Li, Hongxiang and Khang, Tsung Fei (2023) clrDV: a differential variability test for RNA-Seq data based on the skew-normal distribution. PeerJ, 11. ISSN 2167-8359, DOI https://doi.org/10.7717/peerj.16126.
Full text not available from this repository.Abstract
Background. Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean. Methods. Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribu-tion for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data. Results. Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer's disease.
| Item Type: | Article |
|---|---|
| Funders: | United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute on Aging (NIA) P50 AG025711 |
| Uncontrolled Keywords: | Alzheimer's disease; Compositional data; Differential variability; RNA-Seq data; Skew-normal distribution |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Divisions: | Faculty of Science > Institute of Mathematical Sciences |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 01 Nov 2025 14:12 |
| Last Modified: | 01 Nov 2025 14:12 |
| URI: | http://eprints.um.edu.my/id/eprint/48626 |
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