GeneExpressionComputingForMicroarrayImage基因表达微阵列图像计算.doc

Microarray Image Pre-Analysis for Critical Gene Expression Computation with Implemented Algorithmic Kernel 中華民國94年12月 Hwa Kang Journal of Agriculture 華岡農科學報第十六期：43-50 Vol. 16:43 -50, December 2005 PAGE 50  PAGE 51 中華民國94年12月 Hwa Kang Journal of Agriculture 華岡農科學報第十六期：43-50 Vol. 16:43-50, December 2005  PAGE 43 Microarray Image Pre-Analysis for Critical Gene Expression Computation with Implemented Algorithmic Kernel Ming-Yueh Tsai1, Chun-Fan Chang2, Hsueh-Ting Chu3, Chen-hsiung Chan4, King-Jen Chang5, Cheng-Yao Kao6, and Chaur-Chin Chen7 Abstract: Microarray hybridization analysis on transcriptomic specimens has become an efficient technology platform towards identifying valid biomarkers of phenotypic traits or diseases by interrogating simultaneously almost genome-wide genes simply with corresponding probes microarrayed on matrix of glass slide or nylon membrane. Still, the computational processing on microarray image is profoundly essential for mining transcriptomic biomarkers by means of acquiring truthful intensity data of respective spot objects in order for accurate gene expression analysis through critical preprocessing procedures including displayable TIFF image data input, applicable RGB-CMYK 24/16-bit greyscaling, global object layout gridding, intensity 16/8-bit converting, and so forth. This paper implemented an in-house algorithmic kernel of Otsu’s Simple Thresholding, Gaussian Mixture Model (GMM), and Iterated Conditional Modes (ICM) for reliable gridding and segmentation pre-analysis which computes gene expression pattern on microarray image data for subsequent automatic image data analysis (Aida). In comparison with commercial microarray software, our Aida algorithmic kernel demonstrated higher average Pearson correlation coefficient of 0.9933 (cy3, ICM/Otsu’s) and 0.9721 (cy3, ICM/GMM) despite of the inferior result of 0.9538 (cy3, ICM/ArrayPro) with commercial software. Additional procedures with implemented algorithmic Aida modules we