A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.
TL;DR
A system-level understanding of the regulation and coordination mechanisms of gene expression is essential for studying the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The scLink R package is available at https://github.com/Vivianstats/scLink.
Paper Link
https://www.sciencedirect.com/science/article/pii/S1672022921001455
Author/Institution
Wei Vivian Li (Department of Biostatistics and Epidemiology, The State University of New Jersey)
Overview
Contributions and Distinctions from Previous Works
Methods
Results
Cite
Wei Vivian Li, Yanzeng Li, scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data, Genomics, Proteomics & Bioinformatics, Volume 19, Issue 3, 2021, Pages 475-492, ISSN 1672-0229, https://doi.org/10.1016/j.gpb.2020.11.006. (https://www.sciencedirect.com/science/article/pii/S1672022921001455)
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