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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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.