Rahnenführer J
Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany.
Methods Inf Med. 2005;44(3):405-7.
We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed.
We describe sources for errors in microarray images and present algorithms that have been specifically developed to deal with such experimental imperfections.
For the image analysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss.
The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.
我们对微阵列图像分析中遇到的典型问题进行了特征描述,并提出了处理背景估计、斑点识别和强度提取的算法方法。还讨论了对所得测量质量的验证。
我们描述了微阵列图像中的误差来源,并提出了专门为处理此类实验缺陷而开发的算法。
对于杂交实验的图像分析,将斑点区域与背景区分开是最关键的步骤。已经提出了斑点形状检测算法、强度直方图方法和混合方法。最终强度估计的正确性很难验证。然而,复杂算法的应用显著减少了可能的信息损失。
阵列杂交实验的初始分析步骤是表达强度的估计。这一过程的质量对于后续分析步骤解释的有效性至关重要。