Joint Inference of Clonal Structure using Single-cell DNA-Seq and RNA-Seq data [article]

Xiangqi Bai, Lin Wan, Charlie Xia
2020 bioRxiv   pre-print
Understanding how genome changes shape gene expression in individual cells is essential to understand complex genetic diseases such as cancers. Latest high-throughput single-cell RNA (scRNA-) and DNA-sequencing (scDNA-seq) technologies enabled cell-resolved investigation of pathological tissue clones. However, it is still technically challenging to simultaneously measure the genome and transcriptome content of a single cell. In this work, we developed CCNMF--a new computational tool utilizing
more » ... e Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structures in single-cell genomics and transcriptomics data. We benchmarked CCNMF using both simulated and real cell mixture derived datasets and fully demonstrated its robustness and accuracy. We also applied CCNMF to the paired scRNA and scDNA data from a triple-negative breast cancer xenograft, resolved its underlying clonal structures, and identified differential genes between cell clusters. In summary, CCNMF presents a joint and coherent approach to resolve the clonal genome and transcriptome structures, which will facilitate a better understanding of the cellular and tissue changes associated with disease development. CCNMF is freely available at https://github.com/XQBai/CCNMF.
doi:10.1101/2020.02.04.934455 fatcat:taghxbtpavcsvdiyyjrtcb2c3a