title: variancePartition: interpreting drivers of variation in complex gene expression studies.
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 .
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA. gabriel.hoffman@mssm.edu.
authors.forename: Gabriel E
authors.identifier: http://orcid.org/0000-0002-0957-0224
authors.initials: GE
authors.lastname: Hoffman
authors.affiliation: Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA.
authors.forename: Eric E
authors.initials: EE
authors.lastname: Schadt
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D014644:Genetic Variation / Q000235:genetics*; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016014:Linear Models; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
keywords: Linear mixed model; RNA-seq; Transcriptome profiling