BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data

The rapid development of single-cell DNA sequencing (scDNA-seq) technology has greatly enhanced the resolution of tumor cell profiling, providing an unprecedented perspective in characterizing intra-tumoral heterogeneity and understanding tumor progression and metastasis. However, prominent algorithms for constructing tumor phylogeny based on scDNA-seq data usually only take single nucleotide variations (SNVs) as markers, failing to consider the effect caused by copy number alterations (CNAs). Here, we propose BiTSC 2, B ayesian i nference of T umor clonal T ree by joint analysis of S ingle- C ell S NV and C NA data. BiTSC 2 takes raw reads from scDNA-seq as input, accounts for sequencing errors, models dropout rate and assigns single cells into subclones. By applying Markov Chain Monte Carlo (MCMC) sampling, BiTSC 2 can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, sub-clonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC 2 shows high accuracy in genotype recovery and sub-clonal assignment. BiTSC 2 also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant dropout rate. ### Competing Interest Statement The authors have declared no competing interest.