The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

Abstract In genomics, we often impose the assumption that gene expression data follows a specific distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene’s distribution shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.