nrow(genes_to_highlight), function(i) {\n gene <- genes_to_highlight$Symble[i]\n color <- genes_to_highlight$color[i]\n node <- dplyr::filter(tree_data, .data$label == gene) %>%\n dplyr::select(node) %>%\n dplyr::pull()\n if (!is.na(node)) {\n list(\n ggtree::geom_hilight(node = node, fill = color, alpha = 0.3, extend = hilight_extend),\n ggtree::geom_point2(ggtree::aes(subset = (.data$label == gene)), color = color, size = 2, alpha = 0.6)\n )\n } else {\n warning(paste(\"Gene\", gene, \"not found in the ggtree object.\"))\n NULL\n }\n })\n\n # Remove NULL elements from `highlight_commands` since they may exist\n highlight_commands <- Filter(Negate(is.null), highlight_commands)\n\n # Apply the commands to the `ggtree object`\n ggtree_obj <- ggtree_obj + do.call(c, highlight_commands)\n\n return(ggtree_obj)\n}\n" }, { "path": "R/LimmaAnalyze.R", "content": "#' Differential Gene Expression Analysis using limma and voom\n#'\n#' This function performs differential gene expression analysis using the 'limma' package with voom normalization.\n#' It reads tumor and normal expression data, merges them, filters low-expressed genes,\n#' normalizes the data, performs limma analysis, and outputs the results along with information\n#' on gene expression changes.\n#'\n#' @importFrom limma lmFit contrasts.fit eBayes topTable\n#' @importFrom edgeR DGEList filterByExpr calcNormFactors\n#' @importFrom dplyr mutate\n#' @importFrom stats na.omit\n#' @param tumor_file Path to the tumor data file (RDS format).\n#' @param normal_file Path to the normal data file (RDS format).\n#' @param output_file Path to save the output DEG data (RDS format).\n#' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes.\n#' @param p_value_threshold Threshold for p-value for filtering significant genes.\n#' @return A data frame of differential expression results.\n#' @references\n#' limma:Linear Models for Microarray and RNA-Seq Data User’s Guide.\n#' For more information, visit the page:\n#' https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf\n#' @export\n#'\n#' @examples\n#' # Define file paths for tumor and normal data from the data folder\n#' tumor_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' normal_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n#' package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"DEG_limma_voom.rds\")\n#'\n#' DEG_limma_voom <- limma_analyze(\n#' tumor_file = tumor_file,\n#' normal_file = normal_file,\n#' output_file = output_file,\n#' logFC_threshold = 2.5,\n#' p_value_threshold = 0.01\n#' )\n#'\n#' # View the top 5 rows of the result\n#' head(DEG_limma_voom, 5)\nlimma_analyze <- function(tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01) {\n tumor <- readRDS(tumor_file)\n normal <- readRDS(normal_file)\n\n # Merge the datasets, ensuring both have genes as row names\n all_count_exp <- merge(tumor, normal, by = \"row.names\")\n all_count_exp <- tibble::column_to_rownames(all_count_exp, var = \"Row.names\")\n\n # Define groups for tumor and normal samples\n group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal)))\n group <- factor(group, levels = c(\"normal\", \"tumor\"))\n group_table <- table(group)\n\n message(\"Group Table:\")\n message(paste(names(group_table), group_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Create matrix\n design <- model.matrix(~0 + factor(group))\n colnames(design) <- levels(factor(group))\n rownames(design) <- colnames(all_count_exp)\n\n # Create DGEList object for gene expression data and group information\n dge <- edgeR::DGEList(counts = all_count_exp, group = group)\n\n # Filter lowly expressed genes\n keep <- edgeR::filterByExpr(dge)\n dge <- dge[keep, , keep.lib.sizes = FALSE]\n\n # The first step (TMM) scales the raw counts to adjust for library size differences, while the second step (quantile normalization in voom) ensures that the overall distribution of gene expression values is consistent across samples.\n # Normalize the data using the TMM method\n dge <- edgeR::calcNormFactors(dge)\n # Use voom method for normalization:Quantile Normalization\n v <- limma::voom(dge, design, plot = FALSE, normalize = \"quantile\")\n\n # Fit the linear model\n fit <- limma::lmFit(v, design)\n\n # Specify contrast\n con <- paste(rev(levels(group)), collapse = \"-\")\n\n # Create contrast matrix\n cont.matrix <- limma::makeContrasts(contrasts = c(con), levels = design)\n fit2 <- limma::contrasts.fit(fit, cont.matrix)\n fit2 <- limma::eBayes(fit2)\n\n # Get differential expression results\n tempOutput <- limma::topTable(fit2, coef = con, n = Inf)\n DEG_limma_voom <- stats::na.omit(tempOutput)\n\n # Add 'change' column to mark up/down-regulated genes\n k1 <- (DEG_limma_voom$P.Value < p_value_threshold) & (DEG_limma_voom$logFC < -logFC_threshold)\n k2 <- (DEG_limma_voom$P.Value < p_value_threshold) & (DEG_limma_voom$logFC > logFC_threshold)\n DEG_limma_voom <- dplyr::mutate(DEG_limma_voom, change = ifelse(k1, \"down\", ifelse(k2, \"up\", \"stable\")))\n\n change_table <- table(DEG_limma_voom$change)\n\n message(\"Change Table:\")\n message(paste(names(change_table), change_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n\n # Save results to the specified output file\n #save(DEG_limma_voom, file = output_file)\n saveRDS(DEG_limma_voom, file = output_file)\n\n return(DEG_limma_voom)\n}\n\n\n" }, { "path": "R/LogTransform.R", "content": "#' Log transformation decision and application on data\n#'\n#' This function evaluates the need for a log transformation based on a set of criteria\n#' and applies a log2 transformation if necessary.\n#'\n#' @author Dongyue Yu\n#' @param data A numeric matrix or data frame.\n#' @return The original data or the data transformed with log2.\n#' @importFrom stats quantile\n#' @export\n#' @examples\n#' file_path <- system.file(\"extdata\",\n#' \"all_count_exp_test.csv\",\n#' package = \"TransProR\")\n#' your_data <- read.csv(file_path,\n#' row.names = 1) # Assuming first column is row names (e.g., gene names)\n#'\n#' TransformedData <- log_transform(data = your_data)\n#'\nlog_transform <- function(data) {\n # Calculate quantiles\n qx <- as.numeric(quantile(data, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=TRUE))\n\n # Define conditions for log transformation\n LogC <- (qx[5] > 100) ||\n (qx[6]-qx[1] > 50 && qx[2] > 0) ||\n (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2)\n\n # Apply log transformation based on conditions\n if (LogC) {\n # Apply +1 to all values before log2 transformation\n result <- log2(data + 1)\n message(\"log2 transform finished\")\n } else {\n result <- data\n message(\"log2 transform not needed\")\n }\n\n return(result)\n}\n" }, { "path": "R/MergeDensityFoldchange.R", "content": "#' Create high-density region plot with optional points, density rugs, and contours\n#'\n#' This function creates a high-density region plot using hdr methods to\n#' add density rug and filled contours. It also adds a regression line\n#' and Pearson correlation label. Points can be added to the plot optionally.\n#'\n#' @param data Data frame containing variables for plotting.\n#' @param x_var Name of the x-axis variable as a string.\n#' @param y_var Name of the y-axis variable as a string.\n#' @param group_var Name of the grouping variable for color mapping as a string.\n#' @param palette Color palette for the plot as a character vector.\n#' @param show_points Logical, if TRUE adds points to the plot.\n#' @param point_size Size of the points, relevant if show_points is TRUE.\n#' @param point_alpha Transparency level of the points, relevant if show_points is TRUE.\n#' @param x_lim Numeric vector of length 2, giving the x-axis limits.\n#' @param y_lim Numeric vector of length 2, giving the y-axis limits.\n#' @param cor_method Method to calculate correlation (\"pearson\" or \"spearman\").\n#' @param line_size Size of the smoothing line.\n#' @param cor_label_pos Vector of length 2 indicating the position of the correlation label (x and y).\n#' @return A ggplot object representing the high-density region plot.\n#' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth scale_fill_manual scale_color_manual scale_x_continuous scale_y_continuous theme element_rect margin\n#' @importFrom hrbrthemes theme_ipsum\n#' @importFrom grid unit\n#' @importFrom ggdensity geom_hdr geom_hdr_rug\n#' @importFrom ggpubr stat_cor\n#' @examples\n#' combined_df_file <- system.file(\"extdata\", \"combined_df.rds\", package = \"TransProR\")\n#' combined_df <- readRDS(combined_df_file)\n#' pal1 = c(\"#3949ab\",\"#1e88e5\",\"#039be5\",\"#00897b\",\"#43a047\",\"#7cb342\")\n#'\n#' all_density_foldchange_name1 <- merge_density_foldchange(\n#' data = combined_df,\n#' x_var = \"log2FoldChange_1\",\n#' y_var = \"log2FoldChange_2\",\n#' group_var = \"name\",\n#' palette = pal1,\n#' show_points = FALSE,\n#' point_size = 2.5,\n#' point_alpha = 0.1,\n#' x_lim = c(0, 20),\n#' y_lim = c(0, 20),\n#' cor_method = \"pearson\",\n#' line_size = 1.6,\n#' cor_label_pos = c(\"left\", \"top\")\n#' )\n#'\n#' @export\nmerge_density_foldchange <- function(data, x_var, y_var, group_var,\n palette = c(\"#3949ab\",\"#1e88e5\",\"#039be5\",\"#00897b\",\"#43a047\",\"#7cb342\"),\n show_points = FALSE, point_size = 2.5, point_alpha = 0.2,\n x_lim = c(0, 20), y_lim = c(0, 20),\n cor_method = \"pearson\", line_size = 1.6,\n cor_label_pos = c(\"left\", 0.97)) {\n # Begin constructing the ggplot\n plot <- ggplot2::ggplot(data, ggplot2::aes_string(x = x_var, y = y_var, fill = group_var))\n\n # Optionally add points\n if (show_points) {\n plot <- plot + ggplot2::geom_point(ggplot2::aes_string(color = group_var), shape = 21,\n size = point_size, alpha = point_alpha)\n }\n\n # Add density rug and contours\n plot <- plot + ggdensity::geom_hdr_rug() + ggdensity::geom_hdr()\n\n # Add regression line and correlation label\n plot <- plot +\n ggplot2::geom_smooth(ggplot2::aes_string(x = x_var, y = y_var, color = group_var),\n method = 'lm', level = 0.95, size = line_size) +\n ggpubr::stat_cor(ggplot2::aes_string(color = group_var), method = cor_method,\n label.x.npc = cor_label_pos[1], label.y.npc = cor_label_pos[2])\n\n # Customize scales and theme\n plot <- plot +\n ggplot2::scale_fill_manual(values = palette) +\n ggplot2::scale_color_manual(values = palette) +\n ggplot2::scale_x_continuous(limits = x_lim, expand = c(0, 0)) +\n ggplot2::scale_y_continuous(limits = y_lim, expand = c(0, 0)) +\n hrbrthemes::theme_ipsum() +\n ggplot2::theme(plot.margin = ggplot2::margin(10, 10, 10, 10),\n plot.background = ggplot2::element_rect(fill = \"white\", color = \"white\"),\n panel.spacing = grid::unit(2, \"mm\"))\n\n # Return the ggplot object\n return(plot)\n}\n" }, { "path": "R/MergeGtexTcga.R", "content": "#' Merge gene expression data from GTEx and TCGA datasets\n#'\n#' This function merges gene expression data obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) datasets.\n#' It is assumed that both datasets are in '.rds' format and have genes as row names. The merged dataset is saved as an RDS file at the specified output path.\n#'\n#' @param gtex_data_path A string that specifies the file path to the GTEx data saved in RDS format.\n#' @param tcga_exp_path A string that specifies the file path to the TCGA expression data saved in RDS format.\n#' This should be a data.frame with rows as genes and columns as samples.\n#' @param output_path A string that specifies the path where the merged dataset should be saved.\n#' The file is saved in '.rds' format. The default path is \"./merged_gtex_tcga_data.rds\".\n#'\n#' @details It is assumed that both datasets are in '.rds' format and have genes as row names.\n#'\n#' @return A data frame where rows represent genes and columns represent samples.\n#' The data frame contains expression values from both GTEx and TCGA datasets.\n#' It saves the merged dataset to the path specified by 'output_path'.\n#'\n#' @examples\n#' tumor_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' Normal_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n#' package = \"TransProR\")\n#' ouput_file <- file.path(tempdir(), \"all_data.rds\")\n#'\n#' all_data <- merge_gtex_tcga(gtex_data_path = tumor_file,\n#' tcga_exp_path = Normal_file,\n#' output_path = ouput_file)\n#'\n#' @note CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\n#' that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/merged.your_sample_type.gtex.tcga.data.rds'.\n#'\n#' @importFrom tibble column_to_rownames\n#' @export\nmerge_gtex_tcga <- function(gtex_data_path,\n tcga_exp_path,\n output_path = \"./merged_gtex_tcga_data.rds\") {\n\n # Load the GTEx data\n gtex_data <- readRDS(gtex_data_path)\n message(\"Number of GTEx samples:\", ncol(gtex_data), \"\\n\")\n\n # Load the TCGA data\n tcga.exp <- readRDS(tcga_exp_path)\n message(\"Number of TCGA samples:\", ncol(tcga.exp), \"\\n\")\n\n # Merge the datasets, ensuring both have genes as row names\n all_data <- merge(gtex_data, tcga.exp, by = \"row.names\")\n all_data <- tibble::column_to_rownames(all_data, var = \"Row.names\") # Set the row names\n\n message(\"Number of samples after merging:\", ncol(all_data), \"\\n\")\n\n # Save the merged dataset\n saveRDS(all_data, file = output_path)\n\n return(all_data)\n}\n" }, { "path": "R/MergeIDPosition.R", "content": "#' Merge Data Frames by Common Row Names with Additional Columns\n#'\n#' This function merges a list of data frames based on common row names. It adds an 'id' column to track the row order and a 'point_position' column calculated based on the maximum 'Count' value across all data frames. It filters data frames to include only common rows, sorts rows by the length of the 'Description' in descending order, and then merges them by rows.\n#'\n#' @param df_list A list of data frames, each with a 'Description' and 'Count' column and set row names.\n#' @return A single data frame merged from the list, with additional 'id' and 'point_position' columns.\n#' @importFrom dplyr arrange\n#' @examples\n#' df1 <- data.frame(Description = c(\"DataA\", \"DataB\"), Count = c(10, 20), row.names = c(\"R1\", \"R2\"))\n#' df2 <- data.frame(Description = c(\"DataC\", \"DataD\"), Count = c(30, 40), row.names = c(\"R1\", \"R3\"))\n#' df_list <- list(df1, df2)\n#' combined_df_test <- merge_id_position(df_list)\n#'\n#' @export\nmerge_id_position <- function(df_list) {\n # Find common row names across all data frames\n common_row_names <- Reduce(intersect, lapply(df_list, row.names))\n\n # Get the maximum value of the 'Count' column across all data frames\n max_count <- max(sapply(df_list, function(df) max(as.numeric(df$Count), na.rm = TRUE)))\n\n # Calculate the smallest multiple of 10 greater than the maximum count\n ceiling_max_count <- ceiling(max_count / 10) * 10\n\n # Initialize a list to store processed data frames\n processed_dfs <- list()\n number_of_dfs <- length(df_list)\n\n # Process each data frame\n for (i in seq_along(df_list)) {\n # Filter for common rows\n filtered_df <- df_list[[i]][common_row_names, , drop = FALSE]\n\n # Sort 'Description' column by character length in descending order\n filtered_df <- filtered_df %>% dplyr::arrange(desc(nchar(.data$Description)))\n\n # Add 'id' column\n filtered_df$id <- seq_len(nrow(filtered_df))\n\n # Calculate 'point_position' value\n point_position_value <- ceiling_max_count * i / number_of_dfs\n\n # Add 'point_position' column\n filtered_df$point_position <- point_position_value\n\n # Append processed data frame to the list\n processed_dfs[[i]] <- filtered_df\n }\n\n # Check if any data frames are empty after filtering\n if (any(sapply(processed_dfs, nrow) == 0)) {\n stop(\"One or more dataframes have no rows after filtering for common row names.\")\n }\n\n # Merge data frames by rows\n combined_df <- do.call(rbind, processed_dfs)\n\n # Remove original row names and set new row names as NULL\n rownames(combined_df) <- NULL\n\n return(combined_df)\n}\n" }, { "path": "R/MergeMethodColor.R", "content": "#' Merge Data Frames with Specific Method and Color Columns\n#'\n#' This function takes a list of data frames, a method name, and a list of colors.\n#' It adds a 'method' column and a 'test_color' column to each data frame, then merges all data frames by rows.\n#' It ensures that the color list length matches the list of data frames.\n#'\n#' @param df_list A list of data frames, each containing at least 'Description' and 'Count' columns.\n#' @param method_name A string representing the method name to be added to each data frame.\n#' @param color_list A list of colors corresponding to each data frame for the 'test_color' column.\n#' @return A single data frame merged from the list, with each originally provided data frame now having a 'method' and a 'test_color' column.\n#' @importFrom dplyr bind_rows\n#' @examples\n#' df1 <- data.frame(Description = c(\"A\", \"B\"), Count = c(10, 20))\n#' df2 <- data.frame(Description = c(\"C\", \"D\"), Count = c(30, 40))\n#' df_list <- list(df1, df2)\n#' method_name <- \"Method1\"\n#' color_list <- c(\"Red\", \"Blue\")\n#' combined_df_test <- merge_method_color(df_list, method_name, color_list)\n#'\n#' @export\nmerge_method_color <- function(df_list, method_name, color_list) {\n # Validate the length of color list matches the length of data frame list\n if (length(color_list) != length(df_list)) {\n stop(\"The length of the color list must match the length of the data frame list\")\n }\n\n # Initialize a list to store processed data frames\n processed_dfs <- list()\n\n # Iterate over all data frames\n for (i in seq_along(df_list)) {\n # Extract 'Description' and 'Count' columns\n temp_df <- df_list[[i]][, c(\"Description\", \"Count\"), drop = FALSE]\n\n # Add 'method' column\n temp_df$method <- method_name\n\n # Add 'test_color' column using corresponding color\n temp_df$test_color <- color_list[i]\n\n # Append the processed data frame to the list\n processed_dfs[[i]] <- temp_df\n }\n\n # Combine all data frames by rows using bind_rows from dplyr\n combined_df <- dplyr::bind_rows(processed_dfs)\n\n return(combined_df)\n}\n\n\n\n" }, { "path": "R/NewGgraph.R", "content": "#' Generate a graphical representation of pathway gene maps\n#'\n#' This function merges multiple gene-pathway related dataframes, processes them\n#' for graph creation, and visualizes the relationships in a dendrogram layout using\n#' the provided node and edge gathering functions from the 'ggraph' package.\n#'\n#' @param BP_dataframe Dataframe for Biological Process.\n#' @param BP_ids IDs for Biological Process.\n#' @param KEGG_dataframe Dataframe for KEGG pathways.\n#' @param KEGG_ids IDs for KEGG pathways.\n#' @param MF_dataframe Dataframe for Molecular Function.\n#' @param MF_ids IDs for Molecular Function.\n#' @param REACTOME_dataframe Dataframe for REACTOME pathways.\n#' @param REACTOME_ids IDs for REACTOME pathways.\n#' @param CC_dataframe Dataframe for Cellular Component.\n#' @param CC_ids IDs for Cellular Component.\n#' @param DO_dataframe Dataframe for Disease Ontology.\n#' @param DO_ids IDs for Disease Ontology.\n#' @importFrom tidygraph tbl_graph\n#' @importFrom ggraph ggraph geom_edge_diagonal geom_node_point geom_node_text scale_edge_colour_brewer node_angle\n#' @importFrom ggplot2 theme element_rect scale_size scale_color_brewer coord_cartesian\n#' @return A 'ggraph' object representing the pathway gene map visualization.\n#' @export\n#' \nnew_ggraph <- function(BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids,\n MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids,\n CC_dataframe, CC_ids, DO_dataframe, DO_ids) {\n\n new_dataframe <- gene_map_pathway(BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids,\n MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids,\n CC_dataframe, CC_ids, DO_dataframe, DO_ids)\n\n # Prepare the data for graph creation using 'ggraph'\n index_ggraph <- c(\"type\", \"pathway\", \"gene\") # columns other than the lowest level\n nodes_ggraph <- gather_graph_node(new_dataframe, index = index_ggraph, root = \"combination\")\n edges_ggraph <- gather_graph_edge(new_dataframe, index = index_ggraph, root = \"combination\")\n\n # Create and plot the graph using 'tidygraph' and 'ggraph'\n graph_ggraph <- tidygraph::tbl_graph(nodes = nodes_ggraph, edges = edges_ggraph)\n\n plot <- ggraph::ggraph(graph_ggraph, layout = 'dendrogram', circular = TRUE) +\n ggraph::geom_edge_diagonal(aes(color = .data$node1.node.branch, filter = .data$node1.node.level != \"combination\", alpha = .data$node1.node.level), edge_width = 1) +\n ggraph::geom_node_point(aes(size = .data$node.size, color = .data$node.branch, filter = .data$node.level != \"combination\"), alpha = 0.45) +\n ggplot2::scale_size(range = c(15, 90)) +\n ggplot2::theme(legend.position = \"none\") +\n ggraph::scale_edge_colour_brewer(palette= \"Dark2\") +\n ggplot2::scale_color_brewer(palette = \"Dark2\") +\n ggraph::geom_node_text(aes(x = 1.058 * .data$x, y = 1.058 * .data$y, label = .data$node.short_name, angle = -((-ggraph::node_angle(.data$x, .data$y) + 90) %% 180) + 60, filter = .data$leaf, color = .data$node.branch), size = 4, hjust = 'outward') +\n ggraph::geom_node_text(aes(label = .data$node.short_name, filter = !.data$leaf & (.data$node.level == \"type\"), color = .data$node.branch), fontface = \"bold\", size = 8, family = \"sans\") +\n ggraph::geom_node_text(aes(label = .data$node.short_name, filter = !.data$leaf & (.data$node.level == \"pathway\"), color = .data$node.branch, angle = -((-ggraph::node_angle(.data$x, .data$y) + 90) %% 180) + 36), fontface = \"bold\", size = 4.5, family = \"sans\") +\n ggplot2::theme(panel.background = ggplot2::element_rect(fill = NA)) +\n ggplot2::coord_cartesian(xlim = c(-1.3, 1.3), ylim = c(-1.3, 1.3))\n\n return(plot)\n}\n" }, { "path": "R/PathwayCount.R", "content": "#' Count Genes Present in Pathways Above a Threshold\n#'\n#' This function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways.\n#'\n#' @importFrom dplyr filter\n#' @importFrom rlang .data\n#' @param GO A character vector of gene symbols.\n#' @param count_threshold An integer specifying the count threshold for selecting pathways.\n#' @param enrich_data A data frame containing pathway enrichment analysis results.\n#' @return A data frame with columns \"Symble\" (gene symbol), \"Description\" (pathway description), and \"Exists\" (1 if gene is present, 0 otherwise).\n#' @export\n#'\n#' @examples\n#' # Simulated gene list\n#' GO <- c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\")\n#' # Simulated enrichment analysis data\n#' enrich_data <- data.frame(\n#' ID = c(\"GO:0001\", \"GO:0002\", \"GO:0003\"),\n#' Description = c(\"Pathway A\", \"Pathway B\", \"Pathway C\"),\n#' Count = c(10, 4, 6),\n#' geneID = c(\"Gene1/Gene2/Gene3\", \"Gene4/Gene5\", \"Gene2/Gene6/Gene7\")\n#' )\n#'\n#' # Example usage\n#' count_threshold <- 5\n#' result_df <- pathway_count(GO, count_threshold, enrich_data)\n#'\npathway_count <- function(GO, count_threshold, enrich_data) {\n # Filter pathways meeting the count threshold\n selected_pathways <- enrich_data %>%\n dplyr::filter(.data$Count > count_threshold)\n\n final_df <- data.frame(Symble = character(), Description = character(), Exists = integer())\n\n # Iterate through each selected pathway\n for (i in 1:nrow(selected_pathways)) {\n pathway_info <- selected_pathways[i, ]\n genes_in_pathway <- unlist(strsplit(as.character(pathway_info$geneID), \"/\"))\n\n # Create a record for each gene in the current pathway\n for (gene in GO) {\n final_df <- rbind(final_df, data.frame(\n Symble = gene,\n Description = pathway_info$Description,\n Exists = as.integer(gene %in% genes_in_pathway)\n ))\n }\n }\n\n return(final_df)\n}\n" }, { "path": "R/PathwayDescription.R", "content": "#' Describe Genes Present in Selected Pathways\n#'\n#' This function identifies genes present in selected pathways based on gene enrichment analysis results.\n#'\n#' @importFrom dplyr filter\n#' @importFrom rlang .data\n#' @param GO A character vector of gene symbols.\n#' @param selected_pathways_names A character vector specifying the names of selected pathways.\n#' @param enrich_data A data frame containing pathway enrichment analysis results.\n#' @return A data frame with columns \"Symble\" (gene symbol), \"Description\" (pathway description), and \"Exists\" (1 if gene is present, 0 otherwise).\n#' @export\n#'\n#' @examples\n#' GO <- c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\")\n#' # Simulated enrichment analysis data\n#' enrich_data <- data.frame(\n#' ID = c(\"Pathway1\", \"Pathway2\", \"Pathway3\", \"Pathway4\"),\n#' Description = c(\"Apoptosis\", \"Cell Cycle\", \"Signal Transduction\", \"Metabolism\"),\n#' geneID = c(\"Gene1/Gene3\", \"Gene2/Gene4\", \"Gene1/Gene2/Gene3\", \"Gene5\"),\n#' Count = c(2, 2, 3, 1),\n#' stringsAsFactors = FALSE\n#' )\n#'\n#' # Example usage\n#' result <- pathway_description(GO,\n#' selected_pathways_names=\"Apoptosis\",\n#' enrich_data)\n#'\npathway_description <- function(GO, selected_pathways_names, enrich_data) {\n # Filter selected pathways\n selected_pathways <- dplyr::filter(enrich_data, .data$Description %in% selected_pathways_names)\n\n final_df <- data.frame(Symble = character(), Description = character(), Exists = integer())\n\n # Iterate through each selected pathway\n for (i in 1:nrow(selected_pathways)) {\n pathway_info <- selected_pathways[i, ]\n genes_in_pathway <- unlist(strsplit(as.character(pathway_info$geneID), \"/\"))\n\n # Create a record for each gene in the current pathway\n for (gene in GO) {\n final_df <- rbind(final_df, data.frame(\n Symble = gene,\n Description = pathway_info$Description,\n Exists = as.integer(gene %in% genes_in_pathway)\n ))\n }\n }\n\n return(final_df)\n}\n" }, { "path": "R/PrepDeseq2.R", "content": "#' Prepare DESeq2 data for plotting\n#'\n#' This function reads a DESeq2 DEG data frame from an RDS file, filters it,\n#' adjusts the log2FoldChange to absolute values, adds a pseudo-count to pvalues,\n#' and transforms pvalues for plotting. The final data frame is returned and\n#' optionally saved to a new RDS file.\n#'\n#' @param input_path Path to the RDS file containing the DESeq2 DEG data frame.\n#' @param output_name Name for the processed data frame, also used as the RDS file name.\n#' @return A data frame with processed DESeq2 DEG data.\n#' @export\n#' @examples\n#' deseq2_file <- system.file(\"extdata\",\n#' \"DEG_deseq2_test.rds\",\n#' package = \"TransProR\")\n#' deseq2 <- prep_deseq2(deseq2_file)\n#'\nprep_deseq2 <- function(input_path, output_name = NULL) {\n # Read the DESeq2 DEG data frame from an RDS file\n DEG_deseq2 <- readRDS(input_path)\n\n # Filter DEG data using the deg_filter function from the same package\n DESeq2 <- deg_filter(DEG_deseq2)\n DEG_deseq2 <- DEG_deseq2[rownames(DEG_deseq2) %in% DESeq2, ]\n\n # Extract the Gene column as a regular column\n DEG_deseq2$Gene <- rownames(DEG_deseq2)\n\n # Select columns of interest\n DEG_deseq2 <- DEG_deseq2[, c('log2FoldChange', 'pvalue', \"change\", 'Gene')]\n\n # Adjust log2FoldChange values to absolute values\n DEG_deseq2$log2FoldChange <- abs(DEG_deseq2$log2FoldChange)\n\n # Add a small pseudo-count to pvalue to avoid log of zero\n DEG_deseq2$pvalue <- DEG_deseq2$pvalue + .Machine$double.eps\n\n # Transform p-value for plotting\n DEG_deseq2$pvalue <- -log10(DEG_deseq2$pvalue)\n\n # Rename columns\n names(DEG_deseq2) <- c('logFC', 'Pvalue', \"change\", 'Gene')\n\n # Optionally save the processed data frame as an RDS file\n if (!is.null(output_name)) {\n saveRDS(DEG_deseq2, paste0(output_name, \".Rdata\"))\n }\n\n # Return the processed data frame\n return(DEG_deseq2)\n}\n\n\n\n" }, { "path": "R/PrepEdgeR.R", "content": "#' Prepare edgeR DEG data for plotting\n#'\n#' This function reads an edgeR DEG data frame from an RDS file, filters it using\n#' \\code{\\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to PValue,\n#' and transforms PValue for plotting. The final data frame is returned and\n#' optionally saved to a new RDS file.\n#'\n#' @param input_path Path to the RDS file containing the edgeR DEG data frame.\n#' @param output_name Name for the processed data frame, also used as the RDS file name.\n#' @return A data frame with processed edgeR DEG data.\n#' @export\n#' @examples\n#' edgeR_file <- system.file(\"extdata\",\n#' \"DEG_edgeR_test.rds\",\n#' package = \"TransProR\")\n#' edgeR <- prep_edgeR(edgeR_file)\n#'\nprep_edgeR <- function(input_path, output_name = NULL) {\n # Read the edgeR DEG data frame from an RDS file\n DEG_edgeR <- readRDS(input_path)\n\n # Filter DEG data using the deg_filter function from the same package\n edgeR <- deg_filter(DEG_edgeR)\n DEG_edgeR <- DEG_edgeR[rownames(DEG_edgeR) %in% edgeR, ]\n\n # Select columns of interest and adjust logFC values to absolute values\n DEG_edgeR <- DEG_edgeR[, c('logFC', \"PValue\", \"change\")]\n DEG_edgeR$logFC <- abs(DEG_edgeR$logFC)\n\n # Add a small pseudo-count to PValue to avoid log of zero and transform PValue for plotting\n DEG_edgeR$PValue <- DEG_edgeR$PValue + .Machine$double.eps\n DEG_edgeR$PValue <- -log10(DEG_edgeR$PValue)\n\n # Extract the Gene column from row names\n DEG_edgeR$Gene <- rownames(DEG_edgeR)\n\n # Rename columns\n names(DEG_edgeR) <- c('logFC', 'Pvalue', \"change\", 'Gene')\n\n # Optionally save the processed data frame as an RDS file\n if (!is.null(output_name)) {\n saveRDS(DEG_edgeR, paste0(output_name, \".Rdata\"))\n }\n\n # Return the processed data frame\n return(DEG_edgeR)\n}\n" }, { "path": "R/PrepLimma.R", "content": "#' Prepare limma-voom DEG data for plotting\n#'\n#' This function reads a limma-voom DEG data frame from an RDS file, filters it using\n#' \\code{\\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value,\n#' and transforms P.Value for plotting. The final data frame is returned and\n#' optionally saved to a new RDS file.\n#'\n#' @param input_path Path to the RDS file containing the limma-voom DEG data frame.\n#' @param output_name Name for the processed data frame, also used as the RDS file name.\n#' @return A data frame with processed limma-voom DEG data.\n#' @export\n#' @examples\n#' limma_file <- system.file(\"extdata\",\n#' \"DEG_limma_voom_test.rds\",\n#' package = \"TransProR\")\n#' limma <- prep_limma(limma_file)\n#'\nprep_limma <- function(input_path, output_name = NULL) {\n # Read the limma-voom DEG data frame from an RDS file\n limma <- readRDS(input_path)\n\n # Filter DEG data using the deg_filter function from the same package\n limma_filter <- deg_filter(limma)\n limma <- limma[rownames(limma) %in% limma_filter, ]\n\n # Select columns of interest and adjust logFC values to absolute values\n limma <- limma[, c('logFC', \"P.Value\", \"change\")]\n limma$logFC <- abs(limma$logFC)\n\n # Add a small pseudo-count to P.Value to avoid log of zero and transform P.Value for plotting\n limma$P.Value <- limma$P.Value + .Machine$double.eps\n limma$P.Value <- -log10(limma$P.Value)\n\n # Extract the Gene column from row names\n limma$Gene <- rownames(limma)\n\n # Rename columns\n names(limma) <- c('logFC', 'Pvalue', \"change\", 'Gene')\n\n # Optionally save the processed data frame as an RDS file\n if (!is.null(output_name)) {\n saveRDS(limma, paste0(output_name, \".Rdata\"))\n }\n\n # Return the processed data frame\n return(limma)\n}\n" }, { "path": "R/PrepWilcoxon.R", "content": "#' Prepare Wilcoxon DEG data for plotting\n#'\n#' This function reads a Wilcoxon DEG data frame from an RDS file, filters it using\n#' \\code{\\link{deg_filter}} function, adjusts the log2foldChange to absolute values, adds a pseudo-count to pValues,\n#' and transforms pValues for plotting. The final data frame is returned and\n#' optionally saved to a new RDS file.\n#'\n#' @param input_path Path to the RDS file containing the Wilcoxon DEG data frame.\n#' @param output_name Optional; name for the processed data frame, also used as the RDS file name.\n#' If not provided, the data frame will not be saved to file.\n#' @return A data frame with processed Wilcoxon DEG data.\n#' @export\n#' @examples\n#' wilcoxon_file <- system.file(\"extdata\",\n#' \"Wilcoxon_rank_sum_testoutRst_test.rds\",\n#' package = \"TransProR\")\n#' Wilcoxon <- prep_wilcoxon(wilcoxon_file)\n#'\nprep_wilcoxon <- function(input_path, output_name = NULL) {\n # Read the Wilcoxon DEG data frame from an RDS file\n Wilcoxon <- readRDS(input_path)\n\n # Filter DEG data using the deg_filter function from the same package\n Wilcoxon_filter <- deg_filter(Wilcoxon)\n Wilcoxon <- Wilcoxon[rownames(Wilcoxon) %in% Wilcoxon_filter, ]\n\n # Select columns of interest and adjust log2foldChange values to absolute values\n Wilcoxon <- Wilcoxon[, c('log2foldChange', \"pValues\", \"change\")]\n Wilcoxon$log2foldChange <- abs(Wilcoxon$log2foldChange)\n\n # Add a small pseudo-count to pValues to avoid log of zero and transform pValues for plotting\n Wilcoxon$pValues <- Wilcoxon$pValues + .Machine$double.eps\n Wilcoxon$pValues <- -log10(Wilcoxon$pValues)\n\n # Extract the Gene column from row names\n Wilcoxon$Gene <- rownames(Wilcoxon)\n\n # Rename columns\n names(Wilcoxon) <- c('logFC', 'Pvalue', \"change\", 'Gene')\n\n # Optionally save the processed data frame as an RDS file\n if (!is.null(output_name) && nzchar(output_name)) {\n saveRDS(Wilcoxon, paste0(output_name, \".Rdata\"))\n }\n\n # Return the processed data frame\n return(Wilcoxon)\n}\n" }, { "path": "R/ProcessHeatdata.R", "content": "#' Process Heatmap Data with Various Selection Options\n#'\n#' This function processes heatmap data (`heatdata`) based on a given selection option.\n#' It allows customization of column names, selection of specific columns per group,\n#' or averaging columns based on a common prefix.\n#'\n#' @param heatdata A data frame containing the heatmap data.\n#' @param selection An integer specifying the processing method:\n#' - 1: Use custom names for columns.\n#' - 2: Select a given number of columns per group based on a prefix.\n#' - 3: Calculate the average of columns per group based on a prefix.\n#' @param custom_names A character vector of custom names for columns (used when `selection = 1`).\n#' The length of this vector must match the number of columns in `heatdata`.\n#' @param num_names_per_group An integer specifying the number of columns to select per group (used when `selection = 2`).\n#' @param prefix_length An integer specifying the length of the prefix for grouping columns (used when `selection = 2` or `selection = 3`).\n#' Default is 4.\n#' @return A processed data frame based on the specified selection option.\n#' @export\n#'\n#' @examples\n#' # Example heatmap data frame\n#' heatdata <- data.frame(\n#' groupA_1 = c(1, 2, 3),\n#' groupA_2 = c(4, 5, 6),\n#' groupB_1 = c(7, 8, 9),\n#' groupB_2 = c(10, 11, 12)\n#' )\n#'\n#' # Selection 1: Use custom names for columns\n#' custom_names <- c(\"Sample1\", \"Sample2\", \"Sample3\", \"Sample4\")\n#' processed_data1 <- process_heatdata(heatdata, selection = 1, custom_names = custom_names)\n#'\n#' # Selection 2: Select a given number of columns per group based on a prefix\n#' processed_data2 <- process_heatdata(heatdata, selection = 2, num_names_per_group = 1)\n#'\n#' # Selection 3: Calculate the average of columns per group based on a prefix\n#' processed_data3 <- process_heatdata(heatdata, selection = 3, prefix_length = 6)\n\nprocess_heatdata <- function(heatdata,\n selection = 1,\n custom_names = NULL,\n num_names_per_group = NULL,\n prefix_length = 4) {\n\n if (selection == 1) {\n # Option 1: Use custom names for columns\n if (length(custom_names) != ncol(heatdata)) {\n stop(\"Length of custom_names must match number of columns in heatdata\")\n }\n names(heatdata) <- custom_names\n } else if (selection == 2) {\n # Option 2: Select a given number of columns per group based on a prefix\n group_names <- unique(substr(names(heatdata), 1, prefix_length)) # Get unique group names based on prefix\n selected_columns <- integer(0)\n new_names <- character(0)\n\n for (group in group_names) {\n group_cols <- grep(group, names(heatdata)) # Find columns for each group\n num_selected <- min(length(group_cols), num_names_per_group)\n selected_columns <- c(selected_columns, sample(group_cols, num_selected))\n\n # Generate new names for selected columns\n new_group_names <- paste(group, seq_len(num_selected), sep = \"_\")\n new_names <- c(new_names, new_group_names)\n }\n\n heatdata <- heatdata[, selected_columns, drop = FALSE] # Keep only selected columns\n names(heatdata) <- new_names\n } else if (selection == 3) {\n # Option 3: Calculate the average of columns per group based on a prefix\n group_names <- unique(substr(names(heatdata), 1, prefix_length))\n\n # Create a list to collect mean values for each group\n mean_list <- list()\n\n # Calculate mean values for each group and store them in the list\n for (group in group_names) {\n group_cols <- grep(group, names(heatdata), value = TRUE)\n mean_list[[paste(group, \"mean\", sep = \"_\")]] <- rowMeans(heatdata[, group_cols], na.rm = TRUE)\n }\n\n # Convert list to data frame and set row names\n heatdata <- as.data.frame(do.call(cbind, mean_list))\n rownames(heatdata) <- rownames(heatdata)\n } else {\n stop(\"Invalid selection parameter\")\n }\n\n return(heatdata)\n}\n" }, { "path": "R/SeekGtexOrgan.R", "content": "#' Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts\n#'\n#' This function reads the GTEX phenotype data from a specified path, renames its columns for better readability,\n#' and then returns a table of primary site counts.\n#'\n#' @param path The path to the GTEX phenotype data file. Default is \"./download_data/GTEX_phenotype\".\n#'\n#' @return A table representing the count of samples per primary site.\n#' @importFrom utils read.table\n#' @examples\n#' # Get the file path to the example data in the package\n#' path <- system.file(\"extdata\", \"GTEX_phenotype_test\", package = \"TransProR\")\n#' # Call the `seek_gtex_organ` function with the path and print the result\n#' SeekGtexOrgan <- seek_gtex_organ(path = path)\n#'\n#' @export\n\nseek_gtex_organ <- function(path = \"./download_data/GTEX_phenotype\") {\n # Read GTEX phenotype data\n # gtex.phe <- data.table::fread(path, header = TRUE, sep = '\\t', data.table = FALSE)\n\n gtex.phe <- utils::read.table(path,\n header = TRUE,\n sep = '\\t',\n stringsAsFactors = FALSE,\n check.names = FALSE)\n\n rownames(gtex.phe) <- gtex.phe$Sample\n\n # Rename columns\n colnames(gtex.phe) <- c(\"Sample\", \"body_site_detail (SMTSD)\", \"primary_site\", \"gender\", \"patient\", \"cohort\")\n\n # Create table of primary sites\n primary_site_counts <- table(gtex.phe$primary_site)\n\n return(primary_site_counts)\n}\n\n\n" }, { "path": "R/WilcoxonAnalyze.R", "content": "#' Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test\n#'\n#' This function performs differential gene expression analysis using Wilcoxon rank-sum tests.\n#' It reads tumor and normal expression data, performs TMM normalization using 'edgeR', and uses Wilcoxon rank-sum tests to identify differentially expressed genes.\n#'\n#' @importFrom tibble column_to_rownames\n#' @importFrom edgeR DGEList filterByExpr calcNormFactors cpm\n#' @importFrom dplyr mutate\n#' @importFrom stats wilcox.test p.adjust\n#' @param tumor_file Path to the tumor data file (RDS format).\n#' @param normal_file Path to the normal data file (RDS format).\n#' @param output_file Path to save the output DEG data (RDS format).\n#' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes.\n#' @param fdr_threshold Threshold for FDR for filtering significant genes.\n#' @return A data frame of differential expression results.\n#' @references\n#' Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022). Exaggerated False Positives by Popular\n#' Differential Expression Methods When Analyzing Human Population Samples. Genome Biology, 23(1), 79.\n#' DOI: https://doi.org/10.1186/s13059-022-02648-4.\n#' @export\n#'\n#' @examples\n#' # Define file paths for tumor and normal data from the data folder\n#' tumor_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n#' package = \"TransProR\")\n#' normal_file <- system.file(\"extdata\",\n#' \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n#' package = \"TransProR\")\n#' output_file <- file.path(tempdir(), \"Wilcoxon_rank_sum_testoutRst.rds\")\n#'\n#' # Run the Wilcoxon rank sum test\n#' outRst <- Wilcoxon_analyze(\n#' tumor_file = tumor_file,\n#' normal_file = normal_file,\n#' output_file = output_file,\n#' logFC_threshold = 2.5,\n#' fdr_threshold = 0.01\n#' )\n#'\n#' # View the top 5 rows of the result\n#' head(outRst, 5)\nWilcoxon_analyze <- function(tumor_file,\n normal_file,\n output_file,\n logFC_threshold = 2.5,\n fdr_threshold = 0.05) {\n # Read data\n tumor <- readRDS(tumor_file)\n normal <- readRDS(normal_file)\n\n # Merge the datasets and set row names\n all_count_exp <- merge(tumor, normal, by = \"row.names\")\n all_count_exp <- tibble::column_to_rownames(all_count_exp, var = \"Row.names\")\n\n # Define groups\n group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal)))\n group <- factor(group, levels = c(\"normal\", \"tumor\"))\n group_table <- table(group)\n\n message(\"Group Table:\")\n message(paste(names(group_table), group_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # EdgeR TMM normalization\n y <- edgeR::DGEList(counts = all_count_exp, group = group)\n keep <- edgeR::filterByExpr(y)\n y <- y[keep, keep.lib.sizes = FALSE]\n\n # Perform TMM normalization and transfer to CPM (Counts Per Million)\n y <- edgeR::calcNormFactors(y, method = \"TMM\")\n count_norm <- edgeR::cpm(y)\n count_norm <- as.data.frame(count_norm)\n\n # Wilcoxon rank-sum test for each gene\n pvalues <- sapply(1:nrow(count_norm), function(i) {\n data <- cbind.data.frame(gene = as.numeric(t(count_norm[i, ])), group)\n stats::wilcox.test(gene ~ group, data)$p.value\n })\n fdr <- stats::p.adjust(pvalues, method = \"fdr\")\n\n # Calculate fold-change for each gene\n conditionsLevel <- levels(group)\n dataCon1 <- count_norm[, which(group == conditionsLevel[1])]\n dataCon2 <- count_norm[, which(group == conditionsLevel[2])]\n # The addition of a pseudo-count allows for robust statistical analysis of genes with low expression levels, while mitigating computational issues caused by zero expression values.\n # It prevents the occurrence of negative infinity (-Inf) when the numerator is zero, and positive infinity (Inf) when the denominator is zero.\n foldChanges <- log2((rowMeans(dataCon2) + 0.005) / (rowMeans(dataCon1) + 0.005))\n #foldChanges <- log2(rowMeans(dataCon2) / rowMeans(dataCon1))\n\n # Output results based on FDR threshold\n outRst <- data.frame(log2foldChange = foldChanges, pValues = pvalues, FDR = fdr)\n rownames(outRst) <- rownames(count_norm)\n outRst <- na.omit(outRst)\n\n # Mark up/down-regulated genes\n k1 <- (outRst$FDR < fdr_threshold) & (outRst$log2foldChange < -logFC_threshold)\n k2 <- (outRst$FDR < fdr_threshold) & (outRst$log2foldChange > logFC_threshold)\n outRst <- dplyr::mutate(outRst, change = ifelse(k1, \"down\", ifelse(k2, \"up\", \"stable\")))\n\n change_table <- table(outRst$change)\n\n message(\"Change Table:\")\n message(paste(names(change_table), change_table, sep = \": \", collapse = \"\\n\"))\n # Add a space after the output for separation\n message(\" \")\n\n # Save results\n saveRDS(outRst, file = output_file)\n\n return(outRst)\n}\n" }, { "path": "R/data.R", "content": "# The annotation file for the DATA in the data directory can all be commented in this file. Although LazyData: true is marked in the DESCRIPTION,\n# it ensures that the data is not read initially but is automatically read when it is actually used. This automatic process requires this part of the file to ensure it.\n\n#' All DEGs Venn Diagram Data\n#'\n#' A dataset containing the differentially expressed genes (DEGs) from four different statistical analysis methods: DESeq2, edgeR, limma, and Wilcoxon test.\n#' This dataset is used for generating Venn diagrams to compare the overlap of DEGs identified by different methods.\n#'\n#' @format A list with the following components:\n#' \\describe{\n#' \\item{DESeq2}{A vector of gene IDs or gene symbols identified as DEGs by the DESeq2 method.}\n#' \\item{edgeR}{A vector of gene IDs or gene symbols identified as DEGs by the edgeR method.}\n#' \\item{limma}{A vector of gene IDs or gene symbols identified as DEGs by the limma method. }\n#' \\item{Wilcoxon_test}{A vector of gene IDs or gene symbols identified as DEGs by the Wilcoxon test method. }\n#' }\n#'\n#' @source The data was derived from differential expression analyses performed on a gene expression dataset using four commonly used statistical methods (DESeq2, edgeR, limma, and Wilcoxon test).\n#'\n#' @usage data(all_degs_venn)\n#'\n#' @examples\n#' data(all_degs_venn)\n#' # Example of plotting a Venn diagram using the dataset\n#'\n#' edge_colors <- c(\"#1b62bb\",\"#13822e\",\"#332c3a\",\"#9e2d39\")\n#' name_color <- c(\"#1b64bb\",\"#13828e\",\"#337c3a\",\"#9e9d39\")\n#' fill_colors <- c(\"#e3f2fa\", \"#0288d1\")\n#'\n#' Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)\n#'\n#' @keywords datasets\n\"all_degs_venn\"\n\n\n#' Phylogenetic Tree Object\n#'\n#' A dataset containing a phylogenetic tree object created using the `ggtree` package.\n#' This tree represents the evolutionary relationships among a set of species or genes.\n#'\n#' @format A `ggtree` object.\n#'\n#' @source The phylogenetic tree was constructed based on sequence alignment data obtained from [Data Source, e.g., NCBI database, specific study, etc.].\n#'\n#' @usage data(gtree)\n#'\n#' @keywords datasets phylogenetics\n\"gtree\"\n" }, { "path": "R/utils-pipe.R", "content": "#' Pipe operator\n#'\n#' See \\code{magrittr::\\link[magrittr:pipe]{\\%>\\%}} for details.\n#'\n#' @name %>%\n#' @rdname pipe\n#' @keywords internal\n#' @export\n#' @importFrom magrittr %>%\n#' @usage lhs \\%>\\% rhs\n#' @param lhs A value or the magrittr placeholder.\n#' @param rhs A function call using the magrittr semantics.\n#' @return The result of calling `rhs(lhs)`.\nNULL\n" }, { "path": "R/zzz.R", "content": ".onAttach <- function(libname, pkgname) {\n # Display ASCII art\n ascii_art_path <- system.file(\"extdata\", \"ascii_art.txt\", package = \"TransProR\")\n if (file.exists(ascii_art_path)) {\n ascii_art_lines <- readLines(ascii_art_path)\n ascii_art <- paste(ascii_art_lines, collapse = \"\\n\")\n packageStartupMessage(ascii_art)\n } else {\n packageStartupMessage(\"Welcome to TransProR!\")\n }\n}\n\n\n" }, { "path": "README.md", "content": "# TransProR\n[![CRAN status](https://www.r-pkg.org/badges/version/TransProR)](https://cran.r-project.org/package=TransProR)\n\n\n\n\n\n\n\"TransProR\nAnalysis and visualization of transcriptomic data are currently in progress. Future directions include multi-modal fusion, sparse learning, and the investigation of spatio-temporal effects.\n\n## Installation\n\nYou can install the development version of TransProR like so:\n\n``` r\ninstall.packages(\"devtools\")\ndevtools::install_github(\"SSSYDYSSS/TransProR\", build_vignettes = TRUE)\n\ninstall.packages(\"remotes\")\nremotes::install_github(\"SSSYDYSSS/TransProR\", build_vignettes = TRUE)\n```\n## More examples see\n\nTransProR Manual：https://sssydysss.github.io/TransProRBook/\n\n## System Requirements\n\n- R (>= 4.3.0)\n\n## Example\n\nThis is a basic example which shows you how to solve a common problem:\n\n``` r\nlibrary(TransProR)\n## basic example code\n```\n\n## Citation\n\nIf you use TransPro in your research, please cite:\n\nDongyue Yu; Chen Li; Shuo Yan; Lujiale Guo; Jingyu Liang; Shengquan Chen*; Wenjun Bu* (2026). Comparative Evaluation of Differential Gene Selection Methods in Transcriptomics: Bias Correction and Visualization with TransPro. Manuscript in preparation.\n\n**Correspondence:** \n\nShengquan Chen — School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China. \n\nWenjun Bu — Institute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China.\n\n\n## Code of Conduct\n\nPlease note that the TransProR project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms. \"\\# TransProR\"\n" }, { "path": "TransProR.Rproj", "content": "Version: 1.0\n\nRestoreWorkspace: Default\nSaveWorkspace: Default\nAlwaysSaveHistory: Default\n\nEnableCodeIndexing: Yes\nUseSpacesForTab: Yes\nNumSpacesForTab: 2\nEncoding: UTF-8\n\nRnwWeave: Sweave\nLaTeX: pdfLaTeX\n\nAutoAppendNewline: Yes\nStripTrailingWhitespace: Yes\n\nBuildType: Package\nPackageUseDevtools: Yes\nPackageInstallArgs: --no-multiarch --with-keep.source\n" }, { "path": "data-raw/MAKEDATA.R", "content": "# Data that the package will use, and is explorable by users:\nusethis::use_data(x) # This will create a file at data/x.Rda\n\n# Data that the package will use, and is not explorable by users, internal data:\nusethis::use_data(y, internal = TRUE) # This will create a file at R/sysdata.rda. Note, this data does not require documentation, nor does it need to be exported, it is not visible to users.\n" }, { "path": "dev/build.R", "content": "# pipe\nusethis::use_pipe(export = TRUE)\n\n## code to prepare MAKEDATA.R\nusethis::use_data_raw(\"MAKEDATA\")\n\ndevtools::load_all()\n\n\ndevtools::document()\ndevtools::check()\n\n\n#BiocManager::install(\"BiocCheck\")\n#BiocCheck::BiocCheck()\n\n\n\n# Ensure that all dependencies are correctly installed, and generate the pak lock file.\npak::pak()\npak::pak(\"jokergoo/ComplexHeatmap\")# 单独安装\npak::lockfile_create()# for github action\nfile.rename(\"pkg.lock\", \".github/pkg.lock\")\n\n\n# NEWS.md\nusethis::use_news_md() #生成一个标准模板。\n\n" }, { "path": "dev/dev.R", "content": "# dependency package\nusethis::use_package(\"stats\")\nusethis::use_package(\"utils\")\nusethis::use_package(\"DESeq2\")\nusethis::use_package(\"BiocGenerics\")\nusethis::use_package(\"ggplot2\")\nusethis::use_package(\"ggpubr\")\nusethis::use_package(\"tibble\")\n\n# license\nusethis::use_mit_license(\"Dongyue Yu\")\n\n# readme\nusethis::use_readme_md()\n\n# adds a Code of Conduct\nusethis::use_code_of_conduct(contact = \"yudongyue@mail.nankai.edu.cn\")\n\n# vignette\nusethis::use_vignette(\"a_example_workflow\") # optional\ndevtools::install(build_vignettes = TRUE) # optional\n\n## a website\n# Run once to configure your package to use pkgdown\nusethis::use_pkgdown() # optional\npkgdown::build_site() # optional\n\n## add git\nusethis::use_git()\nusethis::use_github()\n\n# CI\nusethis::use_github_action_check_standard()\n\n# CRAN\n# Spell check\ndevtools::spell_check()\n# Regular local tests\ndevtools::check()\n# rhub cross-platform tests\nrhub::check_for_cran()\n# Windows platform-only test\ndevtools::check_win_devel() # optional\n# rhub Windows platform-only test\nrhub::check_for_cran(\n platform=\"windows-x86_64-devel\",\n env_vars=c(R_COMPILE_AND_INSTALL_PACKAGES = \"always\")\n) # optional\n\n\n\n# Check before release\ndevtools::release()\n" }, { "path": "inst/extdata/GTEX_phenotype_test", "content": "Sample\tbody_site_detail (SMTSD)\t_primary_site\t_gender\t_patient\t_cohort\nGTEX-1117F-0226-SM-5GZZ7\tAdipose - Subcutaneous\tAdipose Tissue\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-0426-SM-5EGHI\tMuscle - Skeletal\tMuscle\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-0526-SM-5EGHJ\tArtery - Tibial\tBlood Vessel\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-0626-SM-5N9CS\tArtery - Coronary\tBlood Vessel\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-0726-SM-5GIEN\tHeart - Atrial Appendage\tHeart\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-1326-SM-5EGHH\tAdipose - Visceral (Omentum)\tAdipose Tissue\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-2226-SM-5N9CH\tOvary\tOvary\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-2426-SM-5EGGH\tUterus\tUterus\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-2526-SM-5GZY6\tVagina\tVagina\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-2826-SM-5GZXL\tBreast - Mammary Tissue\tBreast\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-2926-SM-5GZYI\tSkin - Not Sun Exposed (Suprapubic)\tSkin\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-3026-SM-5GZYU\tMinor Salivary Gland\tSalivary Gland\tfemale\tGTEX-1117F\tGTEX\nGTEX-1117F-3226-SM-5N9CT\tBrain - Cortex\tBrain\tfemale\tGTEX-1117F\tGTEX\nGTEX-111CU-0126-SM-5GZWZ\tAdrenal Gland\tAdrenal Gland\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0226-SM-5GZXC\tThyroid\tThyroid\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0326-SM-5GZXO\tLung\tLung\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0426-SM-5GZY1\tSpleen\tSpleen\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0526-SM-5EGHK\tPancreas\tPancreas\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0626-SM-5EGHL\tEsophagus - Muscularis\tEsophagus\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0726-SM-5GZYD\tEsophagus - Mucosa\tEsophagus\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0826-SM-5EGIJ\tEsophagus - Gastroesophageal Junction\tEsophagus\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-0926-SM-5EGIK\tStomach\tStomach\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1026-SM-5EGIL\tAdipose - Visceral (Omentum)\tAdipose Tissue\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1126-SM-5EGIM\tSkin - Not Sun Exposed (Suprapubic)\tSkin\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1226-SM-5EGIN\tColon - Sigmoid\tColon\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1326-SM-5NQ8L\tSmall Intestine - Terminal Ileum\tSmall Intestine\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1426-SM-5GZYP\tColon - Transverse\tColon\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1526-SM-5N9FS\tProstate\tProstate\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1726-SM-5EGHM\tTestis\tTestis\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1826-SM-5GZYN\tAdipose - Subcutaneous\tAdipose Tissue\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-1926-SM-5GZYZ\tSkin - Sun Exposed (Lower leg)\tSkin\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-2026-SM-5GZZC\tMuscle - Skeletal\tMuscle\tmale\tGTEX-111CU\tGTEX\nGTEX-111CU-2226-SM-5N9G5\tNerve - Tibial\tNerve\tmale\tGTEX-111CU\tGTEX\nGTEX-111FC-0126-SM-5N9DL\tSkin - Sun Exposed (Lower leg)\tSkin\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0226-SM-5N9B8\tAdipose - Subcutaneous\tAdipose Tissue\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0326-SM-5GZZ1\tMuscle - Skeletal\tMuscle\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0426-SM-5N9CV\tArtery - Tibial\tBlood Vessel\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0526-SM-5GZZ8\tNerve - Tibial\tNerve\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0626-SM-5N9CU\tHeart - Atrial Appendage\tHeart\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-0826-SM-5GZWO\tHeart - Left Ventricle\tHeart\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-1026-SM-5GZX1\tThyroid\tThyroid\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-1126-SM-5GZWU\tLung\tLung\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-1326-SM-5N9D9\tSpleen\tSpleen\tmale\tGTEX-111FC\tGTEX\nGTEX-111FC-1426-SM-5N9C7\tAdipose - Subcutaneous\tAdipose Tissue\tmale\tGTEX-111FC\tGTEX" }, { "path": "inst/extdata/TCGA-SKCM.GDC_phenotype_test.tsv", "content": "submitter_id.samples\tage_at_initial_pathologic_diagnosis\tbatch_number\tbcr\tbcr_followup_barcode\tbcr_followup_uuid\tsubmitter_id\tbreslow_depth_value\tday_of_dcc_upload\tday_of_form_completion\tdays_to_initial_pathologic_diagnosis\tdays_to_submitted_specimen_dx\tdistant_metastasis_anatomic_site\tfile_uuid\tfollowup_case_report_form_submission_reason\thistory_of_neoadjuvant_treatment\tinformed_consent_verified\tinterferon_90_day_prior_excision_admin_indicator\tlactate_dehydrogenase_result\tlost_follow_up\tmalignant_neoplasm_mitotic_count_rate\tmelanoma_clark_level_value\tmelanoma_origin_skin_anatomic_site\tmelanoma_ulceration_indicator\tmonth_of_dcc_upload\tmonth_of_form_completion\tnew_tumor_dx_prior_submitted_specimen_dx\tother_dx\tpathologic_M\tpathologic_N\tpathologic_T\tpatient_id\tperson_neoplasm_cancer_status\tpostoperative_rx_tx\tprimary_melanoma_at_diagnosis_count\tprimary_neoplasm_melanoma_dx\tprimary_tumor_multiple_present_ind\tprior_radiation_therapy\tprior_systemic_therapy\tprior_systemic_therapy_type\tradiation_therapy\tradiation_therapy_to_primary\tsubmitted_tumor_location\tsubsequent_primary_melanoma_during_followup\tsystem_version\ttissue_prospective_collection_indicator\ttissue_retrospective_collection_indicator\ttissue_source_site\tweight\twithdrawn\tyear_of_dcc_upload\tyear_of_form_completion\tyear_of_initial_pathologic_diagnosis\tdays_to_index\tdbgap_registration_code\tdisease_code\tpathology_report_file_name\tprogram\tproject_code\tvial_number\tage_at_index.demographic\tdays_to_birth.demographic\tdays_to_death.demographic\tethnicity.demographic\tgender.demographic\trace.demographic\tvital_status.demographic\tyear_of_birth.demographic\tyear_of_death.demographic\tage_at_diagnosis.diagnoses\tclassification_of_tumor.diagnoses\tdays_to_diagnosis.diagnoses\tdays_to_last_follow_up.diagnoses\ticd_10_code.diagnoses\tlast_known_disease_status.diagnoses\tmorphology.diagnoses\tprimary_diagnosis.diagnoses\tprior_malignancy.diagnoses\tprior_treatment.diagnoses\tprogression_or_recurrence.diagnoses\tsite_of_resection_or_biopsy.diagnoses\tsynchronous_malignancy.diagnoses\ttissue_or_organ_of_origin.diagnoses\ttumor_grade.diagnoses\ttumor_stage.diagnoses\tyear_of_diagnosis.diagnoses\tdisease_type\talcohol_history.exposures\tbmi.exposures\theight.exposures\tweight.exposures\tprimary_site\tname.project\tproject_id.project\treleasable.project\tbcr_id.tissue_source_site\tcode.tissue_source_site\tname.tissue_source_site\tproject.tissue_source_site\tdays_to_collection.samples\tdays_to_sample_procurement.samples\tinitial_weight.samples\tis_ffpe.samples\toct_embedded.samples\tpreservation_method.samples\tsample_type.samples\tsample_type_id.samples\tstate.samples\ttissue_type.samples\nTCGA-D9-A4Z2-01A\t50\t262.74.0\tNationwide Children's Hospital\tTCGA-D9-A4Z2-F57868\tAB5F6B14-DAAE-4D19-B227-EC6E2D5ED0D0\tTCGA-D9-A4Z2\t25\t14\t28\t0\t7\t\t8FC5ACEA-E493-4C8D-A3AC-5A68B1B8FC26\t\tNo\tYES\t\t\tNO\t10\tIV\tNon-glabrous skin\tYES\t3\t3\tNO\tNo\tM0\tN3\tT4b\tA4Z2\tWITH TUMOR\tNO\t2\tYES\tYES\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tD9\t54\tfalse\t2017\t2014\t2012\t0\t\tSKCM\tTCGA-D9-A4Z2.FEA752ED-7670-4D44-8402-009CF8356C44.pdf\t\tTCGA\tA\t50.0\t-18462.0\t190.0\tnot hispanic or latino\tmale\twhite\tDead\t1962.0\t\t18462.0\tnot reported\t0.0\t93.0\tC44.5\tnot reported\t8721/3\tNodular melanoma\tno\tNo\tnot reported\tSkin of trunk\tNo\tSkin, NOS\tnot reported\tstage iiic\t2012.0\tNevi and Melanomas\tNot Reported\t20.07733491969066\t164.0\t54.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD9\tGreater Poland Cancer Center\tSkin Cutaneous Melanoma\t66.0\t\t160.0\tFalse\tfalse\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-ER-A2NH-06A\t49\t180.87.0\tNationwide Children's Hospital\tTCGA-ER-A2NH-F69370\tCF52E6AD-959C-446F-B88F-E7E73B83E405\tTCGA-ER-A2NH\t4\t14\t7\t0\t618\t\t7E2595AA-7EF7-4E83-AC3C-44179A594F47\t\tNo\tYES\t\t\tNO\t1\tIV\tNon-glabrous skin\tNO\t3\t1\t\tNo\tM0\tN3\tT3a\tA2NH\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Lymph Node\t\t6th\tYES\tNO\tER\t\tfalse\t2017\t2015\t2009\t0\t\tSKCM\tTCGA-ER-A2NH.875D53B1-DFAE-4DA3-84B0-70B14B280EA1.pdf\t\tTCGA\tA\t49.0\t-18220.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1960.0\t\t18220.0\tnot reported\t0.0\t1264.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage iiic\t2009.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t744.0\t\t\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-BF-A5EO-01A\t65\t291.69.0\tNationwide Children's 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Hospital\tTCGA-D9-A6EA-F57860\tC0168BC0-E336-4944-86D9-C332EA962EC0\tTCGA-D9-A6EA\t6\t14\t28\t0\t422\t\tA6210DB2-5211-43B3-9FB8-177784EAC4B2\t\tNo\tYES\t\tNormal\tNO\t\tIV\tNon-glabrous skin\tNO\t3\t3\t\tNo\tM0\tN3\tT4a\tA6EA\tTUMOR FREE\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\t\tNO\t7th\tYES\tNO\tD9\t58\tfalse\t2017\t2014\t2012\t0\t000178\tSKCM\tTCGA-D9-A6EA.78D2592F-845C-4A33-AC8B-48C109E4D50B.pdf\tTCGA\tTCGA\tA\t70.0\t-25582.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1942.0\t\t25582.0\tnot reported\t0.0\t766.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage iiic\t2012.0\tNevi and Melanomas\tNot Reported\t21.048047612135285\t166.0\t58.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD9\tGreater Poland Cancer Center\tSkin Cutaneous Melanoma\t478.0\t\t270.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-D9-A4Z3-01A\t73\t277.76.0\tNationwide Children's Hospital\tTCGA-D9-A4Z3-F58483\t54923FD3-981A-4649-BEDD-860A06350154\tTCGA-D9-A4Z3\t75\t14\t11\t0\t0\t\tACF6C126-DFBA-431C-B768-7C5F0CD7E36E\t\tNo\tYES\t\t\tNO\t10\tV\tNon-glabrous skin\tYES\t3\t4\t\tNo\tM0\tN1b\tT4b\tA4Z3\tTUMOR FREE\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tD9\t70\tfalse\t2017\t2014\t2012\t0\t\tSKCM\tTCGA-D9-A4Z3.9B96B7FD-D3BA-42C8-A0C6-1C93C48F4BB2.pdf\t\tTCGA\tA\t73.0\t-26812.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1939.0\t\t26812.0\tnot reported\t0.0\t505.0\tC44.6\tnot reported\t8721/3\tNodular melanoma\tno\tNo\tnot reported\tSkin of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage iiic\t2012.0\tNevi and Melanomas\tNot Reported\t24.22145328719723\t170.0\t70.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD9\tGreater Poland Cancer Center\tSkin Cutaneous Melanoma\t16.0\t\t170.0\tFalse\tfalse\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-GN-A26A-06A\t63\t180.87.0\tNationwide Children's Hospital\t\t\tTCGA-GN-A26A\t2.3\t14\t\t0\t618\t\t0663AA03-5F7F-4537-B1A3-E5576F7A327A\t\tNo\tYES\t\t\t\t11\tIV\tNon-glabrous skin\tNO\t3\t\tNO\tNo\tM0\tN1a\tT3a\tA26A\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t6th\tNO\tYES\tGN\t66\tfalse\t2017\t\t2006\t0\t\tSKCM\tTCGA-GN-A26A.5F794422-0A58-4224-8108-E0E4DE11D4C6.pdf\t\tTCGA\tA\t63.0\t-23288.0\t988.0\tnot hispanic or latino\tfemale\twhite\tDead\t1943.0\t2008.0\t23288.0\tnot reported\t0.0\t\tC44.5\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of trunk\tNo\tSkin, NOS\tnot reported\tstage iiia\t2006.0\tNevi and Melanomas\tNot Reported\t24.242424242424242\t165.0\t66.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tGN\tRoswell\tSkin Cutaneous Melanoma\t1766.0\t\t270.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-D3-A3BZ-06A\t63\t180.87.0\tNationwide Children's Hospital\tTCGA-D3-A3BZ-F45520\tE3E4F520-07D7-42C1-B74D-A30F7A4708DE\tTCGA-D3-A3BZ\t4.6\t14\t16\t0\t488\t\tBF045C42-5822-46B0-B7C4-4441E5472CFB\t\tNo\tYES\t\t\tNO\t1\tV\tNon-glabrous skin\tNO\t3\t7\tNO\tNo\tM0\tN0\tT4a\tA3BZ\tTUMOR FREE\tNO\t\tYES\tNO\tNO\tNO\t\tYES\tNO\tRegional Lymph Node\tNO\t7th\tNO\tYES\tD3\t99\tfalse\t2017\t2013\t2002\t0\t\tSKCM\tTCGA-D3-A3BZ.968FCFF6-6D7A-48F2-BC66-F9F7E61B095D.pdf\t\tTCGA\tA\t63.0\t-23286.0\t\thispanic or latino\tmale\twhite\tAlive\t1939.0\t\t23286.0\tnot reported\t0.0\t3976.0\tC77.0\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of head, face and neck\tNo\tSkin, NOS\tnot reported\tstage iib\t2002.0\tNevi and Melanomas\tNot Reported\t34.2560553633218\t170.0\t99.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t3332.0\t\t80.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-D3-A51G-06A\t\t277.76.0\tNationwide Children's Hospital\tTCGA-D3-A51G-F60911\t390FFB4F-D2AB-4D2A-B42D-883652921A7F\tTCGA-D3-A51G\t0\t14\t18\t\t\t\t50E32A14-9C29-4994-AC7E-2BA337500C31\t\tNo\tYES\t\t\tNO\t\tI\tNon-glabrous skin\t\t3\t6\tYES\tYes\tM0\tN0\tTis\tA51G\tTUMOR FREE\tYES\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\tNO\t7th\tNO\tYES\tD3\t112\tfalse\t2017\t2014\t1990\t\t\tSKCM\tTCGA-D3-A51G.27A8D7FA-6317-4B5E-9CE6-D65790474BDF.pdf\t\tTCGA\tA\t\t\t\tnot hispanic or latino\tmale\twhite\tAlive\t\t\t\tnot reported\t\t\tC49.2\tnot reported\t8720/3\tMalignant melanoma, NOS\tyes\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of lower limb and hip\tNot Reported\tSkin, NOS\tnot reported\tstage 0\t1990.0\tNevi and Melanomas\tNot Reported\t31.024930747922436\t190.0\t112.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t\t\t70.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A29R-06A\t48\t180.87.0\tNationwide Children's Hospital\tTCGA-EE-A29R-F59763\t3DAB4A16-5952-460C-9B59-DC23C644B2C4\tTCGA-EE-A29R\t3.5\t14\t20\t0\t171\t\tF3442500-209D-45C7-997C-187B53CE576A\t\tNo\tYES\t\t\tYES\t6\tV\tNon-glabrous skin\tYES\t3\t5\tNO\tNo\tM0\tN1b\tT3b\tA29R\t\t\t\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Lymph Node\t\t7th\tYES\tNO\tEE\t\tfalse\t2017\t2014\t2010\t0\t\tSKCM\tTCGA-EE-A29R.8DEEF9EA-3585-4047-A2BD-E7AB4DD1306F.pdf\t\tTCGA\tA\t48.0\t-17857.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1962.0\t\t17857.0\tnot reported\t0.0\t440.0\tC49.0\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of head, face and neck\tNo\tSkin, NOS\tnot reported\tstage iiic\t2010.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t359.0\t\t520.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-D3-A2JE-06A\t75\t388.48.0\tNationwide Children's Hospital\t\t\tTCGA-D3-A2JE\t\t14\t\t0\t140\t\tB1B3D3F4-4A48-4614-8D98-DBA8B33C14EA\t\tNo\tYES\t\t\t\t\t\tNon-glabrous skin\t\t3\t\tNO\tYes\tM0\tN3\tTX\tA2JE\t\t\t\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Lymph Node\t\t6th\tNO\tYES\tD3\t81\tfalse\t2017\t\t2009\t0\t\tSKCM\tTCGA-D3-A2JE.B07C0D10-3397-4F61-A119-F1D7CDB9E792.pdf\t\tTCGA\tA\t75.0\t-27604.0\t841.0\tnot hispanic or latino\tfemale\twhite\tDead\t1934.0\t2011.0\t27604.0\tnot reported\t0.0\t\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tyes\tNo\tnot reported\tLymph nodes of axilla or arm\tNot Reported\tSkin, NOS\tnot reported\tstage iiic\t2009.0\tNevi and Melanomas\tNot Reported\t31.640625\t160.0\t81.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t890.0\t\t170.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-W3-AA1V-06B\t63\t416.38.0\tNationwide Children's Hospital\t\t\tTCGA-W3-AA1V\t1.3\t14\t\t0\t946\t\t4769724A-5993-4103-B31E-EDB352D182B4\t\tNo\tYES\t\t\t\t\tIV\tNon-glabrous skin\tNO\t3\t\tNO\tNo\tM0\tN0\tT3\tAA1V\t\t\t\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Lymph Node\t\t4th\tNO\tYES\tW3\t91\tfalse\t2017\t\t1994\t0\t\tSKCM\tTCGA-W3-AA1V.11DF82C1-C746-4D00-9119-1FEBE3FBC0A2.pdf\t\tTCGA\tB\t63.0\t-23314.0\t1280.0\tnot hispanic or latino\tmale\twhite\tDead\t1931.0\t1997.0\t23314.0\tnot reported\t0.0\t\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage ii\t1994.0\tNevi and Melanomas\tNot Reported\t28.086419753086417\t180.0\t91.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tW3\t John Wayne Cancer Center\tSkin Cutaneous Melanoma\t7097.0\t\t60.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-DA-A1IA-06A\t32\t180.87.0\tNationwide Children's Hospital\tTCGA-DA-A1IA-F70089\tFC51240A-2001-4FE9-97CF-CD55C0B430B7\tTCGA-DA-A1IA\t1.3\t14\t27\t0\t1855\tbrain\t4E62D848-9309-4819-8DF7-8DF15CCD865D\t\tNo\tYES\t\t\tNO\t\tIV\tNon-glabrous skin\tNO\t3\t1\t\tNo\tM0\tN1b\tT2a\tA1IA\tWITH TUMOR\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tDistant Metastasis\tNO\t6th\tNO\tYES\tDA\t\tfalse\t2017\t2015\t2005\t0\t\tSKCM\tTCGA-DA-A1IA.A8C5B4DF-C8BD-43FF-9613-04942F68DCAB.pdf\t\tTCGA\tA\t32.0\t-11744.0\t2005.0\tnot hispanic or latino\tfemale\twhite\tDead\t1973.0\t\t11744.0\tnot reported\t0.0\t1887.0\tC71.1\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tFrontal lobe\tNo\tSkin, NOS\tnot reported\tstage iiib\t2005.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tDA\tYale\tSkin Cutaneous Melanoma\t2074.0\t\t280.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A29T-06A\t51\t180.87.0\tNationwide Children's Hospital\tTCGA-EE-A29T-F55117\t96B388B2-BFA4-4033-BA80-177C7061EC5D\tTCGA-EE-A29T\t\t14\t29\t0\t10326\t\t43BA748D-707E-4752-BBFD-527E41F9BDBD\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t12\tYES\tNo\tM0\tNX\tTX\tA29T\tTUMOR FREE\tNO\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Lymph Node\tNO\t1st\tNO\tYES\tEE\t\tfalse\t2017\t2013\t1982\t0\t\tSKCM\tTCGA-EE-A29T.4F109FA0-76B1-46AA-B90C-AB1CA81C387B.pdf\t\tTCGA\tA\t51.0\t-18779.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1931.0\t\t18779.0\tnot reported\t0.0\t11252.0\tC77.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph node, NOS\tNo\tSkin, NOS\tnot reported\tnot reported\t1982.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t10559.0\t\t200.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EB-A3XB-01A\t63\t240.79.0\tNationwide Children's Hospital\tTCGA-EB-A3XB-F41213\t6592ABBE-555B-4782-92AD-AFE3D1F8EB3F\tTCGA-EB-A3XB\t\t14\t13\t0\t0\t\tDE01D624-A389-4BD4-BD6D-CE09E2DD1902\t\tNo\tYES\t\t\tNO\t\tIV\tNon-glabrous skin\t\t3\t3\t\tNo\tM0\tNX\tT4\tA3XB\tTUMOR FREE\tNO\t\tYES\t\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tEB\t80\tfalse\t2017\t2013\t2011\t0\t\tSKCM\tTCGA-EB-A3XB.73D45CD8-5E91-4A51-B3E2-BE6B071F1A04.pdf\t\tTCGA\tA\t63.0\t-23091.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1948.0\t\t23091.0\tnot reported\t0.0\t796.0\tC44.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin, NOS\tNo\tSkin, NOS\tnot reported\tstage ii\t2011.0\tNevi and Melanomas\tNot Reported\t29.7441998810232\t164.0\t80.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t225.0\t\t540.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-FS-A1ZQ-06A\t31\t180.87.0\tNationwide Children's Hospital\t\t\tTCGA-FS-A1ZQ\t\t14\t\t0\t3799\tlung\t81048A54-134D-47F8-8C6B-DC9E5F390BF7\t\tNo\tYES\t\t\t\t\t\tNon-glabrous skin\t\t3\t\t\tNo\tM0\tN0\tTX\tA1ZQ\t\t\t1\tYES\tNO\tNO\tNO\t\t\tYES\tDistant Metastasis\t\t6th\tNO\tYES\tFS\t\tfalse\t2017\t\t1997\t0\t\tSKCM\tTCGA-FS-A1ZQ.A1E704CD-0D4D-4CCC-82B2-19289EEE84EB.pdf\t\tTCGA\tA\t31.0\t-11599.0\t4062.0\tnot hispanic or latino\tmale\twhite\tDead\t1966.0\t2008.0\t11599.0\tnot reported\t0.0\t\tC34.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLung, NOS\tNo\tSkin, NOS\tnot reported\ti/ii nos\t1997.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFS\tEssen\tSkin Cutaneous Melanoma\t4897.0\t\t300.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EB-A85I-01A\t66\t358.51.0\tNationwide Children's Hospital\tTCGA-EB-A85I-F61877\t74E78246-6218-4314-B8A7-AF5043FB42EA\tTCGA-EB-A85I\t10\t14\t9\t0\t0\t\t1CD3B59D-00C1-4EE5-AD71-A8DCD1D3D6E0\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\tYES\t3\t7\t\tNo\tM0\tN0\tT4b\tA85I\tTUMOR FREE\tNO\t1\tYES\t\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tEB\t82\tfalse\t2017\t2014\t2013\t0\t\tSKCM\tTCGA-EB-A85I.D35BCE2A-8132-4CAA-8E22-559B68BDAF07.pdf\t\tTCGA\tA\t66.0\t-24408.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1947.0\t\t24408.0\tnot reported\t0.0\t362.0\tC44.7\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of lower limb and hip\tNo\tSkin, NOS\tnot reported\tstage iic\t2013.0\tNevi and Melanomas\tNot Reported\t28.37370242214533\t170.0\t82.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t135.0\t\t370.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-D3-A5GO-06A\t61\t291.69.0\tNationwide Children's Hospital\tTCGA-D3-A5GO-F60915\t9ED5547B-C48F-402B-8211-A8D16CD11034\tTCGA-D3-A5GO\t4.3\t14\t18\t0\t2739\tSoft tissue of right upper arm\t88AAC1C5-A560-46F0-8A25-A7FE85F4B55B\t\tNo\tYES\t\t\tYES\t\tV\tNon-glabrous skin\t\t3\t6\tYES\tNo\tM0\tN0\tT4\tA5GO\t\t\t\tYES\tNO\tNO\tNO\t\t\tNO\tDistant Metastasis\t\t7th\tNO\tYES\tD3\t83\tfalse\t2017\t2014\t2001\t0\t\tSKCM\tTCGA-D3-A5GO.FFFA2FD5-C008-4583-99C9-EE71B0F21F07.pdf\t\tTCGA\tA\t61.0\t-22287.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1940.0\t\t22287.0\tnot reported\t0.0\t4195.0\tC44.6\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage ii\t2001.0\tNevi and Melanomas\tNot Reported\t29.060607121599382\t169.0\t83.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t4259.0\t\t50.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-FR-A728-01A\t54\t332.57.0\tNationwide Children's Hospital\tTCGA-FR-A728-F66624\tF878CF96-2289-4593-8E4A-5741242A96AB\tTCGA-FR-A728\t12.00\t14\t21\t0\t16\t\t8AA7655C-0D37-407F-96F0-DE8112D92BDA\t\tNo\tYES\t\t\tNO\t6\tIV\tNon-glabrous skin\tYES\t3\t10\tNO\tNo\tM0\tN2a\tT4b\tA728\tTUMOR FREE\tYES\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tFR\t87\tfalse\t2017\t2014\t2013\t0\t\tSKCM\tTCGA-FR-A728.F3C14D17-8681-4925-BDC5-CFE56F68342E.pdf\t\tTCGA\tA\t54.0\t-19936.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1959.0\t\t19936.0\tnot reported\t0.0\t583.0\tC44.6\tnot reported\t8743/3\tSuperficial spreading melanoma\tno\tNo\tnot reported\tSkin of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage iiib\t2013.0\tNevi and Melanomas\tNot Reported\t31.955922865013772\t165.0\t87.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFR\tUniversity of North Carolina\tSkin Cutaneous Melanoma\t136.0\t\t170.0\tFalse\tfalse\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-EB-A85J-01A\t66\t358.51.0\tNationwide Children's Hospital\tTCGA-EB-A85J-F61878\tED3B4C1F-767C-45E0-8E9B-C36248D3EDD4\tTCGA-EB-A85J\t5.5\t14\t9\t0\t0\t\t52641746-173A-4430-BC34-D858B64E1915\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\tNO\t3\t7\t\tNo\tM0\tN0\tT4a\tA85J\tTUMOR FREE\tYES\t1\tYES\t\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tEB\t128\tfalse\t2017\t2014\t2013\t0\t\tSKCM\tTCGA-EB-A85J.AD298262-1D6D-4023-9DBA-A38A8ED1E3EB.pdf\t\tTCGA\tA\t66.0\t-24263.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1947.0\t\t24263.0\tnot reported\t0.0\t360.0\tC44.7\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of lower limb and hip\tNo\tSkin, NOS\tnot reported\tstage iib\t2013.0\tNevi and Melanomas\tNot Reported\t46.45086369574684\t166.0\t128.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t133.0\t\t220.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-D3-A2JA-06A\t68\t180.87.0\tNationwide Children's Hospital\tTCGA-D3-A2JA-F40740\t1504323B-9883-4CF4-B02D-E44197CB64BA\tTCGA-D3-A2JA\t1.2\t14\t7\t0\t822\t\t4386D921-85F8-4A4F-8674-08FBE0C798BE\t\tNo\tYES\t\t\tNO\t0\tIII\tNon-glabrous skin\tNO\t3\t3\tYES\tNo\tM0\tN1a\tT2a\tA2JA\tTUMOR FREE\tNO\t\tYES\tNO\tNO\tNO\t\tNO\tYES\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\tNO\t7th\tNO\tYES\tD3\t113\tfalse\t2017\t2013\t2003\t0\t\tSKCM\tTCGA-D3-A2JA.75ADE88D-F7C8-4450-8B93-5706A25A13EA.pdf\t\tTCGA\tA\t68.0\t-25085.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1935.0\t\t25085.0\tnot reported\t0.0\t3514.0\tC49.6\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of trunk, NOS\tNo\tSkin, NOS\tnot reported\tstage iiia\t2003.0\tNevi and Melanomas\tNot Reported\t36.89795918367347\t175.0\t113.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t2945.0\t\t80.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-YG-AA3O-06A\t62\t393.46.0\tNationwide Children's Hospital\tTCGA-YG-AA3O-F67869\t48BA783C-56D0-4AAB-9F7F-16184D748565\tTCGA-YG-AA3O\t\t14\t13\t0\t908\t\t61B97687-4FB4-4631-BED7-D5F5F8E97B0E\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t11\t\tNo\t\t\t\tAA3O\tWITH TUMOR\tYES\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Lymph Node\tYES\t\tYES\tNO\tYG\t92\tfalse\t2017\t2014\t2011\t0\t\tSKCM\tTCGA-YG-AA3O.4C358082-E41F-491D-8F0D-39B2B698995A.pdf\t\tTCGA\tA\t62.0\t-22999.0\t1154.0\thispanic or latino\tmale\tnot reported\tDead\t1949.0\t\t22999.0\tnot reported\t0.0\t1096.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tnot reported\t2011.0\tNevi and Melanomas\tNot Reported\t30.73941661933242\t173.0\t92.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tYG\tUniversity of Puerto Rico\tSkin Cutaneous Melanoma\t956.0\t\t170.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A3AB-06A\t30\t180.87.0\tNationwide Children's Hospital\tTCGA-EE-A3AB-F59751\t1FD5BD7A-75C7-4FFD-BB97-36E6A641D315\tTCGA-EE-A3AB\t\t14\t20\t0\t0\t\t46D5759D-A919-4E67-AC58-A0C09E5E0F58\t\tNo\tYES\t\t\tYES\t\t\tNon-glabrous skin\t\t3\t5\t\tNo\tM0\tN2a\tT0\tA3AB\t\t\t\tNO\t\tNO\tNO\t\t\t\tRegional Lymph Node\t\t5th\tNO\tYES\tEE\t\tfalse\t2017\t2014\t2001\t0\t\tSKCM\tTCGA-EE-A3AB.0782D02E-4CD6-4031-B62B-41E1C408E4BC.pdf\t\tTCGA\tA\t30.0\t-11054.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1971.0\t\t11054.0\tnot reported\t0.0\t3733.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage iii\t2001.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t3725.0\t\t160.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-D3-A1QB-06A\t75\t180.87.0\tNationwide Children's Hospital\tTCGA-D3-A1QB-F45519\tDC07AAD2-679E-4F92-BD3B-282B3DAAB030\tTCGA-D3-A1QB\t\t14\t16\t0\t441\t\tFFF48EB4-5011-44D6-A958-C98ACFD2CBB2\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t7\tYES\tNo\tM0\tN2c\tT0\tA1QB\tTUMOR FREE\tNO\t\tNO\t\tNO\tNO\t\tNO\t\tRegional Lymph Node\tNO\t7th\tNO\tYES\tD3\t86\tfalse\t2017\t2013\t2005\t0\t\tSKCM\tTCGA-D3-A1QB.EA89AFF8-7133-4B7F-89E4-9AF1901C300D.pdf\t\tTCGA\tA\t75.0\t-27566.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1930.0\t\t27566.0\tnot reported\t0.0\t2912.0\tC77.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph node, NOS\tNo\tSkin, NOS\tnot reported\tstage iii\t2005.0\tNevi and Melanomas\tNot Reported\t39.79823221805729\t147.0\t86.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t2135.0\t\t100.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-GN-A4U9-06A\t71\t332.57.0\tNationwide Children's Hospital\tTCGA-GN-A4U9-F61269\t26A50AA3-CD8D-4044-A0D5-52F338BCFA50\tTCGA-GN-A4U9\t1.7\t14\t26\t0\t60\t\tEE40F343-4E34-4182-940F-320CDB780B4B\t\tNo\tYES\t\t\tNO\t5\tIV\tNon-glabrous skin\tYES\t3\t6\tNO\tNo\tM0\tN3\tT2b\tA4U9\tWITH TUMOR\tYES\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Lymph Node\tNO\t7th\tNO\tYES\tGN\t104\tfalse\t2017\t2014\t2012\t0\t\tSKCM\tTCGA-GN-A4U9.13096A76-D4DF-412C-B713-B2BB538B23DA.pdf\t\tTCGA\tA\t71.0\t-26118.0\t673.0\tnot hispanic or latino\tmale\twhite\tDead\t1941.0\t\t26118.0\tnot reported\t0.0\t384.0\tC77.4\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of inguinal region or leg\tNo\tSkin, NOS\tnot reported\tstage iiic\t2012.0\tNevi and Melanomas\tNot Reported\t33.574380165289256\t176.0\t104.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tGN\tRoswell\tSkin Cutaneous Melanoma\t167.0\t\t170.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-ER-A3ET-06A\t64\t198.83.0\tNationwide Children's Hospital\tTCGA-ER-A3ET-F69372\tBCB8141F-EE7F-4047-993A-32F4D2F80315\tTCGA-ER-A3ET\t3.55\t14\t7\t0\t2243\t\tBA70B15C-01ED-448D-9EED-CBAAE4A7C9B3\t\tNo\tYES\t\t\tNO\t\tIV\tNon-glabrous skin\t\t3\t1\tNO\tNo\tM0\tN1a\tT3a\tA3ET\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tNO\t\tYES\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t6th\tYES\tNO\tER\t\tfalse\t2017\t2015\t2005\t0\t\tSKCM\tTCGA-ER-A3ET.49CF1653-1EE3-4C4C-B79D-1DEC59490210.pdf\t\tTCGA\tA\t64.0\t-23650.0\t2829.0\tnot hispanic or latino\tfemale\twhite\tDead\t1941.0\t\t23650.0\tnot reported\t0.0\t2443.0\tC49.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues, NOS\tNo\tSkin, NOS\tnot reported\tstage iiia\t2005.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t2362.0\t\t150.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-HR-A5NC-01A\t90\t291.69.0\tNationwide Children's Hospital\tTCGA-HR-A5NC-F70729\tFD13F733-A6FD-4F02-AB1C-A67967D369F2\tTCGA-HR-A5NC\t8\t14\t2\t0\t0\t\tC9C15001-C075-4639-8E67-5C7BBBE2FE1C\t\tNo\tYES\t\t\tYES\t\tV\tNon-glabrous skin\t\t3\t3\tNO\tNo\tM0\tNX\tT4\tA5NC\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tPrimary Tumor\t\t6th\tNO\tYES\tHR\t\tfalse\t2017\t2015\t\t\t\tSKCM\tTCGA-HR-A5NC.B8892054-09BB-4E2F-90FC-E84EBA310C7D.pdf\t\tTCGA\tA\t90.0\t-32872.0\t\tnot reported\tfemale\tnot reported\tAlive\t\t\t32872.0\tnot reported\t0.0\t0.0\tC44.5\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of trunk\tNo\tSkin, NOS\tnot reported\tnot reported\t\tNevi and Melanomas\tNot Reported\t\t152.0\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tHR\tOntario Institute for Cancer Research (OICR)\tSkin Cutaneous Melanoma\t\t\t150.0\tFalse\tfalse\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-FR-A729-06A\t38\t358.51.0\tNationwide Children's Hospital\tTCGA-FR-A729-F66634\tA152F2A2-94BF-461A-9985-B6DFD58F7CAF\tTCGA-FR-A729\t0.25\t14\t21\t0\t6186\t\t8AEF3E1F-D069-4289-9823-DC1B39D14BDD\t\tNo\tYES\t\t\tNO\t\tII\t\tNO\t3\t10\t\tNo\tM0\tN0\tT1\tA729\tWITH TUMOR\tYES\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Lymph Node\tNO\t4th\tYES\tNO\tFR\t83\tfalse\t2017\t2014\t1996\t0\t\tSKCM\tTCGA-FR-A729.E1B37296-5114-47F3-A48E-2046B7939823.pdf\t\tTCGA\tA\t38.0\t-14043.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1958.0\t\t14043.0\tnot reported\t0.0\t6716.0\tC77.4\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of inguinal region or leg\tNo\tSkin, NOS\tnot reported\tstage i\t1996.0\tNevi and Melanomas\tNot Reported\t27.414453692693883\t174.0\t83.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFR\tUniversity of North Carolina\tSkin Cutaneous Melanoma\t6307.0\t\t400.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-BF-A1PZ-01A\t71\t180.87.0\tNationwide Children's Hospital\tTCGA-BF-A1PZ-F68909\t4B1D5ACA-C24D-4E0A-A8A9-A843108AE1CD\tTCGA-BF-A1PZ\t8\t14\t23\t0\t0\t\tDB0F34CD-3594-4DE7-9F20-D6BE76122271\t\tNo\tYES\t\t\tNO\t\tIII\tNon-glabrous skin\tNO\t3\t12\t\tNo\tM0\tN0\tT4a\tA1PZ\tTUMOR FREE\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tPrimary Tumor\t\t7th\tYES\tNO\tBF\t56\tfalse\t2017\t2014\t2010\t0\t\tSKCM\tTCGA-BF-A1PZ.1FAA6F42-BEE4-47B5-9D6A-87D310719766.pdf\t\tTCGA\tA\t71.0\t-26240.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1939.0\t\t26240.0\tnot reported\t0.0\t853.0\tC44.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin, NOS\tNo\tSkin, NOS\tnot reported\tstage iib\t2010.0\tNevi and Melanomas\tNot Reported\t21.077195227520793\t163.0\t56.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tBF\tCureline\tSkin Cutaneous Melanoma\t97.0\t\t130.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-WE-AAA0-06A\t47\t393.46.0\tNationwide Children's Hospital\tTCGA-WE-AAA0-F67339\t826AB2CF-0860-4D9E-B61C-9B084E997C2D\tTCGA-WE-AAA0\t0.8\t14\t4\t0\t311\t\t27E82066-0F36-481D-A837-1F4ADDEE802F\t\tNo\tYES\t\t\tNO\t0\t\tNon-glabrous skin\tNO\t3\t11\t\tNo\tM0\tN0\tT1a\tAAA0\tWITH TUMOR\tNO\t1\tYES\tNO\tNO\tNO\t\tYES\tNO\tRegional Lymph Node\tNO\t7th\tNO\tYES\tWE\t\tfalse\t2017\t2014\t2011\t0\t\tSKCM\tTCGA-WE-AAA0.7C5BE93A-F3F3-4DE3-84EC-5BC54C644E18.pdf\t\tTCGA\tA\t47.0\t-17261.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1964.0\t\t17261.0\tnot reported\t0.0\t1229.0\tC77.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph node, NOS\tNo\tSkin, NOS\tnot reported\tstage ia\t2011.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tWE\t Norfolk and Norwich Hospital\tSkin Cutaneous Melanoma\t819.0\t\t60.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EB-A430-01A\t83\t262.74.0\tNationwide Children's Hospital\tTCGA-EB-A430-F57899\tFBB7092B-338C-40FB-B72A-7B1FF6B34BE4\tTCGA-EB-A430\t18\t14\t21\t0\t0\t\tF978C499-243C-42F0-9B93-5405CF1AE2B8\t\tNo\tYES\t\t\tYES\t\tIV\tNon-glabrous skin\tYES\t3\t4\t\tNo\tM0\tN0\tT4b\tA430\t\t\t\tYES\tNO\tNO\tNO\t\t\tNO\tPrimary Tumor\t\t7th\tYES\tNO\tEB\t85\tfalse\t2017\t2014\t2012\t0\t\tSKCM\tTCGA-EB-A430.CAF377CF-8A98-4334-B6A7-831024B5A5A6.pdf\t\tTCGA\tA\t83.0\t-30344.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1929.0\t\t30344.0\tnot reported\t0.0\t-2.0\tC44.5\tnot reported\t8721/3\tNodular melanoma\tno\tNo\tnot reported\tSkin of trunk\tNo\tSkin, NOS\tnot reported\tstage iic\t2012.0\tNevi and Melanomas\tNot Reported\t30.116213151927436\t168.0\t85.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t125.0\t\t1800.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-EE-A3J5-06A\t71\t198.83.0\tNationwide Children's Hospital\t\t\tTCGA-EE-A3J5\t4.80\t14\t\t0\t467\t\t80D2316D-82AF-47CA-A4CA-E14BD5055385\t\tNo\tYES\t\t\t\t8\tV\tNon-glabrous skin\tNO\t3\t\tNO\tNo\tM0\tN1\tT4a\tA3J5\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t5th\tNO\tYES\tEE\t\tfalse\t2017\t\t2000\t0\t\tSKCM\tTCGA-EE-A3J5.86A187C5-2FAC-4DEA-9431-020F323E6E5D.pdf\t\tTCGA\tA\t71.0\t-26226.0\t1124.0\tnot hispanic or latino\tmale\twhite\tDead\t1929.0\t2003.0\t26226.0\tnot reported\t0.0\t\tC44.5\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of trunk\tNo\tSkin, NOS\tnot reported\tstage iii\t2000.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t4025.0\t\t40.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-WE-A8K4-01A\t85\t388.48.0\tNationwide Children's Hospital\tTCGA-WE-A8K4-F67276\tDBDCB180-0011-41E1-B16A-4A2AA7417124\tTCGA-WE-A8K4\t12\t14\t3\t0\t0\t\tAAD4D726-4E81-4F9B-8433-F1CA0F0CC8A5\t\tNo\tYES\t\t\tNO\t4\t\tNon-glabrous skin\t\t3\t11\t\tYes\tM0\tNX\tT4a\tA8K4\tTUMOR FREE\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tWE\t\tfalse\t2017\t2014\t2013\t0\t\tSKCM\tTCGA-WE-A8K4.1B421DA3-59DD-4246-8D3B-58F4AE13F36B.pdf\t\tTCGA\tA\t85.0\t-31308.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1928.0\t\t31308.0\tnot reported\t0.0\t614.0\tC44.6\tnot reported\t8721/3\tNodular melanoma\tyes\tNo\tnot reported\tSkin of upper limb and shoulder\tNot Reported\tSkin, NOS\tnot reported\tstage iib\t2013.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tWE\t Norfolk and Norwich Hospital\tSkin Cutaneous Melanoma\t203.0\t\t220.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-D3-A5GR-06A\t23\t291.69.0\tNationwide Children's Hospital\tTCGA-D3-A5GR-F55314\tFE2E3CF3-E00B-4BB5-AD4A-A471B05F7CC1\tTCGA-D3-A5GR\t.98\t14\t3\t0\t5008\t\t9EAEF1C7-C789-4A36-927B-DB444B867387\t\tYes\tYES\tYES\t\tNO\t1\tIII\tNon-glabrous skin\t\t3\t1\tYES\tNo\tM0\tN1\tT1b\tA5GR\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tYES\tInterferon\tNO\tNO\tRegional Lymph Node\tNO\t7th\tNO\tYES\tD3\t114\tfalse\t2017\t2014\t1998\t0\t\tSKCM\tTCGA-D3-A5GR.2055FAF3-FB7C-4E13-8A17-FF4B9964F49F.pdf\t\tTCGA\tA\t23.0\t-8637.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1975.0\t\t8637.0\tnot reported\t0.0\t5424.0\tC77.4\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tYes\tnot reported\tLymph nodes of inguinal region or leg\tNo\tSkin, NOS\tnot reported\tstage iii\t1998.0\tNevi and Melanomas\tNot Reported\t43.43850022862368\t162.0\t114.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tD3\tMD Anderson\tSkin Cutaneous Melanoma\t5119.0\t\t40.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A2GS-06A\t28\t180.87.0\tNationwide Children's Hospital\tTCGA-EE-A2GS-F59674\tB4D6FD51-2575-4523-9F4D-79EDC5B690D9\tTCGA-EE-A2GS\t1.8\t14\t20\t0\t1295\t\tB20AB398-5E86-4811-BA49-EAAD22C127EA\t\tNo\tYES\t\t\tNO\t6\tIV\tNon-glabrous skin\tNO\t3\t5\tNO\tNo\tM0\tN0\tT2a\tA2GS\tWITH TUMOR\tYES\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\tNO\t6th\tNO\tYES\tEE\t\tfalse\t2017\t2014\t2006\t0\t\tSKCM\tTCGA-EE-A2GS.57868CFE-20D2-4893-BF42-8E94E290D42F.pdf\t\tTCGA\tA\t28.0\t-10421.0\t2470.0\tnot hispanic or latino\tfemale\twhite\tDead\t1978.0\t\t10421.0\tnot reported\t0.0\t1691.0\tC49.2\tnot reported\t8730/3\tAmelanotic melanoma\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of lower limb and hip\tNo\tSkin, NOS\tnot reported\tstage ib\t2006.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t1804.0\t\t110.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A29Q-06A\t70\t180.87.0\tNationwide Children's Hospital\tTCGA-EE-A29Q-F55045\tEEF32021-01AE-4851-8408-2D2AEDA944D2\tTCGA-EE-A29Q\t2.3\t14\t27\t0\t890\t\tFF6EF291-7764-4725-A355-848442D49F6D\t\tNo\tYES\t\t\tNO\t8\tIV\tNon-glabrous skin\tYES\t3\t12\t\tNo\tM0\tN0\tT3b\tA29Q\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\tNO\t6th\tNO\tYES\tEE\t\tfalse\t2017\t2013\t2007\t0\t\tSKCM\tTCGA-EE-A29Q.4ED10518-B475-433F-945A-1A97C8B6041C.pdf\t\tTCGA\tA\t70.0\t-25847.0\t2030.0\tnot hispanic or latino\tfemale\twhite\tDead\t1937.0\t\t25847.0\tnot reported\t0.0\t1136.0\tC49.1\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage iib\t2007.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t1263.0\t\t60.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EE-A3J7-06A\t43\t198.83.0\tNationwide Children's Hospital\tTCGA-EE-A3J7-F59752\t744C4C44-2DE1-4A70-87E1-D6BFCB5CC54B\tTCGA-EE-A3J7\t1.12\t14\t20\t0\t1292\t\tE6EB26AF-C93F-4031-9336-BC91F5A64728\t\tNo\tYES\t\t\tYES\t\tII\tNon-glabrous skin\tYES\t3\t5\tYES\tNo\tM0\tN0\tT2\tA3J7\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t5th\tNO\tYES\tEE\t\tfalse\t2017\t2014\t1998\t0\t\tSKCM\tTCGA-EE-A3J7.F2479711-8EEC-44A5-877C-7804A631FCBD.pdf\t\tTCGA\tA\t43.0\t-15896.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1955.0\t\t15896.0\tnot reported\t0.0\t1949.0\tC49.1\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage i\t1998.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEE\tUniversity of Sydney\tSkin Cutaneous Melanoma\t4744.0\t\t100.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EB-A3XE-01A\t77\t240.79.0\tNationwide Children's Hospital\tTCGA-EB-A3XE-F42007\t9F87F66F-2863-44D4-BEDC-5EBCFF5195EF\tTCGA-EB-A3XE\t3\t14\t9\t0\t0\t\tEE78DF3D-A074-404D-8287-BF218737BC7C\t\tNo\tYES\t\t\tYES\t\t\tNon-glabrous skin\tNO\t3\t4\t\tNo\tM0\tN0\tT3a\tA3XE\tTUMOR FREE\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\t\tPrimary Tumor\tNO\t7th\tYES\tNO\tEB\t62\tfalse\t2017\t2013\t2012\t0\t\tSKCM\tTCGA-EB-A3XE.0495197B-B5CD-484A-8531-34ADE693294B.pdf\t\tTCGA\tA\t77.0\t-28148.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1935.0\t\t28148.0\tnot reported\t0.0\t180.0\tC44.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin, NOS\tNo\tSkin, NOS\tnot reported\tstage iia\t2012.0\tNevi and Melanomas\tNot Reported\t24.835763499439192\t158.0\t62.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t113.0\t\t590.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-W3-A824-06A\t63\t358.51.0\tNationwide Children's Hospital\tTCGA-W3-A824-F64489\tFC6C70DC-2DF1-43AF-8043-E8B542A2FA84\tTCGA-W3-A824\t1.5\t14\t26\t0\t1461\t\t6A841BC6-BF69-4121-B5AB-5B87F9CBD9E1\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t9\tNO\tNo\tM0\tN0\tT2\tA824\tWITH TUMOR\tYES\t\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Lymph Node\tYES\t4th\tNO\tYES\tW3\t79\tfalse\t2017\t2014\t1995\t0\t\tSKCM\tTCGA-W3-A824.E8E86625-8D7E-419F-A4BB-86499B9E4E88.pdf\t\tTCGA\tA\t63.0\t-23131.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1932.0\t\t23131.0\tnot reported\t0.0\t6940.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage i\t1995.0\tNevi and Melanomas\tNot Reported\t27.335640138408305\t170.0\t79.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tW3\t John Wayne Cancer Center\tSkin Cutaneous Melanoma\t6686.0\t\t80.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-FS-A4FD-06A\t39\t277.76.0\tNationwide Children's Hospital\tTCGA-FS-A4FD-F62033\tEB67D0C9-E4BA-45E1-BE59-6F48AF9F31C4\tTCGA-FS-A4FD\t2\t14\t11\t0\t2300\t\t085DACD4-0C14-4FB7-90BD-8D152BD2D199\t\tYes\tYES\tYES\t\tNO\t\tIV\t\t\t3\t7\t\tNo\tM0\tN3\tT2\tA4FD\tWITH TUMOR\tNO\t1\tYES\tYES\tNO\tYES\tInterferon\tNO\tNO\tRegional Lymph Node\tNO\t6th\tNO\tYES\tFS\t\tfalse\t2017\t2014\t2004\t0\t\tSKCM\tTCGA-FS-A4FD.6412A296-EDCB-4C6F-B2A7-BC3D4643B843.pdf\t\tTCGA\tA\t39.0\t-14426.0\t2454.0\tnot hispanic or latino\tmale\twhite\tDead\t1965.0\t\t14426.0\tnot reported\t0.0\t2369.0\tC77.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tYes\tnot reported\tLymph node, NOS\tNo\tSkin, NOS\tnot reported\tstage iiic\t2004.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFS\tEssen\tSkin Cutaneous Melanoma\t3065.0\t\t80.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-ER-A19H-06A\t40\t180.87.0\tNationwide Children's Hospital\t\t\tTCGA-ER-A19H\t0.4\t14\t\t0\t3573\t\t29E1FFF6-033F-4261-9CFC-F5AAD3E1820C\t\tNo\tYES\t\t\t\t\tII\tNon-glabrous skin\t\t3\t\t\tNo\tM0\tN0\t\tA19H\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t4th\tYES\tNO\tER\t\tfalse\t2017\t\t1995\t0\t\tSKCM\tTCGA-ER-A19H.8C9DBA9E-E8F5-4080-938D-B58F675461ED.pdf\t\tTCGA\tA\t40.0\t-14935.0\t4634.0\tnot hispanic or latino\tmale\twhite\tDead\t1955.0\t2007.0\t14935.0\tnot reported\t0.0\t\tC49.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of thorax\tNo\tSkin, NOS\tnot reported\tnot reported\t1995.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t5757.0\t\t620.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-EB-A4OY-01A\t65\t277.76.0\tNationwide Children's Hospital\tTCGA-EB-A4OY-F57676\t56E5C92C-0D28-4EEE-9145-DDAFF18DCA7A\tTCGA-EB-A4OY\t10\t14\t19\t0\t-2\t\tD6A3C15D-F713-43FB-B5B9-344465482132\t\tNo\tYES\t\t\tNO\t\tV\tNon-glabrous skin\t\t3\t3\t\tNo\tM0\tN1a\tT4b\tA4OY\tTUMOR FREE\tYES\t\tYES\t\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t7th\tYES\tNO\tEB\t87\tfalse\t2017\t2014\t2012\t0\t\tSKCM\tTCGA-EB-A4OY.F5BCA762-3B92-4B86-96D9-A88C7D06967A.pdf\t\tTCGA\tA\t65.0\t-23773.0\t\tnot hispanic or latino\tfemale\twhite\tAlive\t1947.0\t\t23773.0\tnot reported\t0.0\t977.0\tC44.7\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of lower limb and hip\tNo\tSkin, NOS\tnot reported\tstage iiib\t2012.0\tNevi and Melanomas\tNot Reported\t29.407787993510006\t172.0\t87.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tEB\tAsterand\tSkin Cutaneous Melanoma\t186.0\t\t670.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-ER-A3PL-06A\t30\t240.79.0\tNationwide Children's Hospital\tTCGA-ER-A3PL-F69342\t4940156A-6D72-42F6-A95E-AD17C919F121\tTCGA-ER-A3PL\t2.2\t14\t6\t0\t958\t\t6A29BE6F-4360-406A-B2F9-9C4B2DE80E86\t\tNo\tYES\t\t\tNO\t\tV\tNon-glabrous skin\tYES\t3\t1\tNO\tNo\tM1a\tN0\tT3b\tA3PL\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tNO\t\tNO\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t6th\tYES\tNO\tER\t\tfalse\t2017\t2015\t2009\t0\t\tSKCM\tTCGA-ER-A3PL.249C231D-FD24-4E23-87D3-11D844B41EBF.pdf\t\tTCGA\tA\t30.0\t-11032.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1979.0\t\t11032.0\tnot reported\t0.0\t1010.0\tC49.1\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of upper limb and shoulder\tNo\tSkin, NOS\tnot reported\tstage iv\t2009.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t1017.0\t\t440.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-ER-A19A-06A\t79\t180.87.0\tNationwide Children's Hospital\tTCGA-ER-A19A-F48977\tC163BEA8-E3BA-437C-9B9F-791B7DBFC78A\tTCGA-ER-A19A\t\t14\t24\t0\t41\t\t109A9BB6-4D6A-436C-919E-5F05220E029E\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t9\tNO\tNo\tM1\tN0\tTX\tA19A\tTUMOR FREE\tNO\t\tNO\t\tNO\tNO\t\tNO\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t6th\tYES\tNO\tER\t\tfalse\t2017\t2013\t2006\t0\t\tSKCM\tTCGA-ER-A19A.B48FFC2C-4D9A-4451-B237-3B004D773260.pdf\t\tTCGA\tA\t79.0\t-29167.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1927.0\t\t29167.0\tnot reported\t0.0\t2365.0\tC49.6\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues of trunk, NOS\tNo\tSkin, NOS\tnot reported\tstage iv\t2006.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t1672.0\t\t110.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-FS-A1ZR-06A\t36\t180.87.0\tNationwide Children's Hospital\t\t\tTCGA-FS-A1ZR\t2\t14\t\t0\t79\t\tAB1AE23C-0421-46C1-8675-C7FAC33E4738\t\tNo\tYES\t\t\t\t\tIV\tNon-glabrous skin\t\t3\t\t\tNo\tM0\tN0\tT2\tA1ZR\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t4th\tNO\tYES\tFS\t\tfalse\t2017\t\t1995\t0\t\tSKCM\tTCGA-FS-A1ZR.CBE0DB60-637C-4D5D-85EA-C112E308F476.pdf\t\tTCGA\tA\t36.0\t-13325.0\t347.0\tnot hispanic or latino\tmale\twhite\tDead\t1959.0\t1995.0\t13325.0\tnot reported\t0.0\t\tC77.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph node, NOS\tNo\tSkin, NOS\tnot reported\tstage ii\t1995.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFS\tEssen\tSkin Cutaneous Melanoma\t5645.0\t\t100.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-DA-A3F3-06A\t52\t198.83.0\tNationwide Children's Hospital\tTCGA-DA-A3F3-F70096\tF284EA79-1E79-4396-800D-C2AB6AB6BC03\tTCGA-DA-A3F3\t\t14\t27\t0\t106\t\t1D1C4D82-3FFB-4F8D-A403-A53CE1299278\t\tNo\tYES\t\t\tNO\t\t\tNon-glabrous skin\t\t3\t1\t\tNo\tM0\tN2b\tT0\tA3F3\tWITH TUMOR\tNO\t\tNO\t\tNO\tNO\t\tNO\t\tRegional Lymph Node\tNO\t7th\tNO\tYES\tDA\t\tfalse\t2017\t2015\t2010\t0\t\tSKCM\tTCGA-DA-A3F3.66035FA9-7600-46C5-A5F5-CBD1CC1C1AD4.pdf\t\tTCGA\tA\t52.0\t-19330.0\t319.0\tnot hispanic or latino\tmale\twhite\tDead\t1958.0\t\t19330.0\tnot reported\t0.0\t151.0\tC77.0\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of head, face and neck\tNo\tSkin, NOS\tnot reported\tstage iiib\t2010.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tDA\tYale\tSkin Cutaneous Melanoma\t353.0\t\t250.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-FS-A4F4-06A\t64\t277.76.0\tNationwide Children's Hospital\t\t\tTCGA-FS-A4F4\t2.0\t14\t\t0\t1804\t\tE30CDB48-A1C6-494D-B81E-6CAE7B71D583\t\tNo\tYES\t\t\t\t\tIV\tNon-glabrous skin\tYES\t3\t\t\tNo\tM0\tN0\tT3a\tA4F4\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tRegional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis)\t\t5th\tNO\tYES\tFS\t\tfalse\t2017\t\t2001\t0\t\tSKCM\tTCGA-FS-A4F4.8F51786F-405B-495A-B662-D0C17B68DC33.pdf\t\tTCGA\tA\t64.0\t-23663.0\t2028.0\tnot reported\tmale\twhite\tDead\t1937.0\t2006.0\t23663.0\tnot reported\t0.0\t\tC49.9\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tConnective, subcutaneous and other soft tissues, NOS\tNo\tSkin, NOS\tnot reported\tstage ii\t2001.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFS\tEssen\tSkin Cutaneous Melanoma\t4103.0\t\t60.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-FR-A2OS-01A\t49\t206.84.0\tNationwide Children's Hospital\tTCGA-FR-A2OS-F31116\t29630931-6C4E-45CC-892C-EABCD517BB01\tTCGA-FR-A2OS\t\t14\t24\t0\t0\t\t84C055E7-16FF-45A2-BA60-D6548B84A70F\t\tNo\tYES\t\tElevated\tNO\t\tV\tNon-glabrous skin\tYES\t3\t4\tNO\tNo\tM0\tN0\tT4b\tA2OS\tWITH TUMOR\tNO\t1\tYES\tNO\tNO\tNO\t\tNO\tNO\tPrimary Tumor\tNO\t6th\tNO\tYES\tFR\t84\tfalse\t2017\t2012\t2008\t0\t\tSKCM\tTCGA-FR-A2OS.518D48F1-6A74-4376-85CB-E1F2C8C46011.pdf\t\tTCGA\tA\t49.0\t-18122.0\t368.0\tnot reported\tfemale\twhite\tDead\t1959.0\t2009.0\t18122.0\tnot reported\t0.0\t\tC44.7\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tSkin of lower limb and hip\tNo\tSkin, NOS\tnot reported\tstage iic\t2008.0\tNevi and Melanomas\tNot Reported\t31.23140987507436\t164.0\t84.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFR\tUniversity of North Carolina\tSkin Cutaneous Melanoma\t1257.0\t\t70.0\tFalse\tfalse\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-Z2-AA3S-06A\t58\t408.40.0\tNationwide Children's Hospital\tTCGA-Z2-AA3S-F68278\tD473B9CF-0187-46F0-93F3-02DEEBB567EE\tTCGA-Z2-AA3S\t0.5\t14\t14\t0\t2628\t\tECEAAEB1-B2C0-4C6E-B99C-59A00836E1EE\t\tNo\tYES\t\t\tNO\t\tII\tNon-glabrous skin\tNO\t3\t11\tNO\tNo\tM0\tN0\tT1a\tAA3S\tWITH TUMOR\tNO\t\tYES\tNO\tNO\tNO\t\tYES\tNO\tRegional Lymph Node\tNO\t6th\tYES\tNO\tZ2\t78\tfalse\t2017\t2014\t2006\t0\t\tSKCM\tTCGA-Z2-AA3S.48FB1191-7102-4E62-8A56-A3991807AB6D.pdf\t\tTCGA\tA\t58.0\t-21318.0\t\tnot hispanic or latino\tmale\twhite\tAlive\t1948.0\t\t21318.0\tnot reported\t0.0\t2950.0\tC77.3\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tLymph nodes of axilla or arm\tNo\tSkin, NOS\tnot reported\tstage ia\t2006.0\tNevi and Melanomas\tNot Reported\t26.365603028664143\t172.0\t78.0\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tZ2\tIDI-IRCCS\tSkin Cutaneous Melanoma\t2646.0\t\t270.0\tFalse\ttrue\t\tMetastatic\t06\treleased\tNot Reported\nTCGA-ER-A42H-01A\t76\t262.74.0\tNationwide Children's Hospital\t\t\tTCGA-ER-A42H\t\t14\t\t0\t114\t\t14798F61-224E-4BD0-A01A-588823CD7CAF\t\tNo\tYES\t\t\t\t\t\tNon-glabrous skin\t\t3\t\tNO\tNo\t\t\t\tA42H\t\t\t\tYES\tYES\tNO\tNO\t\t\t\tPrimary Tumor\t\t\tYES\tNO\tER\t\tfalse\t2017\t\t2010\t0\t\tSKCM\tTCGA-ER-A42H.A0ED997A-DE1F-4339-B149-9B0C2A8FF8EB.pdf\t\tTCGA\tA\t76.0\t-28110.0\t426.0\tnot hispanic or latino\tmale\twhite\tDead\t1934.0\t2011.0\t28110.0\tnot reported\t0.0\t\tC43.51\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tOverlapping lesion of rectum, anus and anal canal\tNo\tSkin, NOS\tnot reported\tnot reported\t2010.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tER\tUniversity of Pittsburgh\tSkin Cutaneous Melanoma\t736.0\t\t220.0\tFalse\ttrue\t\tPrimary Tumor\t01\treleased\tNot Reported\nTCGA-FS-A1YY-06A\t55\t180.87.0\tNationwide Children's Hospital\t\t\tTCGA-FS-A1YY\t2.7\t14\t\t0\t6618\tabdomen\tB29E5CCD-665D-4CF4-B010-501A66151BF8\t\tNo\tYES\t\t\t\t\tIV\tNon-glabrous skin\tNO\t3\t\t\tNo\tM0\tN0\tT3a\tA1YY\t\t\t1\tYES\tNO\tNO\tNO\t\t\tNO\tDistant Metastasis\t\t2nd\tNO\tYES\tFS\t\tfalse\t2017\t\t1987\t0\t\tSKCM\tTCGA-FS-A1YY.A91D1EF0-6010-4536-8071-39A935247466.pdf\t\tTCGA\tA\t55.0\t-20396.0\t6953.0\tnot hispanic or latino\tfemale\twhite\tDead\t1932.0\t2006.0\t20396.0\tnot reported\t0.0\t\tC76.2\tnot reported\t8720/3\tMalignant melanoma, NOS\tno\tNo\tnot reported\tAbdomen, NOS\tNo\tSkin, NOS\tnot reported\tstage iia\t1987.0\tNevi and Melanomas\tNot Reported\t\t\t\tSkin\tSkin Cutaneous Melanoma\tTCGA-SKCM\tFalse\tNCH\tFS\tEssen\tSkin Cutaneous Melanoma\t8800.0\t\t60.0\tFalse\tfalse\t\tMetastatic\t06\treleased\tNot Reported" }, { "path": "inst/extdata/TCGA-SKCM.htseq_counts_test.tsv", "content": 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}, { "path": "inst/extdata/ascii_art.txt", "content": " _______ _____ _ _ _____ _____ _____ ____ _____ \n |__ __| __ \\ /\\ | \\ | |/ ____| __ \\| __ \\ / __ \\| __ \\ \n | | | |__) | / \\ | \\| | (___ | |__) | |__) | | | | |__) |\n | | | _ / / /\\ \\ | . ` |\\___ \\| ___/| _ /| | | | _ / \n | | | | \\ \\ / ____ \\| |\\ |____) | | | | \\ \\| |__| | | \\ \\ \n |_| |_| \\_\\/_/ \\_\\_| \\_|_____/|_| |_| \\_\\\\____/|_| \\_\\\n \n \n \n \n _ _ _______ _____ _____ _______ _________ _______ _____ _____ \n /\\ | | | |__ __| / ____/ ____/ ____\\ \\ / / __ \\ \\ / / ____/ ____/ ____|\n / \\ | | | | | | | (___| (___| (___ \\ \\_/ /| | | \\ \\_/ / (___| (___| (___ \n / /\\ \\| | | | | | \\___ \\\\___ \\\\___ \\ \\ / | | | |\\ / \\___ \\\\___ \\\\___ \\ \n / ____ \\ |__| | | | ____) |___) |___) | | | | |__| | | | ____) |___) |___) |\n/_/ \\_\\____/ |_| |_____/_____/_____/ |_| |_____/ |_| |_____/_____/_____/ \n \n \n" }, { "path": "inst/extdata/gtex_gene_expected_count_test", "content": 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}, { "path": "inst/extdata/gtex_probeMap_gencode.v23.annotation.gene.probemap_test", "content": "id\tgene\tchrom\tchromStart\tchromEnd\tstrand\nENSG00000242268.2\tRP11-368I23.2\tchr3\t168903366\t168921996\t+\nENSG00000259041.1\tRP11-167B3.1\tchr15\t98282075\t98285907\t+\nENSG00000270112.3\tRP11-742D12.2\tchr18\t46756487\t46764408\t+\nENSG00000167578.16\tRAB4B\tchr19\t40778216\t40796944\t+\nENSG00000278814.1\tAC104071.1\tchr4\t31349786\t31349874\t-" }, { "path": "man/Combat_Normal.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CombatNormal.R\n\\name{Combat_Normal}\n\\alias{Combat_Normal}\n\\title{Process and Correct Batch Effects in TCGA's normal tissue and GTEX Data}\n\\usage{\nCombat_Normal(\n TCGA_normal_data_path,\n gtex_data_path,\n CombatNormal_output_path,\n auto_mode = FALSE,\n default_input = \"11,12\"\n)\n}\n\\arguments{\n\\item{TCGA_normal_data_path}{The path to the tumor data stored in an RDS file.}\n\n\\item{gtex_data_path}{The path to the GTEX data stored in an RDS file.}\n\n\\item{CombatNormal_output_path}{A character string specifying the path where the output RDS file will be saved.}\n\n\\item{auto_mode}{Logical. If set to TRUE, the function will not prompt the user for input and\nwill instead use the values provided in default_input. Default is FALSE.}\n\n\\item{default_input}{Character string. When auto_mode is TRUE, this parameter specifies the default\nTGCA's normal tissue types to be retained. It should be provided as a comma-separated string (e.g., \"11,12\").}\n}\n\\value{\nA data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the\n combat_count_df data as an RDS file at the specified output path.\n}\n\\description{\nThe function first extracts histological types from the provided TCGA's normal tissue data set.\nAfter displaying these types, the user is prompted to input specific types to retain.\nThe data is then filtered based on this input.\nThe GTEX and TCGA's normal tissue datasets are then combined and batch corrected.\n\nNote: This function assumes that TCGA's normal samples and GTEX samples represent different batches.\n}\n\\details{\nThis function takes a TCGA's normal tissue data set and a pre-saved GTEX data set, asks the user\nfor specific TCGA's normal tissue types to retain, then merges the two datasets. The merged dataset\nis then corrected for batch effects using the ComBat_seq function from the 'sva' package.\n\n\nThe ComBat_seq function from the 'sva' package is used to correct batch effects.\nThe function requires the 'sva' package to be installed and loaded externally.\n\nThe example code uses `tempfile()` to generate temporary paths dynamically during execution.\nThese paths are valid during the `R CMD check` process, even if no actual files exist,\nbecause `tempfile()` generates a unique file path that does not depend on the user's file system.\nUsing `tempfile()` ensures that the example code does not rely on specific external files and\navoids errors during `R CMD check`. CRAN review checks for documentation correctness\nand syntax parsing, not the existence of actual files, as long as the example code is syntactically valid.\n}\n\\examples{\nTCGA_normal_file <- system.file(\"extdata\",\n \"SKCM_Skin_TCGA_exp_normal_test.rds\",\n package = \"TransProR\")\ngtex_file <- system.file(\"extdata\", \"Skin_SKCM_Gtex_test.rds\", package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"SKCM_Skin_Combat_Normal_TCGA_GTEX_count.rds\")\n\nSKCM_Skin_Combat_Normal_TCGA_GTEX_count <- Combat_Normal(\n TCGA_normal_data_path = TCGA_normal_file,\n gtex_data_path = gtex_file,\n CombatNormal_output_path = output_file,\n auto_mode = TRUE,\n default_input = \"skip\"\n)\nhead(SKCM_Skin_Combat_Normal_TCGA_GTEX_count)[1:5, 1:5]\n\n}\n\\seealso{\n\\code{\\link[sva]{ComBat_seq}}\n}\n" }, { "path": "man/Contrast_Venn.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/Contrast_Venn.R\n\\name{Contrast_Venn}\n\\alias{Contrast_Venn}\n\\title{Function to Create a Venn Diagram of DEGs with Custom Colors}\n\\usage{\nContrast_Venn(\n all_degs_venn,\n edge_colors,\n name_color,\n fill_colors,\n label_size = 4,\n edge_size = 3\n)\n}\n\\arguments{\n\\item{all_degs_venn}{A list of DEG sets for Venn Diagram creation.}\n\n\\item{edge_colors}{A vector of colors for the edges of the Venn Diagram sets.}\n\n\\item{name_color}{A vector of colors for the names of the sets in the Venn Diagram.}\n\n\\item{fill_colors}{A vector of two colors for the gradient fill of the Venn Diagram.}\n\n\\item{label_size}{The size of the labels showing the number of elements in each set (default is 4).}\n\n\\item{edge_size}{The size of the edges of the Venn Diagram sets (default is 3).}\n}\n\\value{\nA `ggplot` object representing the Venn Diagram.\n}\n\\description{\nThis function creates a Venn Diagram using the 'ggVennDiagram' package.\nIt allows customization of various aesthetic elements of the diagram, including colors.\n}\n\\examples{\n\ndata(\"all_degs_venn\", package = \"TransProR\")\n\nedge_colors <- c(\"#1b62bb\",\"#13822e\",\"#332c3a\",\"#9e2d39\")\nname_color <- c(\"#1b64bb\",\"#13828e\",\"#337c3a\",\"#9e9d39\")\nfill_colors <- c(\"#e3f2fa\", \"#0288d1\")\n\nContrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)\n}\n" }, { "path": "man/DESeq2_analyze.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/DESeq2Analyze.R\n\\name{DESeq2_analyze}\n\\alias{DESeq2_analyze}\n\\title{Differential Gene Expression Analysis using 'DESeq2'}\n\\usage{\nDESeq2_analyze(\n tumor_file,\n normal_file,\n output_file,\n logFC = 2.5,\n p_value = 0.01\n)\n}\n\\arguments{\n\\item{tumor_file}{Path to the tumor data file (RDS format).}\n\n\\item{normal_file}{Path to the normal data file (RDS format).}\n\n\\item{output_file}{Path to save the output DEG data (RDS format).}\n\n\\item{logFC}{Threshold for log fold change.}\n\n\\item{p_value}{Threshold for p-value.}\n}\n\\value{\nA data frame of differential expression results.\n}\n\\description{\n'DESeq2': Differential gene expression analysis based on the negative binomial distribution.\nThis function utilizes the 'DESeq2' package to conduct differential gene expression analysis.\nIt processes tumor and normal expression data, applies DESeq2 analysis,\nand outputs the results along with information on gene expression changes.\n}\n\\details{\nThe DESeq2 methodology is based on modeling count data using a negative binomial distribution,\nwhich allows for handling the variability observed in gene expression data, especially in\nsmall sample sizes. This approach is well-suited for RNA-Seq data analysis.\n}\n\\examples{\n# Define file paths for tumor and normal data from the data folder\ntumor_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\nnormal_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"DEG_DESeq2.rds\")\n\nDEG_DESeq2 <- DESeq2_analyze(\n tumor_file = tumor_file,\n normal_file = normal_file,\n output_file = output_file,\n 2.5,\n 0.01\n)\n\n# View the top 5 rows of the result\nhead(DEG_DESeq2, 5)\n\n}\n\\references{\nDESeq2:Differential gene expression analysis based on the negative binomial distribution.\nFor more information, visit the page:\nhttps://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/\n}\n" }, { "path": "man/Wilcoxon_analyze.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/WilcoxonAnalyze.R\n\\name{Wilcoxon_analyze}\n\\alias{Wilcoxon_analyze}\n\\title{Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test}\n\\usage{\nWilcoxon_analyze(\n tumor_file,\n normal_file,\n output_file,\n logFC_threshold = 2.5,\n fdr_threshold = 0.05\n)\n}\n\\arguments{\n\\item{tumor_file}{Path to the tumor data file (RDS format).}\n\n\\item{normal_file}{Path to the normal data file (RDS format).}\n\n\\item{output_file}{Path to save the output DEG data (RDS format).}\n\n\\item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.}\n\n\\item{fdr_threshold}{Threshold for FDR for filtering significant genes.}\n}\n\\value{\nA data frame of differential expression results.\n}\n\\description{\nThis function performs differential gene expression analysis using Wilcoxon rank-sum tests.\nIt reads tumor and normal expression data, performs TMM normalization using 'edgeR', and uses Wilcoxon rank-sum tests to identify differentially expressed genes.\n}\n\\examples{\n# Define file paths for tumor and normal data from the data folder\ntumor_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\nnormal_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"Wilcoxon_rank_sum_testoutRst.rds\")\n\n# Run the Wilcoxon rank sum test\noutRst <- Wilcoxon_analyze(\n tumor_file = tumor_file,\n normal_file = normal_file,\n output_file = output_file,\n logFC_threshold = 2.5,\n fdr_threshold = 0.01\n)\n\n# View the top 5 rows of the result\nhead(outRst, 5)\n}\n\\references{\nLi, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022). Exaggerated False Positives by Popular\nDifferential Expression Methods When Analyzing Human Population Samples. Genome Biology, 23(1), 79.\nDOI: https://doi.org/10.1186/s13059-022-02648-4.\n}\n" }, { "path": "man/add_boxplot.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CircosFruits.R\n\\name{add_boxplot}\n\\alias{add_boxplot}\n\\title{Add a boxplot layer to a `ggtree` plot}\n\\usage{\nadd_boxplot(\n p,\n data,\n fill_color = \"#f28131\",\n alpha = 0.6,\n offset = 0.22,\n pwidth = 0.5\n)\n}\n\\arguments{\n\\item{p}{An existing ggtree plot object.}\n\n\\item{data}{A data frame containing the data to be plotted. Expected to have columns for 'Sample' and 'value'.}\n\n\\item{fill_color}{A character string specifying the fill color for the boxplots. Default is \"#f28131\".}\n\n\\item{alpha}{Numeric value for the transparency of the boxplots. Default is 0.6.}\n\n\\item{offset}{Numeric value, the position of the boxplot on the x-axis relative to its gene name. Default is 0.22.}\n\n\\item{pwidth}{Numeric value, the width of the boxplot. Default is 0.5.}\n}\n\\value{\nA `ggtree` plot object with the added boxplot layer.\n}\n\\description{\nThis function adds a boxplot layer to an existing `ggtree` plot object using ggtreeExtra's geom_fruit for boxplots.\nIt is primarily used to display statistical summaries of the data related to gene expressions or other metrics.\n}\n\\examples{\n\\donttest{\n# Check and load required packages\nif (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n requireNamespace(\"ggplot2\", quietly = TRUE)) {\n library(ggtreeExtra)\n library(ggplot2)\n\n file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n p <- readRDS(file_path)\n\n # Create boxplot data frame\n boxplot_data <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 30),\n value = c(\n rnorm(30, mean = 5, sd = 1), # Data for Species_A\n rnorm(30, mean = 7, sd = 1.5), # Data for Species_B\n rnorm(30, mean = 6, sd = 1.2), # Data for Species_C\n rnorm(30, mean = 8, sd = 1.3) # Data for Species_D\n )\n )\n\n # Call add_boxplot function to add boxplot layer\n p_with_boxplot <- add_boxplot(p, boxplot_data)\n} else {\n message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n}\n}\n\n}\n" }, { "path": "man/add_new_tile_layer.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CircosFruits.R\n\\name{add_new_tile_layer}\n\\alias{add_new_tile_layer}\n\\title{Add a new tile layer with dynamic scales to a `ggtree` plot}\n\\usage{\nadd_new_tile_layer(\n p,\n data,\n gene_colors,\n gene_label,\n alpha_value = c(0.3, 0.9),\n offset = 0.02,\n pwidth = 2\n)\n}\n\\arguments{\n\\item{p}{An existing ggtree plot object.}\n\n\\item{data}{A data frame containing the data to be plotted. Expected to have columns for 'Sample', 'Gene', and 'value'.}\n\n\\item{gene_colors}{A named vector of colors for genes.}\n\n\\item{gene_label}{A character string used as a label in the legend for the genes.}\n\n\\item{alpha_value}{A numeric or named vector for setting the alpha scale based on values.}\n\n\\item{offset}{Numeric value, the position of the tile on the x-axis relative to its gene name. Default is 0.02.}\n\n\\item{pwidth}{Numeric value, the width of the tile. Default is 2.}\n}\n\\value{\nA `ggtree` plot object with the added tile layer and new scales.\n}\n\\description{\nThis function adds a new tile layer to an existing `ggtree` plot object, allowing for separate scales for fill\nand alpha transparency. This is useful when you want to add additional data layers without interfering with\nthe existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers.\n}\n\\examples{\n\\donttest{\n# Check and load required packages\nif (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n requireNamespace(\"ggplot2\", quietly = TRUE) &&\n requireNamespace(\"ggnewscale\", quietly = TRUE)) {\n library(ggtreeExtra)\n library(ggplot2)\n library(ggnewscale)\n\n file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n p <- readRDS(file_path)\n\n # Create new expression data\n new_expression_data <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 3),\n Gene = rep(c(\"Gene6\", \"Gene7\", \"Gene8\"), times = 4),\n Value = runif(12, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n )\n\n # Define new gene colors\n new_gene_colors <- c(\n Gene6 = \"#0b5f63\",\n Gene7 = \"#074d41\",\n Gene8 = \"#1f5e27\"\n )\n\n # Define gene label and alpha values\n gene_label <- \"New Genes\"\n alpha_value <- c(0.3, 0.9)\n\n # Add new tile layer\n p_with_new_layer <- add_new_tile_layer(\n p,\n new_expression_data,\n new_gene_colors,\n gene_label,\n alpha_value,\n offset = 0.02,\n pwidth = 2\n )\n} else {\n message(\"Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.\")\n}\n}\n\n}\n" }, { "path": "man/adjust_alpha_scale.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/AdjustAlphaScale.R\n\\name{adjust_alpha_scale}\n\\alias{adjust_alpha_scale}\n\\title{Adjust Alpha Scale for Data Visualization}\n\\usage{\nadjust_alpha_scale(data, name, range = c(0.2, 0.8))\n}\n\\arguments{\n\\item{data}{A data frame containing the values for which the alpha scale is to be adjusted.}\n\n\\item{name}{Character string that will be used as the title of the legend in the plot.}\n\n\\item{range}{Numeric vector of length 2 specifying the range of alpha values, defaults to c(0.2, 0.8).}\n}\n\\value{\nA ggplot2 alpha scale adjustment layer.\n}\n\\description{\nThis function dynamically adjusts the transparency scale for visualizations,\nespecially useful when the range of data values varies significantly across different sources.\nIt modifies the transparency scale based on the range of values present in the data,\nensuring that the visualization accurately reflects variations within the data.\n}\n\\examples{\n# Assuming 'data' is a DataFrame with a 'value' column\nplot_data <- data.frame(value = c(10, 20, 30, 40, 50))\nggplot2::ggplot(plot_data, ggplot2::aes(x = 1:nrow(plot_data), y = value)) +\n ggplot2::geom_point(ggplot2::aes(alpha = value)) +\n adjust_alpha_scale(plot_data, \"Transparency Scale\")\n\n}\n" }, { "path": "man/adjust_color_tone.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichmentSpiralize.R\n\\name{adjust_color_tone}\n\\alias{adjust_color_tone}\n\\title{Adjust Color Tone by Modifying Saturation and Luminance}\n\\usage{\nadjust_color_tone(color, saturation_scale, luminance_scale)\n}\n\\arguments{\n\\item{color}{A color in hexadecimal format (e.g., \"#FF0000\") or a valid R color name.}\n\n\\item{saturation_scale}{Numeric, the scaling factor for saturation (values < 1 decrease saturation, values > 1 increase saturation).}\n\n\\item{luminance_scale}{Numeric, the scaling factor for luminance (values < 1 darken the color, values > 1 lighten the color).}\n}\n\\value{\nReturns a color in hexadecimal format adjusted according to the provided scales.\n}\n\\description{\nThis function adjusts the saturation and luminance of a given color. It works by converting\nthe color from RGB to Luv color space, applying the scaling factors to the saturation and luminance,\nand then converting it back to RGB.\n}\n\\examples{\n adjusted_color <- adjust_color_tone(\"#FF0000\", saturation_scale = 0.8, luminance_scale = 1.2)\n print(adjusted_color)\n\n}\n" }, { "path": "man/adjust_export_pathway.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCirclize.R\n\\name{adjust_export_pathway}\n\\alias{adjust_export_pathway}\n\\title{Adjust and Export Pathway Analysis Results}\n\\usage{\nadjust_export_pathway(fgseaRes, nTop = 10)\n}\n\\arguments{\n\\item{fgseaRes}{Dataframe containing fgsea results with columns 'pathway', 'ES', and 'pval'.}\n\n\\item{nTop}{Integer, number of top pathways to select based on the p-value.}\n}\n\\value{\nA vector containing combined top upregulated and downregulated pathways.\n}\n\\description{\nThis function processes a dataframe containing fgsea results. It adjusts pathway names\nby removing underscores, converting to lowercase, then capitalizing the first letter,\nand joining the components with spaces. It selects and merges the top upregulated\nand downregulated pathways based on enrichment score (ES) and p-value.\n}\n\\examples{\n# Create a synthetic fgseaRes dataframe\nfgseaRes <- data.frame(\n pathway = c(\"KEGG_APOPTOSIS\",\n \"GO_CELL_CYCLE\",\n \"REACTOME_DNA_REPAIR\",\n \"KEGG_METABOLISM\",\n \"GO_TRANSPORT\"),\n ES = c(0.45, -0.22, 0.56, -0.35, 0.33),\n pval = c(0.001, 0.02, 0.0003, 0.05, 0.01)\n)\n\n# Run the function to get top pathways\nresult <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2)\n\n}\n" }, { "path": "man/all_degs_venn.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data.R\n\\docType{data}\n\\name{all_degs_venn}\n\\alias{all_degs_venn}\n\\title{All DEGs Venn Diagram Data}\n\\format{\nA list with the following components:\n\\describe{\n \\item{DESeq2}{A vector of gene IDs or gene symbols identified as DEGs by the DESeq2 method.}\n \\item{edgeR}{A vector of gene IDs or gene symbols identified as DEGs by the edgeR method.}\n \\item{limma}{A vector of gene IDs or gene symbols identified as DEGs by the limma method. }\n \\item{Wilcoxon_test}{A vector of gene IDs or gene symbols identified as DEGs by the Wilcoxon test method. }\n}\n}\n\\source{\nThe data was derived from differential expression analyses performed on a gene expression dataset using four commonly used statistical methods (DESeq2, edgeR, limma, and Wilcoxon test).\n}\n\\usage{\ndata(all_degs_venn)\n}\n\\description{\nA dataset containing the differentially expressed genes (DEGs) from four different statistical analysis methods: DESeq2, edgeR, limma, and Wilcoxon test.\nThis dataset is used for generating Venn diagrams to compare the overlap of DEGs identified by different methods.\n}\n\\examples{\ndata(all_degs_venn)\n# Example of plotting a Venn diagram using the dataset\n\nedge_colors <- c(\"#1b62bb\",\"#13822e\",\"#332c3a\",\"#9e2d39\")\nname_color <- c(\"#1b64bb\",\"#13828e\",\"#337c3a\",\"#9e9d39\")\nfill_colors <- c(\"#e3f2fa\", \"#0288d1\")\n\nContrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors)\n\n}\n\\keyword{datasets}\n" }, { "path": "man/circos_fruits.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CircosFruits.R\n\\name{circos_fruits}\n\\alias{circos_fruits}\n\\title{Add multiple layers to a `ggtree` plot for visualizing gene expression and enrichment data}\n\\usage{\ncircos_fruits(\n p,\n long_format_HeatdataDeseq,\n ssgsea_kegg_HeatdataDeseq,\n gsva_kegg_HeatdataDeseq,\n gene_colors\n)\n}\n\\arguments{\n\\item{p}{A `ggtree` plot object to which the data and layers will be added.}\n\n\\item{long_format_HeatdataDeseq}{A data frame containing gene expression data with columns for `Samples`, `Genes`, and `Values`.}\n\n\\item{ssgsea_kegg_HeatdataDeseq}{A data frame containing SSGSEA analysis results with columns for `Samples`, `Genes`, and `Values`.}\n\n\\item{gsva_kegg_HeatdataDeseq}{A data frame containing GSVA analysis results with columns for `Samples`, `Genes`, and `Values`.}\n\n\\item{gene_colors}{A named vector of colors for genes, used for coloring tiles in different layers.}\n}\n\\value{\nA `ggtree` plot object with multiple layers added for comprehensive visualization.\n}\n\\description{\nThis function sequentially adds multiple layers to a `ggtree` plot, including gene expression data, boxplots for statistical\nsummaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate\nfunctions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics\nfor each layer. The function is designed to work with specific data structures and assumes all functions for adding layers\nare defined and available.\n}\n\\examples{\n\\donttest{\n# Check and load required packages\nif (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n requireNamespace(\"ggplot2\", quietly = TRUE)) {\n library(ggtreeExtra)\n library(ggplot2)\n\n # Example data for gene expression, SSGSEA, and GSVA\n file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n p <- readRDS(file_path)\n\n # Create gene expression data frame (long_format_HeatdataDeseq)\n long_format_HeatdataDeseq <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 5),\n Genes = rep(paste0(\"Gene\", 1:5), times = 4),\n Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n )\n\n # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq)\n ssgsea_kegg_HeatdataDeseq <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 3),\n Genes = rep(c(\"Pathway1\", \"Pathway2\", \"Pathway3\"), times = 4),\n Value = runif(12, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1\n )\n\n # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq)\n gsva_kegg_HeatdataDeseq <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 4),\n Genes = rep(c(\"PathwayA\", \"PathwayB\", \"PathwayC\", \"PathwayD\"), times = 4),\n Value = runif(16, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1\n )\n\n # Define gene and pathway colors (named vector), including all genes and pathways\n gene_colors <- c(\n # Genes for gene expression\n Gene1 = \"#491588\",\n Gene2 = \"#301b8d\",\n Gene3 = \"#1a237a\",\n Gene4 = \"#11479c\",\n Gene5 = \"#0a5797\",\n # Pathways for SSGSEA\n Pathway1 = \"#0b5f63\",\n Pathway2 = \"#074d41\",\n Pathway3 = \"#1f5e27\",\n # Pathways for GSVA\n PathwayA = \"#366928\",\n PathwayB = \"#827729\",\n PathwayC = \"#a1d99b\",\n PathwayD = \"#c7e9c0\"\n )\n\n # Call circos_fruits function to add multiple layers\n final_plot <- circos_fruits(\n p,\n long_format_HeatdataDeseq,\n ssgsea_kegg_HeatdataDeseq,\n gsva_kegg_HeatdataDeseq,\n gene_colors\n )\n} else {\n message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n}\n}\n\n}\n" }, { "path": "man/combat_tumor.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CombatTumor.R\n\\name{combat_tumor}\n\\alias{combat_tumor}\n\\title{Process and Correct Batch Effects in Tumor Data}\n\\usage{\ncombat_tumor(\n tumor_data_path,\n CombatTumor_output_path,\n auto_mode = FALSE,\n default_input = \"01,06\"\n)\n}\n\\arguments{\n\\item{tumor_data_path}{The path to the tumor data stored in an RDS file.}\n\n\\item{CombatTumor_output_path}{A character string specifying the path where the output RDS file will be saved.}\n\n\\item{auto_mode}{Logical. If set to TRUE, the function will not prompt the user for input and\nwill instead use the values provided in default_input. Default is FALSE.}\n\n\\item{default_input}{Character string. When auto_mode is TRUE, this parameter specifies the default\ntumor types to be retained. It should be provided as a comma-separated string (e.g., \"01,06\").}\n}\n\\value{\nA data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the\n combat_count_df data as an RDS file at the specified output path.\n}\n\\description{\nThe function first extracts histological types from the provided tumor data set.\nAfter displaying these types, the user is prompted to input specific types to retain.\nThe data is then filtered based on this input.\n\nNote: This example assumes that different tumor types represent different batches in a general sense.\nUsers need to adjust the batch and group vectors based on real-life scenarios.\n}\n\\details{\nThis function takes a tumor data set, asks the user for specific tumor types to retain,\nand then corrects for batch effects using the ComBat_seq function from the 'sva' package.\n\n\nThe ComBat_seq function from the sva package is used to correct batch effects.\nThe function requires the 'sva' package to be installed and loaded externally.\n}\n\\examples{\ntumor_file <- system.file(\"extdata\",\n \"SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"SKCM_combat_count.rds\")\n\n SKCM_combat_count <- combat_tumor(\n tumor_data_path = tumor_file,\n CombatTumor_output_path = output_file,\n auto_mode = TRUE,\n default_input = \"01,06\"\n)\n\nhead(SKCM_combat_count)[1:5, 1:5]\n\n}\n\\seealso{\n\\code{\\link[sva]{ComBat_seq}}\n}\n" }, { "path": "man/compare_merge.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CompareMerge.R\n\\name{compare_merge}\n\\alias{compare_merge}\n\\title{Compare and merge specific columns from two DEG data frames}\n\\usage{\ncompare_merge(df1, df2, by_gene, compare_col, suffixes, df_name)\n}\n\\arguments{\n\\item{df1}{First data frame.}\n\n\\item{df2}{Second data frame.}\n\n\\item{by_gene}{Column name by which to join the data frames, typically \"Gene\".}\n\n\\item{compare_col}{Column to compare for identity, which will also be the name of the merged column.}\n\n\\item{suffixes}{Suffixes to use for non-identical column names in the joined data frame.}\n\n\\item{df_name}{Name to assign to the resulting data frame for identification.}\n}\n\\value{\nA data frame with processed columns.\n}\n\\description{\nThis function takes two DEG data frames, inner joins them by a specified gene column,\nchecks if a specified column is identical across both data frames, and merges them if they are.\nThe resulting data frame will have a merged column named after the compared column.\n}\n\\examples{\n# Create simulated DESeq2 data\nDEG_deseq2 <- data.frame(\n Gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\"),\n change = c(\"up\", \"down\", \"no_change\", \"up\", \"down\"),\n log2FoldChange = c(2.5, -3.2, 0.1, 1.8, -2.5),\n pvalue = c(0.01, 0.05, 0.9, 0.02, 0.03)\n)\n\n# Display the first 5 rows of the DESeq2 data\nhead(DEG_deseq2, 5)\n\n# Create simulated edgeR data\nDEG_edgeR <- data.frame(\n Gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\"),\n change = c(\"up\", \"down\", \"no_change\", \"no_change\", \"up\"),\n log2FoldChange = c(2.3, -3.1, 0.2, 0.1, 2.7),\n pvalue = c(0.02, 0.04, 0.8, 0.6, 0.01)\n)\n\n# Display the first 5 rows of the edgeR data\nhead(DEG_edgeR, 5)\n\n# Merge the DESeq2 and edgeR data\ndeseq2_edgeR <- compare_merge(\n df1 = DEG_deseq2,\n df2 = DEG_edgeR,\n by_gene = \"Gene\",\n compare_col = \"change\",\n suffixes = c(\"_1\", \"_2\"),\n df_name = \"deseq2_edgeR\"\n)\n\n}\n" }, { "path": "man/create_base_plot.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/CircosFruits.R\n\\name{create_base_plot}\n\\alias{create_base_plot}\n\\title{Create a base plot with gene expression data on a phylogenetic tree}\n\\usage{\ncreate_base_plot(p, data, gene_colors, gene_label = \"Gene\")\n}\n\\arguments{\n\\item{p}{A ggtree plot object to which the data will be added.}\n\n\\item{data}{A data frame containing gene expression data with columns for Samples, Genes, and Values.}\n\n\\item{gene_colors}{A named vector of colors for genes.}\n\n\\item{gene_label}{A character string used as a label in the legend for the genes. Default is \"Gene\".}\n}\n\\value{\nA `ggtree` plot object with the gene expression data added.\n}\n\\description{\nThis function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression\ndata as colored tiles to the plot. It allows for dynamic coloring of the genes and includes\nadjustments for alpha transparency based on the expression value.\n}\n\\examples{\n\\donttest{\n# Check and load required packages\nif (requireNamespace(\"ggtreeExtra\", quietly = TRUE) &&\n requireNamespace(\"ggplot2\", quietly = TRUE)) {\n library(ggtreeExtra)\n library(ggplot2)\n\n file_path <- system.file(\"extdata\", \"p_tree_test.rds\", package = \"TransProR\")\n p <- readRDS(file_path)\n\n # Create gene expression data frame\n expression_data <- data.frame(\n Sample = rep(c(\"Species_A\", \"Species_B\", \"Species_C\", \"Species_D\"), each = 5),\n Gene = rep(paste0(\"Gene\", 1:5), times = 4),\n Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1\n )\n\n # Define gene colors (named vector)\n gene_colors <- c(\n Gene1 = \"#491588\",\n Gene2 = \"#301b8d\",\n Gene3 = \"#1a237a\",\n Gene4 = \"#11479c\",\n Gene5 = \"#0a5797\"\n )\n\n # Call create_base_plot function to add gene expression data\n p <- create_base_plot(p, expression_data, gene_colors)\n} else {\n message(\"Required packages 'ggtreeExtra' and 'ggplot2' are not installed.\")\n}\n}\n\n}\n" }, { "path": "man/deg_filter.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/FourDegsVenn.R\n\\name{deg_filter}\n\\alias{deg_filter}\n\\title{Function to Filter Differentially Expressed Genes (DEGs)}\n\\usage{\ndeg_filter(df)\n}\n\\arguments{\n\\item{df}{A data frame containing gene expression data.}\n}\n\\value{\nA vector of gene names that are differentially expressed.\n}\n\\description{\nThis function filters out genes based on their expression change status.\nIt returns the names of genes which are not \"stable\".\n}\n\\examples{\nDEG_deseq2_file <- system.file(\"extdata\", \"DEG_deseq2.rds\", package = \"TransProR\")\nDEG_deseq2 <- readRDS(DEG_deseq2_file)\nDEG_deseq2_test <- deg_filter(DEG_deseq2)\n}\n" }, { "path": "man/drawLegends.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCirclize.R\n\\name{drawLegends}\n\\alias{drawLegends}\n\\title{Draw Dual-Sided Legends on a Plot}\n\\usage{\ndrawLegends(\n labels,\n colors,\n legend_width,\n x_positions,\n y_position,\n just_positions,\n text_alignments,\n font_size\n)\n}\n\\arguments{\n\\item{labels}{Vector of labels for the legends.}\n\n\\item{colors}{Vector of colors corresponding to the labels.}\n\n\\item{legend_width}{The width of each legend viewport expressed in grid units.}\n\n\\item{x_positions}{Numeric vector of length 2 specifying the x-positions of the left and right legends.}\n\n\\item{y_position}{The y-position common for both legends, expressed as a fraction of the plot height.}\n\n\\item{just_positions}{List of two vectors, each specifying the horizontal and vertical justification for the legends.}\n\n\\item{text_alignments}{List of two character strings specifying text alignments for the legends ('left' or 'right').}\n\n\\item{font_size}{Numeric value specifying the font size for the legend labels.}\n}\n\\value{\nInvisible. This function is called for its side effects of drawing legends on a plot.\n}\n\\description{\nThis function creates two sets of legends, one on the left and one on the right side of a plot.\nIt displays color-coded legends with labels corresponding to different data categories.\nEach legend entry consists of a colored rectangle and a text label. The left side legend has\ntext aligned to the right of the color block, while the right side legend has text aligned\nto the left of the color block.\n}\n\\examples{\nlabels <- c(\"Label1\", \"Label2\", \"Label3\", \"Label4\", \"Label5\", \"Label6\")\ncolors <- c(\"#ff0000\", \"#00ff00\", \"#0000ff\", \"#ffff00\", \"#ff00ff\", \"#00ffff\")\n\n# Convert to 'unit' objects for grid\ngrid::grid.roundrect(\n x = grid::unit(0.5, \"npc\"), # \"npc\" stands for normalized parent coordinates\n y = grid::unit(0.5, \"npc\"),\n width = grid::unit(0.1, \"npc\"),\n height = grid::unit(0.05, \"npc\"),\n gp = grid::gpar(fill = \"red\"),\n r = grid::unit(0.1, \"npc\") # rounding radius\n)\n\n# Example of drawing legends with specific labels and colors\ndrawLegends(labels, colors, grid::unit(2, \"cm\"), c(0.225, 0.75), 0.5,\n list(c(\"left\", \"center\"), c(\"right\", \"center\")),\n list(\"right\", \"left\"), 10)\n\n}\n" }, { "path": "man/edgeR_analyze.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EdgeRAnalyze.R\n\\name{edgeR_analyze}\n\\alias{edgeR_analyze}\n\\title{Differential Gene Expression Analysis using 'edgeR'}\n\\usage{\nedgeR_analyze(\n tumor_file,\n normal_file,\n output_file,\n logFC_threshold = 2.5,\n p_value_threshold = 0.01\n)\n}\n\\arguments{\n\\item{tumor_file}{Path to the tumor data file (RDS format).}\n\n\\item{normal_file}{Path to the normal data file (RDS format).}\n\n\\item{output_file}{Path to save the output DEG data (RDS format).}\n\n\\item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.}\n\n\\item{p_value_threshold}{Threshold for p-value for filtering significant genes.}\n}\n\\value{\nA data frame of differential expression results.\n}\n\\description{\nThis function performs differential gene expression analysis using the 'edgeR' package.\nIt reads tumor and normal expression data, merges them, filters low-expressed genes,\nnormalizes the data, performs edgeR analysis, and outputs the results along with information\non gene expression changes.\n}\n\\examples{\n# Define file paths for tumor and normal data from the data folder\ntumor_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\nnormal_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"DEG_edgeR.rds\")\n\nDEG_edgeR <- edgeR_analyze(\n tumor_file = tumor_file,\n normal_file = normal_file,\n output_file = output_file,\n 2.5,\n 0.01\n)\n\n# View the top 5 rows of the result\nhead(DEG_edgeR, 5)\n\n}\n\\references{\nedgeR: Differential analysis of sequence read count data.\nFor more information, visit the edgeR Bioconductor page:\nhttps://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf\n}\n" }, { "path": "man/enrich_circo_bar.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCircoBar.R\n\\name{enrich_circo_bar}\n\\alias{enrich_circo_bar}\n\\title{Combine and Visualize Data with Circular Bar Chart}\n\\usage{\nenrich_circo_bar(data_list)\n}\n\\arguments{\n\\item{data_list}{A list of data frames to be combined.}\n}\n\\value{\nA `ggplot` object representing the Circular Bar Chart.\n}\n\\description{\nThis function combines multiple data frames, arranges them, and visualizes the combined data\nin a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.\n}\n\\examples{\n# Create sample data frames for each enrichment category\n\n# 1. Biological Process (BP)\nfiltered_data_BP <- data.frame(\n Description = c(\n \"immune response\",\n \"cell proliferation\",\n \"signal transduction\",\n \"apoptotic process\",\n \"metabolic process\"\n ),\n Count = c(120, 85, 150, 60, 95),\n color = c(\n \"#1f77b4\", # blue\n \"#ff7f0e\", # orange\n \"#2ca02c\", # green\n \"#d62728\", # red\n \"#9467bd\" # purple\n ),\n stringsAsFactors = FALSE\n)\n\n# 2. Cellular Component (CC)\nfiltered_data_CC <- data.frame(\n Description = c(\n \"nucleus\",\n \"cytoplasm\",\n \"membrane\",\n \"mitochondrion\",\n \"extracellular space\"\n ),\n Count = c(90, 110, 75, 65, 80),\n color = c(\n \"#1f77b4\",\n \"#ff7f0e\",\n \"#2ca02c\",\n \"#d62728\",\n \"#9467bd\"\n ),\n stringsAsFactors = FALSE\n)\n\n# 3. Molecular Function (MF)\nfiltered_data_MF <- data.frame(\n Description = c(\n \"protein binding\",\n \"DNA binding\",\n \"enzyme activity\",\n \"transporter activity\",\n \"receptor activity\"\n ),\n Count = c(140, 130, 100, 70, 90),\n color = c(\n \"#1f77b4\",\n \"#ff7f0e\",\n \"#2ca02c\",\n \"#d62728\",\n \"#9467bd\"\n ),\n stringsAsFactors = FALSE\n)\n\n# 4. Disease Ontology (DO)\nfiltered_data_DO <- data.frame(\n Description = c(\n \"cancer\",\n \"cardiovascular disease\",\n \"neurological disorder\",\n \"metabolic disease\",\n \"infectious disease\"\n ),\n Count = c(200, 150, 120, 90, 160),\n color = c(\n \"#1f77b4\",\n \"#ff7f0e\",\n \"#2ca02c\",\n \"#d62728\",\n \"#9467bd\"\n ),\n stringsAsFactors = FALSE\n)\n\n# 5. Reactome Pathways\nfiltered_data_Reactome <- data.frame(\n Description = c(\n \"Cell Cycle\",\n \"Apoptosis\",\n \"DNA Repair\",\n \"Signal Transduction\",\n \"Metabolism of Proteins\"\n ),\n Count = c(110, 95, 80, 130, 85),\n color = c(\n \"#1f77b4\",\n \"#ff7f0e\",\n \"#2ca02c\",\n \"#d62728\",\n \"#9467bd\"\n ),\n stringsAsFactors = FALSE\n)\n\n# 6. KEGG Pathways\nfiltered_data_kegg <- data.frame(\n Description = c(\n \"PI3K-Akt signaling pathway\",\n \"MAPK signaling pathway\",\n \"NF-kappa B signaling pathway\",\n \"JAK-STAT signaling pathway\",\n \"Toll-like receptor signaling pathway\"\n ),\n Count = c(175, 160, 145, 130, 155),\n color = c(\n \"#1f77b4\",\n \"#ff7f0e\",\n \"#2ca02c\",\n \"#d62728\",\n \"#9467bd\"\n ),\n stringsAsFactors = FALSE\n)\n\n# Combine all filtered data frames into a list\ndata_list <- list(\n BP = filtered_data_BP,\n CC = filtered_data_CC,\n MF = filtered_data_MF,\n DO = filtered_data_DO,\n Reactome = filtered_data_Reactome,\n KEGG = filtered_data_kegg\n)\n\n# Create the Circular Bar Chart\ncombined_and_visualized_data <- enrich_circo_bar(data_list)\n\n}\n" }, { "path": "man/enrich_polar_bubble.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichPolarBubble.R\n\\name{enrich_polar_bubble}\n\\alias{enrich_polar_bubble}\n\\title{Enrichment Polar Bubble Plot}\n\\usage{\nenrich_polar_bubble(final_combined_df_with_id_and_position, pal, highlight_ids)\n}\n\\arguments{\n\\item{final_combined_df_with_id_and_position}{A data frame containing 'id', 'Count', 'method', 'Description', 'point_position', 'test_color'.}\n\n\\item{pal}{A named vector of colors corresponding to the 'method' values.}\n\n\\item{highlight_ids}{A vector of IDs to highlight.}\n}\n\\value{\nA `ggplot` object representing the enriched polar bubble plot.\n}\n\\description{\nThis function creates a polar bubble plot using 'ggplot2'. It is designed to visually represent data with methods and positional metrics integrated, highlighting specific IDs if necessary.\n}\n\\examples{\n final_df <- data.frame(id = 1:10, Count = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100),\n method = rep(\"Method1\", 10),\n Description = LETTERS[1:10],\n point_position = seq(10, 100, 10),\n test_color = sample(c(\"red\", \"blue\"), 10, replace = TRUE))\n pal <- c(\"Method1\" = \"blue\")\n highlight_ids <- c(1, 5, 9)\n enrich_polar_bubble(final_df, pal, highlight_ids)\n}\n" }, { "path": "man/enrichment_circlize.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCirclize.R\n\\name{enrichment_circlize}\n\\alias{enrichment_circlize}\n\\title{Draw Chord Diagram with Legends}\n\\usage{\nenrichment_circlize(\n all_combined_df,\n original_colors,\n labels,\n colors,\n labels2,\n colors2,\n font_size = 10\n)\n}\n\\arguments{\n\\item{all_combined_df}{A dataframe containing the matrix for the chord diagram.}\n\n\\item{original_colors}{A vector of colors for the grid columns of the chord diagram.}\n\n\\item{labels}{A vector of labels for the first legend.}\n\n\\item{colors}{A vector of colors corresponding to the first legend's labels.}\n\n\\item{labels2}{A vector of labels for the second legend.}\n\n\\item{colors2}{A vector of colors corresponding to the second legend's labels.}\n\n\\item{font_size}{The font size used for legend texts, defaults to 10.}\n}\n\\value{\nInvisible, primarily used for its side effects of drawing on a graphics device.\n}\n\\description{\nThis function creates a chord diagram from a specified dataframe and draws two sets of legends for it.\nIt adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid.\nLegends are drawn at specified positions with configurable text alignments and font sizes.\n}\n\\examples{\n# Sample Chord Diagram Matrix\nall_combined_df <- data.frame(\n A = c(10, 20, 30),\n B = c(15, 25, 35),\n C = c(5, 10, 15)\n)\nrownames(all_combined_df) <- c(\"A\", \"B\", \"C\")\n\n# Colors for the grid of the chord diagram (corresponding to columns of the matrix)\noriginal_colors <- c(\"red\", \"green\", \"blue\")\n\n# Name the colors according to the sectors (A, B, C)\nnames(original_colors) <- colnames(all_combined_df)\n\n# Labels and Colors for the First Legend\nlabels <- c(\"Label 1\", \"Label 2\", \"Label 3\")\ncolors <- c(\"yellow\", \"purple\", \"cyan\")\n\n# Labels and Colors for the Second Legend\nlabels2 <- c(\"Label A\", \"Label B\", \"Label C\")\ncolors2 <- c(\"orange\", \"pink\", \"brown\")\n\n# Font size for the legend texts (optional, default is 10)\nfont_size <- 10\n\n# Call the enrichment_circlize function with the sample data\n# This is just an example; the plot will be rendered in an appropriate graphics context\n# such as RStudio's plot pane or an external plotting window.\nplot1 <- enrichment_circlize(all_combined_df,\n original_colors,\n labels,\n colors,\n labels2,\n colors2,\n font_size\n )\n\n}\n" }, { "path": "man/enrichment_spiral_plots.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichmentSpiralize.R\n\\name{enrichment_spiral_plots}\n\\alias{enrichment_spiral_plots}\n\\title{Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap'}\n\\usage{\nenrichment_spiral_plots(results)\n}\n\\arguments{\n\\item{results}{A data frame containing 'Pathway', 'Sample', 'Value', 'PathwayColor', and 'SampleColor' columns.}\n}\n\\value{\nNo return value, called for side effects. This function generates spiral plots and adds legends based on sample and pathway information.\n}\n\\description{\nThis function initializes a spiral plot, adds tracks for pathways and samples,\nand generates legends based on the sample and pathway information in the provided data frame.\nIt uses 'spiralize' for the spiral plot and 'ComplexHeatmap' for handling legends.\n}\n\\examples{\n# Example: Creating enrichment spiral plots with legends\n\n# Define the results data frame\nresults <- data.frame(\n Pathway = c(\"Pathway1\", \"Pathway1\", \"Pathway2\", \"Pathway2\", \"Pathway3\"),\n Sample = c(\"Sample1\", \"Sample1\", \"Sample2\", \"Sample2\", \"Sample3\"),\n Value = c(20, 30, 15, 35, 25),\n PathwayColor = c(\"red\", \"red\", \"blue\", \"blue\", \"orange\"),\n SampleColor = c(\"green\", \"green\", \"purple\", \"purple\", \"cyan\"),\n stringsAsFactors = FALSE\n)\n\n# Create the enrichment spiral plots with legends\nenrichment_spiral_plots(results)\n\n}\n" }, { "path": "man/extract_descriptions_counts.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCircoBar.R\n\\name{extract_descriptions_counts}\n\\alias{extract_descriptions_counts}\n\\title{Extract and Count Descriptions with Specified Color}\n\\usage{\nextract_descriptions_counts(df, descriptions, color)\n}\n\\arguments{\n\\item{df}{A data frame containing at least 'Description' and 'Count' columns.}\n\n\\item{descriptions}{A vector of descriptions to filter in the data frame.}\n\n\\item{color}{A character string specifying the color to be added as a new column.}\n}\n\\value{\nA data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column.\n}\n\\description{\nThis function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns,\nand adds a new column with a specified color.\n}\n\\examples{\n# Generate Sample Input Data for extract_descriptions_counts Function\n\n# Create a sample data frame with 'Description' and 'Count' columns\ndata <- data.frame(\n Description = c(\n \"immunoglobulin production\",\n \"B cell mediated immunity\",\n \"T cell activation\",\n \"antigen processing and presentation\",\n \"cytokine signaling\",\n \"natural killer cell activity\",\n \"phagocytosis\",\n \"complement activation\",\n \"antibody-dependent cellular cytotoxicity\",\n \"regulatory T cell function\"\n ),\n Count = c(\n 150, # immunoglobulin production\n 200, # B cell mediated immunity\n 175, # T cell activation\n 125, # antigen processing and presentation\n 190, # cytokine signaling\n 160, # natural killer cell activity\n 140, # phagocytosis\n 180, # complement activation\n 130, # antibody-dependent cellular cytotoxicity\n 170 # regulatory T cell function\n ),\n stringsAsFactors = FALSE # Ensure that strings are not converted to factors\n)\n\n\n\ndescriptions_to_filter <- c(\"immunoglobulin production\", \"B cell mediated immunity\")\nspecified_color <- \"red\" # You can specify any color you desire\nfiltered_data_with_color <- extract_descriptions_counts(\n data, descriptions_to_filter,\n specified_color)\nprint(filtered_data_with_color)\n\n}\n" }, { "path": "man/extract_ntop_pathways.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichmentSpiralize.R\n\\name{extract_ntop_pathways}\n\\alias{extract_ntop_pathways}\n\\title{Extract and Store Top Pathways for Each Sample}\n\\usage{\nextract_ntop_pathways(ssgsea_kegg, nTop = 5)\n}\n\\arguments{\n\\item{ssgsea_kegg}{Dataframe containing SSGSEA KEGG results with samples as columns and pathways as rows.}\n\n\\item{nTop}{Integer, number of top pathways to select for each sample.}\n}\n\\value{\nA dataframe with columns 'Pathway', 'Sample', and 'Value' representing the top pathways for each sample.\n}\n\\description{\nThis function processes a dataframe containing SSGSEA KEGG results. It allows specifying the number\nof top pathways to extract for each sample based on their scores, and stores these in a new dataframe\nwith sample names and pathway scores.\n}\n\\examples{\n# Example: Generating input data for the extract_ntop_pathways function\n\n# Define example pathways\npathways <- c(\"Pathway_A\", \"Pathway_B\", \"Pathway_C\", \"Pathway_D\", \"Pathway_E\",\n \"Pathway_F\", \"Pathway_G\", \"Pathway_H\", \"Pathway_I\", \"Pathway_J\")\n\n# Define example samples\nsamples <- c(\"Sample_1\", \"Sample_2\", \"Sample_3\")\n\n# Generate random SSGSEA KEGG scores between 0 and 1\nset.seed(123) # For reproducibility\nssgsea_scores <- matrix(runif(length(pathways) * length(samples), min = 0, max = 1),\n nrow = length(pathways), ncol = length(samples),\n dimnames = list(pathways, samples))\n\n# Convert to a data frame\nssgsea_kegg <- as.data.frame(ssgsea_scores)\n\n# Extract the top 3 pathways for each sample\ntop_pathways <- extract_ntop_pathways(ssgsea_kegg, nTop = 3)\n\n}\n" }, { "path": "man/extract_positive_pathways.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichmentSpiralize.R\n\\name{extract_positive_pathways}\n\\alias{extract_positive_pathways}\n\\title{Extract Positive Pathways from SSGSEA Results and Select Random Samples}\n\\usage{\nextract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 5)\n}\n\\arguments{\n\\item{ssgsea_kegg}{A matrix or data frame with pathways as rows and samples as columns.}\n\n\\item{max_paths_per_sample}{Integer, maximum number of pathways to select per sample.}\n}\n\\value{\nA data frame with selected pathways, samples, and their corresponding values.\n}\n\\description{\nThis function processes the results of SSGSEA, specifically focusing on KEGG pathways.\nIt extracts pathways with positive values from each sample and randomly selects a subset of them.\n}\n\\examples{\n# Example: Generating input data for the extract_positive_pathways function\n\n# Define example pathways\npathways <- c(\"Pathway_1\", \"Pathway_2\", \"Pathway_3\", \"Pathway_4\", \"Pathway_5\",\n \"Pathway_6\", \"Pathway_7\", \"Pathway_8\", \"Pathway_9\", \"Pathway_10\")\n\n# Define example samples\nsamples <- c(\"Sample_A\", \"Sample_B\", \"Sample_C\")\n\n# Generate random SSGSEA KEGG scores including both positive and negative values\nset.seed(456) # For reproducibility\nssgsea_scores <- matrix(rnorm(length(pathways) * length(samples), mean = 0, sd = 1),\n nrow = length(pathways), ncol = length(samples),\n dimnames = list(pathways, samples))\n\n# Convert to a data frame\nssgsea_kegg <- as.data.frame(ssgsea_scores)\n\n# Use the extract_positive_pathways function to extract up to 3 positive pathways per sample\nselected_positive_pathways <- extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 3)\n\n}\n" }, { "path": "man/facet_density_foldchange.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/FacetDensityFoldchange.R\n\\name{facet_density_foldchange}\n\\alias{facet_density_foldchange}\n\\title{Create faceted high-density region plots with optional points and density contours}\n\\usage{\nfacet_density_foldchange(\n data,\n x_var,\n y_var,\n group_var,\n facet_var,\n palette,\n show_points = FALSE,\n show_density = TRUE,\n point_size = 2.5,\n point_alpha = 0.1,\n line_size = 1.6,\n cor_method = \"pearson\",\n cor_label_pos = c(\"left\", 0.97),\n cor_vjust = NULL\n)\n}\n\\arguments{\n\\item{data}{Data frame containing variables for plotting.}\n\n\\item{x_var}{Name of the x-axis variable as a string.}\n\n\\item{y_var}{Name of the y-axis variable as a string.}\n\n\\item{group_var}{Name of the grouping variable for color mapping as a string.}\n\n\\item{facet_var}{Name of the faceting variable.}\n\n\\item{palette}{Color palette for the plot as a character vector.}\n\n\\item{show_points}{Logical, if TRUE adds scatter points to the plot.}\n\n\\item{show_density}{Logical, if TRUE adds filled density contours to the plot.}\n\n\\item{point_size}{Size of the points, relevant if show_points is TRUE.}\n\n\\item{point_alpha}{Transparency level of the points, relevant if show_points is TRUE.}\n\n\\item{line_size}{Size of the regression line.}\n\n\\item{cor_method}{Method to calculate correlation (\"pearson\" or \"spearman\").}\n\n\\item{cor_label_pos}{Vector of length 2 indicating the position of the correlation label (x and y).}\n\n\\item{cor_vjust}{Vertical justification for correlation label, default is NULL.}\n}\n\\value{\nA `ggplot` object representing the high-density region plot.\n}\n\\description{\nThis function creates faceted high-density region plots using ggdensity for\nadding optional density rug and density contours, and scatter points. It also adds a regression line\nand Pearson correlation label. The plot is faceted by a grouping variable.\n}\n\\examples{\ncombined_df_file <- system.file(\"extdata\", \"combined_df.rds\", package = \"TransProR\")\ncombined_df <- readRDS(combined_df_file)\npal2 = c(\"#2787e0\",\"#1a9ae0\",\"#1dabbf\",\"#00897b\",\"#43a047\",\"#7cb342\")\nall_facet_density_foldchange_name1 <- facet_density_foldchange(\n data = combined_df,\n x_var = \"log2FoldChange_1\",\n y_var = \"log2FoldChange_2\",\n group_var = \"name\",\n facet_var = \"name\",\n palette = pal2,\n show_points = TRUE,\n show_density = FALSE,\n point_size = 2,\n point_alpha = 0.1,\n line_size = 1.6,\n cor_method = \"pearson\",\n cor_label_pos = c(\"left\", \"top\"),\n cor_vjust = 1\n)\n}\n" }, { "path": "man/filter_diff_genes.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/FilterDiffGenes.R\n\\name{filter_diff_genes}\n\\alias{filter_diff_genes}\n\\title{Filter Differentially Expressed Genes}\n\\usage{\nfilter_diff_genes(\n data,\n p_val_col = \"adj.P.Val\",\n log_fc_col = \"logFC\",\n p_val_threshold = 0.05,\n log_fc_threshold = 1\n)\n}\n\\arguments{\n\\item{data}{A data frame containing gene expression data.}\n\n\\item{p_val_col}{Character string indicating the column name for p-values. Default is \"adj.P.Val\".}\n\n\\item{log_fc_col}{Character string indicating the column name for log fold change. Default is \"logFC\".}\n\n\\item{p_val_threshold}{Numeric threshold for filtering p-values. Default is 0.05.}\n\n\\item{log_fc_threshold}{Numeric threshold for filtering log fold change. Default is 1.0.}\n}\n\\value{\nA data frame with genes filtered by the specified criteria.\n}\n\\description{\nThis function filters a data frame to identify genes with significant differential expression\nbased on specified thresholds for p-values and log fold change. It allows for flexible\ninput of column names for p-values and log fold change.\n}\n\\examples{\n# Create a sample data frame with p-values and log fold changes\nsample_data <- data.frame(\n adj.P.Val = c(0.03, 0.06, 0.02, 0.07),\n logFC = c(1.5, 0.8, -1.2, 1.1),\n gene = c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\")\n)\n\n# Use the filter_diff_genes function to filter significant genes\nfiltered_genes <- filter_diff_genes(sample_data)\nprint(filtered_genes)\n}\n" }, { "path": "man/four_degs_venn.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/FourDegsVenn.R\n\\name{four_degs_venn}\n\\alias{four_degs_venn}\n\\title{Function to Create a Venn Diagram of DEGs}\n\\usage{\nfour_degs_venn(degs_list)\n}\n\\arguments{\n\\item{degs_list}{A list of DEG sets for Venn Diagram creation.}\n}\n\\value{\nA ggplot object representing the Venn Diagram.\n}\n\\description{\nThis function creates a Venn Diagram using the ggVennDiagram package.\nIt allows customization of various aesthetic elements of the diagram.\n}\n\\examples{\ndata(\"all_degs_venn\", package = \"TransProR\")\nfour_degs_venn <- four_degs_venn(all_degs_venn)\n}\n" }, { "path": "man/gather_graph_edge.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GatherGraphEdge.R\n\\name{gather_graph_edge}\n\\alias{gather_graph_edge}\n\\title{Gather graph edge from data frame\nPlease note that this function is from the 'ggraph' package and has not been altered in functionality,\nbut it has been optimized and iterated.\nIt is not original content of 'TransProR'.\nHowever, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\nthe author directly referenced the involved functions in 'TransProR'.\nThis is not the author's original creation. All users please be aware!}\n\\usage{\ngather_graph_edge(df, index = NULL, root = NULL)\n}\n\\arguments{\n\\item{df}{A data frame}\n\n\\item{index}{A vector of column names to group by}\n\n\\item{root}{Root name for the root node connections, optional}\n}\n\\value{\nA tibble of graph edges\n}\n\\description{\nGather graph edge from data frame\nPlease note that this function is from the 'ggraph' package and has not been altered in functionality,\nbut it has been optimized and iterated.\nIt is not original content of 'TransProR'.\nHowever, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\nthe author directly referenced the involved functions in 'TransProR'.\nThis is not the author's original creation. All users please be aware!\n}\n" }, { "path": "man/gather_graph_node.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GatherGraphNode.R\n\\name{gather_graph_node}\n\\alias{gather_graph_node}\n\\title{Gather graph nodes from a data frame\nPlease note that this function is from the 'ggraph' package and has not been altered in functionality,\nbut it has been optimized and iterated.\nIt is not original content of 'TransProR'.\nHowever, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\nthe author directly referenced the involved functions in 'TransProR'.\nThis is not the author's original creation. All users please be aware!}\n\\usage{\ngather_graph_node(\n df,\n index = NULL,\n value = utils::tail(colnames(df), 1),\n root = NULL\n)\n}\n\\arguments{\n\\item{df}{A data frame}\n\n\\item{index}{A vector of column names to group by}\n\n\\item{value}{Column name used for summarizing node size, defaults to the last column}\n\n\\item{root}{Root name for the root node connections, optional}\n}\n\\value{\na tibble of graph nodes\n}\n\\description{\nGather graph nodes from a data frame\nPlease note that this function is from the 'ggraph' package and has not been altered in functionality,\nbut it has been optimized and iterated.\nIt is not original content of 'TransProR'.\nHowever, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR',\nthe author directly referenced the involved functions in 'TransProR'.\nThis is not the author's original creation. All users please be aware!\n}\n" }, { "path": "man/gene_color.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GeneColor.R\n\\name{gene_color}\n\\alias{gene_color}\n\\title{Merge Genes with Color Information Based on Up/Down Regulation}\n\\usage{\ngene_color(selected_genes, DEG_deseq2, up_color, down_color)\n}\n\\arguments{\n\\item{selected_genes}{A data frame containing selected genes with a column named \"Symble\".}\n\n\\item{DEG_deseq2}{A data frame containing differential expression data with a column named \"Symble\" and a column named \"change\" indicating up/down regulation.}\n\n\\item{up_color}{The color to assign to genes with up-regulated expression.}\n\n\\item{down_color}{The color to assign to genes with down-regulated expression.}\n}\n\\value{\nA data frame containing merged genes with an additional color column.\n}\n\\description{\nThis function merges selected genes with differential expression data and adds a color column based on up/down regulation.\n}\n\\examples{\nselected_genes_deseq2_file <- system.file(\"extdata\",\n \"selected_genes_deseq2.rds\",\n package = \"TransProR\")\nselected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)\nDiff_deseq2_file <- system.file(\"extdata\", \"Diff_deseq2.rds\", package = \"TransProR\")\nDiff_deseq2 <- readRDS(Diff_deseq2_file)\n\nresult_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, \"#0000EE\", \"#fc4746\")\n}\n" }, { "path": "man/gene_highlights.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GeneHighlights.R\n\\name{gene_highlights}\n\\alias{gene_highlights}\n\\title{Add gene highlights to a ggtree object}\n\\usage{\ngene_highlights(ggtree_obj, genes_to_highlight, hilight_extend = 18)\n}\n\\arguments{\n\\item{ggtree_obj}{A ggtree object to which the highlights will be added.}\n\n\\item{genes_to_highlight}{A data frame containing genes and their corresponding colors.}\n\n\\item{hilight_extend}{Integer, the extension of the highlight fan in degrees.}\n}\n\\value{\nA ggtree object with added gene highlights.\n}\n\\description{\nThis function enhances a `ggtree` plot by adding highlights for specific genes. It adds both a semi-transparent fan-shaped\nhighlight and a point at the node corresponding to each gene. Colors for each gene can be customized.\n}\n\\examples{\ndata(\"gtree\", package = \"TransProR\")\n\n# Define genes and their colors\ngenes_df <- data.frame(Symble = c(\"t5\", \"t9\"),\n color = c(\"#FF0000\", \"#0000FF\"))\n\n# Add highlights\ngtree <- gene_highlights(gtree, genes_to_highlight = genes_df)\n\n}\n" }, { "path": "man/gene_map_pathway.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GeneMapPathway.R\n\\name{gene_map_pathway}\n\\alias{gene_map_pathway}\n\\title{Create Pathway-Gene Mapping Data Frame}\n\\usage{\ngene_map_pathway(\n BP_dataframe,\n BP_ids,\n KEGG_dataframe,\n KEGG_ids,\n MF_dataframe,\n MF_ids,\n REACTOME_dataframe,\n REACTOME_ids,\n CC_dataframe,\n CC_ids,\n DO_dataframe,\n DO_ids\n)\n}\n\\arguments{\n\\item{BP_dataframe}{Data frame for Biological Process analysis}\n\n\\item{BP_ids}{Selected pathway IDs for Biological Process analysis}\n\n\\item{KEGG_dataframe}{Data frame for KEGG analysis}\n\n\\item{KEGG_ids}{Selected pathway IDs for KEGG analysis}\n\n\\item{MF_dataframe}{Data frame for Molecular Function analysis}\n\n\\item{MF_ids}{Selected pathway IDs for Molecular Function analysis}\n\n\\item{REACTOME_dataframe}{Data frame for REACTOME analysis}\n\n\\item{REACTOME_ids}{Selected pathway IDs for REACTOME analysis}\n\n\\item{CC_dataframe}{Data frame for Cellular Component analysis}\n\n\\item{CC_ids}{Selected pathway IDs for Cellular Component analysis}\n\n\\item{DO_dataframe}{Data frame for Disease Ontology analysis}\n\n\\item{DO_ids}{Selected pathway IDs for Disease Ontology analysis}\n}\n\\value{\nA new data frame that includes pathways, gene, type, and value columns\n}\n\\description{\nThis function takes multiple data frames and pathway IDs, merging them into a new data frame.\nEach data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.).\n}\n\\examples{\n# Simulating data for different analysis types\n\n# Simulate Biological Process (BP) data frame\nBP_df <- data.frame(\n ID = c(\"GO:0002376\", \"GO:0019724\"),\n geneID = c(\"GENE1/GENE2\", \"GENE3/GENE4\"),\n Description = c(\"Immune response\", \"Glycosylation process\")\n)\n\n# Simulate KEGG data frame\nKEGG_df <- data.frame(\n ID = c(\"12345\", \"67890\"),\n geneID = c(\"GENE5/GENE6\", \"GENE7/GENE8\"),\n Description = c(\"Pathway 1\", \"Pathway 2\")\n)\n\n# Simulate Molecular Function (MF) data frame\nMF_df <- data.frame(\n ID = c(\"ABC123\", \"DEF456\"),\n geneID = c(\"GENE9/GENE10\", \"GENE11/GENE12\"),\n Description = c(\"Molecular function A\", \"Molecular function B\")\n)\n\n# Simulate REACTOME data frame\nREACTOME_df <- data.frame(\n ID = c(\"R-HSA-12345\", \"R-HSA-67890\"),\n geneID = c(\"GENE13/GENE14\", \"GENE15/GENE16\"),\n Description = c(\"Pathway in Reactome 1\", \"Pathway in Reactome 2\")\n)\n\n# Simulate Cellular Component (CC) data frame\nCC_df <- data.frame(\n ID = c(\"GO:0005575\", \"GO:0005634\"),\n geneID = c(\"GENE17/GENE18\", \"GENE19/GENE20\"),\n Description = c(\"Cellular component A\", \"Cellular component B\")\n)\n\n# Simulate Disease Ontology (DO) data frame\nDO_df <- data.frame(\n ID = c(\"DOID:123\", \"DOID:456\"),\n geneID = c(\"GENE21/GENE22\", \"GENE23/GENE24\"),\n Description = c(\"Disease A\", \"Disease B\")\n)\n\n# Example pathway IDs for each analysis\nBP_ids <- c(\"GO:0002376\", \"GO:0019724\")\nKEGG_ids <- c(\"12345\", \"67890\")\nMF_ids <- c(\"ABC123\", \"DEF456\")\nREACTOME_ids <- c(\"R-HSA-12345\", \"R-HSA-67890\")\nCC_ids <- c(\"GO:0005575\", \"GO:0005634\")\nDO_ids <- c(\"DOID:123\", \"DOID:456\")\n\n# Generate the pathway-gene map using the gene_map_pathway function\npathway_gene_map <- gene_map_pathway(\n BP_dataframe = BP_df, BP_ids = BP_ids,\n KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids,\n MF_dataframe = MF_df, MF_ids = MF_ids,\n REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids,\n CC_dataframe = CC_df, CC_ids = CC_ids,\n DO_dataframe = DO_df, DO_ids = DO_ids\n)\n\n# Display the resulting pathway-gene mapping data frame\nprint(pathway_gene_map)\n\n}\n" }, { "path": "man/get_gtex_exp.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GetGtexExp.R\n\\name{get_gtex_exp}\n\\alias{get_gtex_exp}\n\\title{Get GTEx Expression Data for Specific Organ}\n\\usage{\nget_gtex_exp(\n organ_specific,\n file_path,\n probe_map_path,\n pheno_path,\n output_path\n)\n}\n\\arguments{\n\\item{organ_specific}{A character string specifying the organ to filter the gene expression data by.}\n\n\\item{file_path}{A character string specifying the path to the GTEx gene expression data file.}\n\n\\item{probe_map_path}{A character string specifying the path to the gtex_probeMap_gencode data file.}\n\n\\item{pheno_path}{A character string specifying the path to the GTEx phenotype data file.}\n\n\\item{output_path}{A character string specifying the path where the output RDS file will be saved.}\n}\n\\value{\nA data frame containing gene expression data for the specified organ.\n Rows represent genes, and columns represent samples. Note that this function also saves the\n organ-specific GTEx data as an RDS file at the specified output path.\n}\n\\description{\nThis function retrieves gene expression data from the GTEx project that is specific to a certain organ.\nIt performs various checks and processing steps to ensure that the data is consistent and relevant to the\nspecified organ. The filtered and cleaned data is saved as an RDS file for further analysis.\n}\n\\details{\nThe function begins by checking if the gene expression and phenotype data files exist at\n the specified paths. It then loads these data files and processes them by setting appropriate row names,\n modifying column names for clarity, and filtering samples based on the specified organ. The function ensures\n that only samples present in both datasets are retained for consistency. It also removes any duplicate gene\n entries to prevent redundancy. Finally, the processed data is saved as an RDS file.\n}\n\\note{\nThe function will stop and throw an error if the input files do not exist, or if no samples are found\n for the specified organ.\n\nCRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\n that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/your_sample_type.gtex.rds'.\n}\n" }, { "path": "man/get_tcga_exp.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/GetTcgaExp.R\n\\name{get_tcga_exp}\n\\alias{get_tcga_exp}\n\\title{TCGA Expression Data Processing}\n\\usage{\nget_tcga_exp(\n counts_file_path,\n gene_probes_file_path,\n phenotype_file_path,\n output_file_path\n)\n}\n\\arguments{\n\\item{counts_file_path}{File path to the counts data (usually in the form of a large matrix with gene expression data).}\n\n\\item{gene_probes_file_path}{File path containing the gene probes data.}\n\n\\item{phenotype_file_path}{File path to the phenotype data, which includes various sample attributes.}\n\n\\item{output_file_path}{Path where the output files, distinguished between tumor and normal, will be saved.}\n}\n\\value{\nA list containing matrices for tumor and normal expression data.\n}\n\\description{\nThis function processes expression data and phenotype information, separates tumor and normal samples,\nand saves the results into different files. It's specifically designed for data obtained from TCGA.\n}\n\\note{\nIMPORTANT: This function assumes that the input files follow a specific format and structure, typically found in TCGA data releases.\nUsers should verify their data's compatibility. Additionally, the function does not perform error checking on the data's content,\nwhich users should handle through proper preprocessing.\n\nCRITICAL: The 'output_file_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\nthat the path includes specific identifiers related to the target samples, as the function will create further subdivisions in the specified\npath for tumor or normal tissues. Please structure the 'output_file_path' following this pattern: './your_directory/your_sample_type.exp.rds'.\n}\n\\examples{\ncounts_file <- system.file(\"extdata\", \"TCGA-SKCM.htseq_counts_test.tsv\", package = \"TransProR\")\ngene_probes_file <- system.file(\"extdata\",\n \"TCGA_gencode.v22.annotation.gene.probeMap_test\",\n package = \"TransProR\")\nphenotype_file <- system.file(\"extdata\", \"TCGA-SKCM.GDC_phenotype_test.tsv\", package = \"TransProR\")\nouput_file <- file.path(tempdir(), \"SKCM_Skin_TCGA_exp_test.rds\")\n\nSKCM_exp <- get_tcga_exp(\n counts_file_path = counts_file,\n gene_probes_file_path = gene_probes_file,\n phenotype_file_path = phenotype_file,\n output_file_path = ouput_file\n)\nhead(SKCM_exp[[\"tumor_tcga_data\"]])[1:5, 1:5]\nhead(SKCM_exp[[\"normal_tcga_data\"]], n = 10) # Because there is only one column.\n}\n\\author{\nDongyue Yu\n}\n" }, { "path": "man/gtree.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/data.R\n\\docType{data}\n\\name{gtree}\n\\alias{gtree}\n\\title{Phylogenetic Tree Object}\n\\format{\nA `ggtree` object.\n}\n\\source{\nThe phylogenetic tree was constructed based on sequence alignment data obtained from [Data Source, e.g., NCBI database, specific study, etc.].\n}\n\\usage{\ndata(gtree)\n}\n\\description{\nA dataset containing a phylogenetic tree object created using the `ggtree` package.\nThis tree represents the evolutionary relationships among a set of species or genes.\n}\n\\keyword{datasets}\n\\keyword{phylogenetics}\n" }, { "path": "man/highlight_by_node.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/HighlightByNode.R\n\\name{highlight_by_node}\n\\alias{highlight_by_node}\n\\title{Highlight Nodes in a Phylogenetic Tree with Custom Fill Colors and Transparency}\n\\usage{\nhighlight_by_node(\n ggtree_object,\n nodes,\n fill_colors,\n alpha_values,\n extend_values\n)\n}\n\\arguments{\n\\item{ggtree_object}{A `ggtree` object representing the phylogenetic tree.}\n\n\\item{nodes}{A character vector specifying the nodes to highlight.}\n\n\\item{fill_colors}{A character vector specifying the fill colors for the highlighted nodes.}\n\n\\item{alpha_values}{A numeric vector specifying the transparency values for the highlighted nodes (between 0 and 1).}\n\n\\item{extend_values}{A logical vector specifying whether to extend the highlight to the whole clade below each node.}\n}\n\\value{\nA modified `ggtree` object with the specified nodes highlighted.\n}\n\\description{\nThis function adds highlights to specific nodes in a phylogenetic tree represented by a `ggtree` object.\nUsers can specify the nodes to highlight along with custom fill colors, transparency, and extension options.\n}\n\\examples{\nplot_file <- system.file(\"extdata\", \"tree_plot.rds\", package = \"TransProR\")\np2_plot <- readRDS(plot_file)\n\n# Please replace the following vectors with your specific values\nnodes <- c(117, 129, 125, 127, 119,\n 123, 139, 166, 124, 131, 217) # x-values of the nodes you want to highlight\nfill_colors <- c(\"#CD6600\", \"#CD6600\", \"#CD6600\",\n \"#CD6600\", \"#009933\", \"#009933\",\n \"#009933\", \"#009933\", \"#9B30FF\",\n \"#9B30FF\", \"#9B30FF\") # Fill colors\nalpha_values <- c(0.3, 0.3, 0.3, 0.3, 0.2, 0.3,\n 0.3, 0.3, 0.3, 0.3, 0.3) # Transparency values\nextend_values <- c(25, 24, 24, 25, 25, 25,\n 24, 24, 25, 24, 24) # Values for the 'extend' parameter\n\np2 <- highlight_by_node(\n p2_plot,\n nodes,\n fill_colors,\n alpha_values,\n extend_values\n)\n}\n" }, { "path": "man/highlight_genes.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/HighlightGenes.R\n\\name{highlight_genes}\n\\alias{highlight_genes}\n\\title{Add Highlights for Genes on a Phylogenetic Tree}\n\\usage{\nhighlight_genes(ggtree_obj, genes_to_highlight, hilight_extend = 18)\n}\n\\arguments{\n\\item{ggtree_obj}{A ggtree object representing the phylogenetic tree.}\n\n\\item{genes_to_highlight}{A data frame containing gene names and corresponding colors to highlight.}\n\n\\item{hilight_extend}{Numeric value indicating the extension length for highlights.}\n}\n\\value{\nA `ggtree` object with added highlights for specified genes.\n}\n\\description{\nThis function adds highlights for specified genes on a phylogenetic tree object.\n}\n\\examples{\nplot_file <- system.file(\"extdata\", \"tree_plot.rds\", package = \"TransProR\")\np2_plot <- readRDS(plot_file)\n\nselected_genes_deseq2_file <- system.file(\"extdata\",\n \"selected_genes_deseq2.rds\",\n package = \"TransProR\")\nselected_genes_deseq2 <- readRDS(selected_genes_deseq2_file)\n\nDiff_deseq2_file <- system.file(\"extdata\", \"Diff_deseq2.rds\", package = \"TransProR\")\nDiff_deseq2 <- readRDS(Diff_deseq2_file)\n\nresult_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, \"#0000EE\", \"#fc4746\")\n\nadd_gene_highlights_p3 <- highlight_genes(p2_plot, result_deseq2, hilight_extend = 26)\n}\n" }, { "path": "man/limma_analyze.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/LimmaAnalyze.R\n\\name{limma_analyze}\n\\alias{limma_analyze}\n\\title{Differential Gene Expression Analysis using limma and voom}\n\\usage{\nlimma_analyze(\n tumor_file,\n normal_file,\n output_file,\n logFC_threshold = 2.5,\n p_value_threshold = 0.01\n)\n}\n\\arguments{\n\\item{tumor_file}{Path to the tumor data file (RDS format).}\n\n\\item{normal_file}{Path to the normal data file (RDS format).}\n\n\\item{output_file}{Path to save the output DEG data (RDS format).}\n\n\\item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.}\n\n\\item{p_value_threshold}{Threshold for p-value for filtering significant genes.}\n}\n\\value{\nA data frame of differential expression results.\n}\n\\description{\nThis function performs differential gene expression analysis using the 'limma' package with voom normalization.\nIt reads tumor and normal expression data, merges them, filters low-expressed genes,\nnormalizes the data, performs limma analysis, and outputs the results along with information\non gene expression changes.\n}\n\\examples{\n# Define file paths for tumor and normal data from the data folder\ntumor_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\nnormal_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n package = \"TransProR\")\noutput_file <- file.path(tempdir(), \"DEG_limma_voom.rds\")\n\nDEG_limma_voom <- limma_analyze(\n tumor_file = tumor_file,\n normal_file = normal_file,\n output_file = output_file,\n logFC_threshold = 2.5,\n p_value_threshold = 0.01\n)\n\n# View the top 5 rows of the result\nhead(DEG_limma_voom, 5)\n}\n\\references{\nlimma:Linear Models for Microarray and RNA-Seq Data User’s Guide.\nFor more information, visit the page:\nhttps://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf\n}\n" }, { "path": "man/log_transform.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/LogTransform.R\n\\name{log_transform}\n\\alias{log_transform}\n\\title{Log transformation decision and application on data}\n\\usage{\nlog_transform(data)\n}\n\\arguments{\n\\item{data}{A numeric matrix or data frame.}\n}\n\\value{\nThe original data or the data transformed with log2.\n}\n\\description{\nThis function evaluates the need for a log transformation based on a set of criteria\nand applies a log2 transformation if necessary.\n}\n\\examples{\nfile_path <- system.file(\"extdata\",\n \"all_count_exp_test.csv\",\n package = \"TransProR\")\nyour_data <- read.csv(file_path,\n row.names = 1) # Assuming first column is row names (e.g., gene names)\n\nTransformedData <- log_transform(data = your_data)\n\n}\n\\author{\nDongyue Yu\n}\n" }, { "path": "man/merge_density_foldchange.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/MergeDensityFoldchange.R\n\\name{merge_density_foldchange}\n\\alias{merge_density_foldchange}\n\\title{Create high-density region plot with optional points, density rugs, and contours}\n\\usage{\nmerge_density_foldchange(\n data,\n x_var,\n y_var,\n group_var,\n palette = c(\"#3949ab\", \"#1e88e5\", \"#039be5\", \"#00897b\", \"#43a047\", \"#7cb342\"),\n show_points = FALSE,\n point_size = 2.5,\n point_alpha = 0.2,\n x_lim = c(0, 20),\n y_lim = c(0, 20),\n cor_method = \"pearson\",\n line_size = 1.6,\n cor_label_pos = c(\"left\", 0.97)\n)\n}\n\\arguments{\n\\item{data}{Data frame containing variables for plotting.}\n\n\\item{x_var}{Name of the x-axis variable as a string.}\n\n\\item{y_var}{Name of the y-axis variable as a string.}\n\n\\item{group_var}{Name of the grouping variable for color mapping as a string.}\n\n\\item{palette}{Color palette for the plot as a character vector.}\n\n\\item{show_points}{Logical, if TRUE adds points to the plot.}\n\n\\item{point_size}{Size of the points, relevant if show_points is TRUE.}\n\n\\item{point_alpha}{Transparency level of the points, relevant if show_points is TRUE.}\n\n\\item{x_lim}{Numeric vector of length 2, giving the x-axis limits.}\n\n\\item{y_lim}{Numeric vector of length 2, giving the y-axis limits.}\n\n\\item{cor_method}{Method to calculate correlation (\"pearson\" or \"spearman\").}\n\n\\item{line_size}{Size of the smoothing line.}\n\n\\item{cor_label_pos}{Vector of length 2 indicating the position of the correlation label (x and y).}\n}\n\\value{\nA ggplot object representing the high-density region plot.\n}\n\\description{\nThis function creates a high-density region plot using hdr methods to\nadd density rug and filled contours. It also adds a regression line\nand Pearson correlation label. Points can be added to the plot optionally.\n}\n\\examples{\ncombined_df_file <- system.file(\"extdata\", \"combined_df.rds\", package = \"TransProR\")\ncombined_df <- readRDS(combined_df_file)\npal1 = c(\"#3949ab\",\"#1e88e5\",\"#039be5\",\"#00897b\",\"#43a047\",\"#7cb342\")\n\nall_density_foldchange_name1 <- merge_density_foldchange(\n data = combined_df,\n x_var = \"log2FoldChange_1\",\n y_var = \"log2FoldChange_2\",\n group_var = \"name\",\n palette = pal1,\n show_points = FALSE,\n point_size = 2.5,\n point_alpha = 0.1,\n x_lim = c(0, 20),\n y_lim = c(0, 20),\n cor_method = \"pearson\",\n line_size = 1.6,\n cor_label_pos = c(\"left\", \"top\")\n)\n\n}\n" }, { "path": "man/merge_gtex_tcga.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/MergeGtexTcga.R\n\\name{merge_gtex_tcga}\n\\alias{merge_gtex_tcga}\n\\title{Merge gene expression data from GTEx and TCGA datasets}\n\\usage{\nmerge_gtex_tcga(\n gtex_data_path,\n tcga_exp_path,\n output_path = \"./merged_gtex_tcga_data.rds\"\n)\n}\n\\arguments{\n\\item{gtex_data_path}{A string that specifies the file path to the GTEx data saved in RDS format.}\n\n\\item{tcga_exp_path}{A string that specifies the file path to the TCGA expression data saved in RDS format.\nThis should be a data.frame with rows as genes and columns as samples.}\n\n\\item{output_path}{A string that specifies the path where the merged dataset should be saved.\nThe file is saved in '.rds' format. The default path is \"./merged_gtex_tcga_data.rds\".}\n}\n\\value{\nA data frame where rows represent genes and columns represent samples.\n The data frame contains expression values from both GTEx and TCGA datasets.\n It saves the merged dataset to the path specified by 'output_path'.\n}\n\\description{\nThis function merges gene expression data obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) datasets.\nIt is assumed that both datasets are in '.rds' format and have genes as row names. The merged dataset is saved as an RDS file at the specified output path.\n}\n\\details{\nIt is assumed that both datasets are in '.rds' format and have genes as row names.\n}\n\\note{\nCRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended\n that the path includes specific identifiers related to the target samples. Please structure the 'output_path' following this pattern: './your_directory/merged.your_sample_type.gtex.tcga.data.rds'.\n}\n\\examples{\ntumor_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds\",\n package = \"TransProR\")\nNormal_file <- system.file(\"extdata\",\n \"removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds\",\n package = \"TransProR\")\nouput_file <- file.path(tempdir(), \"all_data.rds\")\n\nall_data <- merge_gtex_tcga(gtex_data_path = tumor_file,\n tcga_exp_path = Normal_file,\n output_path = ouput_file)\n\n}\n" }, { "path": "man/merge_id_position.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/MergeIDPosition.R\n\\name{merge_id_position}\n\\alias{merge_id_position}\n\\title{Merge Data Frames by Common Row Names with Additional Columns}\n\\usage{\nmerge_id_position(df_list)\n}\n\\arguments{\n\\item{df_list}{A list of data frames, each with a 'Description' and 'Count' column and set row names.}\n}\n\\value{\nA single data frame merged from the list, with additional 'id' and 'point_position' columns.\n}\n\\description{\nThis function merges a list of data frames based on common row names. It adds an 'id' column to track the row order and a 'point_position' column calculated based on the maximum 'Count' value across all data frames. It filters data frames to include only common rows, sorts rows by the length of the 'Description' in descending order, and then merges them by rows.\n}\n\\examples{\ndf1 <- data.frame(Description = c(\"DataA\", \"DataB\"), Count = c(10, 20), row.names = c(\"R1\", \"R2\"))\ndf2 <- data.frame(Description = c(\"DataC\", \"DataD\"), Count = c(30, 40), row.names = c(\"R1\", \"R3\"))\ndf_list <- list(df1, df2)\ncombined_df_test <- merge_id_position(df_list)\n\n}\n" }, { "path": "man/merge_method_color.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/MergeMethodColor.R\n\\name{merge_method_color}\n\\alias{merge_method_color}\n\\title{Merge Data Frames with Specific Method and Color Columns}\n\\usage{\nmerge_method_color(df_list, method_name, color_list)\n}\n\\arguments{\n\\item{df_list}{A list of data frames, each containing at least 'Description' and 'Count' columns.}\n\n\\item{method_name}{A string representing the method name to be added to each data frame.}\n\n\\item{color_list}{A list of colors corresponding to each data frame for the 'test_color' column.}\n}\n\\value{\nA single data frame merged from the list, with each originally provided data frame now having a 'method' and a 'test_color' column.\n}\n\\description{\nThis function takes a list of data frames, a method name, and a list of colors.\nIt adds a 'method' column and a 'test_color' column to each data frame, then merges all data frames by rows.\nIt ensures that the color list length matches the list of data frames.\n}\n\\examples{\ndf1 <- data.frame(Description = c(\"A\", \"B\"), Count = c(10, 20))\ndf2 <- data.frame(Description = c(\"C\", \"D\"), Count = c(30, 40))\ndf_list <- list(df1, df2)\nmethod_name <- \"Method1\"\ncolor_list <- c(\"Red\", \"Blue\")\ncombined_df_test <- merge_method_color(df_list, method_name, color_list)\n\n}\n" }, { "path": "man/new_ggraph.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/NewGgraph.R\n\\name{new_ggraph}\n\\alias{new_ggraph}\n\\title{Generate a graphical representation of pathway gene maps}\n\\usage{\nnew_ggraph(\n BP_dataframe,\n BP_ids,\n KEGG_dataframe,\n KEGG_ids,\n MF_dataframe,\n MF_ids,\n REACTOME_dataframe,\n REACTOME_ids,\n CC_dataframe,\n CC_ids,\n DO_dataframe,\n DO_ids\n)\n}\n\\arguments{\n\\item{BP_dataframe}{Dataframe for Biological Process.}\n\n\\item{BP_ids}{IDs for Biological Process.}\n\n\\item{KEGG_dataframe}{Dataframe for KEGG pathways.}\n\n\\item{KEGG_ids}{IDs for KEGG pathways.}\n\n\\item{MF_dataframe}{Dataframe for Molecular Function.}\n\n\\item{MF_ids}{IDs for Molecular Function.}\n\n\\item{REACTOME_dataframe}{Dataframe for REACTOME pathways.}\n\n\\item{REACTOME_ids}{IDs for REACTOME pathways.}\n\n\\item{CC_dataframe}{Dataframe for Cellular Component.}\n\n\\item{CC_ids}{IDs for Cellular Component.}\n\n\\item{DO_dataframe}{Dataframe for Disease Ontology.}\n\n\\item{DO_ids}{IDs for Disease Ontology.}\n}\n\\value{\nA 'ggraph' object representing the pathway gene map visualization.\n}\n\\description{\nThis function merges multiple gene-pathway related dataframes, processes them\nfor graph creation, and visualizes the relationships in a dendrogram layout using\nthe provided node and edge gathering functions from the 'ggraph' package.\n}\n" }, { "path": "man/pathway_count.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PathwayCount.R\n\\name{pathway_count}\n\\alias{pathway_count}\n\\title{Count Genes Present in Pathways Above a Threshold}\n\\usage{\npathway_count(GO, count_threshold, enrich_data)\n}\n\\arguments{\n\\item{GO}{A character vector of gene symbols.}\n\n\\item{count_threshold}{An integer specifying the count threshold for selecting pathways.}\n\n\\item{enrich_data}{A data frame containing pathway enrichment analysis results.}\n}\n\\value{\nA data frame with columns \"Symble\" (gene symbol), \"Description\" (pathway description), and \"Exists\" (1 if gene is present, 0 otherwise).\n}\n\\description{\nThis function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways.\n}\n\\examples{\n# Simulated gene list\nGO <- c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\")\n# Simulated enrichment analysis data\nenrich_data <- data.frame(\n ID = c(\"GO:0001\", \"GO:0002\", \"GO:0003\"),\n Description = c(\"Pathway A\", \"Pathway B\", \"Pathway C\"),\n Count = c(10, 4, 6),\n geneID = c(\"Gene1/Gene2/Gene3\", \"Gene4/Gene5\", \"Gene2/Gene6/Gene7\")\n)\n\n# Example usage\ncount_threshold <- 5\nresult_df <- pathway_count(GO, count_threshold, enrich_data)\n\n}\n" }, { "path": "man/pathway_description.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PathwayDescription.R\n\\name{pathway_description}\n\\alias{pathway_description}\n\\title{Describe Genes Present in Selected Pathways}\n\\usage{\npathway_description(GO, selected_pathways_names, enrich_data)\n}\n\\arguments{\n\\item{GO}{A character vector of gene symbols.}\n\n\\item{selected_pathways_names}{A character vector specifying the names of selected pathways.}\n\n\\item{enrich_data}{A data frame containing pathway enrichment analysis results.}\n}\n\\value{\nA data frame with columns \"Symble\" (gene symbol), \"Description\" (pathway description), and \"Exists\" (1 if gene is present, 0 otherwise).\n}\n\\description{\nThis function identifies genes present in selected pathways based on gene enrichment analysis results.\n}\n\\examples{\nGO <- c(\"Gene1\", \"Gene2\", \"Gene3\", \"Gene4\", \"Gene5\")\n# Simulated enrichment analysis data\nenrich_data <- data.frame(\n ID = c(\"Pathway1\", \"Pathway2\", \"Pathway3\", \"Pathway4\"),\n Description = c(\"Apoptosis\", \"Cell Cycle\", \"Signal Transduction\", \"Metabolism\"),\n geneID = c(\"Gene1/Gene3\", \"Gene2/Gene4\", \"Gene1/Gene2/Gene3\", \"Gene5\"),\n Count = c(2, 2, 3, 1),\n stringsAsFactors = FALSE\n)\n\n# Example usage\nresult <- pathway_description(GO,\n selected_pathways_names=\"Apoptosis\",\n enrich_data)\n\n}\n" }, { "path": "man/pipe.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/utils-pipe.R\n\\name{\\%>\\%}\n\\alias{\\%>\\%}\n\\title{Pipe operator}\n\\usage{\nlhs \\%>\\% rhs\n}\n\\arguments{\n\\item{lhs}{A value or the magrittr placeholder.}\n\n\\item{rhs}{A function call using the magrittr semantics.}\n}\n\\value{\nThe result of calling `rhs(lhs)`.\n}\n\\description{\nSee \\code{magrittr::\\link[magrittr:pipe]{\\%>\\%}} for details.\n}\n\\keyword{internal}\n" }, { "path": "man/prep_deseq2.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PrepDeseq2.R\n\\name{prep_deseq2}\n\\alias{prep_deseq2}\n\\title{Prepare DESeq2 data for plotting}\n\\usage{\nprep_deseq2(input_path, output_name = NULL)\n}\n\\arguments{\n\\item{input_path}{Path to the RDS file containing the DESeq2 DEG data frame.}\n\n\\item{output_name}{Name for the processed data frame, also used as the RDS file name.}\n}\n\\value{\nA data frame with processed DESeq2 DEG data.\n}\n\\description{\nThis function reads a DESeq2 DEG data frame from an RDS file, filters it,\nadjusts the log2FoldChange to absolute values, adds a pseudo-count to pvalues,\nand transforms pvalues for plotting. The final data frame is returned and\noptionally saved to a new RDS file.\n}\n\\examples{\ndeseq2_file <- system.file(\"extdata\",\n \"DEG_deseq2_test.rds\",\n package = \"TransProR\")\ndeseq2 <- prep_deseq2(deseq2_file)\n\n}\n" }, { "path": "man/prep_edgeR.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PrepEdgeR.R\n\\name{prep_edgeR}\n\\alias{prep_edgeR}\n\\title{Prepare edgeR DEG data for plotting}\n\\usage{\nprep_edgeR(input_path, output_name = NULL)\n}\n\\arguments{\n\\item{input_path}{Path to the RDS file containing the edgeR DEG data frame.}\n\n\\item{output_name}{Name for the processed data frame, also used as the RDS file name.}\n}\n\\value{\nA data frame with processed edgeR DEG data.\n}\n\\description{\nThis function reads an edgeR DEG data frame from an RDS file, filters it using\n\\code{\\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to PValue,\nand transforms PValue for plotting. The final data frame is returned and\noptionally saved to a new RDS file.\n}\n\\examples{\nedgeR_file <- system.file(\"extdata\",\n \"DEG_edgeR_test.rds\",\n package = \"TransProR\")\nedgeR <- prep_edgeR(edgeR_file)\n\n}\n" }, { "path": "man/prep_limma.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PrepLimma.R\n\\name{prep_limma}\n\\alias{prep_limma}\n\\title{Prepare limma-voom DEG data for plotting}\n\\usage{\nprep_limma(input_path, output_name = NULL)\n}\n\\arguments{\n\\item{input_path}{Path to the RDS file containing the limma-voom DEG data frame.}\n\n\\item{output_name}{Name for the processed data frame, also used as the RDS file name.}\n}\n\\value{\nA data frame with processed limma-voom DEG data.\n}\n\\description{\nThis function reads a limma-voom DEG data frame from an RDS file, filters it using\n\\code{\\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value,\nand transforms P.Value for plotting. The final data frame is returned and\noptionally saved to a new RDS file.\n}\n\\examples{\nlimma_file <- system.file(\"extdata\",\n \"DEG_limma_voom_test.rds\",\n package = \"TransProR\")\nlimma <- prep_limma(limma_file)\n\n}\n" }, { "path": "man/prep_wilcoxon.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/PrepWilcoxon.R\n\\name{prep_wilcoxon}\n\\alias{prep_wilcoxon}\n\\title{Prepare Wilcoxon DEG data for plotting}\n\\usage{\nprep_wilcoxon(input_path, output_name = NULL)\n}\n\\arguments{\n\\item{input_path}{Path to the RDS file containing the Wilcoxon DEG data frame.}\n\n\\item{output_name}{Optional; name for the processed data frame, also used as the RDS file name.\nIf not provided, the data frame will not be saved to file.}\n}\n\\value{\nA data frame with processed Wilcoxon DEG data.\n}\n\\description{\nThis function reads a Wilcoxon DEG data frame from an RDS file, filters it using\n\\code{\\link{deg_filter}} function, adjusts the log2foldChange to absolute values, adds a pseudo-count to pValues,\nand transforms pValues for plotting. The final data frame is returned and\noptionally saved to a new RDS file.\n}\n\\examples{\nwilcoxon_file <- system.file(\"extdata\",\n \"Wilcoxon_rank_sum_testoutRst_test.rds\",\n package = \"TransProR\")\nWilcoxon <- prep_wilcoxon(wilcoxon_file)\n\n}\n" }, { "path": "man/process_heatdata.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/ProcessHeatdata.R\n\\name{process_heatdata}\n\\alias{process_heatdata}\n\\title{Process Heatmap Data with Various Selection Options}\n\\usage{\nprocess_heatdata(\n heatdata,\n selection = 1,\n custom_names = NULL,\n num_names_per_group = NULL,\n prefix_length = 4\n)\n}\n\\arguments{\n\\item{heatdata}{A data frame containing the heatmap data.}\n\n\\item{selection}{An integer specifying the processing method:\n- 1: Use custom names for columns.\n- 2: Select a given number of columns per group based on a prefix.\n- 3: Calculate the average of columns per group based on a prefix.}\n\n\\item{custom_names}{A character vector of custom names for columns (used when `selection = 1`).\nThe length of this vector must match the number of columns in `heatdata`.}\n\n\\item{num_names_per_group}{An integer specifying the number of columns to select per group (used when `selection = 2`).}\n\n\\item{prefix_length}{An integer specifying the length of the prefix for grouping columns (used when `selection = 2` or `selection = 3`).\nDefault is 4.}\n}\n\\value{\nA processed data frame based on the specified selection option.\n}\n\\description{\nThis function processes heatmap data (`heatdata`) based on a given selection option.\nIt allows customization of column names, selection of specific columns per group,\nor averaging columns based on a common prefix.\n}\n\\examples{\n# Example heatmap data frame\nheatdata <- data.frame(\n groupA_1 = c(1, 2, 3),\n groupA_2 = c(4, 5, 6),\n groupB_1 = c(7, 8, 9),\n groupB_2 = c(10, 11, 12)\n)\n\n# Selection 1: Use custom names for columns\ncustom_names <- c(\"Sample1\", \"Sample2\", \"Sample3\", \"Sample4\")\nprocessed_data1 <- process_heatdata(heatdata, selection = 1, custom_names = custom_names)\n\n# Selection 2: Select a given number of columns per group based on a prefix\nprocessed_data2 <- process_heatdata(heatdata, selection = 2, num_names_per_group = 1)\n\n# Selection 3: Calculate the average of columns per group based on a prefix\nprocessed_data3 <- process_heatdata(heatdata, selection = 3, prefix_length = 6)\n}\n" }, { "path": "man/seek_gtex_organ.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/SeekGtexOrgan.R\n\\name{seek_gtex_organ}\n\\alias{seek_gtex_organ}\n\\title{Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts}\n\\usage{\nseek_gtex_organ(path = \"./download_data/GTEX_phenotype\")\n}\n\\arguments{\n\\item{path}{The path to the GTEX phenotype data file. Default is \"./download_data/GTEX_phenotype\".}\n}\n\\value{\nA table representing the count of samples per primary site.\n}\n\\description{\nThis function reads the GTEX phenotype data from a specified path, renames its columns for better readability,\nand then returns a table of primary site counts.\n}\n\\examples{\n# Get the file path to the example data in the package\npath <- system.file(\"extdata\", \"GTEX_phenotype_test\", package = \"TransProR\")\n# Call the `seek_gtex_organ` function with the path and print the result\nSeekGtexOrgan <- seek_gtex_organ(path = path)\n\n}\n" }, { "path": "man/selectPathways.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichCirclize.R\n\\name{selectPathways}\n\\alias{selectPathways}\n\\title{Randomly Select Pathways with Limited Word Count}\n\\usage{\nselectPathways(pathways, max_words = 10, num_select = 10)\n}\n\\arguments{\n\\item{pathways}{Character vector of pathways.}\n\n\\item{max_words}{Integer, maximum number of words allowed in the pathway name.}\n\n\\item{num_select}{Integer, number of pathways to randomly select.}\n}\n\\value{\nA character vector of selected pathways.\n}\n\\description{\nThis function randomly selects a specified number of pathways from a given list, ensuring that each selected pathway name does not exceed a specified number of words. It filters out pathways with names longer than the specified word limit before making the selection.\n}\n\\examples{\npathway_list <- c(\"pathway_one response to stimulus\",\n \"pathway_two cell growth and death\",\n \"pathway_three regulation of cellular process\",\n \"pathway_four metabolic process\")\nselected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2)\n\n}\n" }, { "path": "man/spiral_newrle.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/EnrichmentSpiralize.R\n\\name{spiral_newrle}\n\\alias{spiral_newrle}\n\\title{Render a Spiral Plot Using Run-Length Encoding}\n\\usage{\nspiral_newrle(x, samples, values, colors, labels = FALSE)\n}\n\\arguments{\n\\item{x}{A vector representing categories or segments.}\n\n\\item{samples}{A vector indicating the sample each segment belongs to.}\n\n\\item{values}{Numeric vector indicating the lengths of each segment.}\n\n\\item{colors}{Character vector specifying the colors for each segment.}\n\n\\item{labels}{Logical, whether to add labels to each segment.}\n}\n\\value{\nNo return value, called for side effects. This function generates a spiral plot and optionally adds labels.\n}\n\\description{\nThis function creates a spiral plot for visualizing sequential data in a compact and visually appealing way.\nIt uses run-length encoding to represent the lengths and colors of sequences in the spiral.\n}\n\\examples{\n# Example: Creating a spiral plot using the spiral_newrle function\n\n# Define example data\nx <- c(\"A\", \"A\", \"B\", \"C\")\nsamples <- c(\"Sample1\", \"Sample1\", \"Sample2\", \"Sample2\")\nvalues <- c(20, 30, 15, 35)\ncolors <- c(\"red\", \"blue\", \"green\", \"purple\")\nlabels <- TRUE\n\n# Initialize the spiral plot, setting the x-axis range and scaling\nspiralize::spiral_initialize(xlim = c(0, sum(values)), scale_by = \"curve_length\",\n vp_param = list(x = grid::unit(0, \"npc\"), just = \"left\"))\n\n# Create a track for the spiral plot\nspiralize::spiral_track(height = 0.5)\n\n# Add segments to the spiral plot using run-length encoding\nspiral_newrle(x, samples, values, colors, labels)\n\n}\n" }, { "path": "vignettes/TransProR.Rmd", "content": "---\ntitle: \"TransProR: Analysis and visualization of transcriptomic data are currently in progress. Future directions include multi-modal fusion, sparse learning, and the investigation of spatio-temporal effects.\"\nauthor: \"Dongyue Yu\\\\\n\n Ph.d candiadate at Nankai University \"\ndate: \"`r Sys.Date()`\"\noutput:\n prettydoc::html_pretty:\n toc: false\n theme: hpstr\n highlight: github\n pdf_document:\n toc: true\nvignette: >\n %\\VignetteIndexEntry{TransProR}\n %\\VignetteEngine{knitr::rmarkdown}\n %\\usepackage[utf8]{inputenc}\n---\n\n# Vignette\n\nPlease go to for the full vignette.\n\n# Need helps?\n\nIf you have questions/issues, please visit [TransProR GITHUB](https://sssydysss.github.io/TransProRBook/) first. Your problems are mostly documented.\n\n## Bug Reporting\n\nIf you believe you have identified a bug, please submit a reproducible example via the [GitHub issue tracker](https://github.com/SSSYDYSSS/TransProR/issues). Alternatively, you can reach out to the author, SSSYDYSSS, at [yudongyue\\@mail.nankai.edu.cn](mailto:yudongyue@mail.nankai.edu.cn).\n\n" } ]