Standard computational workflows employ principal components analysis [10] and hierarchical clustering [11] to summarize genome-wide expression patterns, and differential expression [12–16] to perform gene-level analyses. Recently, statistical methods that decompose variation in gene expression into the variance attributable to multiple variables in the experimental design have yielded valuable insight into the biological and technical components driving expression variation [8, 17–22]. Moreover, linear mixed models have been widely used in the analysis and interpretation of genome-wide association studies [23–28].
