Fig. 1

Analysis workflow of gene expression data and meta-data. Standard analysis consists of interpreting gene expression data with respect to variables in the metadata using genome-wide analysis such as a principal components analysis and b hierarchical clustering, and gene-level analysis such as c differential expression. The variancePartition workflow uses a rich statistical framework in the form of a linear mixed model and produces gene-level results and a genome-wide summary to simultaneously interpret gene expression data in the context of multiple variables in the metadata. The workflow produces d gene-level results quantifying the contribution of each metadata variable to the variation in expression of each gene, and e a violin plot to summarize the genome-wide trend and rank the total contribution of each variable. f The gene-level results can be used to identify genes that show high expression variation across individuals (i.e. gene385) or tissue (i.e. gene644). Furthermore, variancePartition facilitates examination of specific genes, and integrating external data enables further interpretation of the drivers of expression variation