Abstract
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.
Highlights
Single-cell RNA sequencing is an emerging technology that has revolutionized the research of the tumor heterogeneity
Model overview scRNASeq-based differential network analysis (scdNet) is devised to analyze differential gene regulatory networks associated with cellular states at the single cell level
MATLAB implementation of scdNet is available at https://github.com/ChenLabGCCRI/scdNet
Summary
Single-cell RNA sequencing (scRNA-Seq) is an emerging technology that has revolutionized the research of the tumor heterogeneity. While many methods have been designed to analyze single-cell DNA-Seq data [1], the analysis of scRNA-Seq data remains challenging due to high sparsity that prevents direct applications of methods originally developed for microarray and bulk RNA sequencing. Recent studies have successfully applied correlation onto the inference of gene regulatory networks by using scRNA-Seq data [2, 3]. Realizing that tumor cells are highly heterogeneous, network topologies may be massively changed between cells of different cellular states [4]. The computational method for studying individual gene pairs of which regulatory strengths alter between conditions was only carried out in the bulk RNA sequencing data [6,7,8]
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