An Integration of Statistical Methods for Array-based Comparative Genomic Hybridization
Date Issued
2006
Date
2006
Author(s)
Lin, Yu-Shu
DOI
zh-TW
Abstract
The DNA microarray is widely used to investigate gene expression profiles of many thousands of genes simultaneously. And it has become a common tool for exploring various questions in many areas of biological and medical sciences. Specifically, array-based comparative genomic hybridization (Array CGH) is applied to screen alteration of DNA copy numbers genomewide. The main purpose of such application is to detect the altered DNA segments among genome sequences from a control (reference) treatment to a test treatment. Typically, efficient statistical tools are developed to compare the intensity ratios of spots representing the competitive hybridization between the control mRNA sample and the test mRNA sample, which are separately labeled with red (Cy5) and green (Cy3) fluorescence dyes. Users usually focus on the gain region and the loss region on each chromosome. In consequence, the differentially altered regions are displayed by graphical plots.
From the simulation results presented in Lai et al. (2005), several competing statistical methods are selected for analysis of Array CGH data, including Adaptive Weights Smoothing method, Circular Binary Segmentation method and CGH Segmentation method. Furthermore, we use Perl, PHP programming language and Apache web server to integrate the chosen statistical methods into an analysis platform under R language environment. The proposed platform offers normalization, identification of the differentially altered regions and plotting of the gain and loss regions genomewide. In addition, users can annotate information through UCSC Genome Browser and ID Converter for advanced analyses.
Subjects
微陣列基因體比較雜合法
循環二元分割法
適應性權重平滑法
CGH分割法
Array-based Comparative genomic hybridization
array CGH
aCGH
Circular Binary Segmentation
CBS
Adaptive weights smoothing
AWS
CGH segmentation
Type
thesis
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