Improving data interpretability with new differential sample variance gene set tests.

ABSTRACT:

Background

Gene set analysis methods have played a major role in generating biological interpretations of omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression while methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.

Results

We used ranking schemes based on minimum spanning t

SUBMITTER: Rahmatallah Y 

PROVIDER: S-EPMC11998189 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature