Analysis and Visualization of Multi-Omics Data
Adjust and Export Pathway Analysis Results
Process and Correct Batch Effects in TCGA's normal tissue and GTEX Dat...
Differential Gene Expression Analysis using 'DESeq2'
Merge Genes with Color Information Based on Up/Down Regulation
Add gene highlights to a ggtree object
Create Pathway-Gene Mapping Data Frame
Create high-density region plot with optional points, density rugs, an...
Merge gene expression data from GTEx and TCGA datasets
Merge Data Frames by Common Row Names with Additional Columns
Adjust Alpha Scale for Data Visualization
Adjust Color Tone by Modifying Saturation and Luminance
Merge Data Frames with Specific Method and Color Columns
Generate a graphical representation of pathway gene maps
Count Genes Present in Pathways Above a Threshold
Describe Genes Present in Selected Pathways
Pipe operator
Prepare DESeq2 data for plotting
Prepare edgeR DEG data for plotting
Prepare limma-voom DEG data for plotting
Prepare Wilcoxon DEG data for plotting
Process Heatmap Data with Various Selection Options
Load and Process GTEX Phenotype Data to Retrieve Primary Site Counts
Randomly Select Pathways with Limited Word Count
Render a Spiral Plot Using Run-Length Encoding
Add a boxplot layer to a ggtree plot
Process and Correct Batch Effects in Tumor Data
Compare and merge specific columns from two DEG data frames
Function to Create a Venn Diagram of DEGs with Custom Colors
Create a base plot with gene expression data on a phylogenetic tree
Function to Filter Differentially Expressed Genes (DEGs)
Draw Dual-Sided Legends on a Plot
Differential Gene Expression Analysis using 'edgeR'
Combine and Visualize Data with Circular Bar Chart
Enrichment Polar Bubble Plot
Draw Chord Diagram with Legends
Create Spiral Plots with Legends Using 'spiralize' and 'ComplexHeatmap...
Extract and Count Descriptions with Specified Color
Extract and Store Top Pathways for Each Sample
Extract Positive Pathways from SSGSEA Results and Select Random Sample...
Create faceted high-density region plots with optional points and dens...
Filter Differentially Expressed Genes
Function to Create a Venn Diagram of DEGs
Gather graph edge from data frame Please note that this function is fr...
Gather graph nodes from a data frame Please note that this function is...
Get GTEx Expression Data for Specific Organ
TCGA Expression Data Processing
Add Highlights for Genes on a Phylogenetic Tree
Differential Gene Expression Analysis using limma and voom
Log transformation decision and application on data
X-spline Statistic for ggplot2 (adapted from ggalt)
Differential Gene Expression Analysis Using Wilcoxon Rank-Sum Test
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, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.
Useful links