Joint Inference of Clonal Structure using Single-cell Genome and Transcriptome Sequencing Data

Abstract

Latest advancements in high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CC-NMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. We validated CC-NMF using both simulated benchmarks and real-world applications, demon-strating its robustness and accuracy. We analyzed scRNA and scDNA data from an ovarian cancer cell lines mixture, a gastric cancer cell line, as well as a primary gastric cancer, successfully resolving underlying clonal structures and identifying high correlations of coexisting clones between genome and transcriptome. Overall, CCNMF is a coherent computational framework that simultaneously resolves genomic and transcriptomic clonal architecture, facilitating understanding of how cellular gene expression changes along with clonal genome alternations.