As gene expression datasets become more complex, the analysis and interpretation of the data is becoming the rate-limiting step. We have developed the variancePartition software and workflow to facilitate rapid analysis and improve interpretation of complex gene expression datasets. The software and workflow enables any analyst to perform a sophisticated analysis and visualize the results in hours using a few lines of R code. variancePartition leverages the power of the linear mixed model [29–31] to jointly quantify the contribution of multiple sources of variation in high-throughput genomics studies. In applications to transcriptome profiling, variancePartition fits a linear mixed model for each gene and partitions the total variance into the fraction attributable to each aspect of the study design, plus the residual variation. Because it is built on the first principles of the linear mixed model, variancePartition has well characterized theoretical properties [29–31] and accurately estimates the variance fractions even for complex experimental designs where the standard ANOVA method is either inaccurate or not applicable. Moreover, variancePartition gives strong interpretations about the drivers of expression variation, and we demonstrate that these findings are reproducible across multiple datasets.
