Quantify and interpret drivers of variation in multilevel gene expression experiments (original) (raw)

variancePartition quantifies and interprets multiple sources of biological and technical variation in gene expression experiments. The package a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. The [dream()](reference/dream-method.html) function performs differential expression analysis for datasets with repeated measures or high dimensional batch effects.

Update

variancePartition 1.31.1 includes a major rewrite of the backend for better error handling. See Changelog. Importantly, the new version is compatible with emprical Bayes moderated t-statistics for linear mixed models using eBayes().

Installation

Latest features from GitHub

Stable release from Bioconductor

BiocManager::install("variancePartition")

Reporting bugs

Please help speed up bug fixes by providing a ‘minimal reproducible example’ that starts with a new R session. I recommend the reprex package to produce a GitHub-ready example that is reproducable from a fresh R session.