Work

A novel approach to highly variable genes selection for scRNA-Seq analysis

Year: 2025

Type: conference-paper

Abstract: Single cell RNA-sequencing (scRNA-Seq) data are typically represented as cell-by-gene count matrices, which capture the expression of each gene as detected in the sampled cells; often a heterogeneous population of multiple different cell types or cell states. Almost all scRNA-Seq analysis workflows have a gene selection step prior to applying clustering algorithms which helps remove genes with low variability and hence reduce the high-dimensional gene space. A defacto method for achieving this selection of highly variable genes (HVG) uses dispersion and mean expression scores to evaluate the variability of each individual gene [3]. However, methods based on mean-variance relationship for gene selection suffer from arbitrary determination of number of genes to use and are susceptible to variance instability.

Source: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

DOI: 10.1145/3765612.3767789

Authors: Enos Dk Kakwambi, Tuan Nguyen, Suryaveer Kapoor, Marmar R. Moussa

Institution: University of Oklahoma

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Science Foundation, University of Oklahoma, National Institutes of Health, National Cancer Institute

Awards: CAREER: Computational and statistical methods for studying single cell genomics data in the context of tissue organization, Collaborative Research: III: Medium: Algorithms for scalable inference and phylodynamic analysis of tumor haplotypes using low-coverage single cell sequencing data, G6671297155, G894646939, Computational approaches to the mechanistic elucidation of the serrated pathway of human colon carcinogenesis