Bayesian Segmentation of Cancer Genomes: A Decision Theoretic Approach

• Activity Number: 466
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: International Society for Bayesian Analysis (ISBA)
• Author(s): Chris Holmes*+
• Companies: Oxford University
• Keywords: DNA sequencing and microarray ; segmentation ; decision theory ; cancer genomes

Abstract

Bayesian Hidden Markov models (HMMs) have been developed for the robust detection and classification of copy-number-aberrations (CNAs) in cancer genomes using sequence and/or array data on upwards of 100,000s of loci (observations) on 100s of samples. CNAs involve stretches of DNA that are duplicated or deleted in tumour cells, and are a key driver of cancer initialization and progression. CNA detection can be thought of as a statistical task in change-point modelling or genome segmentation. We describe how Bayesian signal-processing methods using HMMs scaled to large-data are ideally suited to this task. We pay particular attention to the reporting of posterior summaries (predictions) under the model where decision theoretic loss functions lead to predictions with excellent properties relative to penalised likelihood methods such as those based on the lasso. We also discuss a conditional Viterbi algorithms that allows for exact enumeration of the MAP sample path conditional on a user specified number of change points. The techniques are illustrated using on-going real world studies.