Accurate Probabilistic Reconstruction of Cell Lineage Trees from SNVs and CNAs with ScisTreeCNA

Abstract Cell lineage tree is a fundamental evolutionary model for single-cell evolution. Inference of cell lineage tree from noisy single-cell DNA data has been studied actively in recent years. Existing methods for cell lineage tree inference can be classified into two categories based on the type of genetic variations they work with: single-nucleotide variants (SNVs) or copy-number aberrations (CNAs). Due to various noises and uncertainties in the data, the existing methods are not fully satisfactory, in part because they only used one type of genetic variant. Single-cell DNA sequencing data with both SNVs and CNAs are becoming available. In principle, joint inference of cell lineage trees from both SNVs and CNAs may lead to more accurate results. However, there is a lack of rigorous models and efficient algorithms for such inference. In this paper, we present a new cell lineage tree inference method, called ScisTreeCNA, that jointly infers cell lineage trees from SNVs and CNAs. A key contribution of ScisTreeCNA is a novel probabilistic model for the joint evolution of SNVs and CNAs in single cell data. Based on this model, ScisTreeCNA implemented several efficient algorithms for accelerating probabilistic inference of cell lineage tree. Experiments on both simulated and real biological data show that ScisTreeCNA consistently outperforms existing methods in the accuracy of the inferred cell lineage trees. ScisTreeCNA is available at https://github.com/haotianzh/ScisTreeCNA. Competing Interest Statement The authors have declared no competing interest. Footnotes ↵* Email: haotianzh{at}uconn.edu