Complexity and algorithms for finding a perfect phylogeny from mixed tumor samples
Abstract
Recently, Hajirasouliha and Raphael (WABI 2014) proposed a model for deconvoluting mixed tumor samples measured from a collection of high-throughput sequencing reads. This is related to understanding tumor evolution and critical cancer mutations. In short, their formulation asks to split each row of a binary matrix so that the resulting matrix corresponds to a perfect phylogeny and has the minimum number of rows among all matrices with this property. In this paper we disprove several claims about this problem, including an NP-hardness proof of it. However, we show that the problem is indeed NP-hard, by providing a different proof. We also prove NP-completeness of a variant of this problem proposed in the same paper. On the positive side, we propose an efficient (though not necessarily optimal) heuristic algorithm based on coloring co-comparability graphs, and a polynomial time algorithm for solving the problem optimally on matrix instances in which no column is contained in both columns of a pair of conflicting columns. Implementations of these algorithms are freely available at https://github.com/alexandrutomescu/MixedPerfectPhylogeny
Cite
@article{arxiv.1506.07675,
title = {Complexity and algorithms for finding a perfect phylogeny from mixed tumor samples},
author = {Ademir Hujdurović and Urša Kačar and Martin Milanič and Bernard Ries and Alexandru I. Tomescu},
journal= {arXiv preprint arXiv:1506.07675},
year = {2016}
}
Comments
This is the extended version of Hujdurovi\'c et al, Finding a perfect phylogeny from mixed tumor samples, WABI 2015, DOI: 10.1007/978-3-662-48221-6_6