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

BackgroundSingle-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.ResultsAddressing 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.ConclusionsOverall, 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.

[1]  Kang He,et al.  Identification of peptide regions of SERPINA1 and ENOSF1 and their protein expression as potential serum biomarkers for gastric cancer , 2015, Tumor Biology.

[2]  Megan Mitchell,et al.  Metabolic and Mitochondrial Dysfunction in Early Mouse Embryos Following Maternal Dietary Protein Intervention1 , 2009, Biology of reproduction.

[3]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[4]  Peter J Park,et al.  Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data , 2018, Genome research.

[5]  Tianwei Yu,et al.  Differential gene network analysis from single cell RNA-seq. , 2017, Journal of genetics and genomics = Yi chuan xue bao.

[6]  Samir S Taneja,et al.  Re: Increased survival with enzalutamide in prostate cancer after chemotherapy. , 2013, The Journal of urology.

[7]  Yidong Chen,et al.  Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival , 2017, BMC Bioinformatics.

[8]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[9]  Jie Wang,et al.  Single-Cell Co-expression Analysis Reveals Distinct Functional Modules, Co-regulation Mechanisms and Clinical Outcomes , 2016, PLoS Comput. Biol..

[10]  W. J. Allard,et al.  Circulating tumor cells predict survival in patients with metastatic prostate cancer. , 2005, Urology.

[11]  Tanja Fehm,et al.  Circulating Tumor Cells Predict Survival in Early Average-to-High Risk Breast Cancer Patients , 2014, Journal of the National Cancer Institute.

[12]  W. Isaacs,et al.  AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. , 2014, The New England journal of medicine.

[13]  Kurt Miller,et al.  Increased survival with enzalutamide in prostate cancer after chemotherapy. , 2012, The New England journal of medicine.

[14]  W. Koh,et al.  Single-cell genome sequencing: current state of the science , 2016, Nature Reviews Genetics.

[15]  K. Pienta,et al.  Circulating Tumor Cells Predict Survival Benefit from Treatment in Metastatic Castration-Resistant Prostate Cancer , 2008, Clinical Cancer Research.

[16]  Franziska Michor,et al.  Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq , 2018, Nature Communications.

[17]  Michael J. T. Stubbington,et al.  The Human Cell Atlas: from vision to reality , 2017, Nature.

[18]  Yidong Chen,et al.  Analyzing differential regulatory networks modulated by continuous-state genomic features in glioblastoma multiforme , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  T. Ideker,et al.  Differential network biology , 2012, Molecular systems biology.

[20]  Seiji Naito,et al.  Pro-survival and anti-apoptotic properties of androgen receptor signaling by oxidative stress promote treatment resistance in prostate cancer. , 2012, Endocrine-related cancer.

[21]  M. Schmid,et al.  Activation of mouse ribosomal RNA genes at the 2-cell stage , 1977, Human Genetics.

[22]  Sridhar Ramaswamy,et al.  Circulating Tumor Cell Clusters Are Oligoclonal Precursors of Breast Cancer Metastasis , 2014, Cell.

[23]  S. Orkin,et al.  Mapping the Mouse Cell Atlas by Microwell-Seq , 2018, Cell.

[24]  M. Tenniswood,et al.  Emergence of metastatic hormone‐refractory disease in prostate cancer after anti‐androgen therapy , 2004, Journal of cellular biochemistry.

[25]  Jeong Eon Lee,et al.  Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer , 2017, Nature Communications.

[26]  J. Marioni,et al.  Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos , 2016, Cell.

[27]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[28]  Seiji Naito,et al.  Oxidative stress and androgen receptor signaling in the development and progression of castration-resistant prostate cancer. , 2011, Free radical biology & medicine.

[29]  Sara Ballouz,et al.  Exploiting single-cell expression to characterize co-expression replicability , 2016, Genome Biology.

[30]  Sridhar Ramaswamy,et al.  RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance , 2015, Science.

[31]  Tzu-Pin Lu,et al.  Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers , 2016, Scientific Reports.