Clustering Gene Expression Data Based on Predicted Differential Effects of GV Interaction  

作　　者：Hal-Yah Pan Jun Zhu Dan-Fu Han 

机构地区：[1]Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China [2]Department of Mathematics,Zhejiang University, Hangzhou 310027, China.

出　　处：《Genomics, Proteomics & Bioinformatics》2005年第1期36-41,共6页基因组蛋白质组与生物信息学报（英文版）

基　　金：This research was partially supported by the National Natural Science Foundation of China(No.30470916).

摘　　要：Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.Microarray has become a popular biotechnology in biological and medical research. However, systematic and stochastic variabilities in microarray data are expected and unavoidable, resulting in the problem that the raw measurements have inherent “noise” within microarray experiments. Currently, logarithmic ratios are usually analyzed by various clustering methods directly, which may introduce bias interpretation in identifying groups of genes or samples. In this paper, a statistical method based on mixed model approaches was proposed for microarray data cluster analysis. The underlying rationale of this method is to partition the observed total gene expression level into various variations caused by different factors using an ANOVA model, and to predict the differential effects of GV （gene by variety） interaction using the adjusted unbiased prediction （AUP） method. The predicted GV interaction effects can then be used as the inputs of cluster analysis. We illustrated the application of our method with a gene expression dataset and elucidated the utility of our approach using an external validation.

关 键 词：gene expression clustering analysis predicting G V interaction effects 

分 类 号：Q78[生物学—分子生物学]