期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
卷 65, 期 4, 页码 547-563出版社
WILEY
DOI: 10.1111/rssc.12136
关键词
Categorical Indian buffet process; Feature allocation models; Markov chain Monte Carlo methods; Next generation sequencing; Random matrices; Subclone; Variant calling
资金
- National Institutes of Health [R01 CA132897]
Tissue samples from the same tumour are heterogeneous. They consist of different subclones that can be characterized by differences in DNA nucleotide sequences and copy numbers on multiple loci. Inference on tumour heterogeneity thus involves the identification of the subclonal copy number and single-nucleotide mutations at a selected set of loci. We carry out such inference on the basis of a Bayesian feature allocation model. We jointly model subclonal copy numbers and the corresponding allele sequences for the same loci, using three random matrices, L, Z and w, to represent subclonal copy numbers (L), the number of subclonal variant alleles (Z) and the cellular fractions (w) of subclones in one or more tumour samples respectively. The unknown number of subclones implies a random number of columns. More than one subclone indicates tumour heterogeneity. Using simulation studies and a real data analysis with next generation sequencing data, we demonstrate how posterior inference on the subclonal structure is enhanced with the joint modelling of both structure and sequencing variants on subclonal genomes. An R package is available from http://cran.r-project.org/web/packages/BayClone2/index.html.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据