Multi-sample aCGH Data Analysis via Total Variation and Spectral Regularization

Xiaowei Zhou, Can Yang, Xiang Wan, Hongyu Zhao and Weichuan Yu

Abstract

DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multi-sample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.

Matlab codes: https://bioinformatics.hkust.edu.hk/tvsp/tvsp.zip

1. please run example_real and example_simu to see the use of this package

2. File list:

TVSp: algorithm 1 in the paper

TVSpC: algorithm 2 in the paper

aCGH_TVSp: the main function that calls TVSpC to analyze aCGH data, including parameter tuning and fdr estimation

genSimuData: the function to generate synthesized data described in the paper

flsa.*: external mex functions to solve flsa