Integrating additional data with gene-level results from variancePartition can give a clear interpretation of the drivers of variation. For example, 91.1% of variation in CCDC85B is explained by variation across laboratory. This gene has a very high GC content of 70.9% and is consistent with the genome-wide pattern where the degree of variation across laboratories is positively correlated with GC content (Fig. 2 d). While technical variation in RNA-seq is known to depend on GC content [8, 9], variancePartition gives a clear illustration of how the effect of technical artifacts varies substantially across genes. Moreover, this analysis can be used to identify other correlates underlying technical issues in expression variation.
