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An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods

Bioconductor version: Release (3.19)

This package has for objectives to provide a method to make Linear Models for high-dimensional designed data. limpca applies a GLM (General Linear Model) version of ASCA and APCA to analyse multivariate sample profiles generated by an experimental design. ASCA/APCA provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design and contrarily to MANOVA, it can deal with mutlivariate datasets having more variables than observations. This method can handle unbalanced design.

Author: Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb], Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio [dtc], Manon Martin [aut, cre] , Nadia Benaiche [ctb]

Maintainer: Manon Martin <manon.martin at>

Citation (from within R, enter citation("limpca")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Analysis of the Trout dataset with limpca HTML R Script
Analysis of the UCH dataset with limpca HTML R Script
Get started with limpca HTML R Script
Reference Manual PDF


biocViews ExperimentalDesign, GeneExpression, Metabolomics, MultipleComparison, PrincipalComponent, Regression, Software, StatisticalMethod, Visualization
Version 1.0.0
In Bioconductor since BioC 3.19 (R-4.4) (< 6 months)
License Artistic-2.0
Depends R (>= 3.5.0)
Imports ggplot2, stringr, plyr, ggrepel, reshape2, grDevices, graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci, tidyverse, methods, stats, SummarizedExperiment, S4Vectors
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Suggests BiocStyle, pander, rmarkdown, car, gridExtra, knitr, testthat (>= 3.0.0)
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Follow Installation instructions to use this package in your R session.

Source Package limpca_1.0.0.tar.gz
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macOS Binary (x86_64) limpca_1.0.0.tgz
macOS Binary (arm64) limpca_1.0.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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