Dolphyin: A Combinatorial Algorithm for Identifying 1-Dollo Phylogenies in Cancer

Abstract

Several recent cancer phylogeny inference methods have used the k-Dollo evolutionary model for single-nucleotide variants. Specifically, in this problem one is given an m × n binary matrix B and seeks a rooted tree T with m leaves that correspond to the m rows of B, and each node of T is labeled by a binary state for each of the n characters subject to the restriction that each character is gained at most once (0-to-1 transition) and subsequently lost at most k times (1-to-0 transitions). The 1-Dollo variant, also known as the persistent perfect phylogeny where one is restricted to at most k = 1 losses per character, has been studied extensively, but its hardness remains an open question. Here, we prove that the 1-Dollo Linear Phylogeny (1DLP) problem, where we additionally require the resulting 1-Dollo phylogeny T to be linear, is equivalent to verifying whether the input matrix B adheres to the Consecutive Ones Property (C1P), which can be solved in polynomial time. Due to the equivalence, several known NP-hardness results for relevant variants of C1P carry over to 1DLP, including the minimization of false negatives (0-to-1 modifications to the input matrix B) or the allowance of 2 gains and 2 losses. We furthermore show how we can recursively decompose any, not necessarily linear, 1-Dollo phylogeny T into several 1-Dollo linear phylogenies, connected by matching branching points. We extend this characterization to matrices B that admit 1-Dollo phylogenies, giving necessary and sufficient conditions for the existence of a novel decomposition of B into several submatrices and corresponding branching points. This decomposition forms the basis of Dolphyin, a new exponential-time algorithm for inferring 1-Dollo phylogenies that efficiently leverages the determination of linear 1-Dollo phylogenies as a subroutine. Dolphyin can also be applied to input matrices B with false negatives. We demonstrate that Dolphyin is runtime-competitive with a previous integer linear programming based algorithm SPhyR on simulated datasets. We additionally analyze simulated datasets with false negative errors and find that in the median case, Dolphyin infers 1-Dollo phylogenies with inferred error rates at or below the ground truth rate. Finally, we apply Dolphyin to 99 acute myeloid leukemia single-cell sequencing datasets, finding that the majority of the cancers can be explained by 1-Dollo phylogenies with false negative error rates in line with the used sequencing technology.
 
Availability. Dolphyin is available at: https://github.com/elkebir-group/Dolphyin.