ConTreeDP: A consensus method of tumor trees based on maximum directed partition support problem

Phylogenetic inference has become a crucial tool for interpreting cancer genomic data, but continuing advances in our understanding of somatic mutability in cancer, genomic technologies for profiling it, and the scale of data available have created a persistent need for new algorithms able to deal with these challenges. One particular need has been for new forms of consensus tree algorithms, which present special challenges in the cancer space for dealing with heterogeneous data, short evolutionary time scales, and rapid mutation by a wide variety of somatic mutability mechanisms. We develop a new consensus tree method for clonal phylogenetics, ConTreeDP, based on a formulation of the Maximum Directed Partition Support Consensus Tree (MDPSCT) problem. We demonstrate theoretically and empirically that our approach can efficiently and accurately compute clonal consensus trees from cancer genomic data. Availability: https://github.com/CMUSchwartzLab/ConTreeDP