An R package for simple transcriptome meta-analysis for identifying stress-responsive genes

Stress Response score (SRscore) is a stress responsiveness measure for transcriptome datasets and is based on the vote-counting method. The SRscore is determined to evaluate and scores genes on the basis of the consistency of the direction of their regulation (Up-regulation, Down-regulation, or No changed) under stress conditions across the analyzed, multiple research projects. This package is based on the HN-score of Tamura and Bono (2022), and can calculate both the original method and the calculation method we have extended (Fukuda et al. 2025).

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("BiocStyle", "ComplexHeatmap", "clusterProfiler",
                       "org.At.tair.db", "genefilter"))
install.packages(c("RColorBrewer", "DT"))
install.packages("devtools")
devtools::install_github("fusk-kpu/SRscore", build_vignettes = TRUE)

Documents

library(SRscore)
browseVignettes("SRscore")

The SRscore package is designed to facilitate meta-analysis methods based on vote-counting. It contains three main functions for calculating the SRscore, which represents a numerical value indicating a gene's stress responsiveness among multiple studies. Using the expand_by_groups() function, it is possible to generate a table pairing all possible combinations of two groups, which can be arranged in two columns. To mitigate batch effects, the function only generates pairs among samples within a given dataset (e.g., NCBI GEO series). When the table thus acquired is used as an input to execute the calc_SRratio() function, this function calculates a value designated the Stress Response ratio (SRratio) and, which is stored in an SRratio matrix (gene × sample). SRratio represents the gene expression level and is calculated similarly to a log2 fold change. Using this matrix as an input, executing the calc_SRscore() function yields a gene-specific SRscore.

The primary feature of the SRscore package is its capacity to perform cross-comparative analysis of multiple datasets and to estimate consistent changes in gene expression levels. Commencing with the import of metadata and expression data, the package implements a sequential workflow that includes inter-group comparisons within each dataset, calculation of integrated scores via meta-analysis, and visualization and export of the results.

Updates

version 0.1.2 (December 17, 2025)

License

MIT