title: Comparing nonparametric Bayesian tree priors for clonal reconstruction of tumors.
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…
authors.affiliation: Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada. amit.deshwar@utoronto.ca.
authors.forename: Amit G
authors.initials: AG
authors.lastname: Deshwar
authors.forename: Shankar
authors.initials: S
authors.lastname: Vembu
authors.forename: Quaid
authors.initials: Q
authors.lastname: Morris
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D005787:Gene Frequency; D005838:Genotype; D006801:Humans; D015451:Leukemia, Lymphocytic, Chronic, B-Cell / Q000235:genetics / Q000473:pathology; D016013:Likelihood Functions; D000069550:Machine Learning; D008954:Models, Biological; D015233:Models, Statistical; D009154:Mutation; D009369:Neoplasms / Q000235:genetics / Q000473:pathology*; D014411:Neoplastic Stem Cells / Q000473:pathology; D010802:Phylogeny; D018709:Statistics, Nonparametric