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<br> `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()` function performs differential expression analysis for datasets with repeated measures or high dimensional batch effects. <img src="man/figures/variancePartition.png" align="center" alt="" style="padding-left:10px;" /> <br> ### Update variancePartition 1.31.1 includes a major rewrite of the backend for better error handling. See [Changelog](news/index.html). Importantly, the new version is compatible with emprical Bayes moderated t-statistics for linear mixed models using `eBayes()`. <br> ### Installation #### Latest features from GitHub ```r devtools::install_github("DiseaseNeuroGenomics/variancePartition") ``` #### Stable release from Bioconductor ```r BiocManager::install("variancePartition") ``` ### Notes This is a developmental version. For stable release see [Bioconductor version]( For questions about specifying contrasts with dream, see [examples here]( See [frequently asked questions]( See repo of [examples from the paper]( ### 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. ## References Describes extensions of `dream` including empirical Bayes moderated t-statistics for linear mixed models and applications to single cell data - [Hoffman, et al, biorxiv (2023)]( Describes `dream` for differential expression: - [Hoffman and Roussos, Bioinformatics (2021)]( Describes the `variancePartition` package: - [Hoffman and Schadt, BMC Bioinformatics (2016)](