Exact inference under the perfect phylogeny model

TL;DR

This paper introduces EXACT, a novel computational tool that performs exact inference under the Perfect Phylogeny Model using noisy data, enabling comprehensive analysis of all possible trees and surpassing existing methods in accuracy.

Contribution

The paper presents an exact inference algorithm for the PPM that efficiently explores all possible phylogenetic trees, improving over heuristic approaches.

Findings

EXACT outperforms existing tools in accuracy and efficiency.

It provides exact statistics of tree distributions, not just the most-likely tree.

Applicable when mutations cluster into few groups and trees have small size.

Abstract

Motivation: Many inference tools use the Perfect Phylogeny Model (PPM) to learn trees from noisy variant allele frequency (VAF) data. Learning in this setting is hard, and existing tools use approximate or heuristic algorithms. An algorithmic improvement is important to help disentangle the limitations of the PPM's assumptions from the limitations in our capacity to learn under it. Results: We make such improvement in the scenario, where the mutations that are relevant for evolution can be clustered into a small number of groups, and the trees to be reconstructed have a small number of nodes. We use a careful combination of algorithms, software, and hardware, to develop EXACT: a tool that can explore the space of all possible phylogenetic trees, and performs exact inference under the PPM with noisy data. EXACT allows users to obtain not just the most-likely tree for some input data, but exact statistics about the distribution of trees that might explain the data. We show that EXACT outperforms several existing tools for this same task. Availability: https://github.com/surjray-repos/EXACT