Demirkaya Omer, Asyali Musa H, Shoukri Mohamed M
Department of Biostatistics, Epidemiology, and Scientific Computing King Faisal Specialist Hospital and Research Center MBC No. 03, PO Box 3354, Riyadh 11211, Saudi Arabia.
Bioinformatics. 2005 Jul 1;21(13):2994-3000. doi: 10.1093/bioinformatics/bti455. Epub 2005 Apr 19.
Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background. In this study, we have proposed a new approach based on Markov random field (MRF) modeling and tested its performance on simulated and real microarray images against a widely used segmentation method based on Mann-Whitney test adopted by QuantArray software (Boston, MA). Spot addressing was performed using QuantArray. We have also devised a simulation method to generate microarray images with realistic features. Such images can be used as gold standards for the purposes of testing and comparing different segmentation methods, and optimizing segmentation parameters.
Experiments on simulated and 14 actual microarray image sets show that the proposed MRF-based segmentation method can detect spot areas and estimate spot intensities with higher accuracy.
斑点分割是微阵列基因表达数据分析中的关键步骤。因此，分割的性能可能会极大地影响分析后续阶段的结果，例如差异表达基因的检测。已经开发了几种方法来从周围背景中分割微阵列斑点。在本研究中，我们提出了一种基于马尔可夫随机场（MRF）建模的新方法，并在模拟和真实微阵列图像上针对QuantArray软件（马萨诸塞州波士顿）采用的基于曼-惠特尼检验的广泛使用的分割方法测试了其性能。使用QuantArray进行斑点寻址。我们还设计了一种模拟方法来生成具有逼真特征的微阵列图像。此类图像可作为测试和比较不同分割方法以及优化分割参数的金标准。
在模拟和14个实际微阵列图像集上的实验表明，所提出的基于MRF的分割方法能够更准确地检测斑点区域并估计斑点强度。
