RT Journal Article SR Electronic T1 variancePartition: Interpreting drivers of variation in complex gene expression studies JF bioRxiv FD Cold Spring Harbor Laboratory SP 040170 DO 10.1101/040170 A1 Gabriel E. Hoffman A1 Eric E. Schadt YR 2016 UL http://biorxiv.org/content/early/2016/02/19/040170.abstract AB As genomics studies become more complex and consider multiple sources of biological and technical variation, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation with a genome-wide summary, and identify genes that deviate from the genome-wide trend. variancePartition enables rapid interpretation of complex gene expression studies and is applicable to many genomics assays.