Comparing Nonparametric Bayesian Tree Priors for Clonal Reconstruction of Tumors

TL;DR

This paper introduces the treeCRP, a nonparametric Bayesian method for inferring tumor subclonal phylogeny, demonstrating improved computational efficiency and comparable accuracy to existing methods.

Contribution

The paper presents the treeCRP, an extension of the Chinese restaurant process, with new split-merge updates that enhance mixing time for tumor subclonal reconstruction.

Findings

treeCRP shows superior mixing and faster running time compared to PhyloSub

treeCRP and TSSB have similar accuracy in subclonal structure recovery

New split-merge procedures improve Markov chain convergence

Abstract

Statistical machine learning methods, especially nonparametric Bayesian methods, have become increasingly popular to infer clonal population structure of tumors. Here we describe the treeCRP, an extension of the Chinese restaurant process (CRP), a popular construction used in nonparametric mixture models, to infer the phylogeny and genotype of major subclonal lineages represented in the population of cancer cells. We also propose new split-merge updates tailored to the subclonal reconstruction problem that improve the mixing time of Markov chains. In comparisons with the tree-structured stick breaking prior used in PhyloSub, we demonstrate superior mixing and running time using the treeCRP with our new split-merge procedures. We also show that given the same number of samples, TSSB and treeCRP have similar ability to recover the subclonal structure of a tumor.