Bayesian detection of abnormal segments in multiple time series

TL;DR

This paper introduces a Bayesian method for detecting abnormal segments in multiple time series, effectively identifying localized changes even when present in only a subset of series, with applications to genetic copy number variation data.

Contribution

The paper develops a new Bayesian model and inference algorithm for detecting localized changes in multiple time series, especially when changes are sparse across series.

Findings

More accurate than existing methods on simulated data

Effective in real CNV data analysis

Handles small subsets of series with changes

Abstract

We present a novel Bayesian approach to analysing multiple time-series with the aim of detecting abnormal regions. These are regions where the properties of the data change from some normal or baseline behaviour. We allow for the possibility that such changes will only be present in a, potentially small, subset of the time-series. We develop a general model for this problem, and show how it is possible to accurately and efficiently perform Bayesian inference, based upon recursions that enable independent sampling from the posterior distribution. A motivating application for this problem comes from detecting copy number variation (CNVs), using data from multiple individuals. Pooling information across individuals can increase the power of detecting CNVs, but often a specific CNV will only be present in a small subset of the individuals. We evaluate the Bayesian method on both simulated and real CNV data, and give evidence that this approach is more accurate than a recently proposed method for analysing such data.