RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.

ABSTRACT: Gene selection in unannotated large single cell RNA sequencing (scRNA-seq) data is important and crucial step in the preliminary step of downstream analysis. The existing approaches are primarily based on high variation (highly variable genes) or significant high expression (highly expressed genes) failed to provide stable and predictive feature set due to technical noise present in the data. Here, we propose RgCop, a novel regularized copula based method for gene selection from large single cell RNA-seq data. RgCop utilizes copula correlation (Ccor), a robust equitable dependence measure that captures multivariate dependency among a set of genes in single cell expression data. We formulate an objective function by adding l1 regularization term with Ccor to penalizes the redundant co-effic

SUBMITTER: Lall S 

PROVIDER: S-EPMC8568278 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature