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

研究成果: 雜誌貢獻文章

4 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號124
期刊BMC Systems Biology
12
DOIs
出版狀態已發佈 - 十二月 21 2018

ASJC Scopus subject areas

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

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