NDLI: Improving data interpretability with new differential sample variance gene set tests

Content Provider	Springer Nature : BioMed Central
Author	Rahmatallah, Yasir Glazko, Galina
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 tree to generalize the Cramer-Von Mises and Anderson–Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterized the detection power and Type I error rate of these methods in addition to two methods developed earlier using simulation results with different parameters. We applied the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to within phenotype variability and to heterogeneous differences between two compared phenotypes. Our results show that methods designed to detect differential sample variance provide meaningful biological interpretations by detecting specific hallmark gene sets associated with the two compared phenotypes as documented in available literature. Conclusions The results of this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-025-06117-0.pdf
Ending Page	21
Page Count	21
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-025-06117-0
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	26
Language	English
Publisher	BioMed Central
Publisher Date	2025-04-14
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Gene set analysis Differential variability Minimum spanning tree Anderson–Darling Cramer-Von Mises Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

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