Online Program Home
  My Program

Abstract Details

Activity Number: 108 - Junior Research in Bayesian Modeling for High-Dimensional Data
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: International Society for Bayesian Analysis (ISBA)
Abstract #324399
Title: Cancer Phylogenies and Nonparametric Clustering
Author(s): Jeffrey Miller* and Scott Carter
Companies: Harvard School of Public Health and Dana-Farber Cancer Institute
Keywords: Cancer ; Genomics ; Mixture models ; Clustering ; Bayesian ; Nonparametric
Abstract:

As it progresses within a patient, cancer evolves into multiple subpopulations of cancer cells, some co-located and some at distant sites. Genome sequencing of cancer tissue has great promise for understanding how cancers develop and for personalized treatment, however, in bulk sequencing, the observations come from a mixture of multiple subpopulations as well as normal cells. The problem of inferring the distinct subpopulations and their phylogenetic relationships can be formulated as a tree-structured clustering problem in which the number of clusters and the tree topology are unknown. We propose a novel method of inference for this problem that is advantageous in terms of computation and accuracy, and handles complicating issues that arise in practice.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association