Abstract
Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.