MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells - Huzar, Jared; Kim, Hannah; Kumar, Sudhir; Miura, Sayaka (Frontiers in Genetics, 2022)
Cancer is a disease where somatic mutations keep occurring and cell populations keep evolving in tumors. Tumor cell populations may exhibit distinct gene expression patterns due to functional consequences of these somatic mutations or by changing epigenetic modifications and the environmental milieu. Consequently, convergent and divergent patterns of gene expression arise among cells and their populations. Single-cell RNA sequencing (scRNA-seq) is enabling high-throughput and high-resolution cellular transcriptomics. The scRNA-seq datasets now profile hundreds of single-cells many of which carry generic variants suitable for delineating evolutionary genetic ancestry. Here, we present Multi-Omics Concordance Analysis (MOCA) for evaluating the concordance of gene expression and genetic variation of cells using scRNA-seq data. MOCA maps gene expression changes on molecular evolutionary trajectories of cells to reveal cells and genes showing convergent and divergent patterns in functional genomics. Through example data analyses, we suggest that MOCA can help explore the inferred relationship between molecular and expression evolution during cancer progression.