Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations.

ABSTRACT:

Background

Copy number variation (CNV) analysis has become one of the most important research areas for understanding complex disease. With increasing resolution of array-based comparative genomic hybridization (aCGH) arrays, more and more raw copy number data are collected for multiple arrays. It is natural to realize the co-existence of both recurrent and individual-specific CNVs, together with the possible data contamination during the data generation process. Therefore, there is a great need for an efficient and robust statistical model for simultaneous recovery of both recurrent and individual-specific CNVs.

Result

We develop a penalized weighted low-rank approximation method (WPLA) for robust recovery of recurrent CNVs. In particular, we formulate multiple aCGH arrays

SUBMITTER: Gao X 

PROVIDER: S-EPMC4676147 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature