Comparison of methods for image analysis on cDNA microarray data
Comparison of methods for image analysis on cDNA microarray data
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DOI：
10.1198/106186002317375640
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发表时间：
2002-03-01
影响因子：
2.4
通讯作者：
Speed, TP
中科院分区：
文献类型：
--
作者：
Yang, YH;Buckley, MJ;Speed, TP
微阵列是一类新型生物技术的一部分，它可以同时监测数千个基因的表达水平。图像分析是微阵列实验的一个重要方面，它可以对后续分析产生潜在的巨大影响，例如聚类或差异表达基因的鉴定。本文回顾了cDNA微阵列实验中现有的一些图像分析方法，并提出了新的寻址、分割和背景校正方法来从微阵列扫描图像中提取信息。分割组件使用种子区域生长算法，该算法提供不同形状和大小的斑点。背景估计方法是基于一种称为形态开放的图像分析技术。这些新的图像分析程序是在一个名为Spot的软件包中实现的，该软件包基于R环境构建，用于统计计算。使用来自小鼠脂质代谢研究的微阵列数据检查了不同分割和背景调整方法的统计特性。结果表明，在某些情况下，背景平差会大大降低低强度光斑值的精度，即增加其可变性。相比之下，分割过程的选择影响较小。进一步的对比表明，基于形态背景校正的种子区生长分割方法能够精确估计前景和背景强度。
Microarrays are part of a new class of biotechnologies which allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses such as clustering or the identification of differentially expressed genes. This article reviews a number of existing image analysis approaches for cDNA microarray experiments and proposes new addressing, segmentation, and background correction methods for extracting information from microarray scanned images. The segmentation component uses a seeded region growing algorithm which makes provision for spots of different shapes and sizes. The background estimation approach is based on an image analysis technique known as morphological opening. These new image analysis procedures are implemented in a software package named Spot, built on the R environment for statistical computing. The statistical properties of the different segmentation and background adjustment methods are examined using microarray data from a study of lipid metabolism in mice. It is shown that in some cases background adjustment can substantially reduce the precision-that is, increase the variability-of low-intensity spot values. In contrast, the choice of segmentation procedure has a smaller impact. The comparison further suggests that seeded region growing segmentation with morphological background correction provides precise and accurate estimates of foreground and background intensities.