MOTIVATION: Copy number alterations (CNAs) are a significant driver in cancer growth and development, but remain poorly characterized on the single cell level. Although genome evolution in cancer cells is Markovian through evolutionary time, CNAs are not Markovian along the genome. However, existing methods call copy number profiles with Hidden Markov Models or change point detection algorithms based on changes in observed read depth, corrected by genome content and do not account for the stochastic evolutionary process. RESULTS: We present a theoretical framework to use tumor evolutionary history to accurately call CNAs in a principled manner. To model the tumor evolutionary process and account for technical noise from low coverage single-cell whole genome sequencing data, we developed SCONCE, a method based on a Hidden Markov Model to analyze read depth data from tumor cells using matched normal cells as negative controls. Using a combination of public data sets and simulations, we show SCONCE accurately decodes copy number profiles, and provides a useful tool for understanding tumor evolution. AVAILABILITYAND IMPLEMENTATION: SCONCE is implemented in C++11 and is freely available from https://github.com/NielsenBerkeleyLab/sconce. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Single cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors. RESULTS: We present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method. CONCLUSIONS: SCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution.