A Statistical Learning Method for Simultaneous Copy Number Estimation and Subclone Clustering with Single Cell Sequencing Data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.