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A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.

ABSTRACT: Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such as data normalization, often fail to yield satisfactory results.We demonstrated a novel aCGH normalization algorithm, which provides an accurate aCGH data normalization by utilizing the dependency of neighboring probe measurements in aCGH experiments. To facilitate the study, we have developed a hidden Markov model (HMM) to simulate a series of aCGH experiments with random DNA copy number alterations that are used to validate the performance of our normalization. In addition, we applied the proposed normalization algorithm to an aCGH study of lung cancer cell lines. By using the proposed algorithm, data quality and the reliability of experimental results are significantly improved, and the distinct patterns of DNA copy number alternations are observed among those lung cancer cell lines.Source codes and.gures may be found at http://ntumaps.cgm.ntu.edu.tw/aCGH_supplementary.

SUBMITTER: Chen HI

PROVIDER: S-EPMC2732214 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.

Chen Hung-I Harry HI Hsu Fang-Han FH Jiang Yuan Y Tsai Mong-Hsun MH Yang Pan-Chyr PC Meltzer Paul S PS Chuang Eric Y EY Chen Yidong Y

Bioinformatics (Oxford, England) 20080704 16

<h4>Motivation</h4>Genomic instability is one of the fundamental factors in tumorigenesis and tumor progression. Many studies have shown that copy-number abnormalities at the DNA level are important in the pathogenesis of cancer. Array comparative genomic hybridization (aCGH), developed based on expression microarray technology, can reveal the chromosomal aberrations in segmental copies at a high resolution. However, due to the nature of aCGH, many standard expression data processing tools, such ...[more]

PMID: 18603568

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Project description:BackgroundIn two-channel competitive genomic hybridization microarray experiments, the ratio of the two fluorescent signal intensities at each spot on the microarray is commonly used to infer the relative amounts of the test and reference sample DNA levels. This ratio may be influenced by systematic measurement effects from non-biological sources that can introduce biases in the estimated ratios. These biases should be removed before drawing conclusions about the relative levels of DNA. The performance of existing gene expression microarray normalization strategies has not been evaluated for removing systematic biases encountered in array-based comparative genomic hybridization (CGH), which aims to detect single copy gains and losses typically in samples with heterogeneous cell populations resulting in only slight shifts in signal ratios. The purpose of this work is to establish a framework for correcting the systematic sources of variation in high density CGH array images, while maintaining the true biological variations.ResultsAfter an investigation of the systematic variations in the data from two array CGH platforms, SMRT (Sub Mega base Resolution Tiling) BAC arrays and cDNA arrays of Pollack et al., we have developed a stepwise normalization framework integrating novel and existing normalization methods in order to reduce intensity, spatial, plate and background biases. We used stringent measures to quantify the performance of this stepwise normalization using data derived from 5 sets of experiments representing self-self hybridizations, replicated experiments, detection of single copy changes, array CGH experiments which mimic cell population heterogeneity, and array CGH experiments simulating different levels of gene amplifications and deletions. Our results demonstrate that the three-step normalization procedure provides significant improvement in the sensitivity of detection of single copy changes compared to conventional single step normalization approaches in both SMRT BAC array and cDNA array platforms.ConclusionThe proposed stepwise normalization framework preserves the minute copy number changes while removing the observed systematic biases.

Dataset Information

A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.

Publications

A probe-density-based analysis method for array CGH data: simulation, normalization and centralization.

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