TY - JOUR
T1 - scdNet
T2 - A computational tool for single-cell differential network analysis
AU - Chiu, Yu Chiao
AU - Hsiao, Tzu Hung
AU - Wang, Li Ju
AU - Chen, Yidong
AU - Shao, Yu Hsuan Joni
N1 - Funding Information:
Publication charges for this article have been funded by Taichung Veterans General Hospital. This research was also supported by the Ministry of Science and Technology, Taiwan (MOST104-2314-B-038-044) to YJS; the National Health Research Institutes, Taiwan (NHRI-EX107-10710BC) to THH; the NCI Cancer Center Shared Resources (NIH-NCI P30CA54174), NIH (CTSA 1UL1RR025767–01), and CPRIT (RP160732) to YC; and San Antonio Life Science Institute (SALSI Postdoctoral Research Fellowship 2018) to YCC. The funding sources had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/21
Y1 - 2018/12/21
N2 - 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.
AB - 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.
KW - Differential network analysis
KW - Gene regulatory networks
KW - Single-cell RNA-Seq
UR - http://www.scopus.com/inward/record.url?scp=85058916516&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058916516&partnerID=8YFLogxK
U2 - 10.1186/s12918-018-0652-0
DO - 10.1186/s12918-018-0652-0
M3 - Article
C2 - 30577836
AN - SCOPUS:85058916516
SN - 1752-0509
VL - 12
JO - BMC Systems Biology
JF - BMC Systems Biology
M1 - 124
ER -
