Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability.

ABSTRACT: There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour-normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential vari

SUBMITTER: Roberts AGK 

PROVIDER: S-EPMC8759562 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature