SeqClone: sequential Monte Carlo based inference of tumor subclones.

ABSTRACT:

Background

Tumor samples are heterogeneous. They consist of varying cell populations or subclones and each subclone is characterized with a distinct single nucleotide variant (SNV) profile. This explains the source of genetic heterogeneity observed in tumor sequencing data. To make precise prognosis and design effective therapy for cancer, ascertaining the subclonal composition of a tumor is of great importance.

Results

In this paper, we propose a state-space formulation of the feature allocation model. This model is interpreted as the blind deconvolution of the expected variant allele fractions (VAFs). VAFs are deconvolved into a binary matrix of genotypes and a matrix of genotype proportions in the samples. Specifically, we consider a sequential construction of the genotyp

SUBMITTER: Ogundijo OE 

PROVIDER: S-EPMC6320595 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature