A Fast and Flexible Method for the Segmentation of aCGH Data

TL;DR

This paper introduces a wavelet-based segmentation method for aCGH data that is over 1,000 times faster than existing approaches, maintaining accuracy while offering simplicity and flexibility for incorporating additional information.

Contribution

A novel wavelet decomposition and thresholding approach for aCGH segmentation that significantly improves speed and flexibility over prior methods.

Findings

Over 1,000 times faster than leading methods

Maintains similar segmentation performance

Easily incorporates side information and noise variability

Abstract

Motivation: Array Comparative Genomic Hybridization (aCGH) is used to scan the entire genome for variations in DNA copy number. A central task in the analysis of aCGH data is the segmentation into groups of probes sharing the same DNA copy number. Some well known segmentation methods suffer from very long running times, preventing interactive data analysis. Results: We suggest a new segmentation method based on wavelet decomposition and thresholding, which detects significant breakpoints in the data. Our algorithm is over 1,000 times faster than leading approaches, with similar performance. Another key advantage of the proposed method is its simplicity and flexibility. Due to its intuitive structure it can be easily generalized to incorporate several types of side information. Here we consider two extensions which include side information indicating the reliability of each measurement, and compensating for a changing variability in the measurement noise. The resulting algorithm outperforms existing methods, both in terms of speed and performance, when applied on real high density CGH data. Availability: Implementation is available under software tab at: http://www.ee.technion.ac.il/Sites/People/YoninaEldar/ Contact: yonina@ee.technion.ac.il