Microarray Image Pre-Analysis for Critical Gene Expression Computation with Implemented Algorithmic Kernel

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Title:	Microarray Image Pre-Analysis for Critical Gene Expression Computation with Implemented Algorithmic Kernel
Other Titles:	基因表現程度嚴謹計算的微陣影像初期分析演算核心
Authors:	蔡明岳張春梵朱學亭詹鎮熊張金堅高成炎陳朝欽
Contributors:	生科所
Keywords:	微陣基因表現程度格網化分割簡單閾值高斯混合模態迭代條件模式
Date:	2005-12
Issue Date:	2011-01-17 11:34:55 (UTC+8)
Abstract:	轉錄組檢體的微陣雜合分析是篩選表現型性狀或疾病之實效生物標記的有效技術平台，實際操作係經由同時雜合詢答將近整體基因組相關基因探針所產製的微陣晶片。微陣影像的計算處理至今仍是探採轉錄組生物標記的底層根基，主要任務為攫取個別圖點物件的真實緻密程度以便進行精確的基因表現程度分析，嚴謹的前處理操作程序包括螢幕顯示輸入影像數據、特用型紅綠藍與紅綠黃黑的24與16位元灰階轉換、整體物件佈局格網化、與物件緻密程度16與8位元互換等；本篇報告實作Otsu簡單閾值、高斯混合模態、與迭代條件模式的自有演算核心，執行物件格網化與分割的高度可信前處理，以便計算微陣影像數據的基因表現類型達成後續的阿伊達自動影像數據分析專家系統。經比較微陣影像分析商業軟體，本篇報告的阿伊達演算核心展現較高的平均皮爾森關連係數0.9933與0.9721於ICM/Otsu與ICM/GMM分組的整體cy3數值，而商業軟體卻是呈現稍差的0.9538於ICM/ArrayPro分組；增益的相關議題所實作完成的阿伊達演算模組則是進行整合調校，並應用於統計分析計算源自早先HBV與HCV不同感染病因的50個臨床肝癌檢體微陣影像。 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 were also integrated for statistical computation on microarray images of 50 clinical hepatocellular carcinoma (HCC) specimens with different a priori etiology of hepatitis B virus (HBV) and hepatitis C virus (HCV).
Relation:	華岡農科學報 16期 P.43-50
Appears in Collections:	[Graduate Institute of Biotechnology ] journal articles

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