Abstract
BackgroundAs 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.ResultsWe 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.ConclusionsOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1323-z) contains supplementary material, which is available to authorized users.
Highlights
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
How does cell or tissue type affect the genetic regulation of gene expression, and does it vary by ancestry [1, 2]? What is the relative contribution of experimental stimulus versus regulatory genetics to variation in gene expression [5]? Is technical variability of RNA-seq low enough to study regulatory genetics and disease biology, and what are the major drivers of this technical variability [2, 8, 9]? A rich understanding of complex datasets requires answering these questions with both a genomewide summary and gene-level resolution
Analysis of GEUVADIS RNA-seq dataset Consider 660 RNA-seq experiments from the GEUVADIS study [6, 8] of lymphoblastoid cell lines from 462 individuals of 5 ancestries and 2 sexes sequenced across 7 laboratories
Summary
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. Transcriptome profiling in particular has been widely applied to detect variation in transcript levels attributable to differences in disease state, cell type or regulatory genetics. Recent studies have simultaneously considered multiple dimensions of variation to understand the impact of cell type [1], tissue type [2], brain region [3], experimental stimuli [4], time duration following stimulus [5] or ancestry [1, 4, 6] on the genetic regulation of gene expression. How does cell or tissue type affect the genetic regulation of gene expression, and does it vary by ancestry [1, 2]? What is the relative contribution of experimental stimulus versus regulatory genetics to variation in gene expression [5]? Is technical variability of RNA-seq low enough to study regulatory genetics and disease biology, and what are the major drivers of this technical variability [2, 8, 9]? A rich understanding of complex datasets requires answering these questions with both a genomewide summary and gene-level resolution
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