variancePartition: interpreting drivers of variation in complex gene expression studies

BMC Bioinformatics

Software

Eric E. Schadt¹  Gabriel E. Hoffman¹ 

[1] Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA;

关键词: Transcriptome profiling;    RNA-seq;    Linear mixed model;   

DOI  :  10.1186/s12859-016-1323-z

 received in 2016-06-24, accepted in 2016-11-05,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2016

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