scdNet: A computational tool for single-cell differential network analysis

Yu Chiao Chiu, Tzu Hung Hsiao, Li Ju Wang, Yidong Chen, Yu Hsuan Joni Shao

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Background: Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. However, the highly sparse data matrices generated by the technology have posed an obstacle to the analysis of differential gene regulatory networks. Results: Addressing the challenges, this study presents, as far as we know, the first bioinformatics tool for scRNA-Seq-based differential network analysis (scdNet). The tool features a sample size adjustment of gene-gene correlation, comparison of inter-state correlations, and construction of differential networks. A simulation analysis demonstrated the power of scdNet in the analyses of sparse scRNA-Seq data matrices, with low requirement on the sample size, high computation efficiency, and tolerance of sequencing noises. Applying the tool to analyze two datasets of single circulating tumor cells (CTCs) of prostate cancer and early mouse embryos, our data demonstrated that differential gene regulation plays crucial roles in anti-androgen resistance and early embryonic development. Conclusions: Overall, the tool is widely applicable to datasets generated by the emerging technology to bring biological insights into tumor heterogeneity and other studies. MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet.

Original languageEnglish (US)
Article number124
JournalBMC Systems Biology
Volume12
DOIs
StatePublished - Dec 21 2018

Keywords

  • Differential network analysis
  • Gene regulatory networks
  • Single-cell RNA-Seq

ASJC Scopus subject areas

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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