Yin Wotao, Chen Terrence, Zhou Sean Xiang, Chakraborty Amit
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA.
Bioinformatics. 2005 May 15;21(10):2410-6. doi: 10.1093/bioinformatics/bti341. Epub 2005 Feb 22.
Background correction is an important preprocess in cDNA microarray data analysis. A variety of methods have been used for this purpose. However, many kinds of backgrounds, especially inhomogeneous ones, cannot be estimated correctly using any of the existing methods. In this paper, we propose the use of the TV+L1 model, which minimizes the total variation (TV) of the image subject to an L1-fidelity term, to correct background bias. We demonstrate its advantages over the existing methods by both analytically discussing its properties and numerically comparing it with morphological opening.
Experimental results on both synthetic data and real microarray images demonstrate that the TV+L1 model gives the restored intensity that is closer to the true data than morphological opening. As a result, this method can serve an important role in the preprocessing of cDNA microarray data.
背景校正为cDNA微阵列数据分析中的重要预处理步骤。为此已采用了多种方法。然而，许多类型的背景，尤其是不均匀背景，无法用任何现有方法正确估计。在本文中，我们提议使用TV+L1模型校正背景偏差，该模型在满足L1保真项的条件下使图像的总变差（TV）最小化。我们通过分析讨论其性质并与形态学开运算进行数值比较，证明了它相对于现有方法的优势。
合成数据和真实微阵列图像的实验结果均表明，与形态学开运算相比，TV+L1模型给出的恢复强度更接近真实数据。因此，该方法在cDNA微阵列数据预处理中可发挥重要作用。
