CopyMix: Mixture Model Based Single-Cell Clustering and Copy Number Profiling using Variational Inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. Moreover, clonal copy number profiling has been missing except for one method, CONET, which unfortunately computes it by a post-processing tool. Finally, a common copy number profiling tool, HMMcopy, requires parameter tuning. We avoid the clustering artifact issues and provide clonal copy number profiles without the labor of parameter tuning in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. We evaluate CopyMix using simulated data and published biological data from ovarian cancer. The results reveal high clustering performance and low errors in copy number profiling. These favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.