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 [Formula: see text], Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data. BiTSC [Formula: see text] takes raw reads from scDNA-seq as input, accounts for the overlapping of CNA and SNV, models allelic dropout rate, sequencing errors and missing rate, as well as assigns single cells into subclones. By applying Markov Chain Monte Carlo sampling, BiTSC [Formula: see text] can simultaneously estimate the subclonal scCNA and scSNV genotype matrices, subclonal assignments and tumor subclonal evolutionary tree. In comparison with existing methods on synthetic and real tumor data, BiTSC [Formula: see text] shows high accuracy in genotype recovery, subclonal assignment and tree reconstruction. BiTSC [Formula: see text] also performs robustly in dealing with scDNA-seq data with low sequencing depth and variant missing rate. BiTSC [Formula: see text] software is available at https://github.com/ucasdp/BiTSC2.