TY - JOUR T1 - variancePartition: Interpreting drivers of variation in complex gene expression studies JF - bioRxiv DO - 10.1101/040170 SP - 040170 AU - Gabriel E. Hoffman AU - Eric E. Schadt Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/02/19/040170.abstract N2 - 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. ER -