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.

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.

Installation

You can install the released version of TransProR from CRAN with:

install.packages("TransProR")

You can install the development version of TransProR like so:

install.packages("devtools")
devtools::install_github("SSSYDYSSS/TransProR", build_vignettes = TRUE)

install.packages("remotes")
remotes::install_github("SSSYDYSSS/TransProR", build_vignettes = TRUE)

System Requirements

• R (>= 4.3.0)

Example

This is a basic example which shows you how to solve a common problem:

library(TransProR)
## basic example code

Citation

Yu Dongyue (2023). 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. <https://sssydysss.github.io/TransProRBook/.

Code of Conduct

Please note that the TransProR project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms. "# TransProR"