Optimizing copy number profiles for tumor cell phylogeny inference from low-coverage single-cell sequencing data
Motivation: Accurate reconstruction of tumor cell phylogenies is crucial for understanding cancer evolutionary dynamics, inferring intra-tumor heterogeneity, and designing effective and personalized cancer treatment strategies. One of the most promising and cost-effective approaches for reconstructing tumor phylogenies for large numbers of cells is to use copy number alteration (CNA) data obtained from low-coverage single-cell DNA sequencing of sampled tumor cells. However, error and uncertainty in inferred copy number profiles (CNPs), resulting from low sequence coverage, can have a drastic negative effect on the reconstruction accuracy of tumor cell phylogenies. Results: In this work, we show that careful selection of bin size, a critical tuning parameter for inferring CNPs, is necessary to maximize the accuracy of tumor cell phylogeny inference. Specifically, we (i) use three previously published low-coverage single-cell DNA sequencing datasets from real tumors to demonstrate that even small changes in bin size can have a drastic impact on downstream tumor phylogeny reconstruction, (ii) show that this effect persists even when using simulated low-coverage datasets generated under idealized conditions with minimal noise, and (iii) propose a simple computational approach to select bin sizes that maximize tumor phylogeny accuracy. Our experiments span a wide range of bin sizes and account for the impact of various factors including coverage-level, phylogeny reconstruction method, and CNP inference method, and explicitly account for phylogenetic reconstruction uncertainty. Our evaluation of the proposed computational approach for bin size selection shows that it can significantly improve tumor cell phylogeny reconstruction accuracy compared to baseline methods. Availability: Freely available from https://github.com/Tanveer14/BinSelector.