A novel graph-based k-partitioning approach improves the detection of gene-gene correlations by single-cell RNA sequencing.

ABSTRACT:

Background

Gene expression is regulated by transcription factors, cofactors, and epigenetic mechanisms. Coexpressed genes indicate similar functional categories and gene networks. Detecting gene-gene coexpression is important for understanding the underlying mechanisms of cellular function and human diseases. A common practice of identifying coexpressed genes is to test the correlation of expression in a set of genes. In single-cell RNA-seq data, an important challenge is the abundance of zero values, so-called "dropout", which results in biased estimation of gene-gene correlations for downstream analyses. In recent years, efforts have been made to recover coexpressed genes in scRNA-seq data. Here, our goal is to detect coexpressed gene pairs to reduce the "dropout" effect in scRNA

SUBMITTER: Xu H 

PROVIDER: S-EPMC8740455 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature