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TcGSA

Overview

TcGSA is a package which performs Time-course Gene Set Analysis from microarray data, and provide nice representations of its results.

On top of the CRAN help pdf-file, the following article explains what TcGSA is about:

Hejblum, BP, Skinner, J, & Thiébaut, R (2015). Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. PLOS Computational Biology, 11(6):e1004310. <doi: 10.1371/journal.pcbi.1004310>

Installation

TcGSA imports the multtest package which is not available on CRAN, but is available on the Bioconductor repository. Before installing TcGSA, be sure to have this multtest package installed. If not, you can do so by running the following:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("multtest")

The easiest way to get TcGSA is to install it from CRAN:

install.packages("TcGSA")

or to get the development version from GitHub:

#install.packages("devtools")
devtools::install_github("sistm/TcGSA")

Microarrays vs RNA-seq

TcGSA relies on a Gaussian assumption for the expression data, which is suitable for normalized microarray data. Due to their count and heteroskedastic nature, RNA-seq data need to be handled differently and TcGSA cannot deal with RNA-seq data. For RNA-seq data, please have a look at the Bioconductor package dearseq which incorporates similar functionalities for analyzing RNA-seq data.

– Boris Hejblum

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Version

Install

install.packages('TcGSA')

Monthly Downloads

282

Version

0.12.13

License

GPL-2 | file LICENSE

Maintainer

Boris P. Hejblum

Last Published

July 22nd, 2025

Functions in TcGSA (0.12.13)

TcGSA.LR

Computing the Likelihood Ratios for the Gene Sets under Scrutiny
TcGSA.LR.parallel

Parallel computing the Likelihood Ratios for the Gene Sets under Scrutiny
plotFit.GS

Plotting function for exploring the fitness of the mixed modeling used in TcGSA
plotSelect.GS

Plotting (several) Selected Gene Set(s) in some Subjects
plotPat.1GS

Plotting a Specific Gene Set Stratifying on Patients
plotPat.TcGSA

Plot a Gene Set Trends Heatmap for each Patient.
pval_simu

Computing P-values with a Simulated Sample from the Null Distribution
plotMultipleGS

Plotting Multiple Gene Sets in a single plot
summary.TcGSA

Summarizing TcGSA
signifLRT.TcGSA

Identifying the Significant Gene Sets
TcGSA-internal

Internal TcGSA Functions
data_simu_TcGSA

Simulated Data for TcGSA
rchisqmix

Chi-Squared Mixtures Distribution
TcGSA-package

Time-course Gene Set Analysis
multtest.TcGSA

Computing the P-value of the Likelihood Ratios Applying a Multiple Testing Correction
plot.TcGSA

Plot a Gene Set Trends Heatmap.
plot1GS

Plotting a Specific Gene Set
clustTrend

Cluster the genes dynamics into different dominant trends.