Repository: SSSYDYSSS/TransProR Branch: main Commit: cdf08bed9130 Files: 138 Total size: 928.3 KB Directory structure: gitextract_t_svwp8g/ ├── .Rbuildignore ├── .github/ │ ├── .gitignore │ └── workflows/ │ └── R-CMD-check.yaml ├── .gitignore ├── CODE_OF_CONDUCT.md ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── R/ │ ├── AdjustAlphaScale.R │ ├── CircosFruits.R │ ├── CombatNormal.R │ ├── CombatTumor.R │ ├── CompareMerge.R │ ├── Contrast_Venn.R │ ├── DESeq2Analyze.R │ ├── EdgeRAnalyze.R │ ├── EnrichCirclize.R │ ├── EnrichCircoBar.R │ ├── EnrichPolarBubble.R │ ├── EnrichmentSpiralize.R │ ├── FacetDensityFoldchange.R │ ├── FilterDiffGenes.R │ ├── FourDegsVenn.R │ ├── GatherGraphEdge.R │ ├── GatherGraphNode.R │ ├── GeneColor.R │ ├── GeneHighlights.R │ ├── GeneMapPathway.R │ ├── GetGtexExp.R │ ├── GetTcgaExp.R │ ├── HighlightByNode.R │ ├── HighlightGenes.R │ ├── LimmaAnalyze.R │ ├── LogTransform.R │ ├── MergeDensityFoldchange.R │ ├── MergeGtexTcga.R │ ├── MergeIDPosition.R │ ├── MergeMethodColor.R │ ├── NewGgraph.R │ ├── PathwayCount.R │ ├── PathwayDescription.R │ ├── PrepDeseq2.R │ ├── PrepEdgeR.R │ ├── PrepLimma.R │ ├── PrepWilcoxon.R │ ├── ProcessHeatdata.R │ ├── SeekGtexOrgan.R │ ├── WilcoxonAnalyze.R │ ├── data.R │ ├── utils-pipe.R │ └── zzz.R ├── README.md ├── TransProR.Rproj ├── data/ │ ├── all_degs_venn.rda │ └── gtree.rda ├── data-raw/ │ └── MAKEDATA.R ├── dev/ │ ├── build.R │ └── dev.R ├── inst/ │ └── extdata/ │ ├── DEG_deseq2.rds │ ├── DEG_deseq2_test.rds │ ├── DEG_edgeR_test.rds │ ├── DEG_limma_voom_test.rds │ ├── Diff_deseq2.rds │ ├── GTEX_phenotype_test │ ├── SKCM_Skin_TCGA_exp_normal_test.rds │ ├── SKCM_Skin_TCGA_exp_tumor_test.rds │ ├── Skin_SKCM_Gtex_test.rds │ ├── TCGA-SKCM.GDC_phenotype_test.tsv │ ├── TCGA-SKCM.htseq_counts_test.tsv │ ├── TCGA_gencode.v22.annotation.gene.probeMap_test │ ├── Wilcoxon_rank_sum_testoutRst_test.rds │ ├── all_count_exp_test.csv │ ├── ascii_art.txt │ ├── combined_df.rds │ ├── gtex_gene_expected_count_test │ ├── gtex_probeMap_gencode.v23.annotation.gene.probemap_test │ ├── p_tree_test.rds │ ├── removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds │ ├── removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds │ ├── selected_genes_deseq2.rds │ └── tree_plot.rds ├── man/ │ ├── Combat_Normal.Rd │ ├── Contrast_Venn.Rd │ ├── DESeq2_analyze.Rd │ ├── Wilcoxon_analyze.Rd │ ├── add_boxplot.Rd │ ├── add_new_tile_layer.Rd │ ├── adjust_alpha_scale.Rd │ ├── adjust_color_tone.Rd │ ├── adjust_export_pathway.Rd │ ├── all_degs_venn.Rd │ ├── circos_fruits.Rd │ ├── combat_tumor.Rd │ ├── compare_merge.Rd │ ├── create_base_plot.Rd │ ├── deg_filter.Rd │ ├── drawLegends.Rd │ ├── edgeR_analyze.Rd │ ├── enrich_circo_bar.Rd │ ├── enrich_polar_bubble.Rd │ ├── enrichment_circlize.Rd │ ├── enrichment_spiral_plots.Rd │ ├── extract_descriptions_counts.Rd │ ├── extract_ntop_pathways.Rd │ ├── extract_positive_pathways.Rd │ ├── facet_density_foldchange.Rd │ ├── filter_diff_genes.Rd │ ├── four_degs_venn.Rd │ ├── gather_graph_edge.Rd │ ├── gather_graph_node.Rd │ ├── gene_color.Rd │ ├── gene_highlights.Rd │ ├── gene_map_pathway.Rd │ ├── get_gtex_exp.Rd │ ├── get_tcga_exp.Rd │ ├── gtree.Rd │ ├── highlight_by_node.Rd │ ├── highlight_genes.Rd │ ├── limma_analyze.Rd │ ├── log_transform.Rd │ ├── merge_density_foldchange.Rd │ ├── merge_gtex_tcga.Rd │ ├── merge_id_position.Rd │ ├── merge_method_color.Rd │ ├── new_ggraph.Rd │ ├── pathway_count.Rd │ ├── pathway_description.Rd │ ├── pipe.Rd │ ├── prep_deseq2.Rd │ ├── prep_edgeR.Rd │ ├── prep_limma.Rd │ ├── prep_wilcoxon.Rd │ ├── process_heatdata.Rd │ ├── seek_gtex_organ.Rd │ ├── selectPathways.Rd │ └── spiral_newrle.Rd └── vignettes/ └── TransProR.Rmd ================================================ FILE CONTENTS ================================================ ================================================ FILE: .Rbuildignore ================================================ ^.*\.Rproj$ ^\.Rproj\.user$ ^data-raw$ ^data-raw$ ^dev$ ^CODE_OF_CONDUCT\.md$ ^LICENSE\.md$ ^\.github$ ^\.RDataTmp$ ================================================ FILE: .github/.gitignore ================================================ *.html ================================================ FILE: .github/workflows/R-CMD-check.yaml ================================================ name: R-CMD-check on: push: branches: [main, master] pull_request: branches: [main, master] jobs: R-CMD-check: runs-on: ${{ matrix.config.os }} name: ${{ matrix.config.os }} (${{ matrix.config.r }}) strategy: fail-fast: false matrix: config: - {os: macos-latest, r: 'release'} - {os: windows-latest, r: 'release'} - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - {os: ubuntu-latest, r: 'oldrel-1'} env: R_KEEP_PKG_SOURCE: yes steps: # Checkout repository - uses: actions/checkout@v3 # Set up pandoc - uses: r-lib/actions/setup-pandoc@v2 # Set up R environment - uses: r-lib/actions/setup-r@v2 with: r-version: ${{ matrix.config.r }} http-user-agent: ${{ matrix.config.http-user-agent }} use-public-rspm: true # Automatically install dependencies from DESCRIPTION - uses: r-lib/actions/setup-r-dependencies@v2 with: extra-packages: >- any::rcmdcheck # Manually install Bioconductor and other missing dependencies - name: Install dependencies run: | Rscript -e "if (!requireNamespace('BiocManager', quietly = TRUE)) install.packages('BiocManager')" Rscript -e "BiocManager::install()" Rscript -e "if (!requireNamespace('remotes', quietly = TRUE)) install.packages('remotes')" Rscript -e "remotes::install_deps(dependencies = TRUE)" # Run R CMD check - uses: r-lib/actions/check-r-package@v2 with: upload-snapshots: true ================================================ FILE: .gitignore ================================================ .Rproj.user .Rhistory .RData .Ruserdata .Rdata .httr-oauth .DS_Store .quarto .RDataTmp ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Contributor Covenant Code of Conduct ## Our Pledge We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation. 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Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. ## Scope This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at yudongyue@mail.nankai.edu.cn. All complaints will be reviewed and investigated promptly and fairly. 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Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, available at . Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder][https://github.com/mozilla/inclusion]. For answers to common questions about this code of conduct, see the FAQ at . Translations are available at . [homepage]: https://www.contributor-covenant.org ================================================ FILE: DESCRIPTION ================================================ Package: TransProR Type: Package Title: Analysis and Visualization of Multi-Omics Data Version: 1.0.4 Authors@R: person("Dongyue", "Yu", email = "yudongyue@mail.nankai.edu.cn", role = c("aut", "cre", "cph"), comment = c(ORCID = "0000-0002-7041-2073")) Maintainer: Dongyue Yu Description: 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. Imports: magrittr, stats, dplyr, rlang, tibble, sva, DESeq2, utils, edgeR, limma, ggplot2, ggVennDiagram, ggdensity, ggpubr, ggtree, hrbrthemes, grid, ggraph, tidygraph, tidyr, stringr, geomtextpath, ggalt, ggnewscale, Hmisc, circlize, graphics, spiralize, ComplexHeatmap, grDevices Suggests: prettydoc, knitr, ggtreeExtra, rmarkdown, systemfonts VignetteBuilder: knitr License: MIT + file LICENSE Encoding: UTF-8 URL: https://github.com/SSSYDYSSS/TransProRBook BugReports: https://github.com/SSSYDYSSS/TransProR/issues LazyData: true RoxygenNote: 7.3.2 Depends: R (>= 4.3.0) ================================================ FILE: LICENSE ================================================ YEAR: 2023 COPYRIGHT HOLDER: Dongyue Yu ================================================ FILE: LICENSE.md ================================================ # MIT License Copyright (c) 2023 Dongyue Yu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: NAMESPACE ================================================ # Generated by roxygen2: do not edit by hand export("%>%") export(Combat_Normal) export(Contrast_Venn) export(DESeq2_analyze) export(Wilcoxon_analyze) export(add_boxplot) export(add_new_tile_layer) export(adjust_alpha_scale) export(adjust_color_tone) export(adjust_export_pathway) export(circos_fruits) export(combat_tumor) export(compare_merge) export(create_base_plot) export(deg_filter) export(drawLegends) export(edgeR_analyze) export(enrich_circo_bar) export(enrich_polar_bubble) export(enrichment_circlize) export(enrichment_spiral_plots) export(extract_descriptions_counts) export(extract_ntop_pathways) export(extract_positive_pathways) export(facet_density_foldchange) export(filter_diff_genes) export(four_degs_venn) export(gather_graph_edge) export(gather_graph_node) export(gene_color) export(gene_highlights) export(gene_map_pathway) export(get_gtex_exp) export(get_tcga_exp) export(highlight_by_node) export(highlight_genes) export(limma_analyze) export(log_transform) export(merge_density_foldchange) export(merge_gtex_tcga) export(merge_id_position) export(merge_method_color) export(new_ggraph) export(pathway_count) export(pathway_description) export(prep_deseq2) export(prep_edgeR) export(prep_limma) export(prep_wilcoxon) export(process_heatdata) export(seek_gtex_organ) export(selectPathways) export(spiral_newrle) importFrom(ComplexHeatmap,Legend) importFrom(ComplexHeatmap,draw) importFrom(ComplexHeatmap,packLegend) importFrom(DESeq2,DESeq) importFrom(DESeq2,DESeqDataSetFromMatrix) importFrom(DESeq2,results) importFrom(Hmisc,capitalize) importFrom(circlize,chordDiagram) importFrom(dplyr,across) importFrom(dplyr,all_of) importFrom(dplyr,arrange) importFrom(dplyr,bind_rows) importFrom(dplyr,desc) importFrom(dplyr,distinct) importFrom(dplyr,filter) importFrom(dplyr,group_by) importFrom(dplyr,inner_join) importFrom(dplyr,mutate) importFrom(dplyr,n) importFrom(dplyr,pull) importFrom(dplyr,row_number) importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(edgeR,DGEList) importFrom(edgeR,calcNormFactors) importFrom(edgeR,cpm) importFrom(edgeR,estimateGLMCommonDisp) importFrom(edgeR,estimateGLMTagwiseDisp) importFrom(edgeR,estimateGLMTrendedDisp) importFrom(edgeR,filterByExpr) importFrom(edgeR,glmFit) importFrom(edgeR,glmLRT) importFrom(edgeR,topTags) importFrom(geomtextpath,geom_textpath) importFrom(ggVennDiagram,ggVennDiagram) importFrom(ggalt,stat_xspline) importFrom(ggdensity,geom_hdr) importFrom(ggdensity,geom_hdr_rug) importFrom(ggnewscale,new_scale) importFrom(ggnewscale,new_scale_fill) importFrom(ggplot2,aes) importFrom(ggplot2,aes_string) importFrom(ggplot2,alpha) importFrom(ggplot2,annotate) importFrom(ggplot2,coord_cartesian) importFrom(ggplot2,coord_polar) importFrom(ggplot2,element_blank) importFrom(ggplot2,element_rect) importFrom(ggplot2,expansion) importFrom(ggplot2,facet_wrap) importFrom(ggplot2,geom_bar) importFrom(ggplot2,geom_hline) importFrom(ggplot2,geom_point) importFrom(ggplot2,geom_segment) importFrom(ggplot2,geom_smooth) importFrom(ggplot2,geom_text) importFrom(ggplot2,ggplot) importFrom(ggplot2,guide_legend) importFrom(ggplot2,labs) importFrom(ggplot2,margin) importFrom(ggplot2,scale_alpha_continuous) importFrom(ggplot2,scale_color_brewer) importFrom(ggplot2,scale_color_manual) importFrom(ggplot2,scale_fill_gradient) importFrom(ggplot2,scale_fill_identity) importFrom(ggplot2,scale_fill_manual) importFrom(ggplot2,scale_size) importFrom(ggplot2,scale_x_continuous) importFrom(ggplot2,scale_y_continuous) importFrom(ggplot2,theme) importFrom(ggplot2,theme_minimal) importFrom(ggplot2,theme_void) importFrom(ggplot2,unit) importFrom(ggpubr,stat_cor) importFrom(ggraph,geom_edge_diagonal) importFrom(ggraph,geom_node_point) importFrom(ggraph,geom_node_text) importFrom(ggraph,ggraph) importFrom(ggraph,node_angle) importFrom(ggraph,scale_edge_colour_brewer) importFrom(ggtree,aes) importFrom(ggtree,geom_hilight) importFrom(ggtree,geom_point2) importFrom(grDevices,col2rgb) importFrom(grDevices,convertColor) importFrom(grDevices,rgb) importFrom(graphics,strwidth) importFrom(grid,gpar) importFrom(grid,grid.roundrect) importFrom(grid,grid.text) importFrom(grid,pushViewport) importFrom(grid,unit) importFrom(grid,upViewport) importFrom(grid,viewport) importFrom(hrbrthemes,theme_ipsum) importFrom(limma,contrasts.fit) importFrom(limma,eBayes) importFrom(limma,lmFit) importFrom(limma,topTable) importFrom(magrittr,"%>%") importFrom(rlang,.data) importFrom(rlang,sym) importFrom(spiralize,spiral_initialize) importFrom(spiralize,spiral_rect) importFrom(spiralize,spiral_text) importFrom(spiralize,spiral_track) importFrom(stats,as.formula) importFrom(stats,model.matrix) importFrom(stats,na.omit) importFrom(stats,p.adjust) importFrom(stats,quantile) importFrom(stats,wilcox.test) importFrom(stringr,str_c) importFrom(sva,ComBat_seq) importFrom(tibble,as_tibble) importFrom(tibble,column_to_rownames) importFrom(tibble,tibble) importFrom(tidygraph,tbl_graph) importFrom(tidyr,unite) importFrom(utils,head) importFrom(utils,read.table) importFrom(utils,tail) ================================================ FILE: R/AdjustAlphaScale.R ================================================ #' Adjust Alpha Scale for Data Visualization #' #' This function dynamically adjusts the transparency scale for visualizations, #' especially useful when the range of data values varies significantly across different sources. #' It modifies the transparency scale based on the range of values present in the data, #' ensuring that the visualization accurately reflects variations within the data. #' #' @importFrom ggplot2 scale_alpha_continuous guide_legend ggplot geom_point #' @param data A data frame containing the values for which the alpha scale is to be adjusted. #' @param name Character string that will be used as the title of the legend in the plot. #' @param range Numeric vector of length 2 specifying the range of alpha values, defaults to c(0.2, 0.8). #' @return A ggplot2 alpha scale adjustment layer. #' @export #' #' @examples #' # Assuming 'data' is a DataFrame with a 'value' column #' plot_data <- data.frame(value = c(10, 20, 30, 40, 50)) #' ggplot2::ggplot(plot_data, ggplot2::aes(x = 1:nrow(plot_data), y = value)) + #' ggplot2::geom_point(ggplot2::aes(alpha = value)) + #' adjust_alpha_scale(plot_data, "Transparency Scale") #' adjust_alpha_scale <- function(data, name, range = c(0.2, 0.8)) { min_val <- min(data$value, na.rm = TRUE) # Calculate minimum value, excluding NA max_val <- max(data$value, na.rm = TRUE) # Calculate maximum value, excluding NA # Apply scale_alpha_continuous from 'ggplot2' to adjust transparency scale_alpha_continuous( name = name, # Legend title limits = c(min_val, max_val), # Set the data range for alpha scaling range = range, # Set the alpha transparency range guide = guide_legend(keywidth = 0.65, keyheight = 0.35, order = 2) # Customize legend appearance ) } ================================================ FILE: R/CircosFruits.R ================================================ #' Create a base plot with gene expression data on a phylogenetic tree #' #' This function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression #' data as colored tiles to the plot. It allows for dynamic coloring of the genes and includes #' adjustments for alpha transparency based on the expression value. #' #' @importFrom ggplot2 aes scale_fill_manual scale_alpha_continuous guide_legend #' @param p A ggtree plot object to which the data will be added. #' @param data A data frame containing gene expression data with columns for Samples, Genes, and Values. #' @param gene_colors A named vector of colors for genes. #' @param gene_label A character string used as a label in the legend for the genes. Default is "Gene". #' @return A `ggtree` plot object with the gene expression data added. #' @export #' #' @examples #' \donttest{ #' # Check and load required packages #' if (requireNamespace("ggtreeExtra", quietly = TRUE) && #' requireNamespace("ggplot2", quietly = TRUE)) { #' library(ggtreeExtra) #' library(ggplot2) #' #' file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") #' p <- readRDS(file_path) #' #' # Create gene expression data frame #' expression_data <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5), #' Gene = rep(paste0("Gene", 1:5), times = 4), #' Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1 #' ) #' #' # Define gene colors (named vector) #' gene_colors <- c( #' Gene1 = "#491588", #' Gene2 = "#301b8d", #' Gene3 = "#1a237a", #' Gene4 = "#11479c", #' Gene5 = "#0a5797" #' ) #' #' # Call create_base_plot function to add gene expression data #' p <- create_base_plot(p, expression_data, gene_colors) #' } else { #' message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") #' } #' } #' create_base_plot <- function(p, data, gene_colors, gene_label="Gene") { # Define local variables Sample <- data$Sample value <- data$value Gene <- data$Gene if (!requireNamespace("ggtreeExtra", quietly = TRUE)) { stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE) } if (!requireNamespace("ggplot2", quietly = TRUE)) { stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE) } p <- p + ggtreeExtra::geom_fruit( data=data, geom="geom_tile", mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene), offset=0.001, pwidth=2 ) + ggplot2::scale_fill_manual( name=gene_label, values=gene_colors, guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1) ) + # Assuming the function 'adjust_alpha_scale' is defined elsewhere to adjust alpha scale based on the expression values adjust_alpha_scale(data, gene_label) return(p) } #' Add a boxplot layer to a `ggtree` plot #' #' This function adds a boxplot layer to an existing `ggtree` plot object using ggtreeExtra's geom_fruit for boxplots. #' It is primarily used to display statistical summaries of the data related to gene expressions or other metrics. #' #' @importFrom ggplot2 aes #' @param p An existing ggtree plot object. #' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample' and 'value'. #' @param fill_color A character string specifying the fill color for the boxplots. Default is "#f28131". #' @param alpha Numeric value for the transparency of the boxplots. Default is 0.6. #' @param offset Numeric value, the position of the boxplot on the x-axis relative to its gene name. Default is 0.22. #' @param pwidth Numeric value, the width of the boxplot. Default is 0.5. #' @return A `ggtree` plot object with the added boxplot layer. #' @export #' #' @examples #' \donttest{ #' # Check and load required packages #' if (requireNamespace("ggtreeExtra", quietly = TRUE) && #' requireNamespace("ggplot2", quietly = TRUE)) { #' library(ggtreeExtra) #' library(ggplot2) #' #' file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") #' p <- readRDS(file_path) #' #' # Create boxplot data frame #' boxplot_data <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 30), #' value = c( #' rnorm(30, mean = 5, sd = 1), # Data for Species_A #' rnorm(30, mean = 7, sd = 1.5), # Data for Species_B #' rnorm(30, mean = 6, sd = 1.2), # Data for Species_C #' rnorm(30, mean = 8, sd = 1.3) # Data for Species_D #' ) #' ) #' #' # Call add_boxplot function to add boxplot layer #' p_with_boxplot <- add_boxplot(p, boxplot_data) #' } else { #' message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") #' } #' } #' add_boxplot <- function(p, data, fill_color="#f28131", alpha=0.6, offset=0.22, pwidth=0.5) { # Define local variables Sample <- data$Sample value <- data$value if (!requireNamespace("ggtreeExtra", quietly = TRUE)) { stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE) } if (!requireNamespace("ggplot2", quietly = TRUE)) { stop("ggplot2 is required to use geom_boxplot. Please install it.", call. = FALSE) } p + ggtreeExtra::geom_fruit( data=data, geom="geom_boxplot", mapping=ggplot2::aes(y=Sample, x=value), fill=fill_color, alpha=alpha, offset=offset, pwidth=pwidth, size=0.05, outlier.size=0.3, outlier.stroke=0.06, outlier.shape=21, show.legend=FALSE ) } #' Add a new tile layer with dynamic scales to a `ggtree` plot #' #' This function adds a new tile layer to an existing `ggtree` plot object, allowing for separate scales for fill #' and alpha transparency. This is useful when you want to add additional data layers without interfering with #' the existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers. #' #' @importFrom ggplot2 aes scale_fill_manual guide_legend #' @importFrom ggnewscale new_scale #' @param p An existing ggtree plot object. #' @param data A data frame containing the data to be plotted. Expected to have columns for 'Sample', 'Gene', and 'value'. #' @param gene_colors A named vector of colors for genes. #' @param gene_label A character string used as a label in the legend for the genes. #' @param alpha_value A numeric or named vector for setting the alpha scale based on values. #' @param offset Numeric value, the position of the tile on the x-axis relative to its gene name. Default is 0.02. #' @param pwidth Numeric value, the width of the tile. Default is 2. #' @return A `ggtree` plot object with the added tile layer and new scales. #' @export #' #' @examples #' \donttest{ #' # Check and load required packages #' if (requireNamespace("ggtreeExtra", quietly = TRUE) && #' requireNamespace("ggplot2", quietly = TRUE) && #' requireNamespace("ggnewscale", quietly = TRUE)) { #' library(ggtreeExtra) #' library(ggplot2) #' library(ggnewscale) #' #' file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") #' p <- readRDS(file_path) #' #' # Create new expression data #' new_expression_data <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3), #' Gene = rep(c("Gene6", "Gene7", "Gene8"), times = 4), #' Value = runif(12, min = 0, max = 1) # Randomly generate expression values between 0 and 1 #' ) #' #' # Define new gene colors #' new_gene_colors <- c( #' Gene6 = "#0b5f63", #' Gene7 = "#074d41", #' Gene8 = "#1f5e27" #' ) #' #' # Define gene label and alpha values #' gene_label <- "New Genes" #' alpha_value <- c(0.3, 0.9) #' #' # Add new tile layer #' p_with_new_layer <- add_new_tile_layer( #' p, #' new_expression_data, #' new_gene_colors, #' gene_label, #' alpha_value, #' offset = 0.02, #' pwidth = 2 #' ) #' } else { #' message("Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.") #' } #' } #' add_new_tile_layer <- function(p, data, gene_colors, gene_label, alpha_value=c(0.3, 0.9), offset=0.02, pwidth=2) { # Define local variables Sample <- data$Sample value <- data$value Gene <- data$Gene if (!requireNamespace("ggtreeExtra", quietly = TRUE)) { stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE) } if (!requireNamespace("ggplot2", quietly = TRUE)) { stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE) } p + ggnewscale::new_scale("alpha") + ggnewscale::new_scale("fill") + ggtreeExtra::geom_fruit( data=data, geom="geom_tile", mapping=ggplot2::aes(y=Sample, alpha=value, x=Gene, fill=Gene), offset=offset, pwidth=pwidth ) + ggplot2::scale_fill_manual( name=gene_label, values=gene_colors, guide=ggplot2::guide_legend(keywidth=0.65, keyheight=0.35, order=1) ) + adjust_alpha_scale(data, gene_label, alpha_value) # Assuming function signature and usage } #' Add multiple layers to a `ggtree` plot for visualizing gene expression and enrichment data #' #' This function sequentially adds multiple layers to a `ggtree` plot, including gene expression data, boxplots for statistical #' summaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate #' functions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics #' for each layer. The function is designed to work with specific data structures and assumes all functions for adding layers #' are defined and available. #' #' @param p A `ggtree` plot object to which the data and layers will be added. #' @param long_format_HeatdataDeseq A data frame containing gene expression data with columns for `Samples`, `Genes`, and `Values`. #' @param ssgsea_kegg_HeatdataDeseq A data frame containing SSGSEA analysis results with columns for `Samples`, `Genes`, and `Values`. #' @param gsva_kegg_HeatdataDeseq A data frame containing GSVA analysis results with columns for `Samples`, `Genes`, and `Values`. #' @param gene_colors A named vector of colors for genes, used for coloring tiles in different layers. #' @return A `ggtree` plot object with multiple layers added for comprehensive visualization. #' @export #' #' @examples #' \donttest{ #' # Check and load required packages #' if (requireNamespace("ggtreeExtra", quietly = TRUE) && #' requireNamespace("ggplot2", quietly = TRUE)) { #' library(ggtreeExtra) #' library(ggplot2) #' #' # Example data for gene expression, SSGSEA, and GSVA #' file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") #' p <- readRDS(file_path) #' #' # Create gene expression data frame (long_format_HeatdataDeseq) #' long_format_HeatdataDeseq <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5), #' Genes = rep(paste0("Gene", 1:5), times = 4), #' Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1 #' ) #' #' # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq) #' ssgsea_kegg_HeatdataDeseq <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3), #' Genes = rep(c("Pathway1", "Pathway2", "Pathway3"), times = 4), #' Value = runif(12, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1 #' ) #' #' # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq) #' gsva_kegg_HeatdataDeseq <- data.frame( #' Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 4), #' Genes = rep(c("PathwayA", "PathwayB", "PathwayC", "PathwayD"), times = 4), #' Value = runif(16, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1 #' ) #' #' # Define gene and pathway colors (named vector), including all genes and pathways #' gene_colors <- c( #' # Genes for gene expression #' Gene1 = "#491588", #' Gene2 = "#301b8d", #' Gene3 = "#1a237a", #' Gene4 = "#11479c", #' Gene5 = "#0a5797", #' # Pathways for SSGSEA #' Pathway1 = "#0b5f63", #' Pathway2 = "#074d41", #' Pathway3 = "#1f5e27", #' # Pathways for GSVA #' PathwayA = "#366928", #' PathwayB = "#827729", #' PathwayC = "#a1d99b", #' PathwayD = "#c7e9c0" #' ) #' #' # Call circos_fruits function to add multiple layers #' final_plot <- circos_fruits( #' p, #' long_format_HeatdataDeseq, #' ssgsea_kegg_HeatdataDeseq, #' gsva_kegg_HeatdataDeseq, #' gene_colors #' ) #' } else { #' message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") #' } #' } #' circos_fruits <- function(p, long_format_HeatdataDeseq, ssgsea_kegg_HeatdataDeseq, gsva_kegg_HeatdataDeseq, gene_colors) { if (!requireNamespace("ggtreeExtra", quietly = TRUE)) { stop("ggtreeExtra is required for using create_base_plot. Please install it.", call. = FALSE) } if (!requireNamespace("ggplot2", quietly = TRUE)) { stop("ggplot2 is required to use geom_tile. Please install it.", call. = FALSE) } # Create the base plot with gene expression data p1 <- create_base_plot(p, long_format_HeatdataDeseq, gene_colors) # Add a boxplot layer to the base plot p2 <- add_boxplot(p1, long_format_HeatdataDeseq) # Add a new tile layer for SSGSEA data p3 <- add_new_tile_layer(p2, ssgsea_kegg_HeatdataDeseq, gene_colors, "Ssgsea Term") # Add another boxplot layer with specific aesthetic adjustments p4 <- add_boxplot(p3, ssgsea_kegg_HeatdataDeseq, fill_color="#f28131", alpha=0.65, offset=0.32) # Add a new tile layer for GSVA data with specific alpha and offset adjustments p5 <- add_new_tile_layer(p4, gsva_kegg_HeatdataDeseq, gene_colors, "Gsva Term", alpha_value=c(0.3, 0.9), offset=0.02) # Return the final plot return(p5) } ================================================ FILE: R/CombatNormal.R ================================================ #' Process and Correct Batch Effects in TCGA's normal tissue and GTEX Data #' #' This function takes a TCGA's normal tissue data set and a pre-saved GTEX data set, asks the user #' for specific TCGA's normal tissue types to retain, then merges the two datasets. The merged dataset #' is then corrected for batch effects using the ComBat_seq function from the 'sva' package. #' #' @description #' The function first extracts histological types from the provided TCGA's normal tissue data set. #' After displaying these types, the user is prompted to input specific types to retain. #' The data is then filtered based on this input. #' The GTEX and TCGA's normal tissue datasets are then combined and batch corrected. #' #' Note: This function assumes that TCGA's normal samples and GTEX samples represent different batches. #' #' @param TCGA_normal_data_path The path to the tumor data stored in an RDS file. #' @param gtex_data_path The path to the GTEX data stored in an RDS file. #' @param CombatNormal_output_path A character string specifying the path where the output RDS file will be saved. #' @param auto_mode Logical. If set to TRUE, the function will not prompt the user for input and #' will instead use the values provided in default_input. Default is FALSE. #' @param default_input Character string. When auto_mode is TRUE, this parameter specifies the default #' TGCA's normal tissue types to be retained. It should be provided as a comma-separated string (e.g., "11,12"). #' #' @return A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the #' combat_count_df data as an RDS file at the specified output path. #' #' @details #' The ComBat_seq function from the 'sva' package is used to correct batch effects. #' The function requires the 'sva' package to be installed and loaded externally. #' #' The example code uses `tempfile()` to generate temporary paths dynamically during execution. #' These paths are valid during the `R CMD check` process, even if no actual files exist, #' because `tempfile()` generates a unique file path that does not depend on the user's file system. #' Using `tempfile()` ensures that the example code does not rely on specific external files and #' avoids errors during `R CMD check`. CRAN review checks for documentation correctness #' and syntax parsing, not the existence of actual files, as long as the example code is syntactically valid. #' #' @examples #' TCGA_normal_file <- system.file("extdata", #' "SKCM_Skin_TCGA_exp_normal_test.rds", #' package = "TransProR") #' gtex_file <- system.file("extdata", "Skin_SKCM_Gtex_test.rds", package = "TransProR") #' output_file <- file.path(tempdir(), "SKCM_Skin_Combat_Normal_TCGA_GTEX_count.rds") #' #' SKCM_Skin_Combat_Normal_TCGA_GTEX_count <- Combat_Normal( #' TCGA_normal_data_path = TCGA_normal_file, #' gtex_data_path = gtex_file, #' CombatNormal_output_path = output_file, #' auto_mode = TRUE, #' default_input = "skip" #' ) #' head(SKCM_Skin_Combat_Normal_TCGA_GTEX_count)[1:5, 1:5] #' #' @seealso \code{\link[sva]{ComBat_seq}} #' @importFrom sva ComBat_seq #' @importFrom tibble column_to_rownames #' @export Combat_Normal <- function(TCGA_normal_data_path, gtex_data_path, CombatNormal_output_path, auto_mode = FALSE, default_input = "11,12") { # Load the TGCA's normal tissue data and GTEX data TCGA_normal_data <- readRDS(TCGA_normal_data_path) gtex_data <- readRDS(gtex_data_path) # Extract histological types for the TGCA's normal tissue data's samples NormalHistologicalTypes <- substring(colnames(TCGA_normal_data), 14, 15) # Filter only the normal samples normal_data <- TCGA_normal_data[, as.numeric(NormalHistologicalTypes) > 10] # Display the table to the user normal_hist_table <- table(NormalHistologicalTypes) #print(normal_hist_table) message(" ") message("NormalHistologicalTypes:") message(paste(names(normal_hist_table), normal_hist_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Ask the user for input or use default input in auto_mode if(auto_mode) { selected_types <- strsplit(default_input, ",")[[1]] } else { message("Please input the normal tissue types you wish to retain or 'skip' to only use GTEX data: ") selected_types <- strsplit(readline(), ",")[[1]] } # If the user didn't select 'skip' if (!is.element("skip", selected_types)) { # Filter the tumor data based on user's input normal <- normal_data[, NormalHistologicalTypes %in% selected_types] # Combine GTEX and selected TCGA data # Merge the datasets, ensuring both have genes as row names combined_data <- merge(normal, gtex_data, by = "row.names") combined_data <- tibble::column_to_rownames(combined_data, var = "Row.names") # Set the row names # Create group vector (All samples as same group) combined_group <- rep("all_group", ncol(combined_data)) # Create batch vector for selected normal TCGA samples tcga_batches <- match(NormalHistologicalTypes[NormalHistologicalTypes %in% selected_types], selected_types) # Combine the batch vector for TCGA samples with GTEX batch combined_batch <- c(tcga_batches, rep("GTEX", ncol(gtex_data))) # Modify the tumor values combined_data_count <- 2^(combined_data) - 1 combined_data_count <- apply(combined_data_count, 2, as.integer) rownames(combined_data_count) <- rownames(combined_data) # Correct for batch effects using ComBat_seq combat_count <- sva::ComBat_seq(as.matrix(combined_data_count), batch = combined_batch, group = combined_group) # Convert matrix to data frame combat_count_df <- as.data.frame(combat_count) saveRDS(combat_count_df, file = CombatNormal_output_path) return(combat_count_df) } else { combat_count_matrix <- 2^(gtex_data) - 1 combat_count_matrix <- apply(combat_count_matrix, 2, as.integer) rownames(combat_count_matrix) <- rownames(gtex_data) combat_count_df <- as.data.frame(combat_count_matrix) saveRDS(combat_count_df, file = CombatNormal_output_path) return(combat_count_df) } } ================================================ FILE: R/CombatTumor.R ================================================ #' Process and Correct Batch Effects in Tumor Data #' #' This function takes a tumor data set, asks the user for specific tumor types to retain, #' and then corrects for batch effects using the ComBat_seq function from the 'sva' package. #' #' @description #' The function first extracts histological types from the provided tumor data set. #' After displaying these types, the user is prompted to input specific types to retain. #' The data is then filtered based on this input. #' #' Note: This example assumes that different tumor types represent different batches in a general sense. #' Users need to adjust the batch and group vectors based on real-life scenarios. #' #' @param tumor_data_path The path to the tumor data stored in an RDS file. #' @param CombatTumor_output_path A character string specifying the path where the output RDS file will be saved. #' @param auto_mode Logical. If set to TRUE, the function will not prompt the user for input and #' will instead use the values provided in default_input. Default is FALSE. #' @param default_input Character string. When auto_mode is TRUE, this parameter specifies the default #' tumor types to be retained. It should be provided as a comma-separated string (e.g., "01,06"). #' #' @return A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the #' combat_count_df data as an RDS file at the specified output path. #' #' @details #' The ComBat_seq function from the sva package is used to correct batch effects. #' The function requires the 'sva' package to be installed and loaded externally. #' #' @examples #' tumor_file <- system.file("extdata", #' "SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' output_file <- file.path(tempdir(), "SKCM_combat_count.rds") #' #' SKCM_combat_count <- combat_tumor( #' tumor_data_path = tumor_file, #' CombatTumor_output_path = output_file, #' auto_mode = TRUE, #' default_input = "01,06" #' ) #' #' head(SKCM_combat_count)[1:5, 1:5] #' #' @seealso \code{\link[sva]{ComBat_seq}} #' @importFrom sva ComBat_seq #' @export combat_tumor <- function(tumor_data_path, CombatTumor_output_path, auto_mode = FALSE, default_input = "01,06") { # Load the tumor data tumor_data <- readRDS(tumor_data_path) # Extract histological types TumorHistologicalTypes <- substring(colnames(tumor_data), 14, 15) tumor_hist_table <- table(TumorHistologicalTypes) # Display the table to the user message(" ") message("TumorHistologicalTypes:") message(paste(names(tumor_hist_table), tumor_hist_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Ask the user for input or use default input in auto_mode if(auto_mode) { selected_types <- strsplit(default_input, ",")[[1]] } else { message("Please input the tumor types you wish to retain, separated by commas (e.g., 01,06): ") selected_types <- strsplit(readline(), ",")[[1]] } # Filter the tumor data based on user's input tumor <- tumor_data[, TumorHistologicalTypes %in% selected_types] # Modify the tumor values tumor1 <- 2^(tumor) - 1 tumor1 <- apply(tumor1, 2, as.integer) rownames(tumor1) <- rownames(tumor) # If only one sample type is chosen, skip batch correction and return modified tumor data if(length(selected_types) == 1) { combat_count_df <- as.data.frame(tumor1) } else { # Create group vector selected_group = rep("all_group", length(which(TumorHistologicalTypes %in% selected_types))) # Create batch vector based on group vector selected_batch = match(TumorHistologicalTypes[TumorHistologicalTypes %in% selected_types], selected_types) # Correct for batch effects using ComBat_seq combat_count <- sva::ComBat_seq(as.matrix(tumor1), batch = selected_batch, group = selected_group) # Convert matrix to data frame combat_count_df <- as.data.frame(combat_count) } saveRDS(combat_count_df, file = CombatTumor_output_path) return(combat_count_df) } ================================================ FILE: R/CompareMerge.R ================================================ #' Compare and merge specific columns from two DEG data frames #' #' This function takes two DEG data frames, inner joins them by a specified gene column, #' checks if a specified column is identical across both data frames, and merges them if they are. #' The resulting data frame will have a merged column named after the compared column. #' #' @importFrom dplyr inner_join #' @param df1 First data frame. #' @param df2 Second data frame. #' @param by_gene Column name by which to join the data frames, typically "Gene". #' @param compare_col Column to compare for identity, which will also be the name of the merged column. #' @param suffixes Suffixes to use for non-identical column names in the joined data frame. #' @param df_name Name to assign to the resulting data frame for identification. #' @return A data frame with processed columns. #' @examples #' # Create simulated DESeq2 data #' DEG_deseq2 <- data.frame( #' Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"), #' change = c("up", "down", "no_change", "up", "down"), #' log2FoldChange = c(2.5, -3.2, 0.1, 1.8, -2.5), #' pvalue = c(0.01, 0.05, 0.9, 0.02, 0.03) #' ) #' #' # Display the first 5 rows of the DESeq2 data #' head(DEG_deseq2, 5) #' #' # Create simulated edgeR data #' DEG_edgeR <- data.frame( #' Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"), #' change = c("up", "down", "no_change", "no_change", "up"), #' log2FoldChange = c(2.3, -3.1, 0.2, 0.1, 2.7), #' pvalue = c(0.02, 0.04, 0.8, 0.6, 0.01) #' ) #' #' # Display the first 5 rows of the edgeR data #' head(DEG_edgeR, 5) #' #' # Merge the DESeq2 and edgeR data #' deseq2_edgeR <- compare_merge( #' df1 = DEG_deseq2, #' df2 = DEG_edgeR, #' by_gene = "Gene", #' compare_col = "change", #' suffixes = c("_1", "_2"), #' df_name = "deseq2_edgeR" #' ) #' #' @export compare_merge <- function(df1, df2, by_gene, compare_col, suffixes, df_name) { # Perform an inner join on the 'Gene' column merged_df <- dplyr::inner_join(df1, df2, by = by_gene, suffix = suffixes) # Generate column names for comparison col1 <- paste0(compare_col, suffixes[1]) col2 <- paste0(compare_col, suffixes[2]) # Check if the specified columns are identical if (all(merged_df[[col1]] == merged_df[[col2]])) { # If completely identical, merge these columns into one and rename merged_df[[compare_col]] <- merged_df[[col1]] # Remove original compared columns merged_df[[col1]] <- NULL merged_df[[col2]] <- NULL } else { # Handle non-identical case message("The columns", col1, "and", col2, "are not identical.\n") } # Assign the specified name for identification merged_df$name <- df_name # Return the modified data frame return(merged_df) } ================================================ FILE: R/Contrast_Venn.R ================================================ #' Function to Create a Venn Diagram of DEGs with Custom Colors #' #' This function creates a Venn Diagram using the 'ggVennDiagram' package. #' It allows customization of various aesthetic elements of the diagram, including colors. #' #' @importFrom ggplot2 scale_fill_gradient scale_color_manual scale_x_continuous expansion #' @importFrom ggVennDiagram ggVennDiagram #' @param all_degs_venn A list of DEG sets for Venn Diagram creation. #' @param edge_colors A vector of colors for the edges of the Venn Diagram sets. #' @param name_color A vector of colors for the names of the sets in the Venn Diagram. #' @param fill_colors A vector of two colors for the gradient fill of the Venn Diagram. #' @param label_size The size of the labels showing the number of elements in each set (default is 4). #' @param edge_size The size of the edges of the Venn Diagram sets (default is 3). #' @return A `ggplot` object representing the Venn Diagram. #' @examples #' #' data("all_degs_venn", package = "TransProR") #' #' edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39") #' name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39") #' fill_colors <- c("#e3f2fa", "#0288d1") #' #' Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors) #' @export #' Contrast_Venn <- function(all_degs_venn, edge_colors, name_color, fill_colors, label_size = 4, edge_size = 3) { # Draw Venn Diagram ggVennDiagram::ggVennDiagram(all_degs_venn, set_size = 5, # Font size for set names set_color = name_color, label_alpha = 0, # Transparency of the background box for counts/percentages, default is white label_size = label_size, # Font size for counts/percentages edge_size = edge_size # Thickness of the edges ) + ggplot2::scale_fill_gradient(low = fill_colors[1], high = fill_colors[2]) + # Gradient fill color based on value size ggplot2::scale_color_manual(values = edge_colors) + # Manually set edge colors ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = .3)) } ================================================ FILE: R/DESeq2Analyze.R ================================================ #' Differential Gene Expression Analysis using 'DESeq2' #' #' 'DESeq2': Differential gene expression analysis based on the negative binomial distribution. #' This function utilizes the 'DESeq2' package to conduct differential gene expression analysis. #' It processes tumor and normal expression data, applies DESeq2 analysis, #' and outputs the results along with information on gene expression changes. #' #' The DESeq2 methodology is based on modeling count data using a negative binomial distribution, #' which allows for handling the variability observed in gene expression data, especially in #' small sample sizes. This approach is well-suited for RNA-Seq data analysis. #' #' @importFrom DESeq2 DESeqDataSetFromMatrix DESeq results #' @importFrom dplyr mutate #' @importFrom tibble column_to_rownames #' @importFrom stats na.omit #' @param tumor_file Path to the tumor data file (RDS format). #' @param normal_file Path to the normal data file (RDS format). #' @param output_file Path to save the output DEG data (RDS format). #' @param logFC Threshold for log fold change. #' @param p_value Threshold for p-value. #' @return A data frame of differential expression results. #' @examples #' # Define file paths for tumor and normal data from the data folder #' tumor_file <- system.file("extdata", #' "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' normal_file <- system.file("extdata", #' "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", #' package = "TransProR") #' output_file <- file.path(tempdir(), "DEG_DESeq2.rds") #' #' DEG_DESeq2 <- DESeq2_analyze( #' tumor_file = tumor_file, #' normal_file = normal_file, #' output_file = output_file, #' 2.5, #' 0.01 #' ) #' #' # View the top 5 rows of the result #' head(DEG_DESeq2, 5) #' #' @references #' DESeq2:Differential gene expression analysis based on the negative binomial distribution. #' For more information, visit the page: #' https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/ #' @export DESeq2_analyze <- function(tumor_file, normal_file, output_file, logFC = 2.5, p_value = 0.01) { # Read and merge data tumor <- readRDS(tumor_file) normal <- readRDS(normal_file) all_count_exp <- merge(tumor, normal, by = "row.names") # Merge the datasets, ensuring both have genes as row names all_count_exp <- tibble::column_to_rownames(all_count_exp, var = "Row.names") # Set the row names # Create group factor group <- factor(c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal))), levels = c("normal", "tumor")) group_table <- table(group) message("Group Table:") message(paste(names(group_table), group_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Prepare DESeq2 dataset 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. 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. 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. 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. # Perform differential expression analysis. dds <- DESeq2::DESeq(dds) # Perform DESeq2 analysis # Extract the results of differential expression and perform a comparison. Here, the 'contrast' parameter specifies the groups to be compared. # The 'contrast' parameter must be written in a vector format with three elements, and the order cannot be reversed. res <- DESeq2::results(dds, contrast = c("group", "tumor", "normal")) # 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. resOrdered <- res[order(res$padj), ] DEG <- as.data.frame(stats::na.omit(resOrdered)) # Remove missing values. If this step is not taken, some genes with very low expression levels will result in NA values after calculation, causing difficulties in subsequent analysis and plotting. # Add a 'change' column to mark up- or down-regulated genes, with the threshold set according to requirements. DEG <- dplyr::mutate(DEG, change = ifelse(DEG$pvalue < p_value & DEG$log2FoldChange < -logFC, "down", ifelse(DEG$pvalue < p_value & DEG$log2FoldChange > logFC, "up", "stable"))) # Output table of gene expression changes change_table <- table(DEG$change) message(" ") message("Change Table:") message(paste(names(change_table), change_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Save results #save(DEG, file = output_file) saveRDS(DEG, file = output_file) # Return results return(DEG) } ================================================ FILE: R/EdgeRAnalyze.R ================================================ #' Differential Gene Expression Analysis using 'edgeR' #' #' This function performs differential gene expression analysis using the 'edgeR' package. #' It reads tumor and normal expression data, merges them, filters low-expressed genes, #' normalizes the data, performs edgeR analysis, and outputs the results along with information #' on gene expression changes. #' #' @importFrom tibble column_to_rownames #' @importFrom dplyr mutate #' @importFrom edgeR DGEList cpm calcNormFactors estimateGLMCommonDisp estimateGLMTrendedDisp estimateGLMTagwiseDisp glmFit glmLRT topTags #' @importFrom stats model.matrix #' @param tumor_file Path to the tumor data file (RDS format). #' @param normal_file Path to the normal data file (RDS format). #' @param output_file Path to save the output DEG data (RDS format). #' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes. #' @param p_value_threshold Threshold for p-value for filtering significant genes. #' @return A data frame of differential expression results. #' @references #' edgeR: Differential analysis of sequence read count data. #' For more information, visit the edgeR Bioconductor page: #' https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf #' @export #' #' @examples #' # Define file paths for tumor and normal data from the data folder #' tumor_file <- system.file("extdata", #' "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' normal_file <- system.file("extdata", #' "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", #' package = "TransProR") #' output_file <- file.path(tempdir(), "DEG_edgeR.rds") #' #' DEG_edgeR <- edgeR_analyze( #' tumor_file = tumor_file, #' normal_file = normal_file, #' output_file = output_file, #' 2.5, #' 0.01 #' ) #' #' # View the top 5 rows of the result #' head(DEG_edgeR, 5) #' edgeR_analyze <- function(tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01) { tumor <- readRDS(tumor_file) normal <- readRDS(normal_file) # Merge the datasets, ensuring both have genes as row names all_count_exp <- merge(tumor, normal, by = "row.names") all_count_exp <- tibble::column_to_rownames(all_count_exp, var = "Row.names") # Define groups for tumor and normal samples group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal))) group <- factor(group, levels = c("normal", "tumor")) group_table <- table(group) message("Group Table:") message(paste(names(group_table), group_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Create DGEList object for gene expression data and group information d <- edgeR::DGEList(counts = all_count_exp, group = group) # Filter lowly expressed genes based on CPM values keep <- rowSums(edgeR::cpm(d) > 1) >= 2 d <- d[keep, , keep.lib.sizes = FALSE] # Update library size information in the samples d$samples$lib.size <- colSums(d$counts) # Normalize the data using the TMM method d <- edgeR::calcNormFactors(d) # Create design matrix for differential analysis model design <- stats::model.matrix(~0 + factor(group)) rownames(design) <- colnames(d) colnames(design) <- levels(factor(group)) # Estimate dispersions - common dispersion, trended dispersion, tagwise dispersion d <- edgeR::estimateGLMCommonDisp(d, design) d <- edgeR::estimateGLMTrendedDisp(d, design) d <- edgeR::estimateGLMTagwiseDisp(d, design) # Fit the model using Generalized Linear Model (GLM) fit <- edgeR::glmFit(d, design) # Perform differential expression analysis using Likelihood Ratio Test (LRT) lrt <- edgeR::glmLRT(fit, contrast = c(-1, 1)) # Note that the 'contrast' here is different from 'DESeq2'. Here, we only need to input c(-1, 1): -1 corresponds to normal, 1 corresponds to tumor. # Retrieve top differentially expressed genes nrDEG <- edgeR::topTags(lrt, n = nrow(d)) DEG_edgeR <- as.data.frame(nrDEG) # Add 'change' column to mark up/down-regulated genes k1 <- (DEG_edgeR$PValue < p_value_threshold) & (DEG_edgeR$logFC < -logFC_threshold) k2 <- (DEG_edgeR$PValue < p_value_threshold) & (DEG_edgeR$logFC > logFC_threshold) DEG_edgeR <- dplyr::mutate(DEG_edgeR, change = ifelse(k1, "down", ifelse(k2, "up", "stable"))) change_table <- table(DEG_edgeR$change) message("Change Table:") message(paste(names(change_table), change_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Save results to the specified output file saveRDS(DEG_edgeR, file = output_file) return(DEG_edgeR) } ================================================ FILE: R/EnrichCirclize.R ================================================ #' Adjust and Export Pathway Analysis Results #' #' This function processes a dataframe containing fgsea results. It adjusts pathway names #' by removing underscores, converting to lowercase, then capitalizing the first letter, #' and joining the components with spaces. It selects and merges the top upregulated #' and downregulated pathways based on enrichment score (ES) and p-value. #' #' @param fgseaRes Dataframe containing fgsea results with columns 'pathway', 'ES', and 'pval'. #' @param nTop Integer, number of top pathways to select based on the p-value. #' @return A vector containing combined top upregulated and downregulated pathways. #' @importFrom Hmisc capitalize #' @export #' @examples #' # Create a synthetic fgseaRes dataframe #'fgseaRes <- data.frame( #' pathway = c("KEGG_APOPTOSIS", #' "GO_CELL_CYCLE", #' "REACTOME_DNA_REPAIR", #' "KEGG_METABOLISM", #' "GO_TRANSPORT"), #' ES = c(0.45, -0.22, 0.56, -0.35, 0.33), #' pval = c(0.001, 0.02, 0.0003, 0.05, 0.01) #') #' #' # Run the function to get top pathways #'result <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2) #' adjust_export_pathway <- function(fgseaRes, nTop = 10) { # Adjust pathway names fgseaRes$pathway <- as.character(fgseaRes$pathway) for(i in 1:nrow(fgseaRes)){ message("Processing row ", i) term = fgseaRes$pathway[i] ### 1. Split the string term = unlist(strsplit(term, split="_", fixed=TRUE))[-1] ### 2. Convert to lowercase, then capitalize the first letter term = Hmisc::capitalize(tolower(term)) ### 3. Concatenate with spaces term = paste(term, collapse=" ") ### 4. Data export fgseaRes$pathway[i] = term } # Select top upregulated pathways topPathwaysUp <- fgseaRes[fgseaRes$ES > 0,][order(fgseaRes$pval[fgseaRes$ES > 0]), 'pathway'][1:nTop] # Select top downregulated pathways topPathwaysDown <- fgseaRes[fgseaRes$ES < 0,][order(fgseaRes$pval[fgseaRes$ES < 0]), 'pathway'][1:nTop] # Combine top pathways and convert any potential list to a vector combinedPathways <- unlist(c(topPathwaysUp, rev(topPathwaysDown)), use.names = FALSE) return(list(combinedPathways = combinedPathways, fgseaRes = fgseaRes)) } #' Randomly Select Pathways with Limited Word Count #' #' 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. #' #' @param pathways Character vector of pathways. #' @param max_words Integer, maximum number of words allowed in the pathway name. #' @param num_select Integer, number of pathways to randomly select. #' @return A character vector of selected pathways. #' @export #' @examples #' pathway_list <- c("pathway_one response to stimulus", #' "pathway_two cell growth and death", #' "pathway_three regulation of cellular process", #' "pathway_four metabolic process") #' selected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2) #' selectPathways <- function(pathways, max_words = 10, num_select = 10) { # Check input if (!is.character(pathways)) { stop("Please provide a character vector of pathways.") } # Filter pathways with word count not exceeding max_words filtered_pathways <- pathways[sapply(pathways, function(x) length(strsplit(x, " ")[[1]]) <= max_words)] # Randomly select num_select pathways from the filtered list if (length(filtered_pathways) >= num_select) { selected_pathways <- sample(filtered_pathways, num_select) } else { warning("Not enough pathways with <= ", max_words, " words. Returning as many as possible.") selected_pathways <- sample(filtered_pathways, length(filtered_pathways)) } return(selected_pathways) } #' Draw Dual-Sided Legends on a Plot #' #' This function creates two sets of legends, one on the left and one on the right side of a plot. #' It displays color-coded legends with labels corresponding to different data categories. #' Each legend entry consists of a colored rectangle and a text label. The left side legend has #' text aligned to the right of the color block, while the right side legend has text aligned #' to the left of the color block. #' #' @param labels Vector of labels for the legends. #' @param colors Vector of colors corresponding to the labels. #' @param legend_width The width of each legend viewport expressed in grid units. #' @param x_positions Numeric vector of length 2 specifying the x-positions of the left and right legends. #' @param y_position The y-position common for both legends, expressed as a fraction of the plot height. #' @param just_positions List of two vectors, each specifying the horizontal and vertical justification for the legends. #' @param text_alignments List of two character strings specifying text alignments for the legends ('left' or 'right'). #' @param font_size Numeric value specifying the font size for the legend labels. #' @return Invisible. This function is called for its side effects of drawing legends on a plot. #' @importFrom grid pushViewport viewport grid.roundrect grid.text upViewport unit #' @export #' @examples #' labels <- c("Label1", "Label2", "Label3", "Label4", "Label5", "Label6") #' colors <- c("#ff0000", "#00ff00", "#0000ff", "#ffff00", "#ff00ff", "#00ffff") #' #' # Convert to 'unit' objects for grid #' grid::grid.roundrect( #' x = grid::unit(0.5, "npc"), # "npc" stands for normalized parent coordinates #' y = grid::unit(0.5, "npc"), #' width = grid::unit(0.1, "npc"), #' height = grid::unit(0.05, "npc"), #' gp = grid::gpar(fill = "red"), #' r = grid::unit(0.1, "npc") # rounding radius #' ) #' #' # Example of drawing legends with specific labels and colors #' drawLegends(labels, colors, grid::unit(2, "cm"), c(0.225, 0.75), 0.5, #' list(c("left", "center"), c("right", "center")), #' list("right", "left"), 10) #' drawLegends <- function(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size) { half_length <- length(labels) / 2 legend_height <- grid::unit(1, "lines") * half_length # Draw left-side legend grid::pushViewport(grid::viewport( width = legend_width, height = legend_height, x = x_positions[1], y = y_position, just = just_positions[[1]] )) for (i in seq_len(half_length)) { grid::grid.roundrect( x = grid::unit(1, "npc") - grid::unit(0.5, "cm"), y = grid::unit(1, "npc") - grid::unit(i / half_length, "npc") + grid::unit(0.5 / half_length, "npc"), width = grid::unit(0.7, "cm"), height = grid::unit(0.9 / half_length, "npc"), gp = grid::gpar(fill = colors[i], col = NA), r = grid::unit(0.3, "snpc") ) grid::grid.text( labels[i], x = grid::unit(1, "npc") - grid::unit(1, "cm"), y = grid::unit(1, "npc") - grid::unit(i / half_length, "npc") + grid::unit(0.5 / half_length, "npc"), gp = grid::gpar(col = colors[i], fontsize = font_size), just = text_alignments[[1]] ) } grid::upViewport() # Draw right-side legend grid::pushViewport(grid::viewport( width = legend_width, height = legend_height, x = x_positions[2], y = y_position, just = just_positions[[2]] )) for (i in (half_length + 1):length(labels)) { grid::grid.roundrect( x = grid::unit(1, "npc") - grid::unit(0.6, "cm"), y = grid::unit(1, "npc") - grid::unit((i - half_length) / half_length, "npc") + grid::unit(0.5 / half_length, "npc"), width = grid::unit(0.7, "cm"), height = grid::unit(0.9 / half_length, "npc"), gp = grid::gpar(fill = colors[i], col = NA), r = grid::unit(0.3, "snpc") ) grid::grid.text( labels[i], x = grid::unit(1, "npc") - grid::unit(0.1, "cm"), y = grid::unit(1, "npc") - grid::unit((i - half_length) / half_length, "npc") + grid::unit(0.5 / half_length, "npc"), gp = grid::gpar(col = colors[i], fontsize = font_size), just = text_alignments[[2]] ) } grid::upViewport() } #' Draw Chord Diagram with Legends #' #' This function creates a chord diagram from a specified dataframe and draws two sets of legends for it. #' It adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid. #' Legends are drawn at specified positions with configurable text alignments and font sizes. #' #' @param all_combined_df A dataframe containing the matrix for the chord diagram. #' @param original_colors A vector of colors for the grid columns of the chord diagram. #' @param labels A vector of labels for the first legend. #' @param colors A vector of colors corresponding to the first legend's labels. #' @param labels2 A vector of labels for the second legend. #' @param colors2 A vector of colors corresponding to the second legend's labels. #' @param font_size The font size used for legend texts, defaults to 10. #' @return Invisible, primarily used for its side effects of drawing on a graphics device. #' @importFrom circlize chordDiagram #' @importFrom grid unit #' @importFrom graphics strwidth #' @export #' @examples #' # Sample Chord Diagram Matrix #' all_combined_df <- data.frame( #' A = c(10, 20, 30), #' B = c(15, 25, 35), #' C = c(5, 10, 15) #' ) #' rownames(all_combined_df) <- c("A", "B", "C") #' #' # Colors for the grid of the chord diagram (corresponding to columns of the matrix) #' original_colors <- c("red", "green", "blue") #' #' # Name the colors according to the sectors (A, B, C) #' names(original_colors) <- colnames(all_combined_df) #' #' # Labels and Colors for the First Legend #' labels <- c("Label 1", "Label 2", "Label 3") #' colors <- c("yellow", "purple", "cyan") #' #' # Labels and Colors for the Second Legend #' labels2 <- c("Label A", "Label B", "Label C") #' colors2 <- c("orange", "pink", "brown") #' #' # Font size for the legend texts (optional, default is 10) #' font_size <- 10 #' #' # Call the enrichment_circlize function with the sample data #' # This is just an example; the plot will be rendered in an appropriate graphics context #' # such as RStudio's plot pane or an external plotting window. #' plot1 <- enrichment_circlize(all_combined_df, #' original_colors, #' labels, #' colors, #' labels2, #' colors2, #' font_size #' ) #' enrichment_circlize <- function(all_combined_df, original_colors, labels, colors, labels2, colors2, font_size = 10) { # Calculate adjusted height for the chord diagram max_height <- max(graphics::strwidth(unlist(dimnames(all_combined_df)), "inches")) * 1.2 # Draw the chord diagram circlize::chordDiagram(all_combined_df, grid.col = original_colors, annotationTrack = "grid", directional = -1, direction.type = c("diffHeight", "arrows"), link.arr.type = "big.arrow", preAllocateTracks = list(track.height = max_height)) # Draw the first set of legends legend_width <- grid::unit(2, "cm") x_positions <- c(0.225, 0.75) y_position <- 0.5 just_positions <- list(c("left", "center"), c("right", "center")) text_alignments <- list("right", "left") drawLegends(labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size) # Draw the second set of legends x_positions2 <- c(0.3, 0.68) y_position2 <- 0.7 drawLegends(labels2, colors2, legend_width, x_positions2, y_position2, just_positions, text_alignments, font_size) } ================================================ FILE: R/EnrichCircoBar.R ================================================ #' Extract and Count Descriptions with Specified Color #' #' This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns, #' and adds a new column with a specified color. #' #' @param df A data frame containing at least 'Description' and 'Count' columns. #' @param descriptions A vector of descriptions to filter in the data frame. #' @param color A character string specifying the color to be added as a new column. #' @return A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column. #' @export #' #' @examples #' # Generate Sample Input Data for extract_descriptions_counts Function #' #' # Create a sample data frame with 'Description' and 'Count' columns #' data <- data.frame( #' Description = c( #' "immunoglobulin production", #' "B cell mediated immunity", #' "T cell activation", #' "antigen processing and presentation", #' "cytokine signaling", #' "natural killer cell activity", #' "phagocytosis", #' "complement activation", #' "antibody-dependent cellular cytotoxicity", #' "regulatory T cell function" #' ), #' Count = c( #' 150, # immunoglobulin production #' 200, # B cell mediated immunity #' 175, # T cell activation #' 125, # antigen processing and presentation #' 190, # cytokine signaling #' 160, # natural killer cell activity #' 140, # phagocytosis #' 180, # complement activation #' 130, # antibody-dependent cellular cytotoxicity #' 170 # regulatory T cell function #' ), #' stringsAsFactors = FALSE # Ensure that strings are not converted to factors #' ) #' #' #' #' descriptions_to_filter <- c("immunoglobulin production", "B cell mediated immunity") #' specified_color <- "red" # You can specify any color you desire #' filtered_data_with_color <- extract_descriptions_counts( #' data, descriptions_to_filter, #' specified_color) #' print(filtered_data_with_color) #' extract_descriptions_counts <- function(df, descriptions, color) { # Filter rows where the Description column values are in the descriptions vector result_df <- df[df$Description %in% descriptions, ] # Select Description and Count columns result_df <- result_df[, c("Description", "Count")] # Add a new column 'color' with the specified color value result_df$color <- color return(result_df) } #' Combine and Visualize Data with Circular Bar Chart #' #' This function combines multiple data frames, arranges them, and visualizes the combined data #' in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages. #' #' @importFrom dplyr bind_rows arrange desc row_number mutate #' @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 #' @importFrom rlang .data #' @param data_list A list of data frames to be combined. #' @return A `ggplot` object representing the Circular Bar Chart. #' @export #' #' @examples #' # Create sample data frames for each enrichment category #' #' # 1. Biological Process (BP) #' filtered_data_BP <- data.frame( #' Description = c( #' "immune response", #' "cell proliferation", #' "signal transduction", #' "apoptotic process", #' "metabolic process" #' ), #' Count = c(120, 85, 150, 60, 95), #' color = c( #' "#1f77b4", # blue #' "#ff7f0e", # orange #' "#2ca02c", # green #' "#d62728", # red #' "#9467bd" # purple #' ), #' stringsAsFactors = FALSE #' ) #' #' # 2. Cellular Component (CC) #' filtered_data_CC <- data.frame( #' Description = c( #' "nucleus", #' "cytoplasm", #' "membrane", #' "mitochondrion", #' "extracellular space" #' ), #' Count = c(90, 110, 75, 65, 80), #' color = c( #' "#1f77b4", #' "#ff7f0e", #' "#2ca02c", #' "#d62728", #' "#9467bd" #' ), #' stringsAsFactors = FALSE #' ) #' #' # 3. Molecular Function (MF) #' filtered_data_MF <- data.frame( #' Description = c( #' "protein binding", #' "DNA binding", #' "enzyme activity", #' "transporter activity", #' "receptor activity" #' ), #' Count = c(140, 130, 100, 70, 90), #' color = c( #' "#1f77b4", #' "#ff7f0e", #' "#2ca02c", #' "#d62728", #' "#9467bd" #' ), #' stringsAsFactors = FALSE #' ) #' #' # 4. Disease Ontology (DO) #' filtered_data_DO <- data.frame( #' Description = c( #' "cancer", #' "cardiovascular disease", #' "neurological disorder", #' "metabolic disease", #' "infectious disease" #' ), #' Count = c(200, 150, 120, 90, 160), #' color = c( #' "#1f77b4", #' "#ff7f0e", #' "#2ca02c", #' "#d62728", #' "#9467bd" #' ), #' stringsAsFactors = FALSE #' ) #' #' # 5. Reactome Pathways #' filtered_data_Reactome <- data.frame( #' Description = c( #' "Cell Cycle", #' "Apoptosis", #' "DNA Repair", #' "Signal Transduction", #' "Metabolism of Proteins" #' ), #' Count = c(110, 95, 80, 130, 85), #' color = c( #' "#1f77b4", #' "#ff7f0e", #' "#2ca02c", #' "#d62728", #' "#9467bd" #' ), #' stringsAsFactors = FALSE #' ) #' #' # 6. KEGG Pathways #' filtered_data_kegg <- data.frame( #' Description = c( #' "PI3K-Akt signaling pathway", #' "MAPK signaling pathway", #' "NF-kappa B signaling pathway", #' "JAK-STAT signaling pathway", #' "Toll-like receptor signaling pathway" #' ), #' Count = c(175, 160, 145, 130, 155), #' color = c( #' "#1f77b4", #' "#ff7f0e", #' "#2ca02c", #' "#d62728", #' "#9467bd" #' ), #' stringsAsFactors = FALSE #' ) #' #' # Combine all filtered data frames into a list #' data_list <- list( #' BP = filtered_data_BP, #' CC = filtered_data_CC, #' MF = filtered_data_MF, #' DO = filtered_data_DO, #' Reactome = filtered_data_Reactome, #' KEGG = filtered_data_kegg #' ) #' #' # Create the Circular Bar Chart #' combined_and_visualized_data <- enrich_circo_bar(data_list) #' enrich_circo_bar <- function(data_list) { # Combine data frames combined_data <- dplyr::bind_rows(data_list) # Sort by 'Count' column in descending order and add an 'id' column combined_data <- combined_data %>% dplyr::arrange(.data$Count) %>% dplyr::mutate(id = dplyr::row_number()) # Ensure 'Description' is a factor with correct level order combined_data <- combined_data %>% dplyr::mutate(Description = factor(.data$Description, levels = unique(.data$Description))) # Extract fill colors, ensuring colors match 'Description' levels fill_colors <- combined_data$color[match(levels(combined_data$Description), combined_data$Description)] # Calculate the expanded max values for Count and id max_count <- max(combined_data$Count) + (max(combined_data$Count) / 5) max_id <- max(combined_data$id) + 1.5 # Create the plot p <- ggplot2::ggplot(combined_data, ggplot2::aes(x = .data$id, y = .data$Count, fill = .data$Description)) + ggplot2::geom_bar(stat = 'identity', width = 0.7) + ggplot2::geom_text(ggplot2::aes(x = .data$id, y = 0, label = .data$Description), hjust = 1.03, size = 3.5, color = fill_colors) + ggplot2::scale_fill_manual(values = fill_colors, guide = "none") + ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0, max_count), position = 'right') + ggplot2::scale_x_continuous(expand = c(0, 0), limits = c(-1, max_id)) + ggplot2::coord_polar(theta = 'y') + ggplot2::labs(title = 'Enrichment CircularBar Chart', subtitle = "Including: BP/MF/CC/DO/KEGG/Reactome") + ggplot2::theme_minimal() + ggplot2::theme(plot.background = ggplot2::element_rect(fill = 'white', color = 'white'), axis.title = ggplot2::element_blank(), axis.text = ggplot2::element_blank()) return(p) } ================================================ FILE: R/EnrichPolarBubble.R ================================================ #' Enrichment Polar Bubble Plot #' #' 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. #' #' @param final_combined_df_with_id_and_position A data frame containing 'id', 'Count', 'method', 'Description', 'point_position', 'test_color'. #' @param pal A named vector of colors corresponding to the 'method' values. #' @param highlight_ids A vector of IDs to highlight. #' @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 #' @importFrom dplyr filter #' @importFrom stringr str_c #' @importFrom tibble tibble #' @importFrom geomtextpath geom_textpath #' @importFrom ggalt stat_xspline #' @importFrom ggnewscale new_scale_fill #' @return A `ggplot` object representing the enriched polar bubble plot. #' @examples #' final_df <- data.frame(id = 1:10, Count = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100), #' method = rep("Method1", 10), #' Description = LETTERS[1:10], #' point_position = seq(10, 100, 10), #' test_color = sample(c("red", "blue"), 10, replace = TRUE)) #' pal <- c("Method1" = "blue") #' highlight_ids <- c(1, 5, 9) #' enrich_polar_bubble(final_df, pal, highlight_ids) #' @export enrich_polar_bubble <- function(final_combined_df_with_id_and_position, pal, highlight_ids) { # Extract unique levels of point_position and prepend 0 levels <- c(0, sort(unique(final_combined_df_with_id_and_position$point_position))) max_level_plus_five <- max(levels) + 5 max_id_plus_one <- max(final_combined_df_with_id_and_position$id) + 1 gg <- ggplot2::ggplot(data = final_combined_df_with_id_and_position, ggplot2::aes(x = .data$id, y = .data$Count, group = .data$method, fill = .data$method, color = .data$method)) + ggplot2::geom_hline(yintercept = levels, color = "grey85") + ggplot2::geom_hline(yintercept = max_level_plus_five, color = "grey15") + ggplot2::geom_segment(data = tibble::tibble(x = 1:max_id_plus_one, y = 0, yend = max_level_plus_five), ggplot2::aes(x = .data$x, xend = .data$x, y = .data$y, yend = .data$yend), inherit.aes = FALSE, color = "grey85") + ggplot2::geom_segment(data = tibble::tibble(x = 1:max_id_plus_one, y = max(levels), yend = max_level_plus_five), ggplot2::aes(x = .data$x, xend = .data$x, y = .data$y, yend = .data$yend), inherit.aes = FALSE, color = "grey15") + geomtextpath::geom_textpath(ggplot2::aes(x = final_combined_df_with_id_and_position$id, y = max_level_plus_five + 5, label = final_combined_df_with_id_and_position$Description, angle = 55), inherit.aes = FALSE, hjust = 0, size = 5.5, color = final_combined_df_with_id_and_position$test_color) + ggplot2::geom_point(ggplot2::aes(x = .data$id, y = .data$point_position, size = .data$Count), shape = 21, alpha = 0.6) + ggalt::stat_xspline(geom = "line", spline_shape = 0.25, linewidth = 0.75, alpha = 0.4) + ggalt::stat_xspline(geom = "area", alpha = 0.25, spline_shape = 0.25, outline.type = "upper") + ggplot2::scale_size(range = c(0, 14), breaks = levels, guide = ggplot2::guide_legend(title = "Count")) + ggplot2::scale_fill_manual(values = pal) + ggplot2::scale_color_manual(values = pal) + ggplot2::scale_x_continuous(expand = c(0, 0)) + ggplot2::scale_y_continuous(limits = c(-50, max_level_plus_five + 15), expand = c(0, 0)) + ggplot2::coord_polar() + ggplot2::annotate("text", x = 1, y = levels, label = "-", hjust = 1, size = 4) + ggplot2::annotate("text", x = 1.1, y = levels, label = levels, hjust = 0, size = 4) + ggplot2::annotate("text", x = 1, y = -50, label = stringr::str_c("BP/CC/MF/KEGG/DO/REACTOME"), size = 4) + ggplot2::theme_void() + ggplot2::theme(plot.background = ggplot2::element_rect(fill = "white", color = NA)) + ggnewscale::new_scale_fill() + ggplot2::geom_rect(data = dplyr::filter(final_combined_df_with_id_and_position, .data$id %in% highlight_ids), ggplot2::aes(xmin = .data$id - 0.5, xmax = .data$id + 0.5, ymin = 0, ymax = max_level_plus_five, fill = .data$test_color), alpha = 0.1, inherit.aes = FALSE) + ggplot2::scale_fill_identity() # Use colors specified in 'test_color' return(gg) } ================================================ FILE: R/EnrichmentSpiralize.R ================================================ #' Extract and Store Top Pathways for Each Sample #' #' This function processes a dataframe containing SSGSEA KEGG results. It allows specifying the number #' of top pathways to extract for each sample based on their scores, and stores these in a new dataframe #' with sample names and pathway scores. #' #' @param ssgsea_kegg Dataframe containing SSGSEA KEGG results with samples as columns and pathways as rows. #' @param nTop Integer, number of top pathways to select for each sample. #' @return A dataframe with columns 'Pathway', 'Sample', and 'Value' representing the top pathways for each sample. #' @importFrom utils head #' @export #' @examples #' # Example: Generating input data for the extract_ntop_pathways function #' #' # Define example pathways #' pathways <- c("Pathway_A", "Pathway_B", "Pathway_C", "Pathway_D", "Pathway_E", #' "Pathway_F", "Pathway_G", "Pathway_H", "Pathway_I", "Pathway_J") #' #' # Define example samples #' samples <- c("Sample_1", "Sample_2", "Sample_3") #' #' # Generate random SSGSEA KEGG scores between 0 and 1 #' set.seed(123) # For reproducibility #' ssgsea_scores <- matrix(runif(length(pathways) * length(samples), min = 0, max = 1), #' nrow = length(pathways), ncol = length(samples), #' dimnames = list(pathways, samples)) #' #' # Convert to a data frame #' ssgsea_kegg <- as.data.frame(ssgsea_scores) #' #' # Extract the top 3 pathways for each sample #' top_pathways <- extract_ntop_pathways(ssgsea_kegg, nTop = 3) #' extract_ntop_pathways <- function(ssgsea_kegg, nTop = 5) { # Initialize an empty data frame to store the results results <- data.frame(Pathway = character(), Sample = character(), Value = numeric(), stringsAsFactors = FALSE) # Iterate through each sample, starting from the first column for (i in 1:ncol(ssgsea_kegg)) { sample_name <- colnames(ssgsea_kegg)[i] # To avoid factor type errors, ensure the data is numeric column_data <- as.numeric(ssgsea_kegg[[i]]) # Create a new data frame with numeric data for sorting and extracting pathway_data <- data.frame(Pathway = rownames(ssgsea_kegg), Value = column_data, stringsAsFactors = FALSE) # Sort by value in descending order and take the top nTop entries top_paths <- utils::head(pathway_data[order(-pathway_data$Value),], nTop) # Bind to the results data frame results <- rbind(results, data.frame(Pathway = top_paths$Pathway, Sample = sample_name, Value = top_paths$Value)) } return(results) } #' Extract Positive Pathways from SSGSEA Results and Select Random Samples #' #' This function processes the results of SSGSEA, specifically focusing on KEGG pathways. #' It extracts pathways with positive values from each sample and randomly selects a subset of them. #' #' @param ssgsea_kegg A matrix or data frame with pathways as rows and samples as columns. #' @param max_paths_per_sample Integer, maximum number of pathways to select per sample. #' @return A data frame with selected pathways, samples, and their corresponding values. #' @export #' @examples #' # Example: Generating input data for the extract_positive_pathways function #' #' # Define example pathways #' pathways <- c("Pathway_1", "Pathway_2", "Pathway_3", "Pathway_4", "Pathway_5", #' "Pathway_6", "Pathway_7", "Pathway_8", "Pathway_9", "Pathway_10") #' #' # Define example samples #' samples <- c("Sample_A", "Sample_B", "Sample_C") #' #' # Generate random SSGSEA KEGG scores including both positive and negative values #' set.seed(456) # For reproducibility #' ssgsea_scores <- matrix(rnorm(length(pathways) * length(samples), mean = 0, sd = 1), #' nrow = length(pathways), ncol = length(samples), #' dimnames = list(pathways, samples)) #' #' # Convert to a data frame #' ssgsea_kegg <- as.data.frame(ssgsea_scores) #' #' # Use the extract_positive_pathways function to extract up to 3 positive pathways per sample #' selected_positive_pathways <- extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 3) #' extract_positive_pathways <- function(ssgsea_kegg, max_paths_per_sample = 5) { # Initialize an empty data frame to store the results results <- data.frame(Pathway = character(), Sample = character(), Value = numeric(), stringsAsFactors = FALSE) # Iterate over each sample for (i in 1:ncol(ssgsea_kegg)) { sample_name <- colnames(ssgsea_kegg)[i] # Ensure the data is numeric column_data <- as.numeric(ssgsea_kegg[[i]]) # Create a new data frame with pathway names and values pathway_data <- data.frame(Pathway = rownames(ssgsea_kegg), Value = column_data, stringsAsFactors = FALSE) # Filter for positive values positive_paths <- pathway_data[pathway_data$Value > 0,] # If there are positive values, randomly select a few pathways if (nrow(positive_paths) > 0) { selected_paths <- positive_paths[sample(nrow(positive_paths), min(max_paths_per_sample, nrow(positive_paths))),] # Bind to the results data frame results <- rbind(results, data.frame(Pathway = selected_paths$Pathway, Sample = sample_name, Value = selected_paths$Value)) } } return(results) } #' Adjust Color Tone by Modifying Saturation and Luminance #' #' This function adjusts the saturation and luminance of a given color. It works by converting #' the color from RGB to Luv color space, applying the scaling factors to the saturation and luminance, #' and then converting it back to RGB. #' #' @param color A color in hexadecimal format (e.g., "#FF0000") or a valid R color name. #' @param saturation_scale Numeric, the scaling factor for saturation (values < 1 decrease saturation, values > 1 increase saturation). #' @param luminance_scale Numeric, the scaling factor for luminance (values < 1 darken the color, values > 1 lighten the color). #' @return Returns a color in hexadecimal format adjusted according to the provided scales. #' @importFrom grDevices convertColor col2rgb rgb #' @export #' @examples #' adjusted_color <- adjust_color_tone("#FF0000", saturation_scale = 0.8, luminance_scale = 1.2) #' print(adjusted_color) #' adjust_color_tone <- function(color, saturation_scale, luminance_scale) { # Convert the input color to RGB, then to Luv color space rgb <- t(grDevices::col2rgb(color) / 255) luv <- grDevices::convertColor(rgb, from = "sRGB", to = "Luv") # Apply scaling factors to saturation and luminance luv[, 2:3] <- luv[, 2:3] * saturation_scale # Adjust saturation luv[, 1] <- luv[, 1] * luminance_scale # Adjust luminance # Convert back to RGB and correct color values to stay within the valid range rgb_new <- grDevices::convertColor(luv, from = "Luv", to = "sRGB") rgb_new <- rgb_new * 255 rgb_new[rgb_new > 255] <- 255 # Prevent color values from exceeding the maximum # Convert adjusted RGB values back to hexadecimal format apply(rgb_new, 1, function(x) grDevices::rgb(x[1], x[2], x[3], maxColorValue = 255)) } #' Render a Spiral Plot Using Run-Length Encoding #' #' This function creates a spiral plot for visualizing sequential data in a compact and visually appealing way. #' It uses run-length encoding to represent the lengths and colors of sequences in the spiral. #' #' @param x A vector representing categories or segments. #' @param samples A vector indicating the sample each segment belongs to. #' @param values Numeric vector indicating the lengths of each segment. #' @param colors Character vector specifying the colors for each segment. #' @param labels Logical, whether to add labels to each segment. #' @importFrom grid gpar unit #' @importFrom spiralize spiral_rect spiral_text spiral_initialize spiral_track #' @export #' @return No return value, called for side effects. This function generates a spiral plot and optionally adds labels. #' @examples #' # Example: Creating a spiral plot using the spiral_newrle function #' #' # Define example data #' x <- c("A", "A", "B", "C") #' samples <- c("Sample1", "Sample1", "Sample2", "Sample2") #' values <- c(20, 30, 15, 35) #' colors <- c("red", "blue", "green", "purple") #' labels <- TRUE #' #' # Initialize the spiral plot, setting the x-axis range and scaling #' spiralize::spiral_initialize(xlim = c(0, sum(values)), scale_by = "curve_length", #' vp_param = list(x = grid::unit(0, "npc"), just = "left")) #' #' # Create a track for the spiral plot #' spiralize::spiral_track(height = 0.5) #' #' # Add segments to the spiral plot using run-length encoding #' spiral_newrle(x, samples, values, colors, labels) #' spiral_newrle <- function(x, samples, values, colors, labels = FALSE) { x <- as.vector(x) # Ensure x is a vector samples <- as.vector(samples) # Ensure samples is a vector values <- as.numeric(values) # Ensure values are numeric position_start <- 0 # Initialize starting position current_sample <- samples[1] # Start with the first sample cumulative_start <- position_start # Initialize cumulative start for labels # Loop through each value for (i in seq_along(values)) { position_end <- position_start + values[i] # Calculate end position # Use the specified color, defaulting to red if missing color <- if (!is.na(colors[i])) colors[i] else "red" # Draw the segment in the spiral spiralize::spiral_rect(position_start, 0, position_end, 1, gp = grid::gpar(fill = color, col = NA)) # Check for sample change or last element if (i == length(values) || samples[i + 1] != current_sample) { if (labels) { label_position <- (cumulative_start + position_end) / 2 spiralize::spiral_text(label_position, 0.5, current_sample, facing = "curved_inside", nice_facing = TRUE) } cumulative_start <- position_end # Reset for next sample if (i < length(values)) { current_sample <- samples[i + 1] } } position_start <- position_end # Move to next start position } } #' Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap' #' #' This function initializes a spiral plot, adds tracks for pathways and samples, #' and generates legends based on the sample and pathway information in the provided data frame. #' It uses 'spiralize' for the spiral plot and 'ComplexHeatmap' for handling legends. #' #' @param results A data frame containing 'Pathway', 'Sample', 'Value', 'PathwayColor', and 'SampleColor' columns. #' @importFrom grid gpar #' @importFrom spiralize spiral_initialize spiral_track #' @importFrom ComplexHeatmap packLegend Legend draw #' @importFrom ggplot2 unit #' @export #' @return No return value, called for side effects. This function generates spiral plots and adds legends based on sample and pathway information. #' @examples #' # Example: Creating enrichment spiral plots with legends #' #' # Define the results data frame #' results <- data.frame( #' Pathway = c("Pathway1", "Pathway1", "Pathway2", "Pathway2", "Pathway3"), #' Sample = c("Sample1", "Sample1", "Sample2", "Sample2", "Sample3"), #' Value = c(20, 30, 15, 35, 25), #' PathwayColor = c("red", "red", "blue", "blue", "orange"), #' SampleColor = c("green", "green", "purple", "purple", "cyan"), #' stringsAsFactors = FALSE #' ) #' #' # Create the enrichment spiral plots with legends #' enrichment_spiral_plots(results) #' enrichment_spiral_plots <- function(results) { if (!requireNamespace("systemfonts", quietly = TRUE)) { stop("ggplot2 is required to use the function. Please install it.", call. = FALSE) } # Calculate the total value for setting the x-axis range n <- sum(results$Value) # Initialize the spiral plot spiralize::spiral_initialize(xlim = c(0, n), scale_by = "curve_length", vp_param = list(x = ggplot2::unit(0, "npc"), just = "left")) # Add a track for pathways spiralize::spiral_track(height = 0.4) spiral_newrle(results$Pathway, results$Sample, results$Value, results$PathwayColor, labels = FALSE) # Add a track for samples spiralize::spiral_track(height = 0.4) spiral_newrle(results$Sample, results$Sample, results$Value, results$SampleColor, labels = TRUE) # Generate legends based on sample, using unique pathway and color information lgd_list <- tapply(1:nrow(results), results$Sample, function(ind) { ComplexHeatmap::Legend(title = results$Sample[ind][1], at = unique(results$Pathway[ind]), legend_gp = grid::gpar(fill = unique(results$PathwayColor[ind]))) }) # Set the maximum height for the legends and draw them lgd <- ComplexHeatmap::packLegend(list = lgd_list, max_height = ggplot2::unit(7, "inch")) ComplexHeatmap::draw(lgd, x = ggplot2::unit(1, "npc") + ggplot2::unit(1, "mm"), just = "left") } ================================================ FILE: R/FacetDensityFoldchange.R ================================================ #' Create faceted high-density region plots with optional points and density contours #' #' This function creates faceted high-density region plots using ggdensity for #' adding optional density rug and density contours, and scatter points. It also adds a regression line #' and Pearson correlation label. The plot is faceted by a grouping variable. #' #' @param data Data frame containing variables for plotting. #' @param x_var Name of the x-axis variable as a string. #' @param y_var Name of the y-axis variable as a string. #' @param group_var Name of the grouping variable for color mapping as a string. #' @param facet_var Name of the faceting variable. #' @param palette Color palette for the plot as a character vector. #' @param show_points Logical, if TRUE adds scatter points to the plot. #' @param show_density Logical, if TRUE adds filled density contours to the plot. #' @param point_size Size of the points, relevant if show_points is TRUE. #' @param point_alpha Transparency level of the points, relevant if show_points is TRUE. #' @param line_size Size of the regression line. #' @param cor_method Method to calculate correlation ("pearson" or "spearman"). #' @param cor_label_pos Vector of length 2 indicating the position of the correlation label (x and y). #' @param cor_vjust Vertical justification for correlation label, default is NULL. #' @return A `ggplot` object representing the high-density region plot. #' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth scale_fill_manual scale_color_manual facet_wrap theme margin #' @importFrom ggdensity geom_hdr geom_hdr_rug #' @importFrom ggpubr stat_cor #' @importFrom hrbrthemes theme_ipsum #' @importFrom grid unit #' @importFrom stats as.formula #' @examples #' combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR") #' combined_df <- readRDS(combined_df_file) #' pal2 = c("#2787e0","#1a9ae0","#1dabbf","#00897b","#43a047","#7cb342") #' all_facet_density_foldchange_name1 <- facet_density_foldchange( #' data = combined_df, #' x_var = "log2FoldChange_1", #' y_var = "log2FoldChange_2", #' group_var = "name", #' facet_var = "name", #' palette = pal2, #' show_points = TRUE, #' show_density = FALSE, #' point_size = 2, #' point_alpha = 0.1, #' line_size = 1.6, #' cor_method = "pearson", #' cor_label_pos = c("left", "top"), #' cor_vjust = 1 #' ) #' @export facet_density_foldchange <- function(data, x_var, y_var, group_var, facet_var, palette, show_points = FALSE, show_density = TRUE, point_size = 2.5, point_alpha = 0.1, line_size = 1.6, cor_method = "pearson", cor_label_pos = c("left", 0.97), cor_vjust = NULL) { # Begin constructing the ggplot plot <- ggplot2::ggplot(data, ggplot2::aes_string(x = x_var, y = y_var, fill = group_var)) # Optionally add density rug plot <- plot + ggdensity::geom_hdr_rug() # Optionally add density contours if (show_density) { plot <- plot + ggdensity::geom_hdr() } # Optionally add points if (show_points) { plot <- plot + ggplot2::geom_point(ggplot2::aes_string(color = group_var), shape = 21, size = point_size, alpha = point_alpha) } # Add regression line and correlation label plot <- plot + ggplot2::geom_smooth(ggplot2::aes_string(x = x_var, y = y_var, color = group_var), method = 'lm', level = 0.95, size = line_size) # Add regression line and correlation label if (is.null(cor_vjust)) { 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]) } else { 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) } # Customize scales and facet wrapping plot <- plot + ggplot2::scale_fill_manual(values = palette) + ggplot2::scale_color_manual(values = palette) + ggplot2::facet_wrap(stats::as.formula(paste0("~ ", facet_var)), scales = "free_x") + hrbrthemes::theme_ipsum() + ggplot2::theme(plot.margin = ggplot2::margin(10, 10, 10, 10), plot.background = ggplot2::element_rect(fill = "white", color = "white"), panel.spacing = grid::unit(2, "mm")) # Return the `ggplot` object return(plot) } ================================================ FILE: R/FilterDiffGenes.R ================================================ #' Filter Differentially Expressed Genes #' #' This function filters a data frame to identify genes with significant differential expression #' based on specified thresholds for p-values and log fold change. It allows for flexible #' input of column names for p-values and log fold change. #' #' @importFrom dplyr filter #' @importFrom rlang sym #' @param data A data frame containing gene expression data. #' @param p_val_col Character string indicating the column name for p-values. Default is "adj.P.Val". #' @param log_fc_col Character string indicating the column name for log fold change. Default is "logFC". #' @param p_val_threshold Numeric threshold for filtering p-values. Default is 0.05. #' @param log_fc_threshold Numeric threshold for filtering log fold change. Default is 1.0. #' @return A data frame with genes filtered by the specified criteria. #' @export #' #' @examples #' # Create a sample data frame with p-values and log fold changes #' sample_data <- data.frame( #' adj.P.Val = c(0.03, 0.06, 0.02, 0.07), #' logFC = c(1.5, 0.8, -1.2, 1.1), #' gene = c("Gene1", "Gene2", "Gene3", "Gene4") #' ) #' #' # Use the filter_diff_genes function to filter significant genes #' filtered_genes <- filter_diff_genes(sample_data) #' print(filtered_genes) filter_diff_genes <- function(data, p_val_col = "adj.P.Val", log_fc_col = "logFC", p_val_threshold = 0.05, log_fc_threshold = 1.0) { # Check for the specified columns in the data frame if (!(p_val_col %in% names(data))) { stop(paste("Column", p_val_col, "not found in the data frame.")) } if (!(log_fc_col %in% names(data))) { stop(paste("Column", log_fc_col, "not found in the data frame.")) } # Filter the data based on the specified thresholds for p-values and log fold change filtered_data <- data %>% dplyr::filter(!!rlang::sym(p_val_col) < p_val_threshold) %>% dplyr::filter(abs(!!rlang::sym(log_fc_col)) > log_fc_threshold) # Return the filtered data frame return(filtered_data) } ================================================ FILE: R/FourDegsVenn.R ================================================ #' Function to Filter Differentially Expressed Genes (DEGs) #' #' This function filters out genes based on their expression change status. #' It returns the names of genes which are not "stable". #' #' @param df A data frame containing gene expression data. #' @return A vector of gene names that are differentially expressed. #' @examples #' DEG_deseq2_file <- system.file("extdata", "DEG_deseq2.rds", package = "TransProR") #' DEG_deseq2 <- readRDS(DEG_deseq2_file) #' DEG_deseq2_test <- deg_filter(DEG_deseq2) #' @export deg_filter <- function(df){ # Selecting gene names where change is not "stable" rownames(df)[df$change != "stable"] } #' Function to Create a Venn Diagram of DEGs #' #' This function creates a Venn Diagram using the ggVennDiagram package. #' It allows customization of various aesthetic elements of the diagram. #' @importFrom ggplot2 alpha scale_fill_gradient scale_color_manual scale_x_continuous expansion #' @importFrom ggVennDiagram ggVennDiagram #' @param degs_list A list of DEG sets for Venn Diagram creation. #' @return A ggplot object representing the Venn Diagram. #' @examples #' data("all_degs_venn", package = "TransProR") #' four_degs_venn <- four_degs_venn(all_degs_venn) #' @export four_degs_venn <- function(degs_list){ # Defining edge colors and alpha transparency for the Venn Diagram edge_colors <- c(ggplot2::alpha("#1b64bb", 0.5), ggplot2::alpha("#13828e", 0.5), ggplot2::alpha("#337c3a", 0.5), ggplot2::alpha("#9e9d39", 0.5)) edge_color <- ggplot2::alpha(c("#1b64bb","#13828e","#337c3a","#9e9d39"), 0.8) # Creating a Venn Diagram using 'ggVennDiagram' ggVennDiagram::ggVennDiagram( degs_list, set_size = 5, # Font size for group names set_color = edge_color, # Color for group names label_alpha= 0, # Transparency of background box for labels label_size = 4, # Font size for labels edge_size = 3 # Thickness of edges ) + ggplot2::scale_fill_gradient(low="#e1f2f1", high = "#11786b") + # Gradient fill based on values ggplot2::scale_color_manual(values = edge_colors) + # Manually set edge colors ggplot2::scale_x_continuous(expand = ggplot2::expansion(mult = .3)) # Adjusting x-axis scaling } ================================================ FILE: R/GatherGraphEdge.R ================================================ #' Gather graph edge from data frame #' Please note that this function is from the 'ggraph' package and has not been altered in functionality, #' but it has been optimized and iterated. #' It is not original content of 'TransProR'. #' However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', #' the author directly referenced the involved functions in 'TransProR'. #' This is not the author's original creation. All users please be aware! #' @param df A data frame #' @param index A vector of column names to group by #' @param root Root name for the root node connections, optional #' @return A tibble of graph edges #' @export #' @importFrom dplyr mutate select group_by summarise bind_rows all_of across #' @importFrom tidyr unite #' @importFrom tibble as_tibble #' @name gather_graph_edge gather_graph_edge <- function(df, index = NULL, root = NULL) { if (length(index) < 2) { stop("Please specify at least two index columns.") } prepare_edge <- function(data, from, to, sep = "/") { data %>% tidyr::unite("from", dplyr::all_of(from), sep = sep, remove = FALSE) %>% tidyr::unite("to", dplyr::all_of(to), sep = sep) %>% dplyr::select(.data$from, .data$to) %>% dplyr::mutate(dplyr::across(c("from", "to"), as.character)) } edges <- lapply(seq_along(index)[-1], function(i) { prepare_edge(df, index[1:(i - 1)], index[1:i]) }) edges <- dplyr::bind_rows(edges) edges <- tibble::as_tibble(edges) if (!is.null(root)) { root_edges <- df %>% dplyr::group_by(.data[[index[1]]]) %>% dplyr::summarise(count = dplyr::n(), .groups = 'drop') %>% dplyr::mutate(from = root, to = as.character(.data[[index[1]]])) %>% dplyr::select(.data$from, .data$to) edges <- dplyr::bind_rows(root_edges, edges) } return(edges) } ================================================ FILE: R/GatherGraphNode.R ================================================ #' Gather graph nodes from a data frame #' Please note that this function is from the 'ggraph' package and has not been altered in functionality, #' but it has been optimized and iterated. #' It is not original content of 'TransProR'. #' However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', #' the author directly referenced the involved functions in 'TransProR'. #' This is not the author's original creation. All users please be aware! #' @inheritParams gather_graph_edge #' @param value Column name used for summarizing node size, defaults to the last column #' @return a tibble of graph nodes #' @export #' @importFrom dplyr group_by summarise mutate bind_rows n all_of across #' @importFrom tidyr unite #' @importFrom tibble as_tibble #' @importFrom utils tail gather_graph_node <- function(df, index = NULL, value = utils::tail(colnames(df), 1), root = NULL) { if (length(index) < 2) { stop("Please specify at least two index columns.") } else { nodes_list <- lapply(seq_along(index), function(i) { dots <- index[1:i] df %>% dplyr::group_by(dplyr::across(dplyr::all_of(dots))) %>% dplyr::summarise(node.size = sum(.data[[value]], na.rm = TRUE), node.level = index[i], node.count = dplyr::n(), .groups = 'drop') %>% dplyr::mutate(node.short_name = as.character(.data[[dots[length(dots)]]]), node.branch = as.character(.data[[dots[1]]])) %>% tidyr::unite(col = "node.name", all_of(dots), sep = "/") }) nodes <- dplyr::bind_rows(nodes_list) %>% tibble::as_tibble() nodes$node.level <- factor(nodes$node.level, levels = index) if (!is.null(root)) { root_data <- data.frame( node.name = root, node.size = sum(df[[value]], na.rm = TRUE), node.level = root, node.count = 1, node.short_name = root, node.branch = root, stringsAsFactors = FALSE ) nodes <- dplyr::bind_rows(root_data, nodes) nodes$node.level <- factor(nodes$node.level, levels = c(root, index)) } return(nodes) } } ================================================ FILE: R/GeneColor.R ================================================ #' Merge Genes with Color Information Based on Up/Down Regulation #' #' This function merges selected genes with differential expression data and adds a color column based on up/down regulation. #' #' @param selected_genes A data frame containing selected genes with a column named "Symble". #' @param DEG_deseq2 A data frame containing differential expression data with a column named "Symble" and a column named "change" indicating up/down regulation. #' @param up_color The color to assign to genes with up-regulated expression. #' @param down_color The color to assign to genes with down-regulated expression. #' @return A data frame containing merged genes with an additional color column. #' @export #' #' @examples #' selected_genes_deseq2_file <- system.file("extdata", #' "selected_genes_deseq2.rds", #' package = "TransProR") #' selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file) #' Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR") #' Diff_deseq2 <- readRDS(Diff_deseq2_file) #' #' result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746") gene_color <- function(selected_genes, DEG_deseq2, up_color, down_color) { # Ensure that selected_genes has a column named "Symble" if (!"Symble" %in% colnames(selected_genes)) { stop("selected_genes data frame must have a 'Symble' column.") } # Ensure that the row names column of DEG_deseq2 is correctly set to "Symble" DEG_deseq2$Symble <- rownames(DEG_deseq2) # Merge data frames: add the 'change' column from DEG_deseq2 to the selected_genes data frame merged_genes <- merge(selected_genes, DEG_deseq2[, c("Symble", "change")], by = "Symble", all.x = TRUE) # Add color column based on the 'change' column values merged_genes$color <- ifelse(merged_genes$change == "up", up_color, ifelse(merged_genes$change == "down", down_color, NA)) # Ensure that the length of the color column matches the number of rows in merged_genes if (length(merged_genes$color) != nrow(merged_genes)) { stop("Color assignment failed due to unexpected values in 'change' column.") } return(merged_genes) } ================================================ FILE: R/GeneHighlights.R ================================================ #' Add gene highlights to a ggtree object #' #' This function enhances a `ggtree` plot by adding highlights for specific genes. It adds both a semi-transparent fan-shaped #' highlight and a point at the node corresponding to each gene. Colors for each gene can be customized. #' #' @param ggtree_obj A ggtree object to which the highlights will be added. #' @param genes_to_highlight A data frame containing genes and their corresponding colors. #' @param hilight_extend Integer, the extension of the highlight fan in degrees. #' @return A ggtree object with added gene highlights. #' @importFrom ggtree geom_hilight geom_point2 #' @importFrom dplyr filter select pull #' @examples #' data("gtree", package = "TransProR") #' #' # Define genes and their colors #' genes_df <- data.frame(Symble = c("t5", "t9"), #' color = c("#FF0000", "#0000FF")) #' #' # Add highlights #' gtree <- gene_highlights(gtree, genes_to_highlight = genes_df) #' #' @export gene_highlights <- function(ggtree_obj, genes_to_highlight, hilight_extend = 18) { # Ensure the input is a `ggtree` object if (!inherits(ggtree_obj, "ggtree")) { stop("The first argument must be a ggtree object.") } # Ensure the second argument is a data frame if (!("data.frame" %in% class(genes_to_highlight))) { stop("The second argument must be a data frame.") } if (!requireNamespace("systemfonts", quietly = TRUE)) { stop("ggplot2 is required to use the function. Please install it.", call. = FALSE) } # Extract tree data and ensure it is in a data frame format tree_data <- as.data.frame(ggtree_obj$data) # Map gene names and colors to the tree nodes and create geom_hilight and geom_point2 layers for each node highlight_commands <- lapply(1:nrow(genes_to_highlight), function(i) { gene <- genes_to_highlight$Symble[i] color <- genes_to_highlight$color[i] node <- dplyr::filter(tree_data, .data$label == gene) %>% dplyr::select(node) %>% dplyr::pull() if (!is.na(node)) { list( ggtree::geom_hilight(node=node, fill=color, alpha = .3, extend = hilight_extend), ggtree::geom_point2(ggtree::aes(subset = (.data$label == gene)), color=color, size=2, alpha=0.6) ) } else { warning(paste("Gene", gene, "not found in the ggtree object.")) NULL } }) # Remove NULL elements from the list of commands highlight_commands <- Filter(Negate(is.null), highlight_commands) # Flatten the list of commands and apply them to the `ggtree` object ggtree_obj <- ggtree_obj + do.call(c, highlight_commands) return(ggtree_obj) } ================================================ FILE: R/GeneMapPathway.R ================================================ #' Create Pathway-Gene Mapping Data Frame #' #' This function takes multiple data frames and pathway IDs, merging them into a new data frame. #' Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.). #' @param BP_dataframe Data frame for Biological Process analysis #' @param BP_ids Selected pathway IDs for Biological Process analysis #' @param KEGG_dataframe Data frame for KEGG analysis #' @param KEGG_ids Selected pathway IDs for KEGG analysis #' @param MF_dataframe Data frame for Molecular Function analysis #' @param MF_ids Selected pathway IDs for Molecular Function analysis #' @param REACTOME_dataframe Data frame for REACTOME analysis #' @param REACTOME_ids Selected pathway IDs for REACTOME analysis #' @param CC_dataframe Data frame for Cellular Component analysis #' @param CC_ids Selected pathway IDs for Cellular Component analysis #' @param DO_dataframe Data frame for Disease Ontology analysis #' @param DO_ids Selected pathway IDs for Disease Ontology analysis #' @return A new data frame that includes pathways, gene, type, and value columns #' @export #' @examples #' # Simulating data for different analysis types #' #' # Simulate Biological Process (BP) data frame #' BP_df <- data.frame( #' ID = c("GO:0002376", "GO:0019724"), #' geneID = c("GENE1/GENE2", "GENE3/GENE4"), #' Description = c("Immune response", "Glycosylation process") #' ) #' #' # Simulate KEGG data frame #' KEGG_df <- data.frame( #' ID = c("12345", "67890"), #' geneID = c("GENE5/GENE6", "GENE7/GENE8"), #' Description = c("Pathway 1", "Pathway 2") #' ) #' #' # Simulate Molecular Function (MF) data frame #' MF_df <- data.frame( #' ID = c("ABC123", "DEF456"), #' geneID = c("GENE9/GENE10", "GENE11/GENE12"), #' Description = c("Molecular function A", "Molecular function B") #' ) #' #' # Simulate REACTOME data frame #' REACTOME_df <- data.frame( #' ID = c("R-HSA-12345", "R-HSA-67890"), #' geneID = c("GENE13/GENE14", "GENE15/GENE16"), #' Description = c("Pathway in Reactome 1", "Pathway in Reactome 2") #' ) #' #' # Simulate Cellular Component (CC) data frame #' CC_df <- data.frame( #' ID = c("GO:0005575", "GO:0005634"), #' geneID = c("GENE17/GENE18", "GENE19/GENE20"), #' Description = c("Cellular component A", "Cellular component B") #' ) #' #' # Simulate Disease Ontology (DO) data frame #' DO_df <- data.frame( #' ID = c("DOID:123", "DOID:456"), #' geneID = c("GENE21/GENE22", "GENE23/GENE24"), #' Description = c("Disease A", "Disease B") #' ) #' #' # Example pathway IDs for each analysis #' BP_ids <- c("GO:0002376", "GO:0019724") #' KEGG_ids <- c("12345", "67890") #' MF_ids <- c("ABC123", "DEF456") #' REACTOME_ids <- c("R-HSA-12345", "R-HSA-67890") #' CC_ids <- c("GO:0005575", "GO:0005634") #' DO_ids <- c("DOID:123", "DOID:456") #' #' # Generate the pathway-gene map using the gene_map_pathway function #' pathway_gene_map <- gene_map_pathway( #' BP_dataframe = BP_df, BP_ids = BP_ids, #' KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids, #' MF_dataframe = MF_df, MF_ids = MF_ids, #' REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids, #' CC_dataframe = CC_df, CC_ids = CC_ids, #' DO_dataframe = DO_df, DO_ids = DO_ids #' ) #' #' # Display the resulting pathway-gene mapping data frame #' print(pathway_gene_map) #' gene_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) { # Create an empty data frame pathway_gene_map <- data.frame( pathway_description = character(), gene4 = character(), type = character(), value = integer() ) # Helper function to extract information from a data frame and add it to the new data frame add_to_map <- function(df, ids, type) { if (is.null(df) || is.null(ids)) return() selected_rows <- df[df$ID %in% ids, ] for (row in seq(nrow(selected_rows))) { genes <- strsplit(as.character(selected_rows$geneID[row]), "/")[[1]] # Here, instead of the ID, we use the Description column description <- as.character(selected_rows$Description[row]) for (gene in genes) { pathway_gene_map <<- rbind(pathway_gene_map, data.frame( pathway = description, gene = gene, type = type, value = 1 )) } } } # Apply the helper function to add data for each type of analysis add_to_map(BP_dataframe, BP_ids, "BP") add_to_map(KEGG_dataframe, KEGG_ids, "KEGG") add_to_map(MF_dataframe, MF_ids, "MF") add_to_map(REACTOME_dataframe, REACTOME_ids, "REACTOME") add_to_map(CC_dataframe, CC_ids, "CC") add_to_map(DO_dataframe, DO_ids, "DO") return(pathway_gene_map) } ================================================ FILE: R/GetGtexExp.R ================================================ #' Get GTEx Expression Data for Specific Organ #' #' This function retrieves gene expression data from the GTEx project that is specific to a certain organ. #' It performs various checks and processing steps to ensure that the data is consistent and relevant to the #' specified organ. The filtered and cleaned data is saved as an RDS file for further analysis. #' #' @param organ_specific A character string specifying the organ to filter the gene expression data by. #' @param file_path A character string specifying the path to the GTEx gene expression data file. #' @param probe_map_path A character string specifying the path to the gtex_probeMap_gencode data file. #' @param pheno_path A character string specifying the path to the GTEx phenotype data file. #' @param output_path A character string specifying the path where the output RDS file will be saved. #' #' @details The function begins by checking if the gene expression and phenotype data files exist at #' the specified paths. It then loads these data files and processes them by setting appropriate row names, #' modifying column names for clarity, and filtering samples based on the specified organ. The function ensures #' that only samples present in both datasets are retained for consistency. It also removes any duplicate gene #' entries to prevent redundancy. Finally, the processed data is saved as an RDS file. #' #' @return A data frame containing gene expression data for the specified organ. #' Rows represent genes, and columns represent samples. Note that this function also saves the #' organ-specific GTEx data as an RDS file at the specified output path. #' #' @note The function will stop and throw an error if the input files do not exist, or if no samples are found #' for the specified organ. #' #' @note CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended #' 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'. #' #' @importFrom utils read.table #' @importFrom dplyr distinct filter #' @importFrom rlang .data #' @export get_gtex_exp <- function(organ_specific, file_path, probe_map_path, pheno_path, output_path) { # Check for the existence of the file paths if (!file.exists(file_path) | !file.exists(pheno_path) | !file.exists(probe_map_path)) { stop("One or more of the input files do not exist.") } # Load the gene expression, probe map, and phenotype data files from the provided paths # gtex.exp <- data.table::fread(file_path, header = TRUE, sep = '\t', data.table = FALSE) # gtex.pro <- data.table::fread(probe_map_path, header = TRUE, sep = '\t', data.table = FALSE) # gtex.phe <- data.table::fread(pheno_path, header = TRUE, sep = '\t', data.table = FALSE) # Load the gene expression, probe map, and phenotype data files gtex.exp <- utils::read.table(file_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) gtex.pro <- utils::read.table(probe_map_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) gtex.phe <- utils::read.table(pheno_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) # Merge the probe map with the expression data gtex.pro <- gtex.pro[, c(1,2)] # Assuming the columns of interest are the first two gtex.count.pro <- merge(gtex.pro, gtex.exp, by.x = "id", by.y = "sample") # Set the row names for the samples, facilitating subsequent operations rownames(gtex.phe) <- gtex.phe$Sample # Modify column names to be more intuitive colnames(gtex.phe) <- c("Sample", "body_site_detail (SMTSD)", "primary_site", "gender", "patient", "cohort") # Filter samples based on the specified organ specific_samples <- dplyr::filter(gtex.phe, .data$primary_site == organ_specific) # If no corresponding samples are found, halt the function with an error message if (nrow(specific_samples) == 0) { stop("No samples found for the specified organ.") } # Print the number of samples found for the specified organ message("Number of samples for", organ_specific, ":", nrow(specific_samples), "\n") # Ensure processing only for samples present in both expression and phenotype data through intersection valid_sample_names <- intersect(rownames(specific_samples), colnames(gtex.count.pro)) # merge_phe_count_gtex gtex_data <- gtex.count.pro[, c("gene", valid_sample_names)] # Extract data for relevant samples # Remove duplicate gene entries and set row names as gene names gtex_data <- dplyr::distinct(gtex_data, .data$gene, .keep_all = TRUE) rownames(gtex_data) <- gtex_data$gene gtex_data <- gtex_data[, -1] # Remove the 'gene' column, keeping only expression data # Save the results as an RDS file for future data analysis tasks saveRDS(gtex_data, output_path) return(gtex_data) } ================================================ FILE: R/GetTcgaExp.R ================================================ #' TCGA Expression Data Processing #' #' This function processes expression data and phenotype information, separates tumor and normal samples, #' and saves the results into different files. It's specifically designed for data obtained from TCGA. #' #' @param counts_file_path File path to the counts data (usually in the form of a large matrix with gene expression data). #' @param gene_probes_file_path File path containing the gene probes data. #' @param phenotype_file_path File path to the phenotype data, which includes various sample attributes. #' @param output_file_path Path where the output files, distinguished between tumor and normal, will be saved. #' #' @return A list containing matrices for tumor and normal expression data. #' #' @note IMPORTANT: This function assumes that the input files follow a specific format and structure, typically found in TCGA data releases. #' Users should verify their data's compatibility. Additionally, the function does not perform error checking on the data's content, #' which users should handle through proper preprocessing. #' #' @note CRITICAL: The 'output_file_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended #' that the path includes specific identifiers related to the target samples, as the function will create further subdivisions in the specified #' path for tumor or normal tissues. Please structure the 'output_file_path' following this pattern: './your_directory/your_sample_type.exp.rds'. #' #' @importFrom dplyr distinct filter #' @importFrom utils read.table #' @importFrom rlang .data #' @export #' @author Dongyue Yu #' #' @examples #' counts_file <- system.file("extdata", "TCGA-SKCM.htseq_counts_test.tsv", package = "TransProR") #' gene_probes_file <- system.file("extdata", #' "TCGA_gencode.v22.annotation.gene.probeMap_test", #' package = "TransProR") #' phenotype_file <- system.file("extdata", "TCGA-SKCM.GDC_phenotype_test.tsv", package = "TransProR") #' ouput_file <- file.path(tempdir(), "SKCM_Skin_TCGA_exp_test.rds") #' #' SKCM_exp <- get_tcga_exp( #' counts_file_path = counts_file, #' gene_probes_file_path = gene_probes_file, #' phenotype_file_path = phenotype_file, #' output_file_path = ouput_file #' ) #' head(SKCM_exp[["tumor_tcga_data"]])[1:5, 1:5] #' head(SKCM_exp[["normal_tcga_data"]], n = 10) # Because there is only one column. get_tcga_exp <- function(counts_file_path, gene_probes_file_path, phenotype_file_path, output_file_path) { # Load expression matrix # count_data <- data.table::fread(counts_file_path, header = TRUE, sep = '\t', data.table = FALSE) count_data <- utils::read.table(counts_file_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) # Load gene ID conversion information # gene_probes <- data.table::fread(gene_probes_file_path, header = TRUE, sep = '\t', data.table = FALSE) gene_probes <- utils::read.table(gene_probes_file_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) # Keep only necessary columns gene_probes <- gene_probes[, c(1, 2)] # Merge gene ID information with expression matrix count_probes_merged <- merge(gene_probes, count_data, by.x = "id", by.y = "Ensembl_ID") # Remove duplicates count_data_unique <- dplyr::distinct(count_probes_merged, .data$gene, .keep_all = TRUE) # Set gene names as row names rownames(count_data_unique) <- count_data_unique$gene count_data_final <- count_data_unique[, -c(1,2)] # Remove extra columns # Load clinical information # phenotype_data <- data.table::fread(phenotype_file_path, header = TRUE, sep = '\t', data.table = FALSE) # Load clinical information with proper column types phenotype_data <- utils::read.table(phenotype_file_path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE, colClasses = list( withdrawn = "logical", releasable.project = "logical", is_ffpe.samples = "logical", oct_embedded.samples = "logical" )) rownames(phenotype_data) <- phenotype_data$submitter_id.samples # Set sample names as row names # Check first if there are any "Metastatic", "Primary Tumor", or "normal" samples in your data table(phenotype_data$sample_type.samples) # Create a data frame for tumor samples tumor_samples <- phenotype_data %>% dplyr::filter(grepl("Metastatic|Primary Tumor", .data$sample_type.samples, ignore.case = TRUE)) # Check for 'Primary Tumor' or 'Metastatic' samples if (nrow(tumor_samples) == 0) { message("No 'Primary Tumor' or 'Metastatic' samples found.\n") } else { message("Number of 'Primary Tumor' or 'Metastatic' samples: ", nrow(tumor_samples), "\n") } # Create a data frame for normal samples normal_samples <- phenotype_data %>% dplyr::filter(grepl("normal", .data$sample_type.samples, ignore.case = TRUE)) if (nrow(normal_samples) == 0) { message("No 'normal' samples found.\n") } else { message("Number of 'normal' samples:", nrow(normal_samples), "\n") } # Get the intersection of clinical information and expression matrix tumor_common_samples <- intersect(rownames(tumor_samples), colnames(count_data_final)) # Extract corresponding expression matrix tumor_expression_data <- count_data_final[, tumor_common_samples] # Get the intersection of clinical information and expression matrix normal_common_samples <- intersect(rownames(normal_samples), colnames(count_data_final)) # Extract corresponding expression matrix normal_expression_data <- count_data_final[, normal_common_samples, drop = FALSE] # Save results tumor_output_path <- gsub("\\.rds$", "_tumor.rds", output_file_path) normal_output_path <- gsub("\\.rds$", "_normal.rds", output_file_path) saveRDS(tumor_expression_data, file = tumor_output_path) saveRDS(normal_expression_data, file = normal_output_path) result <- list( tumor_tcga_data = tumor_expression_data, normal_tcga_data = normal_expression_data ) return(result) } ================================================ FILE: R/HighlightByNode.R ================================================ #' Highlight Nodes in a Phylogenetic Tree with Custom Fill Colors and Transparency #' #' This function adds highlights to specific nodes in a phylogenetic tree represented by a `ggtree` object. #' Users can specify the nodes to highlight along with custom fill colors, transparency, and extension options. #' #' @importFrom ggtree geom_hilight #' @param ggtree_object A `ggtree` object representing the phylogenetic tree. #' @param nodes A character vector specifying the nodes to highlight. #' @param fill_colors A character vector specifying the fill colors for the highlighted nodes. #' @param alpha_values A numeric vector specifying the transparency values for the highlighted nodes (between 0 and 1). #' @param extend_values A logical vector specifying whether to extend the highlight to the whole clade below each node. #' @return A modified `ggtree` object with the specified nodes highlighted. #' @export #' #' @examples #' plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR") #' p2_plot <- readRDS(plot_file) #' #' # Please replace the following vectors with your specific values #' nodes <- c(117, 129, 125, 127, 119, #' 123, 139, 166, 124, 131, 217) # x-values of the nodes you want to highlight #' fill_colors <- c("#CD6600", "#CD6600", "#CD6600", #' "#CD6600", "#009933", "#009933", #' "#009933", "#009933", "#9B30FF", #' "#9B30FF", "#9B30FF") # Fill colors #' alpha_values <- c(0.3, 0.3, 0.3, 0.3, 0.2, 0.3, #' 0.3, 0.3, 0.3, 0.3, 0.3) # Transparency values #' extend_values <- c(25, 24, 24, 25, 25, 25, #' 24, 24, 25, 24, 24) # Values for the 'extend' parameter #' #' p2 <- highlight_by_node( #' p2_plot, #' nodes, #' fill_colors, #' alpha_values, #' extend_values #' ) highlight_by_node <- function(ggtree_object, nodes, fill_colors, alpha_values, extend_values) { # Ensure that the lengths of `nodes`, `fill_colors`, `alpha_values`, and `extend_values` are consistent if (!(length(nodes) == length(fill_colors) && length(nodes) == length(alpha_values) && length(nodes) == length(extend_values))) { stop("Length of nodes, fill_colors, alpha_values, and extend_values must be the same.") } if (!requireNamespace("systemfonts", quietly = TRUE)) { stop("ggplot2 is required to use the function. Please install it.", call. = FALSE) } # For each node, add the corresponding geom_hilight layer layers <- lapply(seq_along(nodes), function(i) { node <- nodes[i] fill_color <- fill_colors[i] alpha_val <- alpha_values[i] extend_val <- extend_values[i] # Create a geom_hilight layer ggtree::geom_hilight(node = node, fill = fill_color, alpha = alpha_val, extend = extend_val) }) # Add all layers to the `ggtree` object for (layer in layers) { ggtree_object <- ggtree_object + layer } return(ggtree_object) } ================================================ FILE: R/HighlightGenes.R ================================================ #' Add Highlights for Genes on a Phylogenetic Tree #' #' This function adds highlights for specified genes on a phylogenetic tree object. #' #' @importFrom ggtree geom_point2 geom_hilight aes #' @importFrom dplyr filter select pull #' @importFrom rlang .data #' @param ggtree_obj A ggtree object representing the phylogenetic tree. #' @param genes_to_highlight A data frame containing gene names and corresponding colors to highlight. #' @param hilight_extend Numeric value indicating the extension length for highlights. #' @return A `ggtree` object with added highlights for specified genes. #' @export #' #' @examples #' plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR") #' p2_plot <- readRDS(plot_file) #' #' selected_genes_deseq2_file <- system.file("extdata", #' "selected_genes_deseq2.rds", #' package = "TransProR") #' selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file) #' #' Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR") #' Diff_deseq2 <- readRDS(Diff_deseq2_file) #' #' result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746") #' #' add_gene_highlights_p3 <- highlight_genes(p2_plot, result_deseq2, hilight_extend = 26) highlight_genes <- function(ggtree_obj, genes_to_highlight, hilight_extend = 18) { # Ensure that the first argument is a `ggtree` object if (!inherits(ggtree_obj, "ggtree")) { stop("The first argument must be a ggtree object.") } # Ensure that the second argument is a data frame if (!("data.frame" %in% class(genes_to_highlight))) { stop("The second argument must be a data frame.") } if (!requireNamespace("systemfonts", quietly = TRUE)) { stop("ggplot2 is required to use the function. Please install it.", call. = FALSE) } # Extract the data from the tree object and ensure it is a data frame tree_data <- as.data.frame(ggtree_obj$data) # Map gene names and colors to nodes in the tree and create geom_hilight and geom_point2 layers for each node highlight_commands <- lapply(1:nrow(genes_to_highlight), function(i) { gene <- genes_to_highlight$Symble[i] color <- genes_to_highlight$color[i] node <- dplyr::filter(tree_data, .data$label == gene) %>% dplyr::select(node) %>% dplyr::pull() if (!is.na(node)) { list( ggtree::geom_hilight(node = node, fill = color, alpha = 0.3, extend = hilight_extend), ggtree::geom_point2(ggtree::aes(subset = (.data$label == gene)), color = color, size = 2, alpha = 0.6) ) } else { warning(paste("Gene", gene, "not found in the ggtree object.")) NULL } }) # Remove NULL elements from `highlight_commands` since they may exist highlight_commands <- Filter(Negate(is.null), highlight_commands) # Apply the commands to the `ggtree object` ggtree_obj <- ggtree_obj + do.call(c, highlight_commands) return(ggtree_obj) } ================================================ FILE: R/LimmaAnalyze.R ================================================ #' Differential Gene Expression Analysis using limma and voom #' #' This function performs differential gene expression analysis using the 'limma' package with voom normalization. #' It reads tumor and normal expression data, merges them, filters low-expressed genes, #' normalizes the data, performs limma analysis, and outputs the results along with information #' on gene expression changes. #' #' @importFrom limma lmFit contrasts.fit eBayes topTable #' @importFrom edgeR DGEList filterByExpr calcNormFactors #' @importFrom dplyr mutate #' @importFrom stats na.omit #' @param tumor_file Path to the tumor data file (RDS format). #' @param normal_file Path to the normal data file (RDS format). #' @param output_file Path to save the output DEG data (RDS format). #' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes. #' @param p_value_threshold Threshold for p-value for filtering significant genes. #' @return A data frame of differential expression results. #' @references #' limma:Linear Models for Microarray and RNA-Seq Data User’s Guide. #' For more information, visit the page: #' https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf #' @export #' #' @examples #' # Define file paths for tumor and normal data from the data folder #' tumor_file <- system.file("extdata", #' "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' normal_file <- system.file("extdata", #' "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", #' package = "TransProR") #' output_file <- file.path(tempdir(), "DEG_limma_voom.rds") #' #' DEG_limma_voom <- limma_analyze( #' tumor_file = tumor_file, #' normal_file = normal_file, #' output_file = output_file, #' logFC_threshold = 2.5, #' p_value_threshold = 0.01 #' ) #' #' # View the top 5 rows of the result #' head(DEG_limma_voom, 5) limma_analyze <- function(tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01) { tumor <- readRDS(tumor_file) normal <- readRDS(normal_file) # Merge the datasets, ensuring both have genes as row names all_count_exp <- merge(tumor, normal, by = "row.names") all_count_exp <- tibble::column_to_rownames(all_count_exp, var = "Row.names") # Define groups for tumor and normal samples group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal))) group <- factor(group, levels = c("normal", "tumor")) group_table <- table(group) message("Group Table:") message(paste(names(group_table), group_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Create matrix design <- model.matrix(~0 + factor(group)) colnames(design) <- levels(factor(group)) rownames(design) <- colnames(all_count_exp) # Create DGEList object for gene expression data and group information dge <- edgeR::DGEList(counts = all_count_exp, group = group) # Filter lowly expressed genes keep <- edgeR::filterByExpr(dge) dge <- dge[keep, , keep.lib.sizes = FALSE] # 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. # Normalize the data using the TMM method dge <- edgeR::calcNormFactors(dge) # Use voom method for normalization:Quantile Normalization v <- limma::voom(dge, design, plot = FALSE, normalize = "quantile") # Fit the linear model fit <- limma::lmFit(v, design) # Specify contrast con <- paste(rev(levels(group)), collapse = "-") # Create contrast matrix cont.matrix <- limma::makeContrasts(contrasts = c(con), levels = design) fit2 <- limma::contrasts.fit(fit, cont.matrix) fit2 <- limma::eBayes(fit2) # Get differential expression results tempOutput <- limma::topTable(fit2, coef = con, n = Inf) DEG_limma_voom <- stats::na.omit(tempOutput) # Add 'change' column to mark up/down-regulated genes k1 <- (DEG_limma_voom$P.Value < p_value_threshold) & (DEG_limma_voom$logFC < -logFC_threshold) k2 <- (DEG_limma_voom$P.Value < p_value_threshold) & (DEG_limma_voom$logFC > logFC_threshold) DEG_limma_voom <- dplyr::mutate(DEG_limma_voom, change = ifelse(k1, "down", ifelse(k2, "up", "stable"))) change_table <- table(DEG_limma_voom$change) message("Change Table:") message(paste(names(change_table), change_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Save results to the specified output file #save(DEG_limma_voom, file = output_file) saveRDS(DEG_limma_voom, file = output_file) return(DEG_limma_voom) } ================================================ FILE: R/LogTransform.R ================================================ #' Log transformation decision and application on data #' #' This function evaluates the need for a log transformation based on a set of criteria #' and applies a log2 transformation if necessary. #' #' @author Dongyue Yu #' @param data A numeric matrix or data frame. #' @return The original data or the data transformed with log2. #' @importFrom stats quantile #' @export #' @examples #' file_path <- system.file("extdata", #' "all_count_exp_test.csv", #' package = "TransProR") #' your_data <- read.csv(file_path, #' row.names = 1) # Assuming first column is row names (e.g., gene names) #' #' TransformedData <- log_transform(data = your_data) #' log_transform <- function(data) { # Calculate quantiles qx <- as.numeric(quantile(data, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=TRUE)) # Define conditions for log transformation LogC <- (qx[5] > 100) || (qx[6]-qx[1] > 50 && qx[2] > 0) || (qx[2] > 0 && qx[2] < 1 && qx[4] > 1 && qx[4] < 2) # Apply log transformation based on conditions if (LogC) { # Apply +1 to all values before log2 transformation result <- log2(data + 1) message("log2 transform finished") } else { result <- data message("log2 transform not needed") } return(result) } ================================================ FILE: R/MergeDensityFoldchange.R ================================================ #' Create high-density region plot with optional points, density rugs, and contours #' #' This function creates a high-density region plot using hdr methods to #' add density rug and filled contours. It also adds a regression line #' and Pearson correlation label. Points can be added to the plot optionally. #' #' @param data Data frame containing variables for plotting. #' @param x_var Name of the x-axis variable as a string. #' @param y_var Name of the y-axis variable as a string. #' @param group_var Name of the grouping variable for color mapping as a string. #' @param palette Color palette for the plot as a character vector. #' @param show_points Logical, if TRUE adds points to the plot. #' @param point_size Size of the points, relevant if show_points is TRUE. #' @param point_alpha Transparency level of the points, relevant if show_points is TRUE. #' @param x_lim Numeric vector of length 2, giving the x-axis limits. #' @param y_lim Numeric vector of length 2, giving the y-axis limits. #' @param cor_method Method to calculate correlation ("pearson" or "spearman"). #' @param line_size Size of the smoothing line. #' @param cor_label_pos Vector of length 2 indicating the position of the correlation label (x and y). #' @return A ggplot object representing the high-density region plot. #' @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 #' @importFrom hrbrthemes theme_ipsum #' @importFrom grid unit #' @importFrom ggdensity geom_hdr geom_hdr_rug #' @importFrom ggpubr stat_cor #' @examples #' combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR") #' combined_df <- readRDS(combined_df_file) #' pal1 = c("#3949ab","#1e88e5","#039be5","#00897b","#43a047","#7cb342") #' #' all_density_foldchange_name1 <- merge_density_foldchange( #' data = combined_df, #' x_var = "log2FoldChange_1", #' y_var = "log2FoldChange_2", #' group_var = "name", #' palette = pal1, #' show_points = FALSE, #' point_size = 2.5, #' point_alpha = 0.1, #' x_lim = c(0, 20), #' y_lim = c(0, 20), #' cor_method = "pearson", #' line_size = 1.6, #' cor_label_pos = c("left", "top") #' ) #' #' @export merge_density_foldchange <- function(data, x_var, y_var, group_var, palette = c("#3949ab","#1e88e5","#039be5","#00897b","#43a047","#7cb342"), show_points = FALSE, point_size = 2.5, point_alpha = 0.2, x_lim = c(0, 20), y_lim = c(0, 20), cor_method = "pearson", line_size = 1.6, cor_label_pos = c("left", 0.97)) { # Begin constructing the ggplot plot <- ggplot2::ggplot(data, ggplot2::aes_string(x = x_var, y = y_var, fill = group_var)) # Optionally add points if (show_points) { plot <- plot + ggplot2::geom_point(ggplot2::aes_string(color = group_var), shape = 21, size = point_size, alpha = point_alpha) } # Add density rug and contours plot <- plot + ggdensity::geom_hdr_rug() + ggdensity::geom_hdr() # Add regression line and correlation label plot <- plot + ggplot2::geom_smooth(ggplot2::aes_string(x = x_var, y = y_var, color = group_var), method = 'lm', level = 0.95, size = line_size) + 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]) # Customize scales and theme plot <- plot + ggplot2::scale_fill_manual(values = palette) + ggplot2::scale_color_manual(values = palette) + ggplot2::scale_x_continuous(limits = x_lim, expand = c(0, 0)) + ggplot2::scale_y_continuous(limits = y_lim, expand = c(0, 0)) + hrbrthemes::theme_ipsum() + ggplot2::theme(plot.margin = ggplot2::margin(10, 10, 10, 10), plot.background = ggplot2::element_rect(fill = "white", color = "white"), panel.spacing = grid::unit(2, "mm")) # Return the ggplot object return(plot) } ================================================ FILE: R/MergeGtexTcga.R ================================================ #' Merge gene expression data from GTEx and TCGA datasets #' #' This function merges gene expression data obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) datasets. #' 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. #' #' @param gtex_data_path A string that specifies the file path to the GTEx data saved in RDS format. #' @param tcga_exp_path A string that specifies the file path to the TCGA expression data saved in RDS format. #' This should be a data.frame with rows as genes and columns as samples. #' @param output_path A string that specifies the path where the merged dataset should be saved. #' The file is saved in '.rds' format. The default path is "./merged_gtex_tcga_data.rds". #' #' @details It is assumed that both datasets are in '.rds' format and have genes as row names. #' #' @return A data frame where rows represent genes and columns represent samples. #' The data frame contains expression values from both GTEx and TCGA datasets. #' It saves the merged dataset to the path specified by 'output_path'. #' #' @examples #' tumor_file <- system.file("extdata", #' "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' Normal_file <- system.file("extdata", #' "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", #' package = "TransProR") #' ouput_file <- file.path(tempdir(), "all_data.rds") #' #' all_data <- merge_gtex_tcga(gtex_data_path = tumor_file, #' tcga_exp_path = Normal_file, #' output_path = ouput_file) #' #' @note CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended #' 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'. #' #' @importFrom tibble column_to_rownames #' @export merge_gtex_tcga <- function(gtex_data_path, tcga_exp_path, output_path = "./merged_gtex_tcga_data.rds") { # Load the GTEx data gtex_data <- readRDS(gtex_data_path) message("Number of GTEx samples:", ncol(gtex_data), "\n") # Load the TCGA data tcga.exp <- readRDS(tcga_exp_path) message("Number of TCGA samples:", ncol(tcga.exp), "\n") # Merge the datasets, ensuring both have genes as row names all_data <- merge(gtex_data, tcga.exp, by = "row.names") all_data <- tibble::column_to_rownames(all_data, var = "Row.names") # Set the row names message("Number of samples after merging:", ncol(all_data), "\n") # Save the merged dataset saveRDS(all_data, file = output_path) return(all_data) } ================================================ FILE: R/MergeIDPosition.R ================================================ #' Merge Data Frames by Common Row Names with Additional Columns #' #' 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. #' #' @param df_list A list of data frames, each with a 'Description' and 'Count' column and set row names. #' @return A single data frame merged from the list, with additional 'id' and 'point_position' columns. #' @importFrom dplyr arrange #' @examples #' df1 <- data.frame(Description = c("DataA", "DataB"), Count = c(10, 20), row.names = c("R1", "R2")) #' df2 <- data.frame(Description = c("DataC", "DataD"), Count = c(30, 40), row.names = c("R1", "R3")) #' df_list <- list(df1, df2) #' combined_df_test <- merge_id_position(df_list) #' #' @export merge_id_position <- function(df_list) { # Find common row names across all data frames common_row_names <- Reduce(intersect, lapply(df_list, row.names)) # Get the maximum value of the 'Count' column across all data frames max_count <- max(sapply(df_list, function(df) max(as.numeric(df$Count), na.rm = TRUE))) # Calculate the smallest multiple of 10 greater than the maximum count ceiling_max_count <- ceiling(max_count / 10) * 10 # Initialize a list to store processed data frames processed_dfs <- list() number_of_dfs <- length(df_list) # Process each data frame for (i in seq_along(df_list)) { # Filter for common rows filtered_df <- df_list[[i]][common_row_names, , drop = FALSE] # Sort 'Description' column by character length in descending order filtered_df <- filtered_df %>% dplyr::arrange(desc(nchar(.data$Description))) # Add 'id' column filtered_df$id <- seq_len(nrow(filtered_df)) # Calculate 'point_position' value point_position_value <- ceiling_max_count * i / number_of_dfs # Add 'point_position' column filtered_df$point_position <- point_position_value # Append processed data frame to the list processed_dfs[[i]] <- filtered_df } # Check if any data frames are empty after filtering if (any(sapply(processed_dfs, nrow) == 0)) { stop("One or more dataframes have no rows after filtering for common row names.") } # Merge data frames by rows combined_df <- do.call(rbind, processed_dfs) # Remove original row names and set new row names as NULL rownames(combined_df) <- NULL return(combined_df) } ================================================ FILE: R/MergeMethodColor.R ================================================ #' Merge Data Frames with Specific Method and Color Columns #' #' This function takes a list of data frames, a method name, and a list of colors. #' It adds a 'method' column and a 'test_color' column to each data frame, then merges all data frames by rows. #' It ensures that the color list length matches the list of data frames. #' #' @param df_list A list of data frames, each containing at least 'Description' and 'Count' columns. #' @param method_name A string representing the method name to be added to each data frame. #' @param color_list A list of colors corresponding to each data frame for the 'test_color' column. #' @return A single data frame merged from the list, with each originally provided data frame now having a 'method' and a 'test_color' column. #' @importFrom dplyr bind_rows #' @examples #' df1 <- data.frame(Description = c("A", "B"), Count = c(10, 20)) #' df2 <- data.frame(Description = c("C", "D"), Count = c(30, 40)) #' df_list <- list(df1, df2) #' method_name <- "Method1" #' color_list <- c("Red", "Blue") #' combined_df_test <- merge_method_color(df_list, method_name, color_list) #' #' @export merge_method_color <- function(df_list, method_name, color_list) { # Validate the length of color list matches the length of data frame list if (length(color_list) != length(df_list)) { stop("The length of the color list must match the length of the data frame list") } # Initialize a list to store processed data frames processed_dfs <- list() # Iterate over all data frames for (i in seq_along(df_list)) { # Extract 'Description' and 'Count' columns temp_df <- df_list[[i]][, c("Description", "Count"), drop = FALSE] # Add 'method' column temp_df$method <- method_name # Add 'test_color' column using corresponding color temp_df$test_color <- color_list[i] # Append the processed data frame to the list processed_dfs[[i]] <- temp_df } # Combine all data frames by rows using bind_rows from dplyr combined_df <- dplyr::bind_rows(processed_dfs) return(combined_df) } ================================================ FILE: R/NewGgraph.R ================================================ #' Generate a graphical representation of pathway gene maps #' #' This function merges multiple gene-pathway related dataframes, processes them #' for graph creation, and visualizes the relationships in a dendrogram layout using #' the provided node and edge gathering functions from the 'ggraph' package. #' #' @param BP_dataframe Dataframe for Biological Process. #' @param BP_ids IDs for Biological Process. #' @param KEGG_dataframe Dataframe for KEGG pathways. #' @param KEGG_ids IDs for KEGG pathways. #' @param MF_dataframe Dataframe for Molecular Function. #' @param MF_ids IDs for Molecular Function. #' @param REACTOME_dataframe Dataframe for REACTOME pathways. #' @param REACTOME_ids IDs for REACTOME pathways. #' @param CC_dataframe Dataframe for Cellular Component. #' @param CC_ids IDs for Cellular Component. #' @param DO_dataframe Dataframe for Disease Ontology. #' @param DO_ids IDs for Disease Ontology. #' @importFrom tidygraph tbl_graph #' @importFrom ggraph ggraph geom_edge_diagonal geom_node_point geom_node_text scale_edge_colour_brewer node_angle #' @importFrom ggplot2 theme element_rect scale_size scale_color_brewer coord_cartesian #' @return A 'ggraph' object representing the pathway gene map visualization. #' @export #' new_ggraph <- 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) { new_dataframe <- gene_map_pathway(BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids, MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids, CC_dataframe, CC_ids, DO_dataframe, DO_ids) # Prepare the data for graph creation using 'ggraph' index_ggraph <- c("type", "pathway", "gene") # columns other than the lowest level nodes_ggraph <- gather_graph_node(new_dataframe, index = index_ggraph, root = "combination") edges_ggraph <- gather_graph_edge(new_dataframe, index = index_ggraph, root = "combination") # Create and plot the graph using 'tidygraph' and 'ggraph' graph_ggraph <- tidygraph::tbl_graph(nodes = nodes_ggraph, edges = edges_ggraph) plot <- ggraph::ggraph(graph_ggraph, layout = 'dendrogram', circular = TRUE) + ggraph::geom_edge_diagonal(aes(color = .data$node1.node.branch, filter = .data$node1.node.level != "combination", alpha = .data$node1.node.level), edge_width = 1) + ggraph::geom_node_point(aes(size = .data$node.size, color = .data$node.branch, filter = .data$node.level != "combination"), alpha = 0.45) + ggplot2::scale_size(range = c(15, 90)) + ggplot2::theme(legend.position = "none") + ggraph::scale_edge_colour_brewer(palette= "Dark2") + ggplot2::scale_color_brewer(palette = "Dark2") + 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$node.branch), size = 4, hjust = 'outward') + ggraph::geom_node_text(aes(label = .data$node.short_name, filter = !.data$leaf & (.data$node.level == "type"), color = .data$node.branch), fontface = "bold", size = 8, family = "sans") + ggraph::geom_node_text(aes(label = .data$node.short_name, filter = !.data$leaf & (.data$node.level == "pathway"), color = .data$node.branch, angle = -((-ggraph::node_angle(.data$x, .data$y) + 90) %% 180) + 36), fontface = "bold", size = 4.5, family = "sans") + ggplot2::theme(panel.background = ggplot2::element_rect(fill = NA)) + ggplot2::coord_cartesian(xlim = c(-1.3, 1.3), ylim = c(-1.3, 1.3)) return(plot) } ================================================ FILE: R/PathwayCount.R ================================================ #' Count Genes Present in Pathways Above a Threshold #' #' This function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways. #' #' @importFrom dplyr filter #' @importFrom rlang .data #' @param GO A character vector of gene symbols. #' @param count_threshold An integer specifying the count threshold for selecting pathways. #' @param enrich_data A data frame containing pathway enrichment analysis results. #' @return A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise). #' @export #' #' @examples #' # Simulated gene list #' GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5") #' # Simulated enrichment analysis data #' enrich_data <- data.frame( #' ID = c("GO:0001", "GO:0002", "GO:0003"), #' Description = c("Pathway A", "Pathway B", "Pathway C"), #' Count = c(10, 4, 6), #' geneID = c("Gene1/Gene2/Gene3", "Gene4/Gene5", "Gene2/Gene6/Gene7") #' ) #' #' # Example usage #' count_threshold <- 5 #' result_df <- pathway_count(GO, count_threshold, enrich_data) #' pathway_count <- function(GO, count_threshold, enrich_data) { # Filter pathways meeting the count threshold selected_pathways <- enrich_data %>% dplyr::filter(.data$Count > count_threshold) final_df <- data.frame(Symble = character(), Description = character(), Exists = integer()) # Iterate through each selected pathway for (i in 1:nrow(selected_pathways)) { pathway_info <- selected_pathways[i, ] genes_in_pathway <- unlist(strsplit(as.character(pathway_info$geneID), "/")) # Create a record for each gene in the current pathway for (gene in GO) { final_df <- rbind(final_df, data.frame( Symble = gene, Description = pathway_info$Description, Exists = as.integer(gene %in% genes_in_pathway) )) } } return(final_df) } ================================================ FILE: R/PathwayDescription.R ================================================ #' Describe Genes Present in Selected Pathways #' #' This function identifies genes present in selected pathways based on gene enrichment analysis results. #' #' @importFrom dplyr filter #' @importFrom rlang .data #' @param GO A character vector of gene symbols. #' @param selected_pathways_names A character vector specifying the names of selected pathways. #' @param enrich_data A data frame containing pathway enrichment analysis results. #' @return A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise). #' @export #' #' @examples #' GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5") #' # Simulated enrichment analysis data #' enrich_data <- data.frame( #' ID = c("Pathway1", "Pathway2", "Pathway3", "Pathway4"), #' Description = c("Apoptosis", "Cell Cycle", "Signal Transduction", "Metabolism"), #' geneID = c("Gene1/Gene3", "Gene2/Gene4", "Gene1/Gene2/Gene3", "Gene5"), #' Count = c(2, 2, 3, 1), #' stringsAsFactors = FALSE #' ) #' #' # Example usage #' result <- pathway_description(GO, #' selected_pathways_names="Apoptosis", #' enrich_data) #' pathway_description <- function(GO, selected_pathways_names, enrich_data) { # Filter selected pathways selected_pathways <- dplyr::filter(enrich_data, .data$Description %in% selected_pathways_names) final_df <- data.frame(Symble = character(), Description = character(), Exists = integer()) # Iterate through each selected pathway for (i in 1:nrow(selected_pathways)) { pathway_info <- selected_pathways[i, ] genes_in_pathway <- unlist(strsplit(as.character(pathway_info$geneID), "/")) # Create a record for each gene in the current pathway for (gene in GO) { final_df <- rbind(final_df, data.frame( Symble = gene, Description = pathway_info$Description, Exists = as.integer(gene %in% genes_in_pathway) )) } } return(final_df) } ================================================ FILE: R/PrepDeseq2.R ================================================ #' Prepare DESeq2 data for plotting #' #' This function reads a DESeq2 DEG data frame from an RDS file, filters it, #' adjusts the log2FoldChange to absolute values, adds a pseudo-count to pvalues, #' and transforms pvalues for plotting. The final data frame is returned and #' optionally saved to a new RDS file. #' #' @param input_path Path to the RDS file containing the DESeq2 DEG data frame. #' @param output_name Name for the processed data frame, also used as the RDS file name. #' @return A data frame with processed DESeq2 DEG data. #' @export #' @examples #' deseq2_file <- system.file("extdata", #' "DEG_deseq2_test.rds", #' package = "TransProR") #' deseq2 <- prep_deseq2(deseq2_file) #' prep_deseq2 <- function(input_path, output_name = NULL) { # Read the DESeq2 DEG data frame from an RDS file DEG_deseq2 <- readRDS(input_path) # Filter DEG data using the deg_filter function from the same package DESeq2 <- deg_filter(DEG_deseq2) DEG_deseq2 <- DEG_deseq2[rownames(DEG_deseq2) %in% DESeq2, ] # Extract the Gene column as a regular column DEG_deseq2$Gene <- rownames(DEG_deseq2) # Select columns of interest DEG_deseq2 <- DEG_deseq2[, c('log2FoldChange', 'pvalue', "change", 'Gene')] # Adjust log2FoldChange values to absolute values DEG_deseq2$log2FoldChange <- abs(DEG_deseq2$log2FoldChange) # Add a small pseudo-count to pvalue to avoid log of zero DEG_deseq2$pvalue <- DEG_deseq2$pvalue + .Machine$double.eps # Transform p-value for plotting DEG_deseq2$pvalue <- -log10(DEG_deseq2$pvalue) # Rename columns names(DEG_deseq2) <- c('logFC', 'Pvalue', "change", 'Gene') # Optionally save the processed data frame as an RDS file if (!is.null(output_name)) { saveRDS(DEG_deseq2, paste0(output_name, ".Rdata")) } # Return the processed data frame return(DEG_deseq2) } ================================================ FILE: R/PrepEdgeR.R ================================================ #' Prepare edgeR DEG data for plotting #' #' This function reads an edgeR DEG data frame from an RDS file, filters it using #' \code{\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to PValue, #' and transforms PValue for plotting. The final data frame is returned and #' optionally saved to a new RDS file. #' #' @param input_path Path to the RDS file containing the edgeR DEG data frame. #' @param output_name Name for the processed data frame, also used as the RDS file name. #' @return A data frame with processed edgeR DEG data. #' @export #' @examples #' edgeR_file <- system.file("extdata", #' "DEG_edgeR_test.rds", #' package = "TransProR") #' edgeR <- prep_edgeR(edgeR_file) #' prep_edgeR <- function(input_path, output_name = NULL) { # Read the edgeR DEG data frame from an RDS file DEG_edgeR <- readRDS(input_path) # Filter DEG data using the deg_filter function from the same package edgeR <- deg_filter(DEG_edgeR) DEG_edgeR <- DEG_edgeR[rownames(DEG_edgeR) %in% edgeR, ] # Select columns of interest and adjust logFC values to absolute values DEG_edgeR <- DEG_edgeR[, c('logFC', "PValue", "change")] DEG_edgeR$logFC <- abs(DEG_edgeR$logFC) # Add a small pseudo-count to PValue to avoid log of zero and transform PValue for plotting DEG_edgeR$PValue <- DEG_edgeR$PValue + .Machine$double.eps DEG_edgeR$PValue <- -log10(DEG_edgeR$PValue) # Extract the Gene column from row names DEG_edgeR$Gene <- rownames(DEG_edgeR) # Rename columns names(DEG_edgeR) <- c('logFC', 'Pvalue', "change", 'Gene') # Optionally save the processed data frame as an RDS file if (!is.null(output_name)) { saveRDS(DEG_edgeR, paste0(output_name, ".Rdata")) } # Return the processed data frame return(DEG_edgeR) } ================================================ FILE: R/PrepLimma.R ================================================ #' Prepare limma-voom DEG data for plotting #' #' This function reads a limma-voom DEG data frame from an RDS file, filters it using #' \code{\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value, #' and transforms P.Value for plotting. The final data frame is returned and #' optionally saved to a new RDS file. #' #' @param input_path Path to the RDS file containing the limma-voom DEG data frame. #' @param output_name Name for the processed data frame, also used as the RDS file name. #' @return A data frame with processed limma-voom DEG data. #' @export #' @examples #' limma_file <- system.file("extdata", #' "DEG_limma_voom_test.rds", #' package = "TransProR") #' limma <- prep_limma(limma_file) #' prep_limma <- function(input_path, output_name = NULL) { # Read the limma-voom DEG data frame from an RDS file limma <- readRDS(input_path) # Filter DEG data using the deg_filter function from the same package limma_filter <- deg_filter(limma) limma <- limma[rownames(limma) %in% limma_filter, ] # Select columns of interest and adjust logFC values to absolute values limma <- limma[, c('logFC', "P.Value", "change")] limma$logFC <- abs(limma$logFC) # Add a small pseudo-count to P.Value to avoid log of zero and transform P.Value for plotting limma$P.Value <- limma$P.Value + .Machine$double.eps limma$P.Value <- -log10(limma$P.Value) # Extract the Gene column from row names limma$Gene <- rownames(limma) # Rename columns names(limma) <- c('logFC', 'Pvalue', "change", 'Gene') # Optionally save the processed data frame as an RDS file if (!is.null(output_name)) { saveRDS(limma, paste0(output_name, ".Rdata")) } # Return the processed data frame return(limma) } ================================================ FILE: R/PrepWilcoxon.R ================================================ #' Prepare Wilcoxon DEG data for plotting #' #' This function reads a Wilcoxon DEG data frame from an RDS file, filters it using #' \code{\link{deg_filter}} function, adjusts the log2foldChange to absolute values, adds a pseudo-count to pValues, #' and transforms pValues for plotting. The final data frame is returned and #' optionally saved to a new RDS file. #' #' @param input_path Path to the RDS file containing the Wilcoxon DEG data frame. #' @param output_name Optional; name for the processed data frame, also used as the RDS file name. #' If not provided, the data frame will not be saved to file. #' @return A data frame with processed Wilcoxon DEG data. #' @export #' @examples #' wilcoxon_file <- system.file("extdata", #' "Wilcoxon_rank_sum_testoutRst_test.rds", #' package = "TransProR") #' Wilcoxon <- prep_wilcoxon(wilcoxon_file) #' prep_wilcoxon <- function(input_path, output_name = NULL) { # Read the Wilcoxon DEG data frame from an RDS file Wilcoxon <- readRDS(input_path) # Filter DEG data using the deg_filter function from the same package Wilcoxon_filter <- deg_filter(Wilcoxon) Wilcoxon <- Wilcoxon[rownames(Wilcoxon) %in% Wilcoxon_filter, ] # Select columns of interest and adjust log2foldChange values to absolute values Wilcoxon <- Wilcoxon[, c('log2foldChange', "pValues", "change")] Wilcoxon$log2foldChange <- abs(Wilcoxon$log2foldChange) # Add a small pseudo-count to pValues to avoid log of zero and transform pValues for plotting Wilcoxon$pValues <- Wilcoxon$pValues + .Machine$double.eps Wilcoxon$pValues <- -log10(Wilcoxon$pValues) # Extract the Gene column from row names Wilcoxon$Gene <- rownames(Wilcoxon) # Rename columns names(Wilcoxon) <- c('logFC', 'Pvalue', "change", 'Gene') # Optionally save the processed data frame as an RDS file if (!is.null(output_name) && nzchar(output_name)) { saveRDS(Wilcoxon, paste0(output_name, ".Rdata")) } # Return the processed data frame return(Wilcoxon) } ================================================ FILE: R/ProcessHeatdata.R ================================================ #' Process Heatmap Data with Various Selection Options #' #' This function processes heatmap data (`heatdata`) based on a given selection option. #' It allows customization of column names, selection of specific columns per group, #' or averaging columns based on a common prefix. #' #' @param heatdata A data frame containing the heatmap data. #' @param selection An integer specifying the processing method: #' - 1: Use custom names for columns. #' - 2: Select a given number of columns per group based on a prefix. #' - 3: Calculate the average of columns per group based on a prefix. #' @param custom_names A character vector of custom names for columns (used when `selection = 1`). #' The length of this vector must match the number of columns in `heatdata`. #' @param num_names_per_group An integer specifying the number of columns to select per group (used when `selection = 2`). #' @param prefix_length An integer specifying the length of the prefix for grouping columns (used when `selection = 2` or `selection = 3`). #' Default is 4. #' @return A processed data frame based on the specified selection option. #' @export #' #' @examples #' # Example heatmap data frame #' heatdata <- data.frame( #' groupA_1 = c(1, 2, 3), #' groupA_2 = c(4, 5, 6), #' groupB_1 = c(7, 8, 9), #' groupB_2 = c(10, 11, 12) #' ) #' #' # Selection 1: Use custom names for columns #' custom_names <- c("Sample1", "Sample2", "Sample3", "Sample4") #' processed_data1 <- process_heatdata(heatdata, selection = 1, custom_names = custom_names) #' #' # Selection 2: Select a given number of columns per group based on a prefix #' processed_data2 <- process_heatdata(heatdata, selection = 2, num_names_per_group = 1) #' #' # Selection 3: Calculate the average of columns per group based on a prefix #' processed_data3 <- process_heatdata(heatdata, selection = 3, prefix_length = 6) process_heatdata <- function(heatdata, selection = 1, custom_names = NULL, num_names_per_group = NULL, prefix_length = 4) { if (selection == 1) { # Option 1: Use custom names for columns if (length(custom_names) != ncol(heatdata)) { stop("Length of custom_names must match number of columns in heatdata") } names(heatdata) <- custom_names } else if (selection == 2) { # Option 2: Select a given number of columns per group based on a prefix group_names <- unique(substr(names(heatdata), 1, prefix_length)) # Get unique group names based on prefix selected_columns <- integer(0) new_names <- character(0) for (group in group_names) { group_cols <- grep(group, names(heatdata)) # Find columns for each group num_selected <- min(length(group_cols), num_names_per_group) selected_columns <- c(selected_columns, sample(group_cols, num_selected)) # Generate new names for selected columns new_group_names <- paste(group, seq_len(num_selected), sep = "_") new_names <- c(new_names, new_group_names) } heatdata <- heatdata[, selected_columns, drop = FALSE] # Keep only selected columns names(heatdata) <- new_names } else if (selection == 3) { # Option 3: Calculate the average of columns per group based on a prefix group_names <- unique(substr(names(heatdata), 1, prefix_length)) # Create a list to collect mean values for each group mean_list <- list() # Calculate mean values for each group and store them in the list for (group in group_names) { group_cols <- grep(group, names(heatdata), value = TRUE) mean_list[[paste(group, "mean", sep = "_")]] <- rowMeans(heatdata[, group_cols], na.rm = TRUE) } # Convert list to data frame and set row names heatdata <- as.data.frame(do.call(cbind, mean_list)) rownames(heatdata) <- rownames(heatdata) } else { stop("Invalid selection parameter") } return(heatdata) } ================================================ FILE: R/SeekGtexOrgan.R ================================================ #' Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts #' #' This function reads the GTEX phenotype data from a specified path, renames its columns for better readability, #' and then returns a table of primary site counts. #' #' @param path The path to the GTEX phenotype data file. Default is "./download_data/GTEX_phenotype". #' #' @return A table representing the count of samples per primary site. #' @importFrom utils read.table #' @examples #' # Get the file path to the example data in the package #' path <- system.file("extdata", "GTEX_phenotype_test", package = "TransProR") #' # Call the `seek_gtex_organ` function with the path and print the result #' SeekGtexOrgan <- seek_gtex_organ(path = path) #' #' @export seek_gtex_organ <- function(path = "./download_data/GTEX_phenotype") { # Read GTEX phenotype data # gtex.phe <- data.table::fread(path, header = TRUE, sep = '\t', data.table = FALSE) gtex.phe <- utils::read.table(path, header = TRUE, sep = '\t', stringsAsFactors = FALSE, check.names = FALSE) rownames(gtex.phe) <- gtex.phe$Sample # Rename columns colnames(gtex.phe) <- c("Sample", "body_site_detail (SMTSD)", "primary_site", "gender", "patient", "cohort") # Create table of primary sites primary_site_counts <- table(gtex.phe$primary_site) return(primary_site_counts) } ================================================ FILE: R/WilcoxonAnalyze.R ================================================ #' Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test #' #' This function performs differential gene expression analysis using Wilcoxon rank-sum tests. #' It reads tumor and normal expression data, performs TMM normalization using 'edgeR', and uses Wilcoxon rank-sum tests to identify differentially expressed genes. #' #' @importFrom tibble column_to_rownames #' @importFrom edgeR DGEList filterByExpr calcNormFactors cpm #' @importFrom dplyr mutate #' @importFrom stats wilcox.test p.adjust #' @param tumor_file Path to the tumor data file (RDS format). #' @param normal_file Path to the normal data file (RDS format). #' @param output_file Path to save the output DEG data (RDS format). #' @param logFC_threshold Threshold for log fold change for marking up/down-regulated genes. #' @param fdr_threshold Threshold for FDR for filtering significant genes. #' @return A data frame of differential expression results. #' @references #' Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022). Exaggerated False Positives by Popular #' Differential Expression Methods When Analyzing Human Population Samples. Genome Biology, 23(1), 79. #' DOI: https://doi.org/10.1186/s13059-022-02648-4. #' @export #' #' @examples #' # Define file paths for tumor and normal data from the data folder #' tumor_file <- system.file("extdata", #' "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", #' package = "TransProR") #' normal_file <- system.file("extdata", #' "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", #' package = "TransProR") #' output_file <- file.path(tempdir(), "Wilcoxon_rank_sum_testoutRst.rds") #' #' # Run the Wilcoxon rank sum test #' outRst <- Wilcoxon_analyze( #' tumor_file = tumor_file, #' normal_file = normal_file, #' output_file = output_file, #' logFC_threshold = 2.5, #' fdr_threshold = 0.01 #' ) #' #' # View the top 5 rows of the result #' head(outRst, 5) Wilcoxon_analyze <- function(tumor_file, normal_file, output_file, logFC_threshold = 2.5, fdr_threshold = 0.05) { # Read data tumor <- readRDS(tumor_file) normal <- readRDS(normal_file) # Merge the datasets and set row names all_count_exp <- merge(tumor, normal, by = "row.names") all_count_exp <- tibble::column_to_rownames(all_count_exp, var = "Row.names") # Define groups group <- c(rep('tumor', ncol(tumor)), rep('normal', ncol(normal))) group <- factor(group, levels = c("normal", "tumor")) group_table <- table(group) message("Group Table:") message(paste(names(group_table), group_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # EdgeR TMM normalization y <- edgeR::DGEList(counts = all_count_exp, group = group) keep <- edgeR::filterByExpr(y) y <- y[keep, keep.lib.sizes = FALSE] # Perform TMM normalization and transfer to CPM (Counts Per Million) y <- edgeR::calcNormFactors(y, method = "TMM") count_norm <- edgeR::cpm(y) count_norm <- as.data.frame(count_norm) # Wilcoxon rank-sum test for each gene pvalues <- sapply(1:nrow(count_norm), function(i) { data <- cbind.data.frame(gene = as.numeric(t(count_norm[i, ])), group) stats::wilcox.test(gene ~ group, data)$p.value }) fdr <- stats::p.adjust(pvalues, method = "fdr") # Calculate fold-change for each gene conditionsLevel <- levels(group) dataCon1 <- count_norm[, which(group == conditionsLevel[1])] dataCon2 <- count_norm[, which(group == conditionsLevel[2])] # 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. # It prevents the occurrence of negative infinity (-Inf) when the numerator is zero, and positive infinity (Inf) when the denominator is zero. foldChanges <- log2((rowMeans(dataCon2) + 0.005) / (rowMeans(dataCon1) + 0.005)) #foldChanges <- log2(rowMeans(dataCon2) / rowMeans(dataCon1)) # Output results based on FDR threshold outRst <- data.frame(log2foldChange = foldChanges, pValues = pvalues, FDR = fdr) rownames(outRst) <- rownames(count_norm) outRst <- na.omit(outRst) # Mark up/down-regulated genes k1 <- (outRst$FDR < fdr_threshold) & (outRst$log2foldChange < -logFC_threshold) k2 <- (outRst$FDR < fdr_threshold) & (outRst$log2foldChange > logFC_threshold) outRst <- dplyr::mutate(outRst, change = ifelse(k1, "down", ifelse(k2, "up", "stable"))) change_table <- table(outRst$change) message("Change Table:") message(paste(names(change_table), change_table, sep = ": ", collapse = "\n")) # Add a space after the output for separation message(" ") # Save results saveRDS(outRst, file = output_file) return(outRst) } ================================================ FILE: R/data.R ================================================ # 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, # 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. #' All DEGs Venn Diagram Data #' #' A dataset containing the differentially expressed genes (DEGs) from four different statistical analysis methods: DESeq2, edgeR, limma, and Wilcoxon test. #' This dataset is used for generating Venn diagrams to compare the overlap of DEGs identified by different methods. #' #' @format A list with the following components: #' \describe{ #' \item{DESeq2}{A vector of gene IDs or gene symbols identified as DEGs by the DESeq2 method.} #' \item{edgeR}{A vector of gene IDs or gene symbols identified as DEGs by the edgeR method.} #' \item{limma}{A vector of gene IDs or gene symbols identified as DEGs by the limma method. } #' \item{Wilcoxon_test}{A vector of gene IDs or gene symbols identified as DEGs by the Wilcoxon test method. } #' } #' #' @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). #' #' @usage data(all_degs_venn) #' #' @examples #' data(all_degs_venn) #' # Example of plotting a Venn diagram using the dataset #' #' edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39") #' name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39") #' fill_colors <- c("#e3f2fa", "#0288d1") #' #' Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors) #' #' @keywords datasets "all_degs_venn" #' Phylogenetic Tree Object #' #' A dataset containing a phylogenetic tree object created using the `ggtree` package. #' This tree represents the evolutionary relationships among a set of species or genes. #' #' @format A `ggtree` object. #' #' @source The phylogenetic tree was constructed based on sequence alignment data obtained from [Data Source, e.g., NCBI database, specific study, etc.]. #' #' @usage data(gtree) #' #' @keywords datasets phylogenetics "gtree" ================================================ FILE: R/utils-pipe.R ================================================ #' Pipe operator #' #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. #' #' @name %>% #' @rdname pipe #' @keywords internal #' @export #' @importFrom magrittr %>% #' @usage lhs \%>\% rhs #' @param lhs A value or the magrittr placeholder. #' @param rhs A function call using the magrittr semantics. #' @return The result of calling `rhs(lhs)`. NULL ================================================ FILE: R/zzz.R ================================================ .onAttach <- function(libname, pkgname) { # Display ASCII art ascii_art_path <- system.file("extdata", "ascii_art.txt", package = "TransProR") if (file.exists(ascii_art_path)) { ascii_art_lines <- readLines(ascii_art_path) ascii_art <- paste(ascii_art_lines, collapse = "\n") packageStartupMessage(ascii_art) } else { packageStartupMessage("Welcome to TransProR!") } } ================================================ FILE: README.md ================================================ # TransProR [![CRAN status](https://www.r-pkg.org/badges/version/TransProR)](https://cran.r-project.org/package=TransProR) TransProR Logo 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. ## Installation You can install the development version of TransProR like so: ``` r install.packages("devtools") devtools::install_github("SSSYDYSSS/TransProR", build_vignettes = TRUE) install.packages("remotes") remotes::install_github("SSSYDYSSS/TransProR", build_vignettes = TRUE) ``` ## More examples see TransProR Manual：https://sssydysss.github.io/TransProRBook/ ## System Requirements - R (>= 4.3.0) ## Example This is a basic example which shows you how to solve a common problem: ``` r library(TransProR) ## basic example code ``` ## Citation If you use TransPro in your research, please cite: Dongyue 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. **Correspondence:** Shengquan Chen — School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China. Wenjun Bu — Institute of Entomology, College of Life Sciences, Nankai University, Tianjin 300071, China. ## Code of Conduct Please 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" ================================================ FILE: TransProR.Rproj ================================================ Version: 1.0 RestoreWorkspace: Default SaveWorkspace: Default AlwaysSaveHistory: Default EnableCodeIndexing: Yes UseSpacesForTab: Yes NumSpacesForTab: 2 Encoding: UTF-8 RnwWeave: Sweave LaTeX: pdfLaTeX AutoAppendNewline: Yes StripTrailingWhitespace: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source ================================================ FILE: data-raw/MAKEDATA.R ================================================ # Data that the package will use, and is explorable by users: usethis::use_data(x) # This will create a file at data/x.Rda # Data that the package will use, and is not explorable by users, internal data: usethis::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. ================================================ FILE: dev/build.R ================================================ # pipe usethis::use_pipe(export = TRUE) ## code to prepare MAKEDATA.R usethis::use_data_raw("MAKEDATA") devtools::load_all() devtools::document() devtools::check() #BiocManager::install("BiocCheck") #BiocCheck::BiocCheck() # Ensure that all dependencies are correctly installed, and generate the pak lock file. pak::pak() pak::pak("jokergoo/ComplexHeatmap")# 单独安装 pak::lockfile_create()# for github action file.rename("pkg.lock", ".github/pkg.lock") # NEWS.md usethis::use_news_md() #生成一个标准模板。 ================================================ FILE: dev/dev.R ================================================ # dependency package usethis::use_package("stats") usethis::use_package("utils") usethis::use_package("DESeq2") usethis::use_package("BiocGenerics") usethis::use_package("ggplot2") usethis::use_package("ggpubr") usethis::use_package("tibble") # license usethis::use_mit_license("Dongyue Yu") # readme usethis::use_readme_md() # adds a Code of Conduct usethis::use_code_of_conduct(contact = "yudongyue@mail.nankai.edu.cn") # vignette usethis::use_vignette("a_example_workflow") # optional devtools::install(build_vignettes = TRUE) # optional ## a website # Run once to configure your package to use pkgdown usethis::use_pkgdown() # optional pkgdown::build_site() # optional ## add git usethis::use_git() usethis::use_github() # CI usethis::use_github_action_check_standard() # CRAN # Spell check devtools::spell_check() # Regular local tests devtools::check() # rhub cross-platform tests rhub::check_for_cran() # Windows platform-only test devtools::check_win_devel() # optional # rhub Windows platform-only test rhub::check_for_cran( platform="windows-x86_64-devel", env_vars=c(R_COMPILE_AND_INSTALL_PACKAGES = "always") ) # optional # Check before release devtools::release() ================================================ FILE: inst/extdata/GTEX_phenotype_test ================================================ Sample body_site_detail (SMTSD) _primary_site _gender _patient _cohort GTEX-1117F-0226-SM-5GZZ7 Adipose - Subcutaneous Adipose Tissue female GTEX-1117F GTEX GTEX-1117F-0426-SM-5EGHI Muscle - Skeletal Muscle female GTEX-1117F GTEX GTEX-1117F-0526-SM-5EGHJ Artery - Tibial Blood Vessel female GTEX-1117F GTEX GTEX-1117F-0626-SM-5N9CS Artery - Coronary Blood Vessel female GTEX-1117F GTEX GTEX-1117F-0726-SM-5GIEN Heart - Atrial Appendage Heart female GTEX-1117F GTEX GTEX-1117F-1326-SM-5EGHH Adipose - Visceral (Omentum) Adipose Tissue female GTEX-1117F GTEX GTEX-1117F-2226-SM-5N9CH Ovary Ovary female GTEX-1117F GTEX GTEX-1117F-2426-SM-5EGGH Uterus Uterus female GTEX-1117F GTEX GTEX-1117F-2526-SM-5GZY6 Vagina Vagina female GTEX-1117F GTEX GTEX-1117F-2826-SM-5GZXL Breast - Mammary Tissue Breast female GTEX-1117F GTEX GTEX-1117F-2926-SM-5GZYI Skin - Not Sun Exposed (Suprapubic) Skin female GTEX-1117F GTEX GTEX-1117F-3026-SM-5GZYU Minor Salivary Gland Salivary Gland female GTEX-1117F GTEX GTEX-1117F-3226-SM-5N9CT Brain - Cortex Brain female GTEX-1117F GTEX GTEX-111CU-0126-SM-5GZWZ Adrenal Gland Adrenal Gland male GTEX-111CU GTEX GTEX-111CU-0226-SM-5GZXC Thyroid Thyroid male GTEX-111CU GTEX GTEX-111CU-0326-SM-5GZXO Lung Lung male GTEX-111CU GTEX GTEX-111CU-0426-SM-5GZY1 Spleen Spleen male GTEX-111CU GTEX GTEX-111CU-0526-SM-5EGHK Pancreas Pancreas male GTEX-111CU GTEX GTEX-111CU-0626-SM-5EGHL Esophagus - Muscularis Esophagus male GTEX-111CU GTEX GTEX-111CU-0726-SM-5GZYD Esophagus - Mucosa Esophagus male GTEX-111CU GTEX GTEX-111CU-0826-SM-5EGIJ Esophagus - Gastroesophageal Junction Esophagus male GTEX-111CU GTEX GTEX-111CU-0926-SM-5EGIK Stomach Stomach male GTEX-111CU GTEX GTEX-111CU-1026-SM-5EGIL Adipose - Visceral (Omentum) Adipose Tissue male GTEX-111CU GTEX GTEX-111CU-1126-SM-5EGIM Skin - Not Sun Exposed (Suprapubic) Skin male GTEX-111CU GTEX GTEX-111CU-1226-SM-5EGIN Colon - Sigmoid Colon male GTEX-111CU GTEX GTEX-111CU-1326-SM-5NQ8L Small Intestine - Terminal Ileum Small Intestine male GTEX-111CU GTEX GTEX-111CU-1426-SM-5GZYP Colon - Transverse Colon male GTEX-111CU GTEX GTEX-111CU-1526-SM-5N9FS Prostate Prostate male GTEX-111CU GTEX GTEX-111CU-1726-SM-5EGHM Testis Testis male GTEX-111CU GTEX GTEX-111CU-1826-SM-5GZYN Adipose - Subcutaneous Adipose Tissue male GTEX-111CU GTEX GTEX-111CU-1926-SM-5GZYZ Skin - Sun Exposed (Lower leg) Skin male GTEX-111CU GTEX GTEX-111CU-2026-SM-5GZZC Muscle - Skeletal Muscle male GTEX-111CU GTEX GTEX-111CU-2226-SM-5N9G5 Nerve - Tibial Nerve male GTEX-111CU GTEX GTEX-111FC-0126-SM-5N9DL Skin - Sun Exposed (Lower leg) Skin male GTEX-111FC GTEX GTEX-111FC-0226-SM-5N9B8 Adipose - Subcutaneous Adipose Tissue male GTEX-111FC GTEX GTEX-111FC-0326-SM-5GZZ1 Muscle - Skeletal Muscle male GTEX-111FC GTEX GTEX-111FC-0426-SM-5N9CV Artery - Tibial Blood Vessel male GTEX-111FC GTEX GTEX-111FC-0526-SM-5GZZ8 Nerve - Tibial Nerve male GTEX-111FC GTEX GTEX-111FC-0626-SM-5N9CU Heart - Atrial Appendage Heart male GTEX-111FC GTEX GTEX-111FC-0826-SM-5GZWO Heart - Left Ventricle Heart male GTEX-111FC GTEX GTEX-111FC-1026-SM-5GZX1 Thyroid Thyroid male GTEX-111FC GTEX GTEX-111FC-1126-SM-5GZWU Lung Lung male GTEX-111FC GTEX GTEX-111FC-1326-SM-5N9D9 Spleen Spleen male GTEX-111FC GTEX GTEX-111FC-1426-SM-5N9C7 Adipose - Subcutaneous Adipose Tissue male GTEX-111FC GTEX ================================================ FILE: inst/extdata/TCGA-SKCM.GDC_phenotype_test.tsv ================================================ submitter_id.samples age_at_initial_pathologic_diagnosis batch_number bcr bcr_followup_barcode bcr_followup_uuid submitter_id breslow_depth_value day_of_dcc_upload day_of_form_completion days_to_initial_pathologic_diagnosis days_to_submitted_specimen_dx distant_metastasis_anatomic_site file_uuid followup_case_report_form_submission_reason history_of_neoadjuvant_treatment informed_consent_verified interferon_90_day_prior_excision_admin_indicator lactate_dehydrogenase_result lost_follow_up malignant_neoplasm_mitotic_count_rate melanoma_clark_level_value melanoma_origin_skin_anatomic_site melanoma_ulceration_indicator month_of_dcc_upload month_of_form_completion new_tumor_dx_prior_submitted_specimen_dx other_dx pathologic_M pathologic_N pathologic_T patient_id person_neoplasm_cancer_status postoperative_rx_tx primary_melanoma_at_diagnosis_count primary_neoplasm_melanoma_dx primary_tumor_multiple_present_ind prior_radiation_therapy prior_systemic_therapy prior_systemic_therapy_type radiation_therapy radiation_therapy_to_primary submitted_tumor_location subsequent_primary_melanoma_during_followup system_version tissue_prospective_collection_indicator tissue_retrospective_collection_indicator tissue_source_site weight withdrawn year_of_dcc_upload year_of_form_completion year_of_initial_pathologic_diagnosis days_to_index dbgap_registration_code disease_code pathology_report_file_name program project_code vial_number age_at_index.demographic days_to_birth.demographic days_to_death.demographic ethnicity.demographic gender.demographic race.demographic vital_status.demographic year_of_birth.demographic year_of_death.demographic age_at_diagnosis.diagnoses classification_of_tumor.diagnoses days_to_diagnosis.diagnoses days_to_last_follow_up.diagnoses icd_10_code.diagnoses last_known_disease_status.diagnoses morphology.diagnoses primary_diagnosis.diagnoses prior_malignancy.diagnoses prior_treatment.diagnoses progression_or_recurrence.diagnoses site_of_resection_or_biopsy.diagnoses synchronous_malignancy.diagnoses tissue_or_organ_of_origin.diagnoses tumor_grade.diagnoses tumor_stage.diagnoses year_of_diagnosis.diagnoses disease_type alcohol_history.exposures bmi.exposures height.exposures weight.exposures primary_site name.project project_id.project releasable.project bcr_id.tissue_source_site code.tissue_source_site name.tissue_source_site project.tissue_source_site days_to_collection.samples days_to_sample_procurement.samples initial_weight.samples is_ffpe.samples oct_embedded.samples preservation_method.samples sample_type.samples sample_type_id.samples state.samples tissue_type.samples TCGA-D9-A4Z2-01A 50 262.74.0 Nationwide Children's Hospital TCGA-D9-A4Z2-F57868 AB5F6B14-DAAE-4D19-B227-EC6E2D5ED0D0 TCGA-D9-A4Z2 25 14 28 0 7 8FC5ACEA-E493-4C8D-A3AC-5A68B1B8FC26 No YES NO 10 IV Non-glabrous skin YES 3 3 NO No M0 N3 T4b A4Z2 WITH TUMOR NO 2 YES YES NO NO NO NO Primary Tumor NO 7th YES NO D9 54 false 2017 2014 2012 0 SKCM TCGA-D9-A4Z2.FEA752ED-7670-4D44-8402-009CF8356C44.pdf TCGA A 50.0 -18462.0 190.0 not hispanic or latino male white Dead 1962.0 18462.0 not reported 0.0 93.0 C44.5 not reported 8721/3 Nodular melanoma no No not reported Skin of trunk No Skin, NOS not reported stage iiic 2012.0 Nevi and Melanomas Not Reported 20.07733491969066 164.0 54.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D9 Greater Poland Cancer Center Skin Cutaneous Melanoma 66.0 160.0 False false Primary Tumor 01 released Not Reported TCGA-ER-A2NH-06A 49 180.87.0 Nationwide Children's Hospital TCGA-ER-A2NH-F69370 CF52E6AD-959C-446F-B88F-E7E73B83E405 TCGA-ER-A2NH 4 14 7 0 618 7E2595AA-7EF7-4E83-AC3C-44179A594F47 No YES NO 1 IV Non-glabrous skin NO 3 1 No M0 N3 T3a A2NH WITH TUMOR NO YES NO NO NO NO NO Regional Lymph Node 6th YES NO ER false 2017 2015 2009 0 SKCM TCGA-ER-A2NH.875D53B1-DFAE-4DA3-84B0-70B14B280EA1.pdf TCGA A 49.0 -18220.0 not hispanic or latino male white Alive 1960.0 18220.0 not reported 0.0 1264.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage iiic 2009.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 744.0 False true Metastatic 06 released Not Reported TCGA-BF-A5EO-01A 65 291.69.0 Nationwide Children's Hospital TCGA-BF-A5EO-F68880 9993CEA1-92A4-48E7-904E-542F5079A3C6 TCGA-BF-A5EO 8 14 23 0 0 480492D8-82C9-4211-8D4B-FBBAB0A8459B No YES NO III Non-glabrous skin YES 3 12 No M0 N0 T4b A5EO TUMOR FREE NO 1 YES NO NO NO NO NO Primary Tumor 7th YES NO BF 110 false 2017 2014 2012 0 SKCM TCGA-BF-A5EO.FD5F5903-7586-4388-970A-36FBE8AE473D.pdf TCGA A 65.0 -23940.0 not hispanic or latino male white Alive 1947.0 23940.0 not reported 0.0 703.0 C44.5 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of trunk No Skin, NOS not reported stage iic 2012.0 Nevi and Melanomas Not Reported 33.20854969206617 182.0 110.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH BF Cureline Skin Cutaneous Melanoma 134.0 14.0 False true Primary Tumor 01 released Not Reported TCGA-D9-A6EA-06A 70 316.66.0 Nationwide Children's Hospital TCGA-D9-A6EA-F57860 C0168BC0-E336-4944-86D9-C332EA962EC0 TCGA-D9-A6EA 6 14 28 0 422 A6210DB2-5211-43B3-9FB8-177784EAC4B2 No YES Normal NO IV Non-glabrous skin NO 3 3 No M0 N3 T4a A6EA TUMOR FREE NO 1 YES NO NO NO NO NO NO 7th YES NO D9 58 false 2017 2014 2012 0 000178 SKCM TCGA-D9-A6EA.78D2592F-845C-4A33-AC8B-48C109E4D50B.pdf TCGA TCGA A 70.0 -25582.0 not hispanic or latino male white Alive 1942.0 25582.0 not reported 0.0 766.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage iiic 2012.0 Nevi and Melanomas Not Reported 21.048047612135285 166.0 58.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D9 Greater Poland Cancer Center Skin Cutaneous Melanoma 478.0 270.0 False true Metastatic 06 released Not Reported TCGA-D9-A4Z3-01A 73 277.76.0 Nationwide Children's Hospital TCGA-D9-A4Z3-F58483 54923FD3-981A-4649-BEDD-860A06350154 TCGA-D9-A4Z3 75 14 11 0 0 ACF6C126-DFBA-431C-B768-7C5F0CD7E36E No YES NO 10 V Non-glabrous skin YES 3 4 No M0 N1b T4b A4Z3 TUMOR FREE NO 1 YES NO NO NO NO NO Primary Tumor NO 7th YES NO D9 70 false 2017 2014 2012 0 SKCM TCGA-D9-A4Z3.9B96B7FD-D3BA-42C8-A0C6-1C93C48F4BB2.pdf TCGA A 73.0 -26812.0 not hispanic or latino female white Alive 1939.0 26812.0 not reported 0.0 505.0 C44.6 not reported 8721/3 Nodular melanoma no No not reported Skin of upper limb and shoulder No Skin, NOS not reported stage iiic 2012.0 Nevi and Melanomas Not Reported 24.22145328719723 170.0 70.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D9 Greater Poland Cancer Center Skin Cutaneous Melanoma 16.0 170.0 False false Primary Tumor 01 released Not Reported TCGA-GN-A26A-06A 63 180.87.0 Nationwide Children's Hospital TCGA-GN-A26A 2.3 14 0 618 0663AA03-5F7F-4537-B1A3-E5576F7A327A No YES 11 IV Non-glabrous skin NO 3 NO No M0 N1a T3a A26A 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 6th NO YES GN 66 false 2017 2006 0 SKCM TCGA-GN-A26A.5F794422-0A58-4224-8108-E0E4DE11D4C6.pdf TCGA A 63.0 -23288.0 988.0 not hispanic or latino female white Dead 1943.0 2008.0 23288.0 not reported 0.0 C44.5 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of trunk No Skin, NOS not reported stage iiia 2006.0 Nevi and Melanomas Not Reported 24.242424242424242 165.0 66.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH GN Roswell Skin Cutaneous Melanoma 1766.0 270.0 False true Metastatic 06 released Not Reported TCGA-D3-A3BZ-06A 63 180.87.0 Nationwide Children's Hospital TCGA-D3-A3BZ-F45520 E3E4F520-07D7-42C1-B74D-A30F7A4708DE TCGA-D3-A3BZ 4.6 14 16 0 488 BF045C42-5822-46B0-B7C4-4441E5472CFB No YES NO 1 V Non-glabrous skin NO 3 7 NO No M0 N0 T4a A3BZ TUMOR FREE NO YES NO NO NO YES NO Regional Lymph Node NO 7th NO YES D3 99 false 2017 2013 2002 0 SKCM TCGA-D3-A3BZ.968FCFF6-6D7A-48F2-BC66-F9F7E61B095D.pdf TCGA A 63.0 -23286.0 hispanic or latino male white Alive 1939.0 23286.0 not reported 0.0 3976.0 C77.0 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of head, face and neck No Skin, NOS not reported stage iib 2002.0 Nevi and Melanomas Not Reported 34.2560553633218 170.0 99.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 3332.0 80.0 False true Metastatic 06 released Not Reported TCGA-D3-A51G-06A 277.76.0 Nationwide Children's Hospital TCGA-D3-A51G-F60911 390FFB4F-D2AB-4D2A-B42D-883652921A7F TCGA-D3-A51G 0 14 18 50E32A14-9C29-4994-AC7E-2BA337500C31 No YES NO I Non-glabrous skin 3 6 YES Yes M0 N0 Tis A51G TUMOR FREE YES YES NO NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) NO 7th NO YES D3 112 false 2017 2014 1990 SKCM TCGA-D3-A51G.27A8D7FA-6317-4B5E-9CE6-D65790474BDF.pdf TCGA A not hispanic or latino male white Alive not reported C49.2 not reported 8720/3 Malignant melanoma, NOS yes No not reported Connective, subcutaneous and other soft tissues of lower limb and hip Not Reported Skin, NOS not reported stage 0 1990.0 Nevi and Melanomas Not Reported 31.024930747922436 190.0 112.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 70.0 False true Metastatic 06 released Not Reported TCGA-EE-A29R-06A 48 180.87.0 Nationwide Children's Hospital TCGA-EE-A29R-F59763 3DAB4A16-5952-460C-9B59-DC23C644B2C4 TCGA-EE-A29R 3.5 14 20 0 171 F3442500-209D-45C7-997C-187B53CE576A No YES YES 6 V Non-glabrous skin YES 3 5 NO No M0 N1b T3b A29R YES NO NO NO NO Regional Lymph Node 7th YES NO EE false 2017 2014 2010 0 SKCM TCGA-EE-A29R.8DEEF9EA-3585-4047-A2BD-E7AB4DD1306F.pdf TCGA A 48.0 -17857.0 not hispanic or latino female white Alive 1962.0 17857.0 not reported 0.0 440.0 C49.0 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of head, face and neck No Skin, NOS not reported stage iiic 2010.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 359.0 520.0 False true Metastatic 06 released Not Reported TCGA-D3-A2JE-06A 75 388.48.0 Nationwide Children's Hospital TCGA-D3-A2JE 14 0 140 B1B3D3F4-4A48-4614-8D98-DBA8B33C14EA No YES Non-glabrous skin 3 NO Yes M0 N3 TX A2JE YES NO NO NO NO Regional Lymph Node 6th NO YES D3 81 false 2017 2009 0 SKCM TCGA-D3-A2JE.B07C0D10-3397-4F61-A119-F1D7CDB9E792.pdf TCGA A 75.0 -27604.0 841.0 not hispanic or latino female white Dead 1934.0 2011.0 27604.0 not reported 0.0 C77.3 not reported 8720/3 Malignant melanoma, NOS yes No not reported Lymph nodes of axilla or arm Not Reported Skin, NOS not reported stage iiic 2009.0 Nevi and Melanomas Not Reported 31.640625 160.0 81.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 890.0 170.0 False true Metastatic 06 released Not Reported TCGA-W3-AA1V-06B 63 416.38.0 Nationwide Children's Hospital TCGA-W3-AA1V 1.3 14 0 946 4769724A-5993-4103-B31E-EDB352D182B4 No YES IV Non-glabrous skin NO 3 NO No M0 N0 T3 AA1V YES NO NO NO NO Regional Lymph Node 4th NO YES W3 91 false 2017 1994 0 SKCM TCGA-W3-AA1V.11DF82C1-C746-4D00-9119-1FEBE3FBC0A2.pdf TCGA B 63.0 -23314.0 1280.0 not hispanic or latino male white Dead 1931.0 1997.0 23314.0 not reported 0.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage ii 1994.0 Nevi and Melanomas Not Reported 28.086419753086417 180.0 91.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH W3 John Wayne Cancer Center Skin Cutaneous Melanoma 7097.0 60.0 False true Metastatic 06 released Not Reported TCGA-DA-A1IA-06A 32 180.87.0 Nationwide Children's Hospital TCGA-DA-A1IA-F70089 FC51240A-2001-4FE9-97CF-CD55C0B430B7 TCGA-DA-A1IA 1.3 14 27 0 1855 brain 4E62D848-9309-4819-8DF7-8DF15CCD865D No YES NO IV Non-glabrous skin NO 3 1 No M0 N1b T2a A1IA WITH TUMOR NO 1 YES NO NO NO NO NO Distant Metastasis NO 6th NO YES DA false 2017 2015 2005 0 SKCM TCGA-DA-A1IA.A8C5B4DF-C8BD-43FF-9613-04942F68DCAB.pdf TCGA A 32.0 -11744.0 2005.0 not hispanic or latino female white Dead 1973.0 11744.0 not reported 0.0 1887.0 C71.1 not reported 8720/3 Malignant melanoma, NOS no No not reported Frontal lobe No Skin, NOS not reported stage iiib 2005.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH DA Yale Skin Cutaneous Melanoma 2074.0 280.0 False true Metastatic 06 released Not Reported TCGA-EE-A29T-06A 51 180.87.0 Nationwide Children's Hospital TCGA-EE-A29T-F55117 96B388B2-BFA4-4033-BA80-177C7061EC5D TCGA-EE-A29T 14 29 0 10326 43BA748D-707E-4752-BBFD-527E41F9BDBD No YES NO Non-glabrous skin 3 12 YES No M0 NX TX A29T TUMOR FREE NO YES NO NO NO NO NO Regional Lymph Node NO 1st NO YES EE false 2017 2013 1982 0 SKCM TCGA-EE-A29T.4F109FA0-76B1-46AA-B90C-AB1CA81C387B.pdf TCGA A 51.0 -18779.0 not hispanic or latino female white Alive 1931.0 18779.0 not reported 0.0 11252.0 C77.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph node, NOS No Skin, NOS not reported not reported 1982.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 10559.0 200.0 False true Metastatic 06 released Not Reported TCGA-EB-A3XB-01A 63 240.79.0 Nationwide Children's Hospital TCGA-EB-A3XB-F41213 6592ABBE-555B-4782-92AD-AFE3D1F8EB3F TCGA-EB-A3XB 14 13 0 0 DE01D624-A389-4BD4-BD6D-CE09E2DD1902 No YES NO IV Non-glabrous skin 3 3 No M0 NX T4 A3XB TUMOR FREE NO YES NO NO NO NO Primary Tumor NO 7th YES NO EB 80 false 2017 2013 2011 0 SKCM TCGA-EB-A3XB.73D45CD8-5E91-4A51-B3E2-BE6B071F1A04.pdf TCGA A 63.0 -23091.0 not hispanic or latino male white Alive 1948.0 23091.0 not reported 0.0 796.0 C44.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin, NOS No Skin, NOS not reported stage ii 2011.0 Nevi and Melanomas Not Reported 29.7441998810232 164.0 80.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 225.0 540.0 False true Primary Tumor 01 released Not Reported TCGA-FS-A1ZQ-06A 31 180.87.0 Nationwide Children's Hospital TCGA-FS-A1ZQ 14 0 3799 lung 81048A54-134D-47F8-8C6B-DC9E5F390BF7 No YES Non-glabrous skin 3 No M0 N0 TX A1ZQ 1 YES NO NO NO YES Distant Metastasis 6th NO YES FS false 2017 1997 0 SKCM TCGA-FS-A1ZQ.A1E704CD-0D4D-4CCC-82B2-19289EEE84EB.pdf TCGA A 31.0 -11599.0 4062.0 not hispanic or latino male white Dead 1966.0 2008.0 11599.0 not reported 0.0 C34.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Lung, NOS No Skin, NOS not reported i/ii nos 1997.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FS Essen Skin Cutaneous Melanoma 4897.0 300.0 False false Metastatic 06 released Not Reported TCGA-EB-A85I-01A 66 358.51.0 Nationwide Children's Hospital TCGA-EB-A85I-F61877 74E78246-6218-4314-B8A7-AF5043FB42EA TCGA-EB-A85I 10 14 9 0 0 1CD3B59D-00C1-4EE5-AD71-A8DCD1D3D6E0 No YES NO Non-glabrous skin YES 3 7 No M0 N0 T4b A85I TUMOR FREE NO 1 YES NO NO NO NO Primary Tumor NO 7th YES NO EB 82 false 2017 2014 2013 0 SKCM TCGA-EB-A85I.D35BCE2A-8132-4CAA-8E22-559B68BDAF07.pdf TCGA A 66.0 -24408.0 not hispanic or latino male white Alive 1947.0 24408.0 not reported 0.0 362.0 C44.7 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of lower limb and hip No Skin, NOS not reported stage iic 2013.0 Nevi and Melanomas Not Reported 28.37370242214533 170.0 82.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 135.0 370.0 False true Primary Tumor 01 released Not Reported TCGA-D3-A5GO-06A 61 291.69.0 Nationwide Children's Hospital TCGA-D3-A5GO-F60915 9ED5547B-C48F-402B-8211-A8D16CD11034 TCGA-D3-A5GO 4.3 14 18 0 2739 Soft tissue of right upper arm 88AAC1C5-A560-46F0-8A25-A7FE85F4B55B No YES YES V Non-glabrous skin 3 6 YES No M0 N0 T4 A5GO YES NO NO NO NO Distant Metastasis 7th NO YES D3 83 false 2017 2014 2001 0 SKCM TCGA-D3-A5GO.FFFA2FD5-C008-4583-99C9-EE71B0F21F07.pdf TCGA A 61.0 -22287.0 not hispanic or latino male white Alive 1940.0 22287.0 not reported 0.0 4195.0 C44.6 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of upper limb and shoulder No Skin, NOS not reported stage ii 2001.0 Nevi and Melanomas Not Reported 29.060607121599382 169.0 83.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 4259.0 50.0 False true Metastatic 06 released Not Reported TCGA-FR-A728-01A 54 332.57.0 Nationwide Children's Hospital TCGA-FR-A728-F66624 F878CF96-2289-4593-8E4A-5741242A96AB TCGA-FR-A728 12.00 14 21 0 16 8AA7655C-0D37-407F-96F0-DE8112D92BDA No YES NO 6 IV Non-glabrous skin YES 3 10 NO No M0 N2a T4b A728 TUMOR FREE YES YES NO NO NO NO NO Primary Tumor NO 7th YES NO FR 87 false 2017 2014 2013 0 SKCM TCGA-FR-A728.F3C14D17-8681-4925-BDC5-CFE56F68342E.pdf TCGA A 54.0 -19936.0 not hispanic or latino female white Alive 1959.0 19936.0 not reported 0.0 583.0 C44.6 not reported 8743/3 Superficial spreading melanoma no No not reported Skin of upper limb and shoulder No Skin, NOS not reported stage iiib 2013.0 Nevi and Melanomas Not Reported 31.955922865013772 165.0 87.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FR University of North Carolina Skin Cutaneous Melanoma 136.0 170.0 False false Primary Tumor 01 released Not Reported TCGA-EB-A85J-01A 66 358.51.0 Nationwide Children's Hospital TCGA-EB-A85J-F61878 ED3B4C1F-767C-45E0-8E9B-C36248D3EDD4 TCGA-EB-A85J 5.5 14 9 0 0 52641746-173A-4430-BC34-D858B64E1915 No YES NO Non-glabrous skin NO 3 7 No M0 N0 T4a A85J TUMOR FREE YES 1 YES NO NO NO NO Primary Tumor NO 7th YES NO EB 128 false 2017 2014 2013 0 SKCM TCGA-EB-A85J.AD298262-1D6D-4023-9DBA-A38A8ED1E3EB.pdf TCGA A 66.0 -24263.0 not hispanic or latino female white Alive 1947.0 24263.0 not reported 0.0 360.0 C44.7 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of lower limb and hip No Skin, NOS not reported stage iib 2013.0 Nevi and Melanomas Not Reported 46.45086369574684 166.0 128.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 133.0 220.0 False true Primary Tumor 01 released Not Reported TCGA-D3-A2JA-06A 68 180.87.0 Nationwide Children's Hospital TCGA-D3-A2JA-F40740 1504323B-9883-4CF4-B02D-E44197CB64BA TCGA-D3-A2JA 1.2 14 7 0 822 4386D921-85F8-4A4F-8674-08FBE0C798BE No YES NO 0 III Non-glabrous skin NO 3 3 YES No M0 N1a T2a A2JA TUMOR FREE NO YES NO NO NO NO YES Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) NO 7th NO YES D3 113 false 2017 2013 2003 0 SKCM TCGA-D3-A2JA.75ADE88D-F7C8-4450-8B93-5706A25A13EA.pdf TCGA A 68.0 -25085.0 not hispanic or latino male white Alive 1935.0 25085.0 not reported 0.0 3514.0 C49.6 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of trunk, NOS No Skin, NOS not reported stage iiia 2003.0 Nevi and Melanomas Not Reported 36.89795918367347 175.0 113.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 2945.0 80.0 False true Metastatic 06 released Not Reported TCGA-YG-AA3O-06A 62 393.46.0 Nationwide Children's Hospital TCGA-YG-AA3O-F67869 48BA783C-56D0-4AAB-9F7F-16184D748565 TCGA-YG-AA3O 14 13 0 908 61B97687-4FB4-4631-BED7-D5F5F8E97B0E No YES NO Non-glabrous skin 3 11 No AA3O WITH TUMOR YES YES NO NO NO NO NO Regional Lymph Node YES YES NO YG 92 false 2017 2014 2011 0 SKCM TCGA-YG-AA3O.4C358082-E41F-491D-8F0D-39B2B698995A.pdf TCGA A 62.0 -22999.0 1154.0 hispanic or latino male not reported Dead 1949.0 22999.0 not reported 0.0 1096.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported not reported 2011.0 Nevi and Melanomas Not Reported 30.73941661933242 173.0 92.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH YG University of Puerto Rico Skin Cutaneous Melanoma 956.0 170.0 False false Metastatic 06 released Not Reported TCGA-EE-A3AB-06A 30 180.87.0 Nationwide Children's Hospital TCGA-EE-A3AB-F59751 1FD5BD7A-75C7-4FFD-BB97-36E6A641D315 TCGA-EE-A3AB 14 20 0 0 46D5759D-A919-4E67-AC58-A0C09E5E0F58 No YES YES Non-glabrous skin 3 5 No M0 N2a T0 A3AB NO NO NO Regional Lymph Node 5th NO YES EE false 2017 2014 2001 0 SKCM TCGA-EE-A3AB.0782D02E-4CD6-4031-B62B-41E1C408E4BC.pdf TCGA A 30.0 -11054.0 not hispanic or latino male white Alive 1971.0 11054.0 not reported 0.0 3733.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage iii 2001.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 3725.0 160.0 False true Metastatic 06 released Not Reported TCGA-D3-A1QB-06A 75 180.87.0 Nationwide Children's Hospital TCGA-D3-A1QB-F45519 DC07AAD2-679E-4F92-BD3B-282B3DAAB030 TCGA-D3-A1QB 14 16 0 441 FFF48EB4-5011-44D6-A958-C98ACFD2CBB2 No YES NO Non-glabrous skin 3 7 YES No M0 N2c T0 A1QB TUMOR FREE NO NO NO NO NO Regional Lymph Node NO 7th NO YES D3 86 false 2017 2013 2005 0 SKCM TCGA-D3-A1QB.EA89AFF8-7133-4B7F-89E4-9AF1901C300D.pdf TCGA A 75.0 -27566.0 not hispanic or latino female white Alive 1930.0 27566.0 not reported 0.0 2912.0 C77.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph node, NOS No Skin, NOS not reported stage iii 2005.0 Nevi and Melanomas Not Reported 39.79823221805729 147.0 86.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 2135.0 100.0 False true Metastatic 06 released Not Reported TCGA-GN-A4U9-06A 71 332.57.0 Nationwide Children's Hospital TCGA-GN-A4U9-F61269 26A50AA3-CD8D-4044-A0D5-52F338BCFA50 TCGA-GN-A4U9 1.7 14 26 0 60 EE40F343-4E34-4182-940F-320CDB780B4B No YES NO 5 IV Non-glabrous skin YES 3 6 NO No M0 N3 T2b A4U9 WITH TUMOR YES 1 YES NO NO NO NO NO Regional Lymph Node NO 7th NO YES GN 104 false 2017 2014 2012 0 SKCM TCGA-GN-A4U9.13096A76-D4DF-412C-B713-B2BB538B23DA.pdf TCGA A 71.0 -26118.0 673.0 not hispanic or latino male white Dead 1941.0 26118.0 not reported 0.0 384.0 C77.4 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of inguinal region or leg No Skin, NOS not reported stage iiic 2012.0 Nevi and Melanomas Not Reported 33.574380165289256 176.0 104.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH GN Roswell Skin Cutaneous Melanoma 167.0 170.0 False false Metastatic 06 released Not Reported TCGA-ER-A3ET-06A 64 198.83.0 Nationwide Children's Hospital TCGA-ER-A3ET-F69372 BCB8141F-EE7F-4047-993A-32F4D2F80315 TCGA-ER-A3ET 3.55 14 7 0 2243 BA70B15C-01ED-448D-9EED-CBAAE4A7C9B3 No YES NO IV Non-glabrous skin 3 1 NO No M0 N1a T3a A3ET WITH TUMOR NO YES NO NO NO YES NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 6th YES NO ER false 2017 2015 2005 0 SKCM TCGA-ER-A3ET.49CF1653-1EE3-4C4C-B79D-1DEC59490210.pdf TCGA A 64.0 -23650.0 2829.0 not hispanic or latino female white Dead 1941.0 23650.0 not reported 0.0 2443.0 C49.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues, NOS No Skin, NOS not reported stage iiia 2005.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 2362.0 150.0 False true Metastatic 06 released Not Reported TCGA-HR-A5NC-01A 90 291.69.0 Nationwide Children's Hospital TCGA-HR-A5NC-F70729 FD13F733-A6FD-4F02-AB1C-A67967D369F2 TCGA-HR-A5NC 8 14 2 0 0 C9C15001-C075-4639-8E67-5C7BBBE2FE1C No YES YES V Non-glabrous skin 3 3 NO No M0 NX T4 A5NC 1 YES NO NO NO NO Primary Tumor 6th NO YES HR false 2017 2015 SKCM TCGA-HR-A5NC.B8892054-09BB-4E2F-90FC-E84EBA310C7D.pdf TCGA A 90.0 -32872.0 not reported female not reported Alive 32872.0 not reported 0.0 0.0 C44.5 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of trunk No Skin, NOS not reported not reported Nevi and Melanomas Not Reported 152.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH HR Ontario Institute for Cancer Research (OICR) Skin Cutaneous Melanoma 150.0 False false Primary Tumor 01 released Not Reported TCGA-FR-A729-06A 38 358.51.0 Nationwide Children's Hospital TCGA-FR-A729-F66634 A152F2A2-94BF-461A-9985-B6DFD58F7CAF TCGA-FR-A729 0.25 14 21 0 6186 8AEF3E1F-D069-4289-9823-DC1B39D14BDD No YES NO II NO 3 10 No M0 N0 T1 A729 WITH TUMOR YES YES NO NO NO NO NO Regional Lymph Node NO 4th YES NO FR 83 false 2017 2014 1996 0 SKCM TCGA-FR-A729.E1B37296-5114-47F3-A48E-2046B7939823.pdf TCGA A 38.0 -14043.0 not hispanic or latino female white Alive 1958.0 14043.0 not reported 0.0 6716.0 C77.4 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of inguinal region or leg No Skin, NOS not reported stage i 1996.0 Nevi and Melanomas Not Reported 27.414453692693883 174.0 83.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FR University of North Carolina Skin Cutaneous Melanoma 6307.0 400.0 False false Metastatic 06 released Not Reported TCGA-BF-A1PZ-01A 71 180.87.0 Nationwide Children's Hospital TCGA-BF-A1PZ-F68909 4B1D5ACA-C24D-4E0A-A8A9-A843108AE1CD TCGA-BF-A1PZ 8 14 23 0 0 DB0F34CD-3594-4DE7-9F20-D6BE76122271 No YES NO III Non-glabrous skin NO 3 12 No M0 N0 T4a A1PZ TUMOR FREE NO 1 YES NO NO NO NO NO Primary Tumor 7th YES NO BF 56 false 2017 2014 2010 0 SKCM TCGA-BF-A1PZ.1FAA6F42-BEE4-47B5-9D6A-87D310719766.pdf TCGA A 71.0 -26240.0 not hispanic or latino female white Alive 1939.0 26240.0 not reported 0.0 853.0 C44.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin, NOS No Skin, NOS not reported stage iib 2010.0 Nevi and Melanomas Not Reported 21.077195227520793 163.0 56.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH BF Cureline Skin Cutaneous Melanoma 97.0 130.0 False true Primary Tumor 01 released Not Reported TCGA-WE-AAA0-06A 47 393.46.0 Nationwide Children's Hospital TCGA-WE-AAA0-F67339 826AB2CF-0860-4D9E-B61C-9B084E997C2D TCGA-WE-AAA0 0.8 14 4 0 311 27E82066-0F36-481D-A837-1F4ADDEE802F No YES NO 0 Non-glabrous skin NO 3 11 No M0 N0 T1a AAA0 WITH TUMOR NO 1 YES NO NO NO YES NO Regional Lymph Node NO 7th NO YES WE false 2017 2014 2011 0 SKCM TCGA-WE-AAA0.7C5BE93A-F3F3-4DE3-84EC-5BC54C644E18.pdf TCGA A 47.0 -17261.0 not hispanic or latino male white Alive 1964.0 17261.0 not reported 0.0 1229.0 C77.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph node, NOS No Skin, NOS not reported stage ia 2011.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH WE Norfolk and Norwich Hospital Skin Cutaneous Melanoma 819.0 60.0 False false Metastatic 06 released Not Reported TCGA-EB-A430-01A 83 262.74.0 Nationwide Children's Hospital TCGA-EB-A430-F57899 FBB7092B-338C-40FB-B72A-7B1FF6B34BE4 TCGA-EB-A430 18 14 21 0 0 F978C499-243C-42F0-9B93-5405CF1AE2B8 No YES YES IV Non-glabrous skin YES 3 4 No M0 N0 T4b A430 YES NO NO NO NO Primary Tumor 7th YES NO EB 85 false 2017 2014 2012 0 SKCM TCGA-EB-A430.CAF377CF-8A98-4334-B6A7-831024B5A5A6.pdf TCGA A 83.0 -30344.0 not hispanic or latino male white Alive 1929.0 30344.0 not reported 0.0 -2.0 C44.5 not reported 8721/3 Nodular melanoma no No not reported Skin of trunk No Skin, NOS not reported stage iic 2012.0 Nevi and Melanomas Not Reported 30.116213151927436 168.0 85.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 125.0 1800.0 False true Primary Tumor 01 released Not Reported TCGA-EE-A3J5-06A 71 198.83.0 Nationwide Children's Hospital TCGA-EE-A3J5 4.80 14 0 467 80D2316D-82AF-47CA-A4CA-E14BD5055385 No YES 8 V Non-glabrous skin NO 3 NO No M0 N1 T4a A3J5 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 5th NO YES EE false 2017 2000 0 SKCM TCGA-EE-A3J5.86A187C5-2FAC-4DEA-9431-020F323E6E5D.pdf TCGA A 71.0 -26226.0 1124.0 not hispanic or latino male white Dead 1929.0 2003.0 26226.0 not reported 0.0 C44.5 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of trunk No Skin, NOS not reported stage iii 2000.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 4025.0 40.0 False true Metastatic 06 released Not Reported TCGA-WE-A8K4-01A 85 388.48.0 Nationwide Children's Hospital TCGA-WE-A8K4-F67276 DBDCB180-0011-41E1-B16A-4A2AA7417124 TCGA-WE-A8K4 12 14 3 0 0 AAD4D726-4E81-4F9B-8433-F1CA0F0CC8A5 No YES NO 4 Non-glabrous skin 3 11 Yes M0 NX T4a A8K4 TUMOR FREE NO 1 YES NO NO NO NO NO Primary Tumor NO 7th YES NO WE false 2017 2014 2013 0 SKCM TCGA-WE-A8K4.1B421DA3-59DD-4246-8D3B-58F4AE13F36B.pdf TCGA A 85.0 -31308.0 not hispanic or latino male white Alive 1928.0 31308.0 not reported 0.0 614.0 C44.6 not reported 8721/3 Nodular melanoma yes No not reported Skin of upper limb and shoulder Not Reported Skin, NOS not reported stage iib 2013.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH WE Norfolk and Norwich Hospital Skin Cutaneous Melanoma 203.0 220.0 False true Primary Tumor 01 released Not Reported TCGA-D3-A5GR-06A 23 291.69.0 Nationwide Children's Hospital TCGA-D3-A5GR-F55314 FE2E3CF3-E00B-4BB5-AD4A-A471B05F7CC1 TCGA-D3-A5GR .98 14 3 0 5008 9EAEF1C7-C789-4A36-927B-DB444B867387 Yes YES YES NO 1 III Non-glabrous skin 3 1 YES No M0 N1 T1b A5GR WITH TUMOR NO YES NO NO YES Interferon NO NO Regional Lymph Node NO 7th NO YES D3 114 false 2017 2014 1998 0 SKCM TCGA-D3-A5GR.2055FAF3-FB7C-4E13-8A17-FF4B9964F49F.pdf TCGA A 23.0 -8637.0 not hispanic or latino female white Alive 1975.0 8637.0 not reported 0.0 5424.0 C77.4 not reported 8720/3 Malignant melanoma, NOS no Yes not reported Lymph nodes of inguinal region or leg No Skin, NOS not reported stage iii 1998.0 Nevi and Melanomas Not Reported 43.43850022862368 162.0 114.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH D3 MD Anderson Skin Cutaneous Melanoma 5119.0 40.0 False true Metastatic 06 released Not Reported TCGA-EE-A2GS-06A 28 180.87.0 Nationwide Children's Hospital TCGA-EE-A2GS-F59674 B4D6FD51-2575-4523-9F4D-79EDC5B690D9 TCGA-EE-A2GS 1.8 14 20 0 1295 B20AB398-5E86-4811-BA49-EAAD22C127EA No YES NO 6 IV Non-glabrous skin NO 3 5 NO No M0 N0 T2a A2GS WITH TUMOR YES YES NO NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) NO 6th NO YES EE false 2017 2014 2006 0 SKCM TCGA-EE-A2GS.57868CFE-20D2-4893-BF42-8E94E290D42F.pdf TCGA A 28.0 -10421.0 2470.0 not hispanic or latino female white Dead 1978.0 10421.0 not reported 0.0 1691.0 C49.2 not reported 8730/3 Amelanotic melanoma no No not reported Connective, subcutaneous and other soft tissues of lower limb and hip No Skin, NOS not reported stage ib 2006.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 1804.0 110.0 False true Metastatic 06 released Not Reported TCGA-EE-A29Q-06A 70 180.87.0 Nationwide Children's Hospital TCGA-EE-A29Q-F55045 EEF32021-01AE-4851-8408-2D2AEDA944D2 TCGA-EE-A29Q 2.3 14 27 0 890 FF6EF291-7764-4725-A355-848442D49F6D No YES NO 8 IV Non-glabrous skin YES 3 12 No M0 N0 T3b A29Q WITH TUMOR NO YES NO NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) NO 6th NO YES EE false 2017 2013 2007 0 SKCM TCGA-EE-A29Q.4ED10518-B475-433F-945A-1A97C8B6041C.pdf TCGA A 70.0 -25847.0 2030.0 not hispanic or latino female white Dead 1937.0 25847.0 not reported 0.0 1136.0 C49.1 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of upper limb and shoulder No Skin, NOS not reported stage iib 2007.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 1263.0 60.0 False true Metastatic 06 released Not Reported TCGA-EE-A3J7-06A 43 198.83.0 Nationwide Children's Hospital TCGA-EE-A3J7-F59752 744C4C44-2DE1-4A70-87E1-D6BFCB5CC54B TCGA-EE-A3J7 1.12 14 20 0 1292 E6EB26AF-C93F-4031-9336-BC91F5A64728 No YES YES II Non-glabrous skin YES 3 5 YES No M0 N0 T2 A3J7 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 5th NO YES EE false 2017 2014 1998 0 SKCM TCGA-EE-A3J7.F2479711-8EEC-44A5-877C-7804A631FCBD.pdf TCGA A 43.0 -15896.0 not hispanic or latino male white Alive 1955.0 15896.0 not reported 0.0 1949.0 C49.1 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of upper limb and shoulder No Skin, NOS not reported stage i 1998.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EE University of Sydney Skin Cutaneous Melanoma 4744.0 100.0 False true Metastatic 06 released Not Reported TCGA-EB-A3XE-01A 77 240.79.0 Nationwide Children's Hospital TCGA-EB-A3XE-F42007 9F87F66F-2863-44D4-BEDC-5EBCFF5195EF TCGA-EB-A3XE 3 14 9 0 0 EE78DF3D-A074-404D-8287-BF218737BC7C No YES YES Non-glabrous skin NO 3 4 No M0 N0 T3a A3XE TUMOR FREE NO 1 YES NO NO NO NO Primary Tumor NO 7th YES NO EB 62 false 2017 2013 2012 0 SKCM TCGA-EB-A3XE.0495197B-B5CD-484A-8531-34ADE693294B.pdf TCGA A 77.0 -28148.0 not hispanic or latino female white Alive 1935.0 28148.0 not reported 0.0 180.0 C44.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin, NOS No Skin, NOS not reported stage iia 2012.0 Nevi and Melanomas Not Reported 24.835763499439192 158.0 62.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 113.0 590.0 False true Primary Tumor 01 released Not Reported TCGA-W3-A824-06A 63 358.51.0 Nationwide Children's Hospital TCGA-W3-A824-F64489 FC6C70DC-2DF1-43AF-8043-E8B542A2FA84 TCGA-W3-A824 1.5 14 26 0 1461 6A841BC6-BF69-4121-B5AB-5B87F9CBD9E1 No YES NO Non-glabrous skin 3 9 NO No M0 N0 T2 A824 WITH TUMOR YES YES NO NO NO NO Regional Lymph Node YES 4th NO YES W3 79 false 2017 2014 1995 0 SKCM TCGA-W3-A824.E8E86625-8D7E-419F-A4BB-86499B9E4E88.pdf TCGA A 63.0 -23131.0 not hispanic or latino male white Alive 1932.0 23131.0 not reported 0.0 6940.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage i 1995.0 Nevi and Melanomas Not Reported 27.335640138408305 170.0 79.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH W3 John Wayne Cancer Center Skin Cutaneous Melanoma 6686.0 80.0 False true Metastatic 06 released Not Reported TCGA-FS-A4FD-06A 39 277.76.0 Nationwide Children's Hospital TCGA-FS-A4FD-F62033 EB67D0C9-E4BA-45E1-BE59-6F48AF9F31C4 TCGA-FS-A4FD 2 14 11 0 2300 085DACD4-0C14-4FB7-90BD-8D152BD2D199 Yes YES YES NO IV 3 7 No M0 N3 T2 A4FD WITH TUMOR NO 1 YES YES NO YES Interferon NO NO Regional Lymph Node NO 6th NO YES FS false 2017 2014 2004 0 SKCM TCGA-FS-A4FD.6412A296-EDCB-4C6F-B2A7-BC3D4643B843.pdf TCGA A 39.0 -14426.0 2454.0 not hispanic or latino male white Dead 1965.0 14426.0 not reported 0.0 2369.0 C77.9 not reported 8720/3 Malignant melanoma, NOS no Yes not reported Lymph node, NOS No Skin, NOS not reported stage iiic 2004.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FS Essen Skin Cutaneous Melanoma 3065.0 80.0 False false Metastatic 06 released Not Reported TCGA-ER-A19H-06A 40 180.87.0 Nationwide Children's Hospital TCGA-ER-A19H 0.4 14 0 3573 29E1FFF6-033F-4261-9CFC-F5AAD3E1820C No YES II Non-glabrous skin 3 No M0 N0 A19H 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 4th YES NO ER false 2017 1995 0 SKCM TCGA-ER-A19H.8C9DBA9E-E8F5-4080-938D-B58F675461ED.pdf TCGA A 40.0 -14935.0 4634.0 not hispanic or latino male white Dead 1955.0 2007.0 14935.0 not reported 0.0 C49.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of thorax No Skin, NOS not reported not reported 1995.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 5757.0 620.0 False true Metastatic 06 released Not Reported TCGA-EB-A4OY-01A 65 277.76.0 Nationwide Children's Hospital TCGA-EB-A4OY-F57676 56E5C92C-0D28-4EEE-9145-DDAFF18DCA7A TCGA-EB-A4OY 10 14 19 0 -2 D6A3C15D-F713-43FB-B5B9-344465482132 No YES NO V Non-glabrous skin 3 3 No M0 N1a T4b A4OY TUMOR FREE YES YES NO NO NO NO Primary Tumor NO 7th YES NO EB 87 false 2017 2014 2012 0 SKCM TCGA-EB-A4OY.F5BCA762-3B92-4B86-96D9-A88C7D06967A.pdf TCGA A 65.0 -23773.0 not hispanic or latino female white Alive 1947.0 23773.0 not reported 0.0 977.0 C44.7 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of lower limb and hip No Skin, NOS not reported stage iiib 2012.0 Nevi and Melanomas Not Reported 29.407787993510006 172.0 87.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH EB Asterand Skin Cutaneous Melanoma 186.0 670.0 False true Primary Tumor 01 released Not Reported TCGA-ER-A3PL-06A 30 240.79.0 Nationwide Children's Hospital TCGA-ER-A3PL-F69342 4940156A-6D72-42F6-A95E-AD17C919F121 TCGA-ER-A3PL 2.2 14 6 0 958 6A29BE6F-4360-406A-B2F9-9C4B2DE80E86 No YES NO V Non-glabrous skin YES 3 1 NO No M1a N0 T3b A3PL WITH TUMOR NO YES NO NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 6th YES NO ER false 2017 2015 2009 0 SKCM TCGA-ER-A3PL.249C231D-FD24-4E23-87D3-11D844B41EBF.pdf TCGA A 30.0 -11032.0 not hispanic or latino male white Alive 1979.0 11032.0 not reported 0.0 1010.0 C49.1 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of upper limb and shoulder No Skin, NOS not reported stage iv 2009.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 1017.0 440.0 False true Metastatic 06 released Not Reported TCGA-ER-A19A-06A 79 180.87.0 Nationwide Children's Hospital TCGA-ER-A19A-F48977 C163BEA8-E3BA-437C-9B9F-791B7DBFC78A TCGA-ER-A19A 14 24 0 41 109A9BB6-4D6A-436C-919E-5F05220E029E No YES NO Non-glabrous skin 3 9 NO No M1 N0 TX A19A TUMOR FREE NO NO NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 6th YES NO ER false 2017 2013 2006 0 SKCM TCGA-ER-A19A.B48FFC2C-4D9A-4451-B237-3B004D773260.pdf TCGA A 79.0 -29167.0 not hispanic or latino male white Alive 1927.0 29167.0 not reported 0.0 2365.0 C49.6 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues of trunk, NOS No Skin, NOS not reported stage iv 2006.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 1672.0 110.0 False true Metastatic 06 released Not Reported TCGA-FS-A1ZR-06A 36 180.87.0 Nationwide Children's Hospital TCGA-FS-A1ZR 2 14 0 79 AB1AE23C-0421-46C1-8675-C7FAC33E4738 No YES IV Non-glabrous skin 3 No M0 N0 T2 A1ZR 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 4th NO YES FS false 2017 1995 0 SKCM TCGA-FS-A1ZR.CBE0DB60-637C-4D5D-85EA-C112E308F476.pdf TCGA A 36.0 -13325.0 347.0 not hispanic or latino male white Dead 1959.0 1995.0 13325.0 not reported 0.0 C77.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph node, NOS No Skin, NOS not reported stage ii 1995.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FS Essen Skin Cutaneous Melanoma 5645.0 100.0 False false Metastatic 06 released Not Reported TCGA-DA-A3F3-06A 52 198.83.0 Nationwide Children's Hospital TCGA-DA-A3F3-F70096 F284EA79-1E79-4396-800D-C2AB6AB6BC03 TCGA-DA-A3F3 14 27 0 106 1D1C4D82-3FFB-4F8D-A403-A53CE1299278 No YES NO Non-glabrous skin 3 1 No M0 N2b T0 A3F3 WITH TUMOR NO NO NO NO NO Regional Lymph Node NO 7th NO YES DA false 2017 2015 2010 0 SKCM TCGA-DA-A3F3.66035FA9-7600-46C5-A5F5-CBD1CC1C1AD4.pdf TCGA A 52.0 -19330.0 319.0 not hispanic or latino male white Dead 1958.0 19330.0 not reported 0.0 151.0 C77.0 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of head, face and neck No Skin, NOS not reported stage iiib 2010.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH DA Yale Skin Cutaneous Melanoma 353.0 250.0 False true Metastatic 06 released Not Reported TCGA-FS-A4F4-06A 64 277.76.0 Nationwide Children's Hospital TCGA-FS-A4F4 2.0 14 0 1804 E30CDB48-A1C6-494D-B81E-6CAE7B71D583 No YES IV Non-glabrous skin YES 3 No M0 N0 T3a A4F4 1 YES NO NO NO NO Regional Cutaneous or Subcutaneous Tissue (includes satellite and in-transit metastasis) 5th NO YES FS false 2017 2001 0 SKCM TCGA-FS-A4F4.8F51786F-405B-495A-B662-D0C17B68DC33.pdf TCGA A 64.0 -23663.0 2028.0 not reported male white Dead 1937.0 2006.0 23663.0 not reported 0.0 C49.9 not reported 8720/3 Malignant melanoma, NOS no No not reported Connective, subcutaneous and other soft tissues, NOS No Skin, NOS not reported stage ii 2001.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FS Essen Skin Cutaneous Melanoma 4103.0 60.0 False false Metastatic 06 released Not Reported TCGA-FR-A2OS-01A 49 206.84.0 Nationwide Children's Hospital TCGA-FR-A2OS-F31116 29630931-6C4E-45CC-892C-EABCD517BB01 TCGA-FR-A2OS 14 24 0 0 84C055E7-16FF-45A2-BA60-D6548B84A70F No YES Elevated NO V Non-glabrous skin YES 3 4 NO No M0 N0 T4b A2OS WITH TUMOR NO 1 YES NO NO NO NO NO Primary Tumor NO 6th NO YES FR 84 false 2017 2012 2008 0 SKCM TCGA-FR-A2OS.518D48F1-6A74-4376-85CB-E1F2C8C46011.pdf TCGA A 49.0 -18122.0 368.0 not reported female white Dead 1959.0 2009.0 18122.0 not reported 0.0 C44.7 not reported 8720/3 Malignant melanoma, NOS no No not reported Skin of lower limb and hip No Skin, NOS not reported stage iic 2008.0 Nevi and Melanomas Not Reported 31.23140987507436 164.0 84.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FR University of North Carolina Skin Cutaneous Melanoma 1257.0 70.0 False false Primary Tumor 01 released Not Reported TCGA-Z2-AA3S-06A 58 408.40.0 Nationwide Children's Hospital TCGA-Z2-AA3S-F68278 D473B9CF-0187-46F0-93F3-02DEEBB567EE TCGA-Z2-AA3S 0.5 14 14 0 2628 ECEAAEB1-B2C0-4C6E-B99C-59A00836E1EE No YES NO II Non-glabrous skin NO 3 11 NO No M0 N0 T1a AA3S WITH TUMOR NO YES NO NO NO YES NO Regional Lymph Node NO 6th YES NO Z2 78 false 2017 2014 2006 0 SKCM TCGA-Z2-AA3S.48FB1191-7102-4E62-8A56-A3991807AB6D.pdf TCGA A 58.0 -21318.0 not hispanic or latino male white Alive 1948.0 21318.0 not reported 0.0 2950.0 C77.3 not reported 8720/3 Malignant melanoma, NOS no No not reported Lymph nodes of axilla or arm No Skin, NOS not reported stage ia 2006.0 Nevi and Melanomas Not Reported 26.365603028664143 172.0 78.0 Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH Z2 IDI-IRCCS Skin Cutaneous Melanoma 2646.0 270.0 False true Metastatic 06 released Not Reported TCGA-ER-A42H-01A 76 262.74.0 Nationwide Children's Hospital TCGA-ER-A42H 14 0 114 14798F61-224E-4BD0-A01A-588823CD7CAF No YES Non-glabrous skin 3 NO No A42H YES YES NO NO Primary Tumor YES NO ER false 2017 2010 0 SKCM TCGA-ER-A42H.A0ED997A-DE1F-4339-B149-9B0C2A8FF8EB.pdf TCGA A 76.0 -28110.0 426.0 not hispanic or latino male white Dead 1934.0 2011.0 28110.0 not reported 0.0 C43.51 not reported 8720/3 Malignant melanoma, NOS no No not reported Overlapping lesion of rectum, anus and anal canal No Skin, NOS not reported not reported 2010.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH ER University of Pittsburgh Skin Cutaneous Melanoma 736.0 220.0 False true Primary Tumor 01 released Not Reported TCGA-FS-A1YY-06A 55 180.87.0 Nationwide Children's Hospital TCGA-FS-A1YY 2.7 14 0 6618 abdomen B29E5CCD-665D-4CF4-B010-501A66151BF8 No YES IV Non-glabrous skin NO 3 No M0 N0 T3a A1YY 1 YES NO NO NO NO Distant Metastasis 2nd NO YES FS false 2017 1987 0 SKCM TCGA-FS-A1YY.A91D1EF0-6010-4536-8071-39A935247466.pdf TCGA A 55.0 -20396.0 6953.0 not hispanic or latino female white Dead 1932.0 2006.0 20396.0 not reported 0.0 C76.2 not reported 8720/3 Malignant melanoma, NOS no No not reported Abdomen, NOS No Skin, NOS not reported stage iia 1987.0 Nevi and Melanomas Not Reported Skin Skin Cutaneous Melanoma TCGA-SKCM False NCH FS Essen Skin Cutaneous Melanoma 8800.0 60.0 False false Metastatic 06 released Not Reported ================================================ FILE: inst/extdata/TCGA-SKCM.htseq_counts_test.tsv ================================================ Ensembl_ID TCGA-EE-A2GJ-06A TCGA-EE-A2GI-06A TCGA-WE-A8ZM-06A TCGA-DA-A1IA-06A TCGA-D3-A51H-06A TCGA-XV-A9VZ-01A TCGA-FS-A1ZE-06A TCGA-D3-A51F-06A TCGA-D3-A8GL-06A TCGA-BF-A5EP-01A TCGA-FS-A1ZS-06A TCGA-D3-A2J6-06A TCGA-EE-A3AF-06A TCGA-BF-AAP6-01A TCGA-D3-A8GM-06A TCGA-D9-A3Z3-06A TCGA-FS-A1ZR-06A TCGA-EE-A2GC-06A TCGA-WE-A8K5-06A TCGA-ER-A198-06A TCGA-FS-A1ZG-06A TCGA-XV-A9W5-01A TCGA-ER-A42H-01A TCGA-EB-A44P-01A TCGA-D3-A3MU-06A TCGA-FS-A1ZT-06A TCGA-GN-A8LK-06A TCGA-FR-A729-06A TCGA-D3-A51J-06A TCGA-D3-A5GR-06A TCGA-WE-A8ZN-06A TCGA-FS-A4FC-06A TCGA-RP-A6K9-06A TCGA-EB-A82B-01A TCGA-FS-A4FB-06A TCGA-EE-A3J8-06A TCGA-D3-A1Q7-06A TCGA-GN-A267-06A 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9.527477006060396 9.906890595608518 11.250890535723341 8.721099188707186 8.751544059089099 8.823367240046235 9.927777962082342 8.73809225962049 9.473705749619416 11.323054760341646 10.290018846932618 7.693486957499325 8.854868383260236 9.505811553919594 9.074141462752506 8.845490050944376 9.095397022792557 9.370687406807217 8.515699838284043 10.393390457443987 10.650154213675094 8.303780748177102 9.972979786066292 8.189824558880018 9.618385502258606 9.632995197142957 9.54882190845875 9.264442600226602 10.091435386323607 9.57553924683453 10.938844457466171 9.366322214245816 7.930737337562887 8.344295907915816 8.422064766172813 9.228818690495881 10.426264754702098 ================================================ FILE: inst/extdata/TCGA_gencode.v22.annotation.gene.probeMap_test ================================================ id gene chrom chromStart chromEnd strand ENSG00000000005.5 TNMD chrX 100584802 100599885 + ENSG00000000457.12 SCYL3 chr1 169849631 169894267 - ENSG00000000460.15 C1orf112 chr1 169662007 169854080 + ================================================ FILE: inst/extdata/all_count_exp_test.csv ================================================ 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================================================ FILE: inst/extdata/ascii_art.txt ================================================ _______ _____ _ _ _____ _____ _____ ____ _____ |__ __| __ \ /\ | \ | |/ ____| __ \| __ \ / __ \| __ \ | | | |__) | / \ | \| | (___ | |__) | |__) | | | | |__) | | | | _ / / /\ \ | . ` |\___ \| ___/| _ /| | | | _ / | | | | \ \ / ____ \| |\ |____) | | | | \ \| |__| | | \ \ |_| |_| \_\/_/ \_\_| \_|_____/|_| |_| \_\\____/|_| \_\ _ _ _______ _____ _____ _______ _________ _______ _____ _____ /\ | | | |__ __| / ____/ ____/ ____\ \ / / __ \ \ / / ____/ ____/ ____| / \ | | | | | | | (___| (___| (___ \ \_/ /| | | \ \_/ / (___| (___| (___ / /\ \| | | | | | \___ \\___ \\___ \ \ / | | | |\ / \___ \\___ \\___ \ / ____ \ |__| | | | ____) |___) |___) | | | | |__| | | | ____) |___) |___) | /_/ \_\____/ |_| |_____/_____/_____/ |_| |_____/ |_| |_____/_____/_____/ ================================================ FILE: inst/extdata/gtex_gene_expected_count_test 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GTEX-11P7K-0326-SM-59871 GTEX-ZYWO-0526-SM-5E45I GTEX-XUJ4-2826-SM-4BOQ2 GTEX-14ASI-0526-SM-5QGQP GTEX-Y8DK-0526-SM-4TT3R GTEX-PW2O-0006-SM-2I3DV GTEX-T5JC-0001-SM-3NMAK GTEX-YB5K-2326-SM-5IFHD GTEX-ZLV1-1326-SM-4WWER GTEX-13112-1126-SM-5GCMY GTEX-ZV6S-0926-SM-57WGB GTEX-P4PQ-0726-SM-3NMCR GTEX-N7MT-1226-SM-2TC6K GTEX-ZDTS-0126-SM-4WAY5 GTEX-13FTW-0005-SM-5LZVZ GTEX-TML8-0001-SM-3NMAF GTEX-13OW6-1526-SM-5L3HX GTEX-11NSD-1226-SM-5986Q GTEX-1399U-0008-SM-5S2VE GTEX-144GM-1326-SM-5LU5E GTEX-X261-0011-R6B-SM-4E3J8 GTEX-13S86-1226-SM-5S2OA GTEX-ZYWO-1726-SM-5E44T GTEX-13JVG-1426-SM-5MR4W GTEX-XXEK-1326-SM-4BRV1 GTEX-12584-1426-SM-5EGJ9 GTEX-13X6K-0011-R4b-SM-5P9HO GTEX-14E6E-1326-SM-5S2NR GTEX-PLZ6-0006-SM-33HBZ GTEX-12WSH-0126-SM-5GCO3 GTEX-11ZTT-0006-SM-5N9FX GTEX-11P81-1326-SM-5GU5S GTEX-1445S-0011-R8a-SM-5PNWX GTEX-147JS-2526-SM-5S2Q8 K-562-SM-5KM2I GTEX-1128S-2826-SM-5N9DI GTEX-WWYW-0011-R6A-SM-3NB3G GTEX-Y9LG-1126-SM-4VBQ7 GTEX-13CF3-0426-SM-5IJEU GTEX-147GR-0011-R4a-SM-5S2V2 GTEX-12WSB-0011-R6b-SM-5GU6J GTEX-PLZ6-0226-SM-3P61I GTEX-11DXY-0426-SM-5H12R GTEX-ZAK1-3026-SM-5S2MJ GTEX-13S7M-0011-R7a-SM-5O9DK GTEX-11XUK-0426-SM-5EQML GTEX-P4QT-0526-SM-2I3EX GTEX-ZVT3-0011-R10b-SM-57WB6 GTEX-11NV4-1126-SM-5HL6J GTEX-WY7C-2726-SM-3NB3P GTEX-ZVE2-1826-SM-51MRZ GTEX-ZLV1-0005-SM-4WWAP GTEX-QDT8-0011-R6A-SM-32PKI GTEX-148VJ-0826-SM-5LU8V GTEX-ZPIC-0626-SM-57WDY GTEX-1192X-1826-SM-5GIE2 GTEX-P78B-0226-SM-3NB1Z GTEX-11PRG-2826-SM-5BC54 GTEX-X638-0126-SM-47JZ8 GTEX-ZE7O-0011-R6a-SM-57WCI GTEX-ZDTT-2126-SM-5S2OJ GTEX-131XW-3226-SM-5LZUO GTEX-13N11-2326-SM-5IJBF GTEX-147F3-0526-SM-5SI98 GTEX-139UC-2926-SM-5J1N6 GTEX-ZYWO-0426-SM-5SI9A GTEX-Y114-2326-SM-4TT7X GTEX-144GL-0226-SM-5QGOX GTEX-1339X-0426-SM-5KLZY GTEX-ZVP2-0005-SM-51MRK GTEX-12WSM-0011-R8a-SM-5DUWJ GTEX-13N11-0926-SM-5IJG2 GTEX-QDT8-0526-SM-32PL5 GTEX-ZPCL-1226-SM-4WWFF GTEX-ZQG8-2426-SM-57WEE GTEX-111FC-0826-SM-5GZWO GTEX-T6MO-0326-SM-32QOK GTEX-13PDP-0011-R8b-SM-5O9CS GTEX-13U4I-1826-SM-5IJD2 GTEX-XQ3S-2726-SM-4BOP2 GTEX-111YS-1326-SM-5EGGK GTEX-11EQ9-1626-SM-5PNY8 GTEX-11I78-0726-SM-5A5M1 GTEX-13NYB-0011-R8a-SM-5KM4R GTEX-1313W-0726-SM-5EGK1 GTEX-1212Z-0426-SM-5FQT6 GTEX-13111-2126-SM-5LZU2 GTEX-WHSE-0526-SM-5HL79 GTEX-Q2AG-0226-SM-2S1P4 GTEX-T6MN-1526-SM-4DM5P GTEX-S3XE-0008-SM-3NM8O GTEX-12ZZZ-1326-SM-59HKW GTEX-RNOR-1426-SM-48FDJ GTEX-13OW8-0126-SM-5IJE5 GTEX-11ZUS-2626-SM-5FQUR GTEX-145MN-0326-SM-5QGQI GTEX-132NY-0005-SM-5O9AC GTEX-R55F-1426-SM-2TF53 GTEX-X4LF-0326-SM-4QAS9 GTEX-P4QS-1826-SM-2S1NI GTEX-U3ZH-1926-SM-4DXTR GTEX-P44H-0011-R5A-SM-2XCEX GTEX-V955-1626-SM-4JBHJ GTEX-13SLW-0011-R10a-SM-5S2UI GTEX-13S7M-0226-SM-5S2UG GTEX-12WSH-2926-SM-5GCMR GTEX-146FH-1326-SM-5NQBI GTEX-ZTTD-0626-SM-4YCCY GTEX-V1D1-0826-SM-3P5ZA GTEX-1399R-0326-SM-5KM1X GTEX-T5JC-0426-SM-32PLO GTEX-Q2AG-0011-R9A-SM-2HMJ6 GTEX-14BIM-0011-R6b-SM-5S2VB GTEX-11OF3-1126-SM-5986C GTEX-Y111-2426-SM-4TT23 GTEX-13O3Q-2626-SM-5KM3S GTEX-11ZVC-3226-SM-5FQV1 GTEX-WFON-1126-SM-4LVMA GTEX-12C56-1826-SM-5EQ6D GTEX-ZAB5-0126-SM-5CVMT GTEX-144FL-3026-SM-5O99C GTEX-XLM4-1526-SM-4AT6D GTEX-X4EP-0126-SM-3P5YV GTEX-X4EO-0526-SM-3P5Z3 GTEX-YFCO-0526-SM-4W1ZJ GTEX-R55F-1526-SM-2TF4X GTEX-13FHP-0426-SM-5J1O5 GTEX-13FLV-0011-R8a-SM-5LZZE GTEX-QXCU-1326-SM-48FE2 GTEX-13CF2-0011-R8b-SM-5IJCX GTEX-ZTTD-2626-SM-57WEX GTEX-131YS-3126-SM-5KLYT GTEX-13OW7-3026-SM-5L3GY GTEX-WFON-1726-SM-4LVMQ GTEX-WHPG-0726-SM-4M1XX GTEX-ZVT2-0226-SM-5GIE3 GTEX-R3RS-0526-SM-3GADG GTEX-13OVI-1526-SM-5K7VX GTEX-111YS-0008-SM-5Q5BH GTEX-13SLX-0011-R8b-SM-5PNWZ GTEX-11ILO-2226-SM-5A5L1 GTEX-11PRG-0626-SM-5BC56 GTEX-139T8-0011-R4a-SM-5HL54 GTEX-T6MO-1226-SM-4DM5S GTEX-11ZUS-0126-SM-5EQM5 GTEX-13OVJ-2426-SM-5KM3I GTEX-145LU-0011-R3a-SM-5P9K2 GTEX-WHSE-0011-R2A-SM-3P5ZL GTEX-VUSG-0126-SM-4KL1X GTEX-ZVZP-1226-SM-5GID7 GTEX-OOBJ-1526-SM-3NB1Q GTEX-TMKS-0005-SM-33OV7 GTEX-RN64-1226-SM-2TC6E GTEX-YEC3-0526-SM-5IFHN GTEX-QDT8-0326-SM-32PL1 GTEX-Z93S-0926-SM-5HL8B GTEX-PLZ4-2826-SM-3P617 GTEX-XBED-2226-SM-47JYQ 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GTEX-1477Z-2826-SM-5SI9J GTEX-13OW7-0826-SM-5L3EL GTEX-Y8DK-0011-R6A-SM-4V6EG GTEX-ZYW4-2126-SM-59HJ9 GTEX-P4PP-1626-SM-2HMJF GTEX-ZU9S-0226-SM-5E441 GTEX-11P7K-1826-SM-5HL5A GTEX-13113-2026-SM-5LZU3 GTEX-X5EB-0526-SM-46MVP GTEX-11DXY-1626-SM-5H12L GTEX-ZF29-1626-SM-4WKFJ GTEX-11O72-2726-SM-5HL6K GTEX-13FTW-1526-SM-5LZXC GTEX-R55E-0011-R7A-SM-2TC5Z GTEX-11O72-0326-SM-5986D GTEX-13VXT-0126-SM-5LU4I GTEX-14C38-0826-SM-5S2U8 GTEX-11P82-0826-SM-5P9GU GTEX-11EMC-0006-SM-5O9DN GTEX-OXRK-0426-SM-3NB2E GTEX-13FTW-0626-SM-5IFEX GTEX-131YS-0011-R1a-SM-5DUXC GTEX-QESD-0826-SM-47J4G GTEX-WFON-2226-SM-3TW8W GTEX-13RTK-1826-SM-5S2P3 GTEX-YF7O-1326-SM-4W1ZR GTEX-OIZI-0005-SM-2XCED GTEX-13OW6-0011-R7b-SM-5L3H4 GTEX-R55E-0006-SM-2TC5G GTEX-V955-1826-SM-4JBIL GTEX-14BMU-1126-SM-5RQJ8 GTEX-13OVG-0326-SM-5KM57 GTEX-ZVT3-3026-SM-5E43N GTEX-ZYW4-2226-SM-5GZYG GTEX-PW2O-1926-SM-2S1OB GTEX-131YS-1426-SM-5IJF7 GTEX-Q2AH-1126-SM-48TZM GTEX-11OC5-0004-SM-5S2O6 GTEX-X4XY-0006-SM-46MV2 GTEX-P4PQ-1526-SM-3NMCK 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9.9817 9.4979 8.7335 10.2407 10.1647 7.6366 10.0927 9.4063 8.9786 9.1447 9.8358 10.9263 9.8728 10.3743 0.0000 9.5908 10.4218 9.2975 11.2018 10.5978 9.6552 7.3129 9.4337 8.7180 9.5785 9.4727 8.5504 10.3698 8.8457 9.7055 7.4099 8.9972 10.8498 9.9051 9.0484 9.6770 9.4432 8.9464 8.8237 8.7544 8.9561 10.0242 8.8958 8.2628 9.7804 9.5621 9.0016 9.7538 8.9553 9.4701 9.6447 8.8236 8.6724 9.5131 10.5820 8.5028 9.1005 10.4915 9.8047 10.0402 8.4127 10.1154 8.6961 8.1167 9.2823 7.0289 9.3715 8.7626 8.6685 10.0744 8.9662 9.6797 9.4262 10.4522 9.7341 9.7153 11.5968 7.9715 9.6386 10.3140 8.4436 11.0362 8.9930 9.1928 10.0890 10.2252 8.9962 10.0664 10.1511 9.6308 7.2436 8.6680 10.2342 8.3657 9.6865 10.1364 8.9011 7.9112 7.5157 10.7768 9.9111 9.3686 9.1496 10.3376 8.9142 9.5097 11.2526 9.1639 8.6718 9.8644 8.8123 8.7313 8.9784 10.1258 8.0607 9.0533 10.4526 10.0140 8.1799 7.2095 9.1684 9.0038 9.7207 10.9058 9.7761 8.9373 9.4193 9.0189 8.8517 9.3587 10.1323 7.9181 10.4781 10.1248 10.2134 9.6460 9.9495 10.2996 8.8830 9.5515 8.8478 9.1846 9.6203 9.8432 9.9604 8.2411 9.8026 7.6080 9.3483 9.6413 8.9449 9.2683 8.7235 10.7715 8.2744 8.6592 9.0835 9.2134 8.7257 10.1387 10.5235 8.9587 11.0306 9.0708 9.7654 9.1510 10.0494 7.6067 8.3565 10.0955 4.4777 7.5763 8.1913 9.5749 10.8847 9.4816 9.5012 10.3184 8.1413 10.0370 10.1065 9.8541 4.4449 9.1410 9.0436 9.0498 10.5192 9.4366 8.5408 8.9687 10.5364 8.3275 6.8704 8.9619 9.2390 9.2388 8.7991 10.9818 9.7979 7.3822 9.6595 9.0815 10.1084 9.8762 8.8922 10.4624 10.1109 10.4940 8.2719 8.9894 9.8308 9.7715 8.1045 9.1617 8.0050 9.6356 9.7426 6.8659 8.2904 8.0419 8.7934 9.5726 9.0900 9.6431 8.6100 10.0402 8.9368 9.5438 9.7087 8.5004 9.5629 10.0898 9.8436 9.2038 9.7978 7.9887 10.0556 9.3386 8.9099 10.4564 8.8526 8.8889 10.3002 10.0536 8.6124 8.9916 9.7273 9.0051 9.8725 8.3831 10.5323 10.0157 9.1539 10.2714 9.8797 8.2928 8.3804 11.3507 8.5333 8.5392 8.1334 9.9121 11.1797 9.7294 10.0670 8.1925 9.7137 10.8596 9.8129 10.1609 11.1418 7.4091 10.0291 6.5078 11.5819 8.6139 9.9113 10.1431 8.4152 9.5931 10.1951 10.0299 10.3579 10.1989 8.7563 9.9061 10.2131 10.6260 10.5164 9.1985 10.3861 10.7141 9.0884 8.0405 9.2039 9.7926 9.3434 8.9355 10.3045 10.6648 9.7614 10.5229 9.7398 8.5196 8.6917 10.5003 8.8540 9.0515 10.2128 11.4523 10.1385 8.8135 9.4925 10.6182 9.9792 9.8161 10.1711 8.9119 9.8814 9.0218 9.3280 9.8442 10.3443 10.5008 9.7157 9.7451 9.1228 9.3755 7.1954 10.0871 9.6507 9.5647 9.7033 8.8125 11.3746 8.4623 9.1516 9.2457 11.0884 10.0052 9.5024 10.1991 10.5000 5.5081 9.4844 9.2039 8.1753 9.6711 9.2969 10.0522 8.4927 10.5197 10.7911 8.8390 10.4755 8.8430 9.9042 10.6441 10.7177 8.1033 9.0848 9.7664 9.9153 9.4322 7.7550 8.3856 6.9389 10.6978 9.6049 10.8080 9.4463 9.3728 9.2053 9.1252 10.2951 9.7000 9.1542 10.3390 9.2893 8.1255 11.3663 8.9614 8.4039 9.3167 9.5307 9.8533 9.5162 9.3685 9.7848 6.9773 8.2149 10.0737 9.0630 9.8791 11.7949 9.9036 9.6904 9.5148 9.5522 9.2015 9.6943 9.4971 9.6853 10.7115 10.1744 9.2328 10.6896 9.1394 9.1281 9.9637 9.5813 9.1429 9.9915 10.1029 10.7443 10.3172 9.6390 8.7166 10.5344 10.0117 10.8642 8.9565 10.2718 9.0234 8.9994 9.4609 10.1786 8.6683 9.4248 10.2099 9.2216 10.0309 8.3531 9.9200 9.1858 10.1322 10.2495 9.3762 9.9635 8.9304 9.5128 10.4971 10.2918 9.8577 8.1604 8.9411 9.3747 10.4016 10.3143 9.2597 9.3218 10.8522 9.2495 9.1599 9.1726 9.7027 11.9259 8.9066 9.0247 8.5413 8.3407 10.1902 11.1265 8.4791 10.2028 8.5947 9.3254 10.2185 9.2572 10.0685 7.0334 10.3627 8.4717 10.0321 9.3269 8.4053 10.4458 8.7044 7.1799 10.0164 10.1970 10.7809 8.9773 9.5864 9.2940 9.7359 0.0000 9.4130 7.0112 9.2505 7.5546 10.1346 7.4250 9.6925 9.7742 7.6366 9.8779 9.7279 10.6447 7.6495 10.1588 10.3967 8.7236 9.9768 9.4023 9.2262 11.1024 8.8732 7.7415 9.9088 9.6204 9.4676 9.3163 9.4041 9.7910 9.1994 6.8982 10.5813 9.2823 9.7852 10.0758 9.2526 8.9591 10.2198 9.7412 10.2005 8.7694 10.1781 10.2979 10.1132 8.8517 8.3869 9.9405 9.2989 10.5732 8.6670 9.8701 9.1220 9.3516 9.5097 9.3652 9.2429 8.4279 9.2090 10.2178 9.5558 6.9676 7.9017 11.5257 10.8406 9.4860 7.9214 10.9463 8.5708 9.6112 10.2778 9.3573 8.9560 9.1912 10.4565 9.2518 7.8126 9.9549 0.0000 10.1056 9.4897 9.2265 7.4245 10.1924 10.6816 9.8878 9.1720 10.2075 11.1176 10.8472 9.0397 7.8580 9.5944 9.0347 12.3821 7.8101 10.5207 10.4433 9.6764 8.7964 9.1564 9.5519 9.4415 10.0330 9.3701 10.4632 7.7342 9.5069 9.3519 9.5516 9.4802 8.8267 9.6813 9.9485 8.4352 7.8226 8.0097 8.1278 9.7296 10.6962 8.9683 8.9983 8.6147 9.4311 9.3486 9.6549 9.8754 10.7394 8.8246 10.2256 10.1098 7.3031 9.9095 9.6671 9.7529 10.4896 8.8223 9.9879 9.6805 9.8851 9.0284 11.1973 7.0629 8.2277 10.1924 8.9167 9.9810 9.3215 10.3062 9.8504 10.6235 9.3584 9.7756 10.1871 9.5265 8.8132 9.5959 9.7036 11.1246 8.8844 11.6319 8.0998 9.9270 10.6044 8.3036 9.8750 10.7072 9.2329 7.6439 7.8816 10.0349 10.0592 10.3211 9.1171 9.6958 8.6144 8.3998 10.1565 9.1387 9.9337 6.8804 10.4251 9.4371 9.4498 10.0637 9.5640 9.5297 10.0858 8.8106 10.0002 9.6629 7.4018 11.3069 8.9857 9.3754 8.6770 9.8864 9.0926 10.3595 9.2755 9.6495 11.0519 9.2357 8.3771 10.6607 8.9316 8.8772 9.6797 9.1707 7.9307 9.3668 9.6801 8.8955 9.7170 8.3301 11.1014 10.3095 9.8036 8.5448 8.1251 9.3417 9.1772 9.0882 10.2938 9.6832 8.8766 9.0713 10.3948 8.1005 11.1170 6.7166 10.8310 10.0876 8.2561 10.1895 10.5983 9.1146 8.3928 8.0661 10.1633 10.4833 9.0927 8.7433 10.0387 9.3751 8.6452 5.3576 11.2645 10.0678 10.2006 9.4264 10.1273 9.6384 9.9742 8.7197 10.5659 9.9823 9.5039 9.6720 9.6538 8.5996 9.9225 7.3129 10.8120 9.0698 9.6474 10.8037 9.9484 10.7505 8.7810 10.4840 9.2809 10.2818 9.6615 8.6743 7.7211 9.6778 10.6306 10.0894 9.5727 9.2650 6.5686 10.0696 9.8979 9.8627 0.0000 9.3623 9.9809 8.2369 8.9248 9.1695 10.5349 11.0777 8.4866 10.5803 9.4159 9.9844 8.9572 10.1032 9.5186 8.4482 9.8205 10.7059 10.3344 9.2201 0.0000 8.7325 10.1794 8.3428 7.1498 8.6026 9.6725 10.2017 10.3532 9.3716 9.1155 9.5921 8.8049 10.6954 11.5543 9.0566 10.0070 9.3320 10.3966 9.2426 10.3545 9.3091 9.5081 11.0260 10.3480 8.9635 9.6273 10.2253 9.3108 9.4036 10.1531 8.4651 10.4359 9.8859 9.9145 10.1894 11.1354 10.6561 8.8809 10.0614 10.1143 10.6514 10.1244 8.4822 10.1954 9.1991 10.4871 7.9413 9.4289 9.3444 10.6983 8.6583 10.2460 10.3756 11.2597 8.7007 9.6180 10.2385 10.7987 8.7678 9.3965 8.5667 9.9573 8.4419 8.9811 9.2841 10.0863 8.8010 8.5807 10.2651 9.0817 9.3914 9.0469 9.1395 10.3851 8.4947 8.8466 9.1451 9.2791 8.7764 9.4483 9.8406 9.7217 9.0306 9.6178 9.6840 8.3542 8.6355 9.4469 10.2484 8.0214 8.3345 9.5994 8.7834 11.3258 9.1140 9.9017 8.6961 8.5459 10.1979 8.9046 9.8461 10.1289 9.3106 9.4024 8.1245 8.5746 9.2313 9.5187 8.6833 8.6754 10.0590 8.1176 9.0575 9.1723 9.3539 10.7371 10.3998 8.9738 9.2344 9.9716 8.9954 9.4830 9.7110 9.9987 9.4572 9.9682 9.8653 8.4007 8.8408 8.4721 8.8977 9.3149 9.1737 8.9402 10.6137 10.0401 8.8585 10.1376 9.4852 10.3374 10.3528 9.6676 9.7877 9.9738 10.2437 8.8095 8.6197 9.4878 8.9465 11.1331 8.8720 10.8797 9.6537 8.7493 9.0636 9.8370 9.2014 9.2983 10.6311 9.0063 9.1765 10.1791 8.3855 8.4388 9.4109 7.7804 11.7178 9.3198 9.6947 9.9141 8.6632 9.4527 11.0963 8.5164 9.2988 10.7817 10.3465 8.8563 8.7046 8.8472 10.5351 11.4624 9.8661 9.4185 10.8685 9.0913 9.2880 9.1378 7.9679 9.4294 9.4691 11.0559 9.8011 9.8976 9.7991 8.0017 10.3902 8.8931 10.2740 9.5701 8.7482 9.3377 9.0533 8.4676 10.4892 10.0404 10.2068 9.3285 10.0614 9.4322 12.0083 10.5106 8.6554 7.9156 9.6596 8.8299 9.2218 8.9646 9.8375 7.8765 10.0223 9.8775 8.8182 8.5248 9.3231 9.9624 9.3086 10.4096 10.3178 9.9054 9.6967 9.5375 9.4909 9.0332 9.7595 8.4526 9.1915 10.1688 8.5392 8.3656 5.7004 9.3444 8.7013 6.8947 9.2547 8.6257 8.2213 4.8580 10.4191 8.5236 8.9928 10.5233 7.8517 9.5770 10.2228 8.5947 9.3156 9.2243 9.2437 9.9945 8.3923 9.3682 9.2003 11.1022 11.4753 9.8412 10.1699 11.3515 8.9204 10.1621 9.4450 11.3377 9.4983 9.2824 9.4725 10.3668 9.5536 10.4823 9.5388 8.2280 10.8001 9.1189 9.5581 9.6549 9.5287 10.5628 8.7169 10.4394 9.1210 11.3384 9.0642 10.2160 8.5521 9.9167 8.5601 8.7700 10.6654 8.7856 9.6257 9.6354 8.8880 8.8647 9.8737 8.8605 10.4104 9.6769 10.1522 9.5069 9.3539 10.0533 8.2344 9.9852 10.6326 9.5451 8.4037 10.6585 10.3546 10.5480 8.8009 10.8057 9.8990 10.3225 9.4960 8.6809 10.9496 8.4914 9.9830 9.0665 8.0460 8.3038 9.8534 9.3917 8.8748 10.3366 10.0617 9.1882 10.2672 9.3784 7.9011 9.9522 9.8968 9.2073 9.8906 8.9824 8.7283 9.0417 10.4832 8.8001 9.4366 9.0032 9.6660 7.4060 9.1970 10.6617 10.0830 9.8986 10.4890 9.8911 10.7648 9.9702 8.2746 9.0314 9.0836 10.7979 9.2896 9.2485 8.9050 9.1520 9.1881 9.7507 11.1384 10.2930 8.8282 9.2503 7.5210 9.4333 8.8647 9.3035 9.6283 10.0388 9.2556 10.1554 8.4099 8.1687 7.7279 8.5425 9.1615 9.7588 8.4265 10.5732 10.2278 9.3551 8.8325 9.8341 9.2066 8.6388 10.2218 9.5246 10.4231 8.4903 9.8627 9.3610 9.4327 8.1063 8.8018 10.3841 9.4999 8.6239 9.7754 9.2935 9.3762 7.6549 10.2478 9.7549 10.9185 8.0851 7.0323 10.7083 10.2316 9.8232 10.3057 9.0363 9.7422 9.4051 9.8955 9.9317 9.4781 10.9835 7.7831 8.9334 8.4919 8.7762 9.8265 9.9789 8.7840 10.5947 8.9039 11.1137 8.9428 9.7214 8.4385 10.1157 9.0486 8.9074 9.3394 9.3808 8.6846 8.4059 10.2365 8.7930 9.3206 8.5834 10.7432 9.4757 10.1715 8.8081 10.2145 9.9922 9.7270 9.4227 10.2688 8.9625 10.1991 9.1279 8.7533 9.0274 8.8066 8.4631 9.8998 9.3057 9.4880 10.0799 9.8725 8.5612 7.3536 9.1135 10.0259 7.9744 9.7360 10.0906 8.3966 9.0944 7.8877 10.0733 9.7315 9.6813 9.0613 9.8332 10.3328 8.9505 9.5051 10.0047 7.6716 10.7853 9.9988 9.5470 10.0644 8.7381 8.8455 8.0279 9.1944 9.8380 8.7393 8.8839 10.2071 8.1174 10.4468 9.8236 9.7852 8.6504 8.5275 10.0673 10.4804 10.3646 8.1497 9.5586 8.3038 10.2569 9.0580 9.9469 9.4229 11.2141 10.5591 9.6261 9.3501 7.0311 9.0014 8.7955 9.5892 9.7154 9.8710 8.7906 9.0900 7.7960 8.5679 9.2312 9.2005 9.9132 9.9886 10.5643 9.2327 10.2219 9.7461 10.7795 8.9972 10.9565 10.4073 8.3257 9.0883 9.0065 9.8434 9.7638 8.7457 10.2956 10.1534 9.4204 10.0699 9.6200 8.3258 9.4790 9.2431 9.3472 8.9499 6.8074 9.1967 9.6473 10.2114 10.3710 9.2010 6.0661 8.4424 11.1271 8.4570 9.3683 9.2658 9.5392 9.3707 10.7530 11.0695 10.3059 11.0008 9.0266 9.5450 8.8344 9.6298 9.2063 10.4238 9.9380 10.2240 9.9228 9.4413 8.1300 9.2157 10.9050 9.0727 8.3598 8.9135 9.6336 9.7523 9.5556 10.5740 8.9114 9.4673 10.1412 9.2758 9.3080 10.0303 9.5947 10.7408 7.6653 9.4002 6.4890 9.3757 9.1715 10.5497 8.0269 10.8566 10.0370 10.3611 0.0000 8.4734 8.8469 10.4712 7.6876 9.0415 9.4865 10.6576 9.8807 9.1139 9.0412 10.5908 10.2895 9.1205 8.4124 10.1340 11.8993 8.5848 8.4931 9.2765 9.7060 8.2808 10.7009 7.8329 10.6872 9.2948 8.6366 7.5306 10.2870 10.6857 9.8894 10.6747 8.8899 9.4817 9.8023 9.9997 9.0438 8.4168 7.6974 10.5235 10.2248 9.2332 7.6712 8.4406 10.6489 9.7800 10.5838 9.4866 10.6550 8.6864 9.5646 10.3869 9.8615 9.7914 8.6823 8.8763 9.0746 11.0807 9.3255 10.0119 8.1017 10.5152 9.5724 12.3264 10.3881 9.8600 9.3406 9.8624 9.3579 9.5079 8.7340 10.9402 10.0303 9.6758 10.1310 7.1945 8.9234 10.9455 9.4483 9.0066 7.8113 10.5189 9.0808 9.5491 8.8202 9.1657 9.9505 6.9887 11.1935 11.2591 9.4861 9.0288 8.5793 8.9766 9.9440 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================================================ FILE: inst/extdata/gtex_probeMap_gencode.v23.annotation.gene.probemap_test ================================================ id gene chrom chromStart chromEnd strand ENSG00000242268.2 RP11-368I23.2 chr3 168903366 168921996 + ENSG00000259041.1 RP11-167B3.1 chr15 98282075 98285907 + ENSG00000270112.3 RP11-742D12.2 chr18 46756487 46764408 + ENSG00000167578.16 RAB4B chr19 40778216 40796944 + ENSG00000278814.1 AC104071.1 chr4 31349786 31349874 - ================================================ FILE: man/Combat_Normal.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CombatNormal.R \name{Combat_Normal} \alias{Combat_Normal} \title{Process and Correct Batch Effects in TCGA's normal tissue and GTEX Data} \usage{ Combat_Normal( TCGA_normal_data_path, gtex_data_path, CombatNormal_output_path, auto_mode = FALSE, default_input = "11,12" ) } \arguments{ \item{TCGA_normal_data_path}{The path to the tumor data stored in an RDS file.} \item{gtex_data_path}{The path to the GTEX data stored in an RDS file.} \item{CombatNormal_output_path}{A character string specifying the path where the output RDS file will be saved.} \item{auto_mode}{Logical. If set to TRUE, the function will not prompt the user for input and will instead use the values provided in default_input. Default is FALSE.} \item{default_input}{Character string. When auto_mode is TRUE, this parameter specifies the default TGCA's normal tissue types to be retained. It should be provided as a comma-separated string (e.g., "11,12").} } \value{ A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the combat_count_df data as an RDS file at the specified output path. } \description{ The function first extracts histological types from the provided TCGA's normal tissue data set. After displaying these types, the user is prompted to input specific types to retain. The data is then filtered based on this input. The GTEX and TCGA's normal tissue datasets are then combined and batch corrected. Note: This function assumes that TCGA's normal samples and GTEX samples represent different batches. } \details{ This function takes a TCGA's normal tissue data set and a pre-saved GTEX data set, asks the user for specific TCGA's normal tissue types to retain, then merges the two datasets. The merged dataset is then corrected for batch effects using the ComBat_seq function from the 'sva' package. The ComBat_seq function from the 'sva' package is used to correct batch effects. The function requires the 'sva' package to be installed and loaded externally. The example code uses `tempfile()` to generate temporary paths dynamically during execution. These paths are valid during the `R CMD check` process, even if no actual files exist, because `tempfile()` generates a unique file path that does not depend on the user's file system. Using `tempfile()` ensures that the example code does not rely on specific external files and avoids errors during `R CMD check`. CRAN review checks for documentation correctness and syntax parsing, not the existence of actual files, as long as the example code is syntactically valid. } \examples{ TCGA_normal_file <- system.file("extdata", "SKCM_Skin_TCGA_exp_normal_test.rds", package = "TransProR") gtex_file <- system.file("extdata", "Skin_SKCM_Gtex_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "SKCM_Skin_Combat_Normal_TCGA_GTEX_count.rds") SKCM_Skin_Combat_Normal_TCGA_GTEX_count <- Combat_Normal( TCGA_normal_data_path = TCGA_normal_file, gtex_data_path = gtex_file, CombatNormal_output_path = output_file, auto_mode = TRUE, default_input = "skip" ) head(SKCM_Skin_Combat_Normal_TCGA_GTEX_count)[1:5, 1:5] } \seealso{ \code{\link[sva]{ComBat_seq}} } ================================================ FILE: man/Contrast_Venn.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/Contrast_Venn.R \name{Contrast_Venn} \alias{Contrast_Venn} \title{Function to Create a Venn Diagram of DEGs with Custom Colors} \usage{ Contrast_Venn( all_degs_venn, edge_colors, name_color, fill_colors, label_size = 4, edge_size = 3 ) } \arguments{ \item{all_degs_venn}{A list of DEG sets for Venn Diagram creation.} \item{edge_colors}{A vector of colors for the edges of the Venn Diagram sets.} \item{name_color}{A vector of colors for the names of the sets in the Venn Diagram.} \item{fill_colors}{A vector of two colors for the gradient fill of the Venn Diagram.} \item{label_size}{The size of the labels showing the number of elements in each set (default is 4).} \item{edge_size}{The size of the edges of the Venn Diagram sets (default is 3).} } \value{ A `ggplot` object representing the Venn Diagram. } \description{ This function creates a Venn Diagram using the 'ggVennDiagram' package. It allows customization of various aesthetic elements of the diagram, including colors. } \examples{ data("all_degs_venn", package = "TransProR") edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39") name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39") fill_colors <- c("#e3f2fa", "#0288d1") Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors) } ================================================ FILE: man/DESeq2_analyze.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/DESeq2Analyze.R \name{DESeq2_analyze} \alias{DESeq2_analyze} \title{Differential Gene Expression Analysis using 'DESeq2'} \usage{ DESeq2_analyze( tumor_file, normal_file, output_file, logFC = 2.5, p_value = 0.01 ) } \arguments{ \item{tumor_file}{Path to the tumor data file (RDS format).} \item{normal_file}{Path to the normal data file (RDS format).} \item{output_file}{Path to save the output DEG data (RDS format).} \item{logFC}{Threshold for log fold change.} \item{p_value}{Threshold for p-value.} } \value{ A data frame of differential expression results. } \description{ 'DESeq2': Differential gene expression analysis based on the negative binomial distribution. This function utilizes the 'DESeq2' package to conduct differential gene expression analysis. It processes tumor and normal expression data, applies DESeq2 analysis, and outputs the results along with information on gene expression changes. } \details{ The DESeq2 methodology is based on modeling count data using a negative binomial distribution, which allows for handling the variability observed in gene expression data, especially in small sample sizes. This approach is well-suited for RNA-Seq data analysis. } \examples{ # Define file paths for tumor and normal data from the data folder tumor_file <- system.file("extdata", "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") normal_file <- system.file("extdata", "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "DEG_DESeq2.rds") DEG_DESeq2 <- DESeq2_analyze( tumor_file = tumor_file, normal_file = normal_file, output_file = output_file, 2.5, 0.01 ) # View the top 5 rows of the result head(DEG_DESeq2, 5) } \references{ DESeq2:Differential gene expression analysis based on the negative binomial distribution. For more information, visit the page: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/ } ================================================ FILE: man/Wilcoxon_analyze.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/WilcoxonAnalyze.R \name{Wilcoxon_analyze} \alias{Wilcoxon_analyze} \title{Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test} \usage{ Wilcoxon_analyze( tumor_file, normal_file, output_file, logFC_threshold = 2.5, fdr_threshold = 0.05 ) } \arguments{ \item{tumor_file}{Path to the tumor data file (RDS format).} \item{normal_file}{Path to the normal data file (RDS format).} \item{output_file}{Path to save the output DEG data (RDS format).} \item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.} \item{fdr_threshold}{Threshold for FDR for filtering significant genes.} } \value{ A data frame of differential expression results. } \description{ This function performs differential gene expression analysis using Wilcoxon rank-sum tests. It reads tumor and normal expression data, performs TMM normalization using 'edgeR', and uses Wilcoxon rank-sum tests to identify differentially expressed genes. } \examples{ # Define file paths for tumor and normal data from the data folder tumor_file <- system.file("extdata", "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") normal_file <- system.file("extdata", "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "Wilcoxon_rank_sum_testoutRst.rds") # Run the Wilcoxon rank sum test outRst <- Wilcoxon_analyze( tumor_file = tumor_file, normal_file = normal_file, output_file = output_file, logFC_threshold = 2.5, fdr_threshold = 0.01 ) # View the top 5 rows of the result head(outRst, 5) } \references{ Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022). Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples. Genome Biology, 23(1), 79. DOI: https://doi.org/10.1186/s13059-022-02648-4. } ================================================ FILE: man/add_boxplot.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CircosFruits.R \name{add_boxplot} \alias{add_boxplot} \title{Add a boxplot layer to a `ggtree` plot} \usage{ add_boxplot( p, data, fill_color = "#f28131", alpha = 0.6, offset = 0.22, pwidth = 0.5 ) } \arguments{ \item{p}{An existing ggtree plot object.} \item{data}{A data frame containing the data to be plotted. Expected to have columns for 'Sample' and 'value'.} \item{fill_color}{A character string specifying the fill color for the boxplots. Default is "#f28131".} \item{alpha}{Numeric value for the transparency of the boxplots. Default is 0.6.} \item{offset}{Numeric value, the position of the boxplot on the x-axis relative to its gene name. Default is 0.22.} \item{pwidth}{Numeric value, the width of the boxplot. Default is 0.5.} } \value{ A `ggtree` plot object with the added boxplot layer. } \description{ This function adds a boxplot layer to an existing `ggtree` plot object using ggtreeExtra's geom_fruit for boxplots. It is primarily used to display statistical summaries of the data related to gene expressions or other metrics. } \examples{ \donttest{ # Check and load required packages if (requireNamespace("ggtreeExtra", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE)) { library(ggtreeExtra) library(ggplot2) file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") p <- readRDS(file_path) # Create boxplot data frame boxplot_data <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 30), value = c( rnorm(30, mean = 5, sd = 1), # Data for Species_A rnorm(30, mean = 7, sd = 1.5), # Data for Species_B rnorm(30, mean = 6, sd = 1.2), # Data for Species_C rnorm(30, mean = 8, sd = 1.3) # Data for Species_D ) ) # Call add_boxplot function to add boxplot layer p_with_boxplot <- add_boxplot(p, boxplot_data) } else { message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") } } } ================================================ FILE: man/add_new_tile_layer.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CircosFruits.R \name{add_new_tile_layer} \alias{add_new_tile_layer} \title{Add a new tile layer with dynamic scales to a `ggtree` plot} \usage{ add_new_tile_layer( p, data, gene_colors, gene_label, alpha_value = c(0.3, 0.9), offset = 0.02, pwidth = 2 ) } \arguments{ \item{p}{An existing ggtree plot object.} \item{data}{A data frame containing the data to be plotted. Expected to have columns for 'Sample', 'Gene', and 'value'.} \item{gene_colors}{A named vector of colors for genes.} \item{gene_label}{A character string used as a label in the legend for the genes.} \item{alpha_value}{A numeric or named vector for setting the alpha scale based on values.} \item{offset}{Numeric value, the position of the tile on the x-axis relative to its gene name. Default is 0.02.} \item{pwidth}{Numeric value, the width of the tile. Default is 2.} } \value{ A `ggtree` plot object with the added tile layer and new scales. } \description{ This function adds a new tile layer to an existing `ggtree` plot object, allowing for separate scales for fill and alpha transparency. This is useful when you want to add additional data layers without interfering with the existing scales in the plot. It utilizes the 'ggnewscale' package to reset scales for new layers. } \examples{ \donttest{ # Check and load required packages if (requireNamespace("ggtreeExtra", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE) && requireNamespace("ggnewscale", quietly = TRUE)) { library(ggtreeExtra) library(ggplot2) library(ggnewscale) file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") p <- readRDS(file_path) # Create new expression data new_expression_data <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3), Gene = rep(c("Gene6", "Gene7", "Gene8"), times = 4), Value = runif(12, min = 0, max = 1) # Randomly generate expression values between 0 and 1 ) # Define new gene colors new_gene_colors <- c( Gene6 = "#0b5f63", Gene7 = "#074d41", Gene8 = "#1f5e27" ) # Define gene label and alpha values gene_label <- "New Genes" alpha_value <- c(0.3, 0.9) # Add new tile layer p_with_new_layer <- add_new_tile_layer( p, new_expression_data, new_gene_colors, gene_label, alpha_value, offset = 0.02, pwidth = 2 ) } else { message("Required packages 'ggtreeExtra', 'ggplot2', and 'ggnewscale' are not installed.") } } } ================================================ FILE: man/adjust_alpha_scale.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/AdjustAlphaScale.R \name{adjust_alpha_scale} \alias{adjust_alpha_scale} \title{Adjust Alpha Scale for Data Visualization} \usage{ adjust_alpha_scale(data, name, range = c(0.2, 0.8)) } \arguments{ \item{data}{A data frame containing the values for which the alpha scale is to be adjusted.} \item{name}{Character string that will be used as the title of the legend in the plot.} \item{range}{Numeric vector of length 2 specifying the range of alpha values, defaults to c(0.2, 0.8).} } \value{ A ggplot2 alpha scale adjustment layer. } \description{ This function dynamically adjusts the transparency scale for visualizations, especially useful when the range of data values varies significantly across different sources. It modifies the transparency scale based on the range of values present in the data, ensuring that the visualization accurately reflects variations within the data. } \examples{ # Assuming 'data' is a DataFrame with a 'value' column plot_data <- data.frame(value = c(10, 20, 30, 40, 50)) ggplot2::ggplot(plot_data, ggplot2::aes(x = 1:nrow(plot_data), y = value)) + ggplot2::geom_point(ggplot2::aes(alpha = value)) + adjust_alpha_scale(plot_data, "Transparency Scale") } ================================================ FILE: man/adjust_color_tone.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichmentSpiralize.R \name{adjust_color_tone} \alias{adjust_color_tone} \title{Adjust Color Tone by Modifying Saturation and Luminance} \usage{ adjust_color_tone(color, saturation_scale, luminance_scale) } \arguments{ \item{color}{A color in hexadecimal format (e.g., "#FF0000") or a valid R color name.} \item{saturation_scale}{Numeric, the scaling factor for saturation (values < 1 decrease saturation, values > 1 increase saturation).} \item{luminance_scale}{Numeric, the scaling factor for luminance (values < 1 darken the color, values > 1 lighten the color).} } \value{ Returns a color in hexadecimal format adjusted according to the provided scales. } \description{ This function adjusts the saturation and luminance of a given color. It works by converting the color from RGB to Luv color space, applying the scaling factors to the saturation and luminance, and then converting it back to RGB. } \examples{ adjusted_color <- adjust_color_tone("#FF0000", saturation_scale = 0.8, luminance_scale = 1.2) print(adjusted_color) } ================================================ FILE: man/adjust_export_pathway.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCirclize.R \name{adjust_export_pathway} \alias{adjust_export_pathway} \title{Adjust and Export Pathway Analysis Results} \usage{ adjust_export_pathway(fgseaRes, nTop = 10) } \arguments{ \item{fgseaRes}{Dataframe containing fgsea results with columns 'pathway', 'ES', and 'pval'.} \item{nTop}{Integer, number of top pathways to select based on the p-value.} } \value{ A vector containing combined top upregulated and downregulated pathways. } \description{ This function processes a dataframe containing fgsea results. It adjusts pathway names by removing underscores, converting to lowercase, then capitalizing the first letter, and joining the components with spaces. It selects and merges the top upregulated and downregulated pathways based on enrichment score (ES) and p-value. } \examples{ # Create a synthetic fgseaRes dataframe fgseaRes <- data.frame( pathway = c("KEGG_APOPTOSIS", "GO_CELL_CYCLE", "REACTOME_DNA_REPAIR", "KEGG_METABOLISM", "GO_TRANSPORT"), ES = c(0.45, -0.22, 0.56, -0.35, 0.33), pval = c(0.001, 0.02, 0.0003, 0.05, 0.01) ) # Run the function to get top pathways result <- adjust_export_pathway(fgseaRes = fgseaRes, nTop = 2) } ================================================ FILE: man/all_degs_venn.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{all_degs_venn} \alias{all_degs_venn} \title{All DEGs Venn Diagram Data} \format{ A list with the following components: \describe{ \item{DESeq2}{A vector of gene IDs or gene symbols identified as DEGs by the DESeq2 method.} \item{edgeR}{A vector of gene IDs or gene symbols identified as DEGs by the edgeR method.} \item{limma}{A vector of gene IDs or gene symbols identified as DEGs by the limma method. } \item{Wilcoxon_test}{A vector of gene IDs or gene symbols identified as DEGs by the Wilcoxon test method. } } } \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). } \usage{ data(all_degs_venn) } \description{ A dataset containing the differentially expressed genes (DEGs) from four different statistical analysis methods: DESeq2, edgeR, limma, and Wilcoxon test. This dataset is used for generating Venn diagrams to compare the overlap of DEGs identified by different methods. } \examples{ data(all_degs_venn) # Example of plotting a Venn diagram using the dataset edge_colors <- c("#1b62bb","#13822e","#332c3a","#9e2d39") name_color <- c("#1b64bb","#13828e","#337c3a","#9e9d39") fill_colors <- c("#e3f2fa", "#0288d1") Contrast_degs_venn <- Contrast_Venn(all_degs_venn, edge_colors, name_color, fill_colors) } \keyword{datasets} ================================================ FILE: man/circos_fruits.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CircosFruits.R \name{circos_fruits} \alias{circos_fruits} \title{Add multiple layers to a `ggtree` plot for visualizing gene expression and enrichment data} \usage{ circos_fruits( p, long_format_HeatdataDeseq, ssgsea_kegg_HeatdataDeseq, gsva_kegg_HeatdataDeseq, gene_colors ) } \arguments{ \item{p}{A `ggtree` plot object to which the data and layers will be added.} \item{long_format_HeatdataDeseq}{A data frame containing gene expression data with columns for `Samples`, `Genes`, and `Values`.} \item{ssgsea_kegg_HeatdataDeseq}{A data frame containing SSGSEA analysis results with columns for `Samples`, `Genes`, and `Values`.} \item{gsva_kegg_HeatdataDeseq}{A data frame containing GSVA analysis results with columns for `Samples`, `Genes`, and `Values`.} \item{gene_colors}{A named vector of colors for genes, used for coloring tiles in different layers.} } \value{ A `ggtree` plot object with multiple layers added for comprehensive visualization. } \description{ This function sequentially adds multiple layers to a `ggtree` plot, including gene expression data, boxplots for statistical summaries, and additional tile layers for pathway enrichment scores from SSGSEA and GSVA analyses. It utilizes separate functions for adding each type of layer and allows for the specification of gene colors as well as adjustments in aesthetics for each layer. The function is designed to work with specific data structures and assumes all functions for adding layers are defined and available. } \examples{ \donttest{ # Check and load required packages if (requireNamespace("ggtreeExtra", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE)) { library(ggtreeExtra) library(ggplot2) # Example data for gene expression, SSGSEA, and GSVA file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") p <- readRDS(file_path) # Create gene expression data frame (long_format_HeatdataDeseq) long_format_HeatdataDeseq <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5), Genes = rep(paste0("Gene", 1:5), times = 4), Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1 ) # Create SSGSEA analysis results data frame (ssgsea_kegg_HeatdataDeseq) ssgsea_kegg_HeatdataDeseq <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 3), Genes = rep(c("Pathway1", "Pathway2", "Pathway3"), times = 4), Value = runif(12, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1 ) # Create GSVA analysis results data frame (gsva_kegg_HeatdataDeseq) gsva_kegg_HeatdataDeseq <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 4), Genes = rep(c("PathwayA", "PathwayB", "PathwayC", "PathwayD"), times = 4), Value = runif(16, min = 0, max = 1) # Randomly generate enrichment scores between 0 and 1 ) # Define gene and pathway colors (named vector), including all genes and pathways gene_colors <- c( # Genes for gene expression Gene1 = "#491588", Gene2 = "#301b8d", Gene3 = "#1a237a", Gene4 = "#11479c", Gene5 = "#0a5797", # Pathways for SSGSEA Pathway1 = "#0b5f63", Pathway2 = "#074d41", Pathway3 = "#1f5e27", # Pathways for GSVA PathwayA = "#366928", PathwayB = "#827729", PathwayC = "#a1d99b", PathwayD = "#c7e9c0" ) # Call circos_fruits function to add multiple layers final_plot <- circos_fruits( p, long_format_HeatdataDeseq, ssgsea_kegg_HeatdataDeseq, gsva_kegg_HeatdataDeseq, gene_colors ) } else { message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") } } } ================================================ FILE: man/combat_tumor.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CombatTumor.R \name{combat_tumor} \alias{combat_tumor} \title{Process and Correct Batch Effects in Tumor Data} \usage{ combat_tumor( tumor_data_path, CombatTumor_output_path, auto_mode = FALSE, default_input = "01,06" ) } \arguments{ \item{tumor_data_path}{The path to the tumor data stored in an RDS file.} \item{CombatTumor_output_path}{A character string specifying the path where the output RDS file will be saved.} \item{auto_mode}{Logical. If set to TRUE, the function will not prompt the user for input and will instead use the values provided in default_input. Default is FALSE.} \item{default_input}{Character string. When auto_mode is TRUE, this parameter specifies the default tumor types to be retained. It should be provided as a comma-separated string (e.g., "01,06").} } \value{ A data.frame with corrected values after the ComBat_seq adjustment. Note that this function also saves the combat_count_df data as an RDS file at the specified output path. } \description{ The function first extracts histological types from the provided tumor data set. After displaying these types, the user is prompted to input specific types to retain. The data is then filtered based on this input. Note: This example assumes that different tumor types represent different batches in a general sense. Users need to adjust the batch and group vectors based on real-life scenarios. } \details{ This function takes a tumor data set, asks the user for specific tumor types to retain, and then corrects for batch effects using the ComBat_seq function from the 'sva' package. The ComBat_seq function from the sva package is used to correct batch effects. The function requires the 'sva' package to be installed and loaded externally. } \examples{ tumor_file <- system.file("extdata", "SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "SKCM_combat_count.rds") SKCM_combat_count <- combat_tumor( tumor_data_path = tumor_file, CombatTumor_output_path = output_file, auto_mode = TRUE, default_input = "01,06" ) head(SKCM_combat_count)[1:5, 1:5] } \seealso{ \code{\link[sva]{ComBat_seq}} } ================================================ FILE: man/compare_merge.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CompareMerge.R \name{compare_merge} \alias{compare_merge} \title{Compare and merge specific columns from two DEG data frames} \usage{ compare_merge(df1, df2, by_gene, compare_col, suffixes, df_name) } \arguments{ \item{df1}{First data frame.} \item{df2}{Second data frame.} \item{by_gene}{Column name by which to join the data frames, typically "Gene".} \item{compare_col}{Column to compare for identity, which will also be the name of the merged column.} \item{suffixes}{Suffixes to use for non-identical column names in the joined data frame.} \item{df_name}{Name to assign to the resulting data frame for identification.} } \value{ A data frame with processed columns. } \description{ This function takes two DEG data frames, inner joins them by a specified gene column, checks if a specified column is identical across both data frames, and merges them if they are. The resulting data frame will have a merged column named after the compared column. } \examples{ # Create simulated DESeq2 data DEG_deseq2 <- data.frame( Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"), change = c("up", "down", "no_change", "up", "down"), log2FoldChange = c(2.5, -3.2, 0.1, 1.8, -2.5), pvalue = c(0.01, 0.05, 0.9, 0.02, 0.03) ) # Display the first 5 rows of the DESeq2 data head(DEG_deseq2, 5) # Create simulated edgeR data DEG_edgeR <- data.frame( Gene = c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5"), change = c("up", "down", "no_change", "no_change", "up"), log2FoldChange = c(2.3, -3.1, 0.2, 0.1, 2.7), pvalue = c(0.02, 0.04, 0.8, 0.6, 0.01) ) # Display the first 5 rows of the edgeR data head(DEG_edgeR, 5) # Merge the DESeq2 and edgeR data deseq2_edgeR <- compare_merge( df1 = DEG_deseq2, df2 = DEG_edgeR, by_gene = "Gene", compare_col = "change", suffixes = c("_1", "_2"), df_name = "deseq2_edgeR" ) } ================================================ FILE: man/create_base_plot.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/CircosFruits.R \name{create_base_plot} \alias{create_base_plot} \title{Create a base plot with gene expression data on a phylogenetic tree} \usage{ create_base_plot(p, data, gene_colors, gene_label = "Gene") } \arguments{ \item{p}{A ggtree plot object to which the data will be added.} \item{data}{A data frame containing gene expression data with columns for Samples, Genes, and Values.} \item{gene_colors}{A named vector of colors for genes.} \item{gene_label}{A character string used as a label in the legend for the genes. Default is "Gene".} } \value{ A `ggtree` plot object with the gene expression data added. } \description{ This function creates a base plot using 'ggtree' and 'ggtreeExtra' libraries, adding gene expression data as colored tiles to the plot. It allows for dynamic coloring of the genes and includes adjustments for alpha transparency based on the expression value. } \examples{ \donttest{ # Check and load required packages if (requireNamespace("ggtreeExtra", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE)) { library(ggtreeExtra) library(ggplot2) file_path <- system.file("extdata", "p_tree_test.rds", package = "TransProR") p <- readRDS(file_path) # Create gene expression data frame expression_data <- data.frame( Sample = rep(c("Species_A", "Species_B", "Species_C", "Species_D"), each = 5), Gene = rep(paste0("Gene", 1:5), times = 4), Value = runif(20, min = 0, max = 1) # Randomly generate expression values between 0 and 1 ) # Define gene colors (named vector) gene_colors <- c( Gene1 = "#491588", Gene2 = "#301b8d", Gene3 = "#1a237a", Gene4 = "#11479c", Gene5 = "#0a5797" ) # Call create_base_plot function to add gene expression data p <- create_base_plot(p, expression_data, gene_colors) } else { message("Required packages 'ggtreeExtra' and 'ggplot2' are not installed.") } } } ================================================ FILE: man/deg_filter.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/FourDegsVenn.R \name{deg_filter} \alias{deg_filter} \title{Function to Filter Differentially Expressed Genes (DEGs)} \usage{ deg_filter(df) } \arguments{ \item{df}{A data frame containing gene expression data.} } \value{ A vector of gene names that are differentially expressed. } \description{ This function filters out genes based on their expression change status. It returns the names of genes which are not "stable". } \examples{ DEG_deseq2_file <- system.file("extdata", "DEG_deseq2.rds", package = "TransProR") DEG_deseq2 <- readRDS(DEG_deseq2_file) DEG_deseq2_test <- deg_filter(DEG_deseq2) } ================================================ FILE: man/drawLegends.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCirclize.R \name{drawLegends} \alias{drawLegends} \title{Draw Dual-Sided Legends on a Plot} \usage{ drawLegends( labels, colors, legend_width, x_positions, y_position, just_positions, text_alignments, font_size ) } \arguments{ \item{labels}{Vector of labels for the legends.} \item{colors}{Vector of colors corresponding to the labels.} \item{legend_width}{The width of each legend viewport expressed in grid units.} \item{x_positions}{Numeric vector of length 2 specifying the x-positions of the left and right legends.} \item{y_position}{The y-position common for both legends, expressed as a fraction of the plot height.} \item{just_positions}{List of two vectors, each specifying the horizontal and vertical justification for the legends.} \item{text_alignments}{List of two character strings specifying text alignments for the legends ('left' or 'right').} \item{font_size}{Numeric value specifying the font size for the legend labels.} } \value{ Invisible. This function is called for its side effects of drawing legends on a plot. } \description{ This function creates two sets of legends, one on the left and one on the right side of a plot. It displays color-coded legends with labels corresponding to different data categories. Each legend entry consists of a colored rectangle and a text label. The left side legend has text aligned to the right of the color block, while the right side legend has text aligned to the left of the color block. } \examples{ labels <- c("Label1", "Label2", "Label3", "Label4", "Label5", "Label6") colors <- c("#ff0000", "#00ff00", "#0000ff", "#ffff00", "#ff00ff", "#00ffff") # Convert to 'unit' objects for grid grid::grid.roundrect( x = grid::unit(0.5, "npc"), # "npc" stands for normalized parent coordinates y = grid::unit(0.5, "npc"), width = grid::unit(0.1, "npc"), height = grid::unit(0.05, "npc"), gp = grid::gpar(fill = "red"), r = grid::unit(0.1, "npc") # rounding radius ) # Example of drawing legends with specific labels and colors drawLegends(labels, colors, grid::unit(2, "cm"), c(0.225, 0.75), 0.5, list(c("left", "center"), c("right", "center")), list("right", "left"), 10) } ================================================ FILE: man/edgeR_analyze.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EdgeRAnalyze.R \name{edgeR_analyze} \alias{edgeR_analyze} \title{Differential Gene Expression Analysis using 'edgeR'} \usage{ edgeR_analyze( tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01 ) } \arguments{ \item{tumor_file}{Path to the tumor data file (RDS format).} \item{normal_file}{Path to the normal data file (RDS format).} \item{output_file}{Path to save the output DEG data (RDS format).} \item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.} \item{p_value_threshold}{Threshold for p-value for filtering significant genes.} } \value{ A data frame of differential expression results. } \description{ This function performs differential gene expression analysis using the 'edgeR' package. It reads tumor and normal expression data, merges them, filters low-expressed genes, normalizes the data, performs edgeR analysis, and outputs the results along with information on gene expression changes. } \examples{ # Define file paths for tumor and normal data from the data folder tumor_file <- system.file("extdata", "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") normal_file <- system.file("extdata", "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "DEG_edgeR.rds") DEG_edgeR <- edgeR_analyze( tumor_file = tumor_file, normal_file = normal_file, output_file = output_file, 2.5, 0.01 ) # View the top 5 rows of the result head(DEG_edgeR, 5) } \references{ edgeR: Differential analysis of sequence read count data. For more information, visit the edgeR Bioconductor page: https://www.bioconductor.org/packages/release/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf } ================================================ FILE: man/enrich_circo_bar.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCircoBar.R \name{enrich_circo_bar} \alias{enrich_circo_bar} \title{Combine and Visualize Data with Circular Bar Chart} \usage{ enrich_circo_bar(data_list) } \arguments{ \item{data_list}{A list of data frames to be combined.} } \value{ A `ggplot` object representing the Circular Bar Chart. } \description{ This function combines multiple data frames, arranges them, and visualizes the combined data in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages. } \examples{ # Create sample data frames for each enrichment category # 1. Biological Process (BP) filtered_data_BP <- data.frame( Description = c( "immune response", "cell proliferation", "signal transduction", "apoptotic process", "metabolic process" ), Count = c(120, 85, 150, 60, 95), color = c( "#1f77b4", # blue "#ff7f0e", # orange "#2ca02c", # green "#d62728", # red "#9467bd" # purple ), stringsAsFactors = FALSE ) # 2. Cellular Component (CC) filtered_data_CC <- data.frame( Description = c( "nucleus", "cytoplasm", "membrane", "mitochondrion", "extracellular space" ), Count = c(90, 110, 75, 65, 80), color = c( "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd" ), stringsAsFactors = FALSE ) # 3. Molecular Function (MF) filtered_data_MF <- data.frame( Description = c( "protein binding", "DNA binding", "enzyme activity", "transporter activity", "receptor activity" ), Count = c(140, 130, 100, 70, 90), color = c( "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd" ), stringsAsFactors = FALSE ) # 4. Disease Ontology (DO) filtered_data_DO <- data.frame( Description = c( "cancer", "cardiovascular disease", "neurological disorder", "metabolic disease", "infectious disease" ), Count = c(200, 150, 120, 90, 160), color = c( "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd" ), stringsAsFactors = FALSE ) # 5. Reactome Pathways filtered_data_Reactome <- data.frame( Description = c( "Cell Cycle", "Apoptosis", "DNA Repair", "Signal Transduction", "Metabolism of Proteins" ), Count = c(110, 95, 80, 130, 85), color = c( "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd" ), stringsAsFactors = FALSE ) # 6. KEGG Pathways filtered_data_kegg <- data.frame( Description = c( "PI3K-Akt signaling pathway", "MAPK signaling pathway", "NF-kappa B signaling pathway", "JAK-STAT signaling pathway", "Toll-like receptor signaling pathway" ), Count = c(175, 160, 145, 130, 155), color = c( "#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd" ), stringsAsFactors = FALSE ) # Combine all filtered data frames into a list data_list <- list( BP = filtered_data_BP, CC = filtered_data_CC, MF = filtered_data_MF, DO = filtered_data_DO, Reactome = filtered_data_Reactome, KEGG = filtered_data_kegg ) # Create the Circular Bar Chart combined_and_visualized_data <- enrich_circo_bar(data_list) } ================================================ FILE: man/enrich_polar_bubble.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichPolarBubble.R \name{enrich_polar_bubble} \alias{enrich_polar_bubble} \title{Enrichment Polar Bubble Plot} \usage{ enrich_polar_bubble(final_combined_df_with_id_and_position, pal, highlight_ids) } \arguments{ \item{final_combined_df_with_id_and_position}{A data frame containing 'id', 'Count', 'method', 'Description', 'point_position', 'test_color'.} \item{pal}{A named vector of colors corresponding to the 'method' values.} \item{highlight_ids}{A vector of IDs to highlight.} } \value{ A `ggplot` object representing the enriched polar bubble plot. } \description{ 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. } \examples{ final_df <- data.frame(id = 1:10, Count = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100), method = rep("Method1", 10), Description = LETTERS[1:10], point_position = seq(10, 100, 10), test_color = sample(c("red", "blue"), 10, replace = TRUE)) pal <- c("Method1" = "blue") highlight_ids <- c(1, 5, 9) enrich_polar_bubble(final_df, pal, highlight_ids) } ================================================ FILE: man/enrichment_circlize.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCirclize.R \name{enrichment_circlize} \alias{enrichment_circlize} \title{Draw Chord Diagram with Legends} \usage{ enrichment_circlize( all_combined_df, original_colors, labels, colors, labels2, colors2, font_size = 10 ) } \arguments{ \item{all_combined_df}{A dataframe containing the matrix for the chord diagram.} \item{original_colors}{A vector of colors for the grid columns of the chord diagram.} \item{labels}{A vector of labels for the first legend.} \item{colors}{A vector of colors corresponding to the first legend's labels.} \item{labels2}{A vector of labels for the second legend.} \item{colors2}{A vector of colors corresponding to the second legend's labels.} \item{font_size}{The font size used for legend texts, defaults to 10.} } \value{ Invisible, primarily used for its side effects of drawing on a graphics device. } \description{ This function creates a chord diagram from a specified dataframe and draws two sets of legends for it. It adjusts the track height of the chord diagram to optimize space and uses specified colors for the grid. Legends are drawn at specified positions with configurable text alignments and font sizes. } \examples{ # Sample Chord Diagram Matrix all_combined_df <- data.frame( A = c(10, 20, 30), B = c(15, 25, 35), C = c(5, 10, 15) ) rownames(all_combined_df) <- c("A", "B", "C") # Colors for the grid of the chord diagram (corresponding to columns of the matrix) original_colors <- c("red", "green", "blue") # Name the colors according to the sectors (A, B, C) names(original_colors) <- colnames(all_combined_df) # Labels and Colors for the First Legend labels <- c("Label 1", "Label 2", "Label 3") colors <- c("yellow", "purple", "cyan") # Labels and Colors for the Second Legend labels2 <- c("Label A", "Label B", "Label C") colors2 <- c("orange", "pink", "brown") # Font size for the legend texts (optional, default is 10) font_size <- 10 # Call the enrichment_circlize function with the sample data # This is just an example; the plot will be rendered in an appropriate graphics context # such as RStudio's plot pane or an external plotting window. plot1 <- enrichment_circlize(all_combined_df, original_colors, labels, colors, labels2, colors2, font_size ) } ================================================ FILE: man/enrichment_spiral_plots.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichmentSpiralize.R \name{enrichment_spiral_plots} \alias{enrichment_spiral_plots} \title{Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap'} \usage{ enrichment_spiral_plots(results) } \arguments{ \item{results}{A data frame containing 'Pathway', 'Sample', 'Value', 'PathwayColor', and 'SampleColor' columns.} } \value{ No return value, called for side effects. This function generates spiral plots and adds legends based on sample and pathway information. } \description{ This function initializes a spiral plot, adds tracks for pathways and samples, and generates legends based on the sample and pathway information in the provided data frame. It uses 'spiralize' for the spiral plot and 'ComplexHeatmap' for handling legends. } \examples{ # Example: Creating enrichment spiral plots with legends # Define the results data frame results <- data.frame( Pathway = c("Pathway1", "Pathway1", "Pathway2", "Pathway2", "Pathway3"), Sample = c("Sample1", "Sample1", "Sample2", "Sample2", "Sample3"), Value = c(20, 30, 15, 35, 25), PathwayColor = c("red", "red", "blue", "blue", "orange"), SampleColor = c("green", "green", "purple", "purple", "cyan"), stringsAsFactors = FALSE ) # Create the enrichment spiral plots with legends enrichment_spiral_plots(results) } ================================================ FILE: man/extract_descriptions_counts.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCircoBar.R \name{extract_descriptions_counts} \alias{extract_descriptions_counts} \title{Extract and Count Descriptions with Specified Color} \usage{ extract_descriptions_counts(df, descriptions, color) } \arguments{ \item{df}{A data frame containing at least 'Description' and 'Count' columns.} \item{descriptions}{A vector of descriptions to filter in the data frame.} \item{color}{A character string specifying the color to be added as a new column.} } \value{ A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column. } \description{ This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns, and adds a new column with a specified color. } \examples{ # Generate Sample Input Data for extract_descriptions_counts Function # Create a sample data frame with 'Description' and 'Count' columns data <- data.frame( Description = c( "immunoglobulin production", "B cell mediated immunity", "T cell activation", "antigen processing and presentation", "cytokine signaling", "natural killer cell activity", "phagocytosis", "complement activation", "antibody-dependent cellular cytotoxicity", "regulatory T cell function" ), Count = c( 150, # immunoglobulin production 200, # B cell mediated immunity 175, # T cell activation 125, # antigen processing and presentation 190, # cytokine signaling 160, # natural killer cell activity 140, # phagocytosis 180, # complement activation 130, # antibody-dependent cellular cytotoxicity 170 # regulatory T cell function ), stringsAsFactors = FALSE # Ensure that strings are not converted to factors ) descriptions_to_filter <- c("immunoglobulin production", "B cell mediated immunity") specified_color <- "red" # You can specify any color you desire filtered_data_with_color <- extract_descriptions_counts( data, descriptions_to_filter, specified_color) print(filtered_data_with_color) } ================================================ FILE: man/extract_ntop_pathways.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichmentSpiralize.R \name{extract_ntop_pathways} \alias{extract_ntop_pathways} \title{Extract and Store Top Pathways for Each Sample} \usage{ extract_ntop_pathways(ssgsea_kegg, nTop = 5) } \arguments{ \item{ssgsea_kegg}{Dataframe containing SSGSEA KEGG results with samples as columns and pathways as rows.} \item{nTop}{Integer, number of top pathways to select for each sample.} } \value{ A dataframe with columns 'Pathway', 'Sample', and 'Value' representing the top pathways for each sample. } \description{ This function processes a dataframe containing SSGSEA KEGG results. It allows specifying the number of top pathways to extract for each sample based on their scores, and stores these in a new dataframe with sample names and pathway scores. } \examples{ # Example: Generating input data for the extract_ntop_pathways function # Define example pathways pathways <- c("Pathway_A", "Pathway_B", "Pathway_C", "Pathway_D", "Pathway_E", "Pathway_F", "Pathway_G", "Pathway_H", "Pathway_I", "Pathway_J") # Define example samples samples <- c("Sample_1", "Sample_2", "Sample_3") # Generate random SSGSEA KEGG scores between 0 and 1 set.seed(123) # For reproducibility ssgsea_scores <- matrix(runif(length(pathways) * length(samples), min = 0, max = 1), nrow = length(pathways), ncol = length(samples), dimnames = list(pathways, samples)) # Convert to a data frame ssgsea_kegg <- as.data.frame(ssgsea_scores) # Extract the top 3 pathways for each sample top_pathways <- extract_ntop_pathways(ssgsea_kegg, nTop = 3) } ================================================ FILE: man/extract_positive_pathways.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichmentSpiralize.R \name{extract_positive_pathways} \alias{extract_positive_pathways} \title{Extract Positive Pathways from SSGSEA Results and Select Random Samples} \usage{ extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 5) } \arguments{ \item{ssgsea_kegg}{A matrix or data frame with pathways as rows and samples as columns.} \item{max_paths_per_sample}{Integer, maximum number of pathways to select per sample.} } \value{ A data frame with selected pathways, samples, and their corresponding values. } \description{ This function processes the results of SSGSEA, specifically focusing on KEGG pathways. It extracts pathways with positive values from each sample and randomly selects a subset of them. } \examples{ # Example: Generating input data for the extract_positive_pathways function # Define example pathways pathways <- c("Pathway_1", "Pathway_2", "Pathway_3", "Pathway_4", "Pathway_5", "Pathway_6", "Pathway_7", "Pathway_8", "Pathway_9", "Pathway_10") # Define example samples samples <- c("Sample_A", "Sample_B", "Sample_C") # Generate random SSGSEA KEGG scores including both positive and negative values set.seed(456) # For reproducibility ssgsea_scores <- matrix(rnorm(length(pathways) * length(samples), mean = 0, sd = 1), nrow = length(pathways), ncol = length(samples), dimnames = list(pathways, samples)) # Convert to a data frame ssgsea_kegg <- as.data.frame(ssgsea_scores) # Use the extract_positive_pathways function to extract up to 3 positive pathways per sample selected_positive_pathways <- extract_positive_pathways(ssgsea_kegg, max_paths_per_sample = 3) } ================================================ FILE: man/facet_density_foldchange.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/FacetDensityFoldchange.R \name{facet_density_foldchange} \alias{facet_density_foldchange} \title{Create faceted high-density region plots with optional points and density contours} \usage{ facet_density_foldchange( data, x_var, y_var, group_var, facet_var, palette, show_points = FALSE, show_density = TRUE, point_size = 2.5, point_alpha = 0.1, line_size = 1.6, cor_method = "pearson", cor_label_pos = c("left", 0.97), cor_vjust = NULL ) } \arguments{ \item{data}{Data frame containing variables for plotting.} \item{x_var}{Name of the x-axis variable as a string.} \item{y_var}{Name of the y-axis variable as a string.} \item{group_var}{Name of the grouping variable for color mapping as a string.} \item{facet_var}{Name of the faceting variable.} \item{palette}{Color palette for the plot as a character vector.} \item{show_points}{Logical, if TRUE adds scatter points to the plot.} \item{show_density}{Logical, if TRUE adds filled density contours to the plot.} \item{point_size}{Size of the points, relevant if show_points is TRUE.} \item{point_alpha}{Transparency level of the points, relevant if show_points is TRUE.} \item{line_size}{Size of the regression line.} \item{cor_method}{Method to calculate correlation ("pearson" or "spearman").} \item{cor_label_pos}{Vector of length 2 indicating the position of the correlation label (x and y).} \item{cor_vjust}{Vertical justification for correlation label, default is NULL.} } \value{ A `ggplot` object representing the high-density region plot. } \description{ This function creates faceted high-density region plots using ggdensity for adding optional density rug and density contours, and scatter points. It also adds a regression line and Pearson correlation label. The plot is faceted by a grouping variable. } \examples{ combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR") combined_df <- readRDS(combined_df_file) pal2 = c("#2787e0","#1a9ae0","#1dabbf","#00897b","#43a047","#7cb342") all_facet_density_foldchange_name1 <- facet_density_foldchange( data = combined_df, x_var = "log2FoldChange_1", y_var = "log2FoldChange_2", group_var = "name", facet_var = "name", palette = pal2, show_points = TRUE, show_density = FALSE, point_size = 2, point_alpha = 0.1, line_size = 1.6, cor_method = "pearson", cor_label_pos = c("left", "top"), cor_vjust = 1 ) } ================================================ FILE: man/filter_diff_genes.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/FilterDiffGenes.R \name{filter_diff_genes} \alias{filter_diff_genes} \title{Filter Differentially Expressed Genes} \usage{ filter_diff_genes( data, p_val_col = "adj.P.Val", log_fc_col = "logFC", p_val_threshold = 0.05, log_fc_threshold = 1 ) } \arguments{ \item{data}{A data frame containing gene expression data.} \item{p_val_col}{Character string indicating the column name for p-values. Default is "adj.P.Val".} \item{log_fc_col}{Character string indicating the column name for log fold change. Default is "logFC".} \item{p_val_threshold}{Numeric threshold for filtering p-values. Default is 0.05.} \item{log_fc_threshold}{Numeric threshold for filtering log fold change. Default is 1.0.} } \value{ A data frame with genes filtered by the specified criteria. } \description{ This function filters a data frame to identify genes with significant differential expression based on specified thresholds for p-values and log fold change. It allows for flexible input of column names for p-values and log fold change. } \examples{ # Create a sample data frame with p-values and log fold changes sample_data <- data.frame( adj.P.Val = c(0.03, 0.06, 0.02, 0.07), logFC = c(1.5, 0.8, -1.2, 1.1), gene = c("Gene1", "Gene2", "Gene3", "Gene4") ) # Use the filter_diff_genes function to filter significant genes filtered_genes <- filter_diff_genes(sample_data) print(filtered_genes) } ================================================ FILE: man/four_degs_venn.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/FourDegsVenn.R \name{four_degs_venn} \alias{four_degs_venn} \title{Function to Create a Venn Diagram of DEGs} \usage{ four_degs_venn(degs_list) } \arguments{ \item{degs_list}{A list of DEG sets for Venn Diagram creation.} } \value{ A ggplot object representing the Venn Diagram. } \description{ This function creates a Venn Diagram using the ggVennDiagram package. It allows customization of various aesthetic elements of the diagram. } \examples{ data("all_degs_venn", package = "TransProR") four_degs_venn <- four_degs_venn(all_degs_venn) } ================================================ FILE: man/gather_graph_edge.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GatherGraphEdge.R \name{gather_graph_edge} \alias{gather_graph_edge} \title{Gather graph edge from data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!} \usage{ gather_graph_edge(df, index = NULL, root = NULL) } \arguments{ \item{df}{A data frame} \item{index}{A vector of column names to group by} \item{root}{Root name for the root node connections, optional} } \value{ A tibble of graph edges } \description{ Gather graph edge from data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware! } ================================================ FILE: man/gather_graph_node.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GatherGraphNode.R \name{gather_graph_node} \alias{gather_graph_node} \title{Gather graph nodes from a data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware!} \usage{ gather_graph_node( df, index = NULL, value = utils::tail(colnames(df), 1), root = NULL ) } \arguments{ \item{df}{A data frame} \item{index}{A vector of column names to group by} \item{value}{Column name used for summarizing node size, defaults to the last column} \item{root}{Root name for the root node connections, optional} } \value{ a tibble of graph nodes } \description{ Gather graph nodes from a data frame Please note that this function is from the 'ggraph' package and has not been altered in functionality, but it has been optimized and iterated. It is not original content of 'TransProR'. However, since 'ggraph' caused frequent GitHub Action errors during the creation of 'TransProR', the author directly referenced the involved functions in 'TransProR'. This is not the author's original creation. All users please be aware! } ================================================ FILE: man/gene_color.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GeneColor.R \name{gene_color} \alias{gene_color} \title{Merge Genes with Color Information Based on Up/Down Regulation} \usage{ gene_color(selected_genes, DEG_deseq2, up_color, down_color) } \arguments{ \item{selected_genes}{A data frame containing selected genes with a column named "Symble".} \item{DEG_deseq2}{A data frame containing differential expression data with a column named "Symble" and a column named "change" indicating up/down regulation.} \item{up_color}{The color to assign to genes with up-regulated expression.} \item{down_color}{The color to assign to genes with down-regulated expression.} } \value{ A data frame containing merged genes with an additional color column. } \description{ This function merges selected genes with differential expression data and adds a color column based on up/down regulation. } \examples{ selected_genes_deseq2_file <- system.file("extdata", "selected_genes_deseq2.rds", package = "TransProR") selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file) Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR") Diff_deseq2 <- readRDS(Diff_deseq2_file) result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746") } ================================================ FILE: man/gene_highlights.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GeneHighlights.R \name{gene_highlights} \alias{gene_highlights} \title{Add gene highlights to a ggtree object} \usage{ gene_highlights(ggtree_obj, genes_to_highlight, hilight_extend = 18) } \arguments{ \item{ggtree_obj}{A ggtree object to which the highlights will be added.} \item{genes_to_highlight}{A data frame containing genes and their corresponding colors.} \item{hilight_extend}{Integer, the extension of the highlight fan in degrees.} } \value{ A ggtree object with added gene highlights. } \description{ This function enhances a `ggtree` plot by adding highlights for specific genes. It adds both a semi-transparent fan-shaped highlight and a point at the node corresponding to each gene. Colors for each gene can be customized. } \examples{ data("gtree", package = "TransProR") # Define genes and their colors genes_df <- data.frame(Symble = c("t5", "t9"), color = c("#FF0000", "#0000FF")) # Add highlights gtree <- gene_highlights(gtree, genes_to_highlight = genes_df) } ================================================ FILE: man/gene_map_pathway.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GeneMapPathway.R \name{gene_map_pathway} \alias{gene_map_pathway} \title{Create Pathway-Gene Mapping Data Frame} \usage{ gene_map_pathway( BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids, MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids, CC_dataframe, CC_ids, DO_dataframe, DO_ids ) } \arguments{ \item{BP_dataframe}{Data frame for Biological Process analysis} \item{BP_ids}{Selected pathway IDs for Biological Process analysis} \item{KEGG_dataframe}{Data frame for KEGG analysis} \item{KEGG_ids}{Selected pathway IDs for KEGG analysis} \item{MF_dataframe}{Data frame for Molecular Function analysis} \item{MF_ids}{Selected pathway IDs for Molecular Function analysis} \item{REACTOME_dataframe}{Data frame for REACTOME analysis} \item{REACTOME_ids}{Selected pathway IDs for REACTOME analysis} \item{CC_dataframe}{Data frame for Cellular Component analysis} \item{CC_ids}{Selected pathway IDs for Cellular Component analysis} \item{DO_dataframe}{Data frame for Disease Ontology analysis} \item{DO_ids}{Selected pathway IDs for Disease Ontology analysis} } \value{ A new data frame that includes pathways, gene, type, and value columns } \description{ This function takes multiple data frames and pathway IDs, merging them into a new data frame. Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.). } \examples{ # Simulating data for different analysis types # Simulate Biological Process (BP) data frame BP_df <- data.frame( ID = c("GO:0002376", "GO:0019724"), geneID = c("GENE1/GENE2", "GENE3/GENE4"), Description = c("Immune response", "Glycosylation process") ) # Simulate KEGG data frame KEGG_df <- data.frame( ID = c("12345", "67890"), geneID = c("GENE5/GENE6", "GENE7/GENE8"), Description = c("Pathway 1", "Pathway 2") ) # Simulate Molecular Function (MF) data frame MF_df <- data.frame( ID = c("ABC123", "DEF456"), geneID = c("GENE9/GENE10", "GENE11/GENE12"), Description = c("Molecular function A", "Molecular function B") ) # Simulate REACTOME data frame REACTOME_df <- data.frame( ID = c("R-HSA-12345", "R-HSA-67890"), geneID = c("GENE13/GENE14", "GENE15/GENE16"), Description = c("Pathway in Reactome 1", "Pathway in Reactome 2") ) # Simulate Cellular Component (CC) data frame CC_df <- data.frame( ID = c("GO:0005575", "GO:0005634"), geneID = c("GENE17/GENE18", "GENE19/GENE20"), Description = c("Cellular component A", "Cellular component B") ) # Simulate Disease Ontology (DO) data frame DO_df <- data.frame( ID = c("DOID:123", "DOID:456"), geneID = c("GENE21/GENE22", "GENE23/GENE24"), Description = c("Disease A", "Disease B") ) # Example pathway IDs for each analysis BP_ids <- c("GO:0002376", "GO:0019724") KEGG_ids <- c("12345", "67890") MF_ids <- c("ABC123", "DEF456") REACTOME_ids <- c("R-HSA-12345", "R-HSA-67890") CC_ids <- c("GO:0005575", "GO:0005634") DO_ids <- c("DOID:123", "DOID:456") # Generate the pathway-gene map using the gene_map_pathway function pathway_gene_map <- gene_map_pathway( BP_dataframe = BP_df, BP_ids = BP_ids, KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids, MF_dataframe = MF_df, MF_ids = MF_ids, REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids, CC_dataframe = CC_df, CC_ids = CC_ids, DO_dataframe = DO_df, DO_ids = DO_ids ) # Display the resulting pathway-gene mapping data frame print(pathway_gene_map) } ================================================ FILE: man/get_gtex_exp.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GetGtexExp.R \name{get_gtex_exp} \alias{get_gtex_exp} \title{Get GTEx Expression Data for Specific Organ} \usage{ get_gtex_exp( organ_specific, file_path, probe_map_path, pheno_path, output_path ) } \arguments{ \item{organ_specific}{A character string specifying the organ to filter the gene expression data by.} \item{file_path}{A character string specifying the path to the GTEx gene expression data file.} \item{probe_map_path}{A character string specifying the path to the gtex_probeMap_gencode data file.} \item{pheno_path}{A character string specifying the path to the GTEx phenotype data file.} \item{output_path}{A character string specifying the path where the output RDS file will be saved.} } \value{ A data frame containing gene expression data for the specified organ. Rows represent genes, and columns represent samples. Note that this function also saves the organ-specific GTEx data as an RDS file at the specified output path. } \description{ This function retrieves gene expression data from the GTEx project that is specific to a certain organ. It performs various checks and processing steps to ensure that the data is consistent and relevant to the specified organ. The filtered and cleaned data is saved as an RDS file for further analysis. } \details{ The function begins by checking if the gene expression and phenotype data files exist at the specified paths. It then loads these data files and processes them by setting appropriate row names, modifying column names for clarity, and filtering samples based on the specified organ. The function ensures that only samples present in both datasets are retained for consistency. It also removes any duplicate gene entries to prevent redundancy. Finally, the processed data is saved as an RDS file. } \note{ The function will stop and throw an error if the input files do not exist, or if no samples are found for the specified organ. CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended 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'. } ================================================ FILE: man/get_tcga_exp.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/GetTcgaExp.R \name{get_tcga_exp} \alias{get_tcga_exp} \title{TCGA Expression Data Processing} \usage{ get_tcga_exp( counts_file_path, gene_probes_file_path, phenotype_file_path, output_file_path ) } \arguments{ \item{counts_file_path}{File path to the counts data (usually in the form of a large matrix with gene expression data).} \item{gene_probes_file_path}{File path containing the gene probes data.} \item{phenotype_file_path}{File path to the phenotype data, which includes various sample attributes.} \item{output_file_path}{Path where the output files, distinguished between tumor and normal, will be saved.} } \value{ A list containing matrices for tumor and normal expression data. } \description{ This function processes expression data and phenotype information, separates tumor and normal samples, and saves the results into different files. It's specifically designed for data obtained from TCGA. } \note{ IMPORTANT: This function assumes that the input files follow a specific format and structure, typically found in TCGA data releases. Users should verify their data's compatibility. Additionally, the function does not perform error checking on the data's content, which users should handle through proper preprocessing. CRITICAL: The 'output_file_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended that the path includes specific identifiers related to the target samples, as the function will create further subdivisions in the specified path for tumor or normal tissues. Please structure the 'output_file_path' following this pattern: './your_directory/your_sample_type.exp.rds'. } \examples{ counts_file <- system.file("extdata", "TCGA-SKCM.htseq_counts_test.tsv", package = "TransProR") gene_probes_file <- system.file("extdata", "TCGA_gencode.v22.annotation.gene.probeMap_test", package = "TransProR") phenotype_file <- system.file("extdata", "TCGA-SKCM.GDC_phenotype_test.tsv", package = "TransProR") ouput_file <- file.path(tempdir(), "SKCM_Skin_TCGA_exp_test.rds") SKCM_exp <- get_tcga_exp( counts_file_path = counts_file, gene_probes_file_path = gene_probes_file, phenotype_file_path = phenotype_file, output_file_path = ouput_file ) head(SKCM_exp[["tumor_tcga_data"]])[1:5, 1:5] head(SKCM_exp[["normal_tcga_data"]], n = 10) # Because there is only one column. } \author{ Dongyue Yu } ================================================ FILE: man/gtree.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{gtree} \alias{gtree} \title{Phylogenetic Tree Object} \format{ A `ggtree` object. } \source{ The phylogenetic tree was constructed based on sequence alignment data obtained from [Data Source, e.g., NCBI database, specific study, etc.]. } \usage{ data(gtree) } \description{ A dataset containing a phylogenetic tree object created using the `ggtree` package. This tree represents the evolutionary relationships among a set of species or genes. } \keyword{datasets} \keyword{phylogenetics} ================================================ FILE: man/highlight_by_node.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/HighlightByNode.R \name{highlight_by_node} \alias{highlight_by_node} \title{Highlight Nodes in a Phylogenetic Tree with Custom Fill Colors and Transparency} \usage{ highlight_by_node( ggtree_object, nodes, fill_colors, alpha_values, extend_values ) } \arguments{ \item{ggtree_object}{A `ggtree` object representing the phylogenetic tree.} \item{nodes}{A character vector specifying the nodes to highlight.} \item{fill_colors}{A character vector specifying the fill colors for the highlighted nodes.} \item{alpha_values}{A numeric vector specifying the transparency values for the highlighted nodes (between 0 and 1).} \item{extend_values}{A logical vector specifying whether to extend the highlight to the whole clade below each node.} } \value{ A modified `ggtree` object with the specified nodes highlighted. } \description{ This function adds highlights to specific nodes in a phylogenetic tree represented by a `ggtree` object. Users can specify the nodes to highlight along with custom fill colors, transparency, and extension options. } \examples{ plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR") p2_plot <- readRDS(plot_file) # Please replace the following vectors with your specific values nodes <- c(117, 129, 125, 127, 119, 123, 139, 166, 124, 131, 217) # x-values of the nodes you want to highlight fill_colors <- c("#CD6600", "#CD6600", "#CD6600", "#CD6600", "#009933", "#009933", "#009933", "#009933", "#9B30FF", "#9B30FF", "#9B30FF") # Fill colors alpha_values <- c(0.3, 0.3, 0.3, 0.3, 0.2, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3) # Transparency values extend_values <- c(25, 24, 24, 25, 25, 25, 24, 24, 25, 24, 24) # Values for the 'extend' parameter p2 <- highlight_by_node( p2_plot, nodes, fill_colors, alpha_values, extend_values ) } ================================================ FILE: man/highlight_genes.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/HighlightGenes.R \name{highlight_genes} \alias{highlight_genes} \title{Add Highlights for Genes on a Phylogenetic Tree} \usage{ highlight_genes(ggtree_obj, genes_to_highlight, hilight_extend = 18) } \arguments{ \item{ggtree_obj}{A ggtree object representing the phylogenetic tree.} \item{genes_to_highlight}{A data frame containing gene names and corresponding colors to highlight.} \item{hilight_extend}{Numeric value indicating the extension length for highlights.} } \value{ A `ggtree` object with added highlights for specified genes. } \description{ This function adds highlights for specified genes on a phylogenetic tree object. } \examples{ plot_file <- system.file("extdata", "tree_plot.rds", package = "TransProR") p2_plot <- readRDS(plot_file) selected_genes_deseq2_file <- system.file("extdata", "selected_genes_deseq2.rds", package = "TransProR") selected_genes_deseq2 <- readRDS(selected_genes_deseq2_file) Diff_deseq2_file <- system.file("extdata", "Diff_deseq2.rds", package = "TransProR") Diff_deseq2 <- readRDS(Diff_deseq2_file) result_deseq2 <- gene_color(selected_genes_deseq2, Diff_deseq2, "#0000EE", "#fc4746") add_gene_highlights_p3 <- highlight_genes(p2_plot, result_deseq2, hilight_extend = 26) } ================================================ FILE: man/limma_analyze.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/LimmaAnalyze.R \name{limma_analyze} \alias{limma_analyze} \title{Differential Gene Expression Analysis using limma and voom} \usage{ limma_analyze( tumor_file, normal_file, output_file, logFC_threshold = 2.5, p_value_threshold = 0.01 ) } \arguments{ \item{tumor_file}{Path to the tumor data file (RDS format).} \item{normal_file}{Path to the normal data file (RDS format).} \item{output_file}{Path to save the output DEG data (RDS format).} \item{logFC_threshold}{Threshold for log fold change for marking up/down-regulated genes.} \item{p_value_threshold}{Threshold for p-value for filtering significant genes.} } \value{ A data frame of differential expression results. } \description{ This function performs differential gene expression analysis using the 'limma' package with voom normalization. It reads tumor and normal expression data, merges them, filters low-expressed genes, normalizes the data, performs limma analysis, and outputs the results along with information on gene expression changes. } \examples{ # Define file paths for tumor and normal data from the data folder tumor_file <- system.file("extdata", "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") normal_file <- system.file("extdata", "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", package = "TransProR") output_file <- file.path(tempdir(), "DEG_limma_voom.rds") DEG_limma_voom <- limma_analyze( tumor_file = tumor_file, normal_file = normal_file, output_file = output_file, logFC_threshold = 2.5, p_value_threshold = 0.01 ) # View the top 5 rows of the result head(DEG_limma_voom, 5) } \references{ limma:Linear Models for Microarray and RNA-Seq Data User’s Guide. For more information, visit the page: https://www.bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf } ================================================ FILE: man/log_transform.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/LogTransform.R \name{log_transform} \alias{log_transform} \title{Log transformation decision and application on data} \usage{ log_transform(data) } \arguments{ \item{data}{A numeric matrix or data frame.} } \value{ The original data or the data transformed with log2. } \description{ This function evaluates the need for a log transformation based on a set of criteria and applies a log2 transformation if necessary. } \examples{ file_path <- system.file("extdata", "all_count_exp_test.csv", package = "TransProR") your_data <- read.csv(file_path, row.names = 1) # Assuming first column is row names (e.g., gene names) TransformedData <- log_transform(data = your_data) } \author{ Dongyue Yu } ================================================ FILE: man/merge_density_foldchange.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/MergeDensityFoldchange.R \name{merge_density_foldchange} \alias{merge_density_foldchange} \title{Create high-density region plot with optional points, density rugs, and contours} \usage{ merge_density_foldchange( data, x_var, y_var, group_var, palette = c("#3949ab", "#1e88e5", "#039be5", "#00897b", "#43a047", "#7cb342"), show_points = FALSE, point_size = 2.5, point_alpha = 0.2, x_lim = c(0, 20), y_lim = c(0, 20), cor_method = "pearson", line_size = 1.6, cor_label_pos = c("left", 0.97) ) } \arguments{ \item{data}{Data frame containing variables for plotting.} \item{x_var}{Name of the x-axis variable as a string.} \item{y_var}{Name of the y-axis variable as a string.} \item{group_var}{Name of the grouping variable for color mapping as a string.} \item{palette}{Color palette for the plot as a character vector.} \item{show_points}{Logical, if TRUE adds points to the plot.} \item{point_size}{Size of the points, relevant if show_points is TRUE.} \item{point_alpha}{Transparency level of the points, relevant if show_points is TRUE.} \item{x_lim}{Numeric vector of length 2, giving the x-axis limits.} \item{y_lim}{Numeric vector of length 2, giving the y-axis limits.} \item{cor_method}{Method to calculate correlation ("pearson" or "spearman").} \item{line_size}{Size of the smoothing line.} \item{cor_label_pos}{Vector of length 2 indicating the position of the correlation label (x and y).} } \value{ A ggplot object representing the high-density region plot. } \description{ This function creates a high-density region plot using hdr methods to add density rug and filled contours. It also adds a regression line and Pearson correlation label. Points can be added to the plot optionally. } \examples{ combined_df_file <- system.file("extdata", "combined_df.rds", package = "TransProR") combined_df <- readRDS(combined_df_file) pal1 = c("#3949ab","#1e88e5","#039be5","#00897b","#43a047","#7cb342") all_density_foldchange_name1 <- merge_density_foldchange( data = combined_df, x_var = "log2FoldChange_1", y_var = "log2FoldChange_2", group_var = "name", palette = pal1, show_points = FALSE, point_size = 2.5, point_alpha = 0.1, x_lim = c(0, 20), y_lim = c(0, 20), cor_method = "pearson", line_size = 1.6, cor_label_pos = c("left", "top") ) } ================================================ FILE: man/merge_gtex_tcga.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/MergeGtexTcga.R \name{merge_gtex_tcga} \alias{merge_gtex_tcga} \title{Merge gene expression data from GTEx and TCGA datasets} \usage{ merge_gtex_tcga( gtex_data_path, tcga_exp_path, output_path = "./merged_gtex_tcga_data.rds" ) } \arguments{ \item{gtex_data_path}{A string that specifies the file path to the GTEx data saved in RDS format.} \item{tcga_exp_path}{A string that specifies the file path to the TCGA expression data saved in RDS format. This should be a data.frame with rows as genes and columns as samples.} \item{output_path}{A string that specifies the path where the merged dataset should be saved. The file is saved in '.rds' format. The default path is "./merged_gtex_tcga_data.rds".} } \value{ A data frame where rows represent genes and columns represent samples. The data frame contains expression values from both GTEx and TCGA datasets. It saves the merged dataset to the path specified by 'output_path'. } \description{ This function merges gene expression data obtained from the GTEx (Genotype-Tissue Expression) and TCGA (The Cancer Genome Atlas) datasets. 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. } \details{ It is assumed that both datasets are in '.rds' format and have genes as row names. } \note{ CRITICAL: The 'output_path' parameter must end with '.rds' to be properly recognized by the function. It is also highly recommended 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'. } \examples{ tumor_file <- system.file("extdata", "removebatch_SKCM_Skin_TCGA_exp_tumor_test.rds", package = "TransProR") Normal_file <- system.file("extdata", "removebatch_SKCM_Skin_Normal_TCGA_GTEX_count_test.rds", package = "TransProR") ouput_file <- file.path(tempdir(), "all_data.rds") all_data <- merge_gtex_tcga(gtex_data_path = tumor_file, tcga_exp_path = Normal_file, output_path = ouput_file) } ================================================ FILE: man/merge_id_position.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/MergeIDPosition.R \name{merge_id_position} \alias{merge_id_position} \title{Merge Data Frames by Common Row Names with Additional Columns} \usage{ merge_id_position(df_list) } \arguments{ \item{df_list}{A list of data frames, each with a 'Description' and 'Count' column and set row names.} } \value{ A single data frame merged from the list, with additional 'id' and 'point_position' columns. } \description{ 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. } \examples{ df1 <- data.frame(Description = c("DataA", "DataB"), Count = c(10, 20), row.names = c("R1", "R2")) df2 <- data.frame(Description = c("DataC", "DataD"), Count = c(30, 40), row.names = c("R1", "R3")) df_list <- list(df1, df2) combined_df_test <- merge_id_position(df_list) } ================================================ FILE: man/merge_method_color.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/MergeMethodColor.R \name{merge_method_color} \alias{merge_method_color} \title{Merge Data Frames with Specific Method and Color Columns} \usage{ merge_method_color(df_list, method_name, color_list) } \arguments{ \item{df_list}{A list of data frames, each containing at least 'Description' and 'Count' columns.} \item{method_name}{A string representing the method name to be added to each data frame.} \item{color_list}{A list of colors corresponding to each data frame for the 'test_color' column.} } \value{ A single data frame merged from the list, with each originally provided data frame now having a 'method' and a 'test_color' column. } \description{ This function takes a list of data frames, a method name, and a list of colors. It adds a 'method' column and a 'test_color' column to each data frame, then merges all data frames by rows. It ensures that the color list length matches the list of data frames. } \examples{ df1 <- data.frame(Description = c("A", "B"), Count = c(10, 20)) df2 <- data.frame(Description = c("C", "D"), Count = c(30, 40)) df_list <- list(df1, df2) method_name <- "Method1" color_list <- c("Red", "Blue") combined_df_test <- merge_method_color(df_list, method_name, color_list) } ================================================ FILE: man/new_ggraph.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/NewGgraph.R \name{new_ggraph} \alias{new_ggraph} \title{Generate a graphical representation of pathway gene maps} \usage{ new_ggraph( BP_dataframe, BP_ids, KEGG_dataframe, KEGG_ids, MF_dataframe, MF_ids, REACTOME_dataframe, REACTOME_ids, CC_dataframe, CC_ids, DO_dataframe, DO_ids ) } \arguments{ \item{BP_dataframe}{Dataframe for Biological Process.} \item{BP_ids}{IDs for Biological Process.} \item{KEGG_dataframe}{Dataframe for KEGG pathways.} \item{KEGG_ids}{IDs for KEGG pathways.} \item{MF_dataframe}{Dataframe for Molecular Function.} \item{MF_ids}{IDs for Molecular Function.} \item{REACTOME_dataframe}{Dataframe for REACTOME pathways.} \item{REACTOME_ids}{IDs for REACTOME pathways.} \item{CC_dataframe}{Dataframe for Cellular Component.} \item{CC_ids}{IDs for Cellular Component.} \item{DO_dataframe}{Dataframe for Disease Ontology.} \item{DO_ids}{IDs for Disease Ontology.} } \value{ A 'ggraph' object representing the pathway gene map visualization. } \description{ This function merges multiple gene-pathway related dataframes, processes them for graph creation, and visualizes the relationships in a dendrogram layout using the provided node and edge gathering functions from the 'ggraph' package. } ================================================ FILE: man/pathway_count.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PathwayCount.R \name{pathway_count} \alias{pathway_count} \title{Count Genes Present in Pathways Above a Threshold} \usage{ pathway_count(GO, count_threshold, enrich_data) } \arguments{ \item{GO}{A character vector of gene symbols.} \item{count_threshold}{An integer specifying the count threshold for selecting pathways.} \item{enrich_data}{A data frame containing pathway enrichment analysis results.} } \value{ A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise). } \description{ This function filters pathways that meet a count threshold and then counts the presence of specified genes in those pathways. } \examples{ # Simulated gene list GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5") # Simulated enrichment analysis data enrich_data <- data.frame( ID = c("GO:0001", "GO:0002", "GO:0003"), Description = c("Pathway A", "Pathway B", "Pathway C"), Count = c(10, 4, 6), geneID = c("Gene1/Gene2/Gene3", "Gene4/Gene5", "Gene2/Gene6/Gene7") ) # Example usage count_threshold <- 5 result_df <- pathway_count(GO, count_threshold, enrich_data) } ================================================ FILE: man/pathway_description.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PathwayDescription.R \name{pathway_description} \alias{pathway_description} \title{Describe Genes Present in Selected Pathways} \usage{ pathway_description(GO, selected_pathways_names, enrich_data) } \arguments{ \item{GO}{A character vector of gene symbols.} \item{selected_pathways_names}{A character vector specifying the names of selected pathways.} \item{enrich_data}{A data frame containing pathway enrichment analysis results.} } \value{ A data frame with columns "Symble" (gene symbol), "Description" (pathway description), and "Exists" (1 if gene is present, 0 otherwise). } \description{ This function identifies genes present in selected pathways based on gene enrichment analysis results. } \examples{ GO <- c("Gene1", "Gene2", "Gene3", "Gene4", "Gene5") # Simulated enrichment analysis data enrich_data <- data.frame( ID = c("Pathway1", "Pathway2", "Pathway3", "Pathway4"), Description = c("Apoptosis", "Cell Cycle", "Signal Transduction", "Metabolism"), geneID = c("Gene1/Gene3", "Gene2/Gene4", "Gene1/Gene2/Gene3", "Gene5"), Count = c(2, 2, 3, 1), stringsAsFactors = FALSE ) # Example usage result <- pathway_description(GO, selected_pathways_names="Apoptosis", enrich_data) } ================================================ FILE: man/pipe.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils-pipe.R \name{\%>\%} \alias{\%>\%} \title{Pipe operator} \usage{ lhs \%>\% rhs } \arguments{ \item{lhs}{A value or the magrittr placeholder.} \item{rhs}{A function call using the magrittr semantics.} } \value{ The result of calling `rhs(lhs)`. } \description{ See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. } \keyword{internal} ================================================ FILE: man/prep_deseq2.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PrepDeseq2.R \name{prep_deseq2} \alias{prep_deseq2} \title{Prepare DESeq2 data for plotting} \usage{ prep_deseq2(input_path, output_name = NULL) } \arguments{ \item{input_path}{Path to the RDS file containing the DESeq2 DEG data frame.} \item{output_name}{Name for the processed data frame, also used as the RDS file name.} } \value{ A data frame with processed DESeq2 DEG data. } \description{ This function reads a DESeq2 DEG data frame from an RDS file, filters it, adjusts the log2FoldChange to absolute values, adds a pseudo-count to pvalues, and transforms pvalues for plotting. The final data frame is returned and optionally saved to a new RDS file. } \examples{ deseq2_file <- system.file("extdata", "DEG_deseq2_test.rds", package = "TransProR") deseq2 <- prep_deseq2(deseq2_file) } ================================================ FILE: man/prep_edgeR.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PrepEdgeR.R \name{prep_edgeR} \alias{prep_edgeR} \title{Prepare edgeR DEG data for plotting} \usage{ prep_edgeR(input_path, output_name = NULL) } \arguments{ \item{input_path}{Path to the RDS file containing the edgeR DEG data frame.} \item{output_name}{Name for the processed data frame, also used as the RDS file name.} } \value{ A data frame with processed edgeR DEG data. } \description{ This function reads an edgeR DEG data frame from an RDS file, filters it using \code{\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to PValue, and transforms PValue for plotting. The final data frame is returned and optionally saved to a new RDS file. } \examples{ edgeR_file <- system.file("extdata", "DEG_edgeR_test.rds", package = "TransProR") edgeR <- prep_edgeR(edgeR_file) } ================================================ FILE: man/prep_limma.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PrepLimma.R \name{prep_limma} \alias{prep_limma} \title{Prepare limma-voom DEG data for plotting} \usage{ prep_limma(input_path, output_name = NULL) } \arguments{ \item{input_path}{Path to the RDS file containing the limma-voom DEG data frame.} \item{output_name}{Name for the processed data frame, also used as the RDS file name.} } \value{ A data frame with processed limma-voom DEG data. } \description{ This function reads a limma-voom DEG data frame from an RDS file, filters it using \code{\link{deg_filter}} function, adjusts the logFC to absolute values, adds a pseudo-count to P.Value, and transforms P.Value for plotting. The final data frame is returned and optionally saved to a new RDS file. } \examples{ limma_file <- system.file("extdata", "DEG_limma_voom_test.rds", package = "TransProR") limma <- prep_limma(limma_file) } ================================================ FILE: man/prep_wilcoxon.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/PrepWilcoxon.R \name{prep_wilcoxon} \alias{prep_wilcoxon} \title{Prepare Wilcoxon DEG data for plotting} \usage{ prep_wilcoxon(input_path, output_name = NULL) } \arguments{ \item{input_path}{Path to the RDS file containing the Wilcoxon DEG data frame.} \item{output_name}{Optional; name for the processed data frame, also used as the RDS file name. If not provided, the data frame will not be saved to file.} } \value{ A data frame with processed Wilcoxon DEG data. } \description{ This function reads a Wilcoxon DEG data frame from an RDS file, filters it using \code{\link{deg_filter}} function, adjusts the log2foldChange to absolute values, adds a pseudo-count to pValues, and transforms pValues for plotting. The final data frame is returned and optionally saved to a new RDS file. } \examples{ wilcoxon_file <- system.file("extdata", "Wilcoxon_rank_sum_testoutRst_test.rds", package = "TransProR") Wilcoxon <- prep_wilcoxon(wilcoxon_file) } ================================================ FILE: man/process_heatdata.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/ProcessHeatdata.R \name{process_heatdata} \alias{process_heatdata} \title{Process Heatmap Data with Various Selection Options} \usage{ process_heatdata( heatdata, selection = 1, custom_names = NULL, num_names_per_group = NULL, prefix_length = 4 ) } \arguments{ \item{heatdata}{A data frame containing the heatmap data.} \item{selection}{An integer specifying the processing method: - 1: Use custom names for columns. - 2: Select a given number of columns per group based on a prefix. - 3: Calculate the average of columns per group based on a prefix.} \item{custom_names}{A character vector of custom names for columns (used when `selection = 1`). The length of this vector must match the number of columns in `heatdata`.} \item{num_names_per_group}{An integer specifying the number of columns to select per group (used when `selection = 2`).} \item{prefix_length}{An integer specifying the length of the prefix for grouping columns (used when `selection = 2` or `selection = 3`). Default is 4.} } \value{ A processed data frame based on the specified selection option. } \description{ This function processes heatmap data (`heatdata`) based on a given selection option. It allows customization of column names, selection of specific columns per group, or averaging columns based on a common prefix. } \examples{ # Example heatmap data frame heatdata <- data.frame( groupA_1 = c(1, 2, 3), groupA_2 = c(4, 5, 6), groupB_1 = c(7, 8, 9), groupB_2 = c(10, 11, 12) ) # Selection 1: Use custom names for columns custom_names <- c("Sample1", "Sample2", "Sample3", "Sample4") processed_data1 <- process_heatdata(heatdata, selection = 1, custom_names = custom_names) # Selection 2: Select a given number of columns per group based on a prefix processed_data2 <- process_heatdata(heatdata, selection = 2, num_names_per_group = 1) # Selection 3: Calculate the average of columns per group based on a prefix processed_data3 <- process_heatdata(heatdata, selection = 3, prefix_length = 6) } ================================================ FILE: man/seek_gtex_organ.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/SeekGtexOrgan.R \name{seek_gtex_organ} \alias{seek_gtex_organ} \title{Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts} \usage{ seek_gtex_organ(path = "./download_data/GTEX_phenotype") } \arguments{ \item{path}{The path to the GTEX phenotype data file. Default is "./download_data/GTEX_phenotype".} } \value{ A table representing the count of samples per primary site. } \description{ This function reads the GTEX phenotype data from a specified path, renames its columns for better readability, and then returns a table of primary site counts. } \examples{ # Get the file path to the example data in the package path <- system.file("extdata", "GTEX_phenotype_test", package = "TransProR") # Call the `seek_gtex_organ` function with the path and print the result SeekGtexOrgan <- seek_gtex_organ(path = path) } ================================================ FILE: man/selectPathways.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichCirclize.R \name{selectPathways} \alias{selectPathways} \title{Randomly Select Pathways with Limited Word Count} \usage{ selectPathways(pathways, max_words = 10, num_select = 10) } \arguments{ \item{pathways}{Character vector of pathways.} \item{max_words}{Integer, maximum number of words allowed in the pathway name.} \item{num_select}{Integer, number of pathways to randomly select.} } \value{ A character vector of selected pathways. } \description{ 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. } \examples{ pathway_list <- c("pathway_one response to stimulus", "pathway_two cell growth and death", "pathway_three regulation of cellular process", "pathway_four metabolic process") selected_pathways <- selectPathways(pathway_list, max_words = 5, num_select = 2) } ================================================ FILE: man/spiral_newrle.Rd ================================================ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/EnrichmentSpiralize.R \name{spiral_newrle} \alias{spiral_newrle} \title{Render a Spiral Plot Using Run-Length Encoding} \usage{ spiral_newrle(x, samples, values, colors, labels = FALSE) } \arguments{ \item{x}{A vector representing categories or segments.} \item{samples}{A vector indicating the sample each segment belongs to.} \item{values}{Numeric vector indicating the lengths of each segment.} \item{colors}{Character vector specifying the colors for each segment.} \item{labels}{Logical, whether to add labels to each segment.} } \value{ No return value, called for side effects. This function generates a spiral plot and optionally adds labels. } \description{ This function creates a spiral plot for visualizing sequential data in a compact and visually appealing way. It uses run-length encoding to represent the lengths and colors of sequences in the spiral. } \examples{ # Example: Creating a spiral plot using the spiral_newrle function # Define example data x <- c("A", "A", "B", "C") samples <- c("Sample1", "Sample1", "Sample2", "Sample2") values <- c(20, 30, 15, 35) colors <- c("red", "blue", "green", "purple") labels <- TRUE # Initialize the spiral plot, setting the x-axis range and scaling spiralize::spiral_initialize(xlim = c(0, sum(values)), scale_by = "curve_length", vp_param = list(x = grid::unit(0, "npc"), just = "left")) # Create a track for the spiral plot spiralize::spiral_track(height = 0.5) # Add segments to the spiral plot using run-length encoding spiral_newrle(x, samples, values, colors, labels) } ================================================ FILE: vignettes/TransProR.Rmd ================================================ --- title: "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." author: "Dongyue Yu\\ Ph.d candiadate at Nankai University " date: "`r Sys.Date()`" output: prettydoc::html_pretty: toc: false theme: hpstr highlight: github pdf_document: toc: true vignette: > %\VignetteIndexEntry{TransProR} %\VignetteEngine{knitr::rmarkdown} %\usepackage[utf8]{inputenc} --- # Vignette Please go to for the full vignette. # Need helps? If you have questions/issues, please visit [TransProR GITHUB](https://sssydysss.github.io/TransProRBook/) first. Your problems are mostly documented. ## Bug Reporting If 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).