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).
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)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.
MIT