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We develop a feature allocation model for inference on genetic tumorvariation using next-generation sequencing data. Specifically, we record singlenucleotide variants (SNVs) based on short reads mapped to human referencegenome and characterize tumor heterogeneity by latent haplotypes defined as ascaffold of SNVs on the same homologous genome. For multiple samples from asingle tumor, assuming that each sample is composed of some sample-specificproportions of these haplotypes, we then fit the observed variant allelefractions of SNVs for each sample and estimate the proportions of haplotypes.Varying proportions of haplotypes across samples is evidence of tumorheterogeneity since it implies varying composition of cell subpopulations.Taking a Bayesian perspective, we proceed with a prior probability model forall relevant unknown quantities, including, in particular, a prior probabilitymodel on the binary indicators that characterize the latent haplotypes. Suchprior models are known as feature allocation models. Specifically, we define asimplified version of the Indian buffet process, one of the most traditionalfeature allocation models. The proposed model allows overlapping clustering ofSNVs in defining latent haplotypes, which reflects the evolutionary process ofsubclonal expansion in tumor samples.

A Bayesian feature allocation model for tumor heterogeneity