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

ABSTRACT:

Background

In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression distributions in three different tumor types from the Cancer Genome Atlas (TCGA).

Results

Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the shape of the e

SUBMITTER: de Torrente L 

PROVIDER: S-EPMC7768656 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature