Abstract
Advanced single-cell profiling technologies promote exploration of cell heterogeneity, and clustering of single-cell RNA (scRNA-seq) data enables discovery of coexpression genes and network relationships between genes. In particular, single-cell profiling of circulating tumor cells (CTCs) can provide unique insights into tumor heterogeneity (including in triple-negative breast cancer (TNBC)), while scRNA-seq leads to better understanding of subclonal architecture and biological function. Despite numerous reports suggesting a direct correlation between circulating tumor cells (CTCs) and poor clinical outcomes, few studies have provided a thorough heterogeneity characterization of CTCs. In addition, TNBC is a disease with not only intertumor but also intratumor heterogeneity and represents various biological distinct subgroups that may have relationships with immune functions that are not clearly established yet. In this article, we introduce a new scheme for detecting genotypic characterization of single-cell heterogeneities and apply it to CTC and TNBC single-cell RNA-seq data. First, we use an existing mixture exponential family graph model to partition the cell-cell network; then, with the Markov random field model, we obtain more flexible network rewiring. Finally, we find the cell heterogeneity and network relationships according to different high coexpression gene modules in different cell subsets. Our results demonstrate that this scheme provides a reasonable and effective way to model different cell clusters and different biological enrichment gene clusters. Thus, using different internal coexpression genes of different cell clusters, we can infer the differences in tumor composition and diversity.
Highlights
Cells in the same tissue are commonly viewed as identical functional units
We excluded the edges between cells if the Pearson correlation coefficient between two cell data arrays in the gene expression matrix was corPearson correlation < 0:27, which corresponds to the 0.95 quantile of the Student t-distribution
Since we focused on finding the clustering results of mixture exponential random graph model (ERGM), we tried to select network statistics, such as the differences of network attributes of nodes, with the attached edges inside or outside one cluster
Summary
The analysis of traditional detection methods is always based on the overall average reaction of cells [1]. It has been suggested that the system-level function of a tissue is produced by heterogeneous cells between which there is a slight difference. Traditional sequencing is always based on the average reaction of cells, so it is difficult to detect the difference. Sequencing studies on bulk tumor tissue can only identify the average gene expression. One basic aspect of cancer cell heterogeneity in the same tumor is the different levels of gene expression. Single-cell RNA-Seq technology is feasible and reproducible for gene expression-based classification of cell subpopulations [4,5,6,7]. Zhang et al have demonstrated that scRNA-seq allows researchers to study the heterogeneity of gene expression in individual cells [8]. We leverage the power of single-cell RNA-seq to identify individual cells with
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