variancePartition: interpreting drivers of variation in complex gene expression studies

TLDR

Large‑scale gene expression studies with multiple biological and technical sources require characterization of variation to understand disease biology and regulatory genetics. The authors present variancePartition, a statistical and visualization framework that prioritizes drivers of variation across the genome and identifies genes deviating from the overall trend. Using a linear mixed model, variancePartition quantifies the proportion of variation in each expression trait attributable to disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large‑scale transcriptome datasets shows that variancePartition recovers reproducible patterns of biological and technical variation across datasets. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.

Abstract

Abstract Background As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition .