Statistics Papers

Document Type

Journal Article

Date of this Version

1-15-2010

Publication Source

Bioinformatics

Volume

26

Issue

2

Start Page

153

Last Page

160

DOI

10.1093/bioinformatics/btp653

Abstract

DNA copy number variants (CNV) are gains and losses of segments of chromosomes, and comprise an important class of genetic variation. Recently, various microarray hybridization based techniques have been developed for high throughput measurement of DNA copy number. In many studies, multiple technical platforms or different versions of the same platform were used to interrogate the same samples; and it became necessary to pool information across these multiple sources to derive a consensus molecular profile for each sample. An integrated analysis is expected to maximize resolution and accuracy, yet currently there is no well formulated statistical method to address the between-platform differences in probe design, assay methods, sensitivity, and analytical complexity.

The conventional approach is to apply one of the CNV detection (a.k.a. “segmentation”) algorithms to search for DNA segments of altered signal intensity. The results from three platforms are combined after segmentation. Here we propose a new method, Multi-Platform Circular Binary Segmentation (MPCBS), which pools statistical evidence across platforms during segmentation, and does not require pre-standardization of different data sources. It involves a weighted sum of t-statistics, which arises naturally from the generalized log-likelihood ratio of a multi-platform model. We show by comparing the integrated analysis of Affymetrix and Illumina SNP array data with fosmid clone end-sequencing results on 8 HapMap samples that MPCBS achieves improved spatial resolution, detection power, and provide a natural consensus across platforms. We also apply the new method to analyze the multi-platform data from TCGA.

The R package for MPCBS is registered on R-Forge under project name MPCBS

Copyright/Permission Statement

This is a post-peer-review, pre-copyedit version of an article published in Bioinformatics.

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Date Posted: 27 November 2017

This document has been peer reviewed.