title: Clustering gene expression data based on predicted differential effects of GV interaction.
abstract: 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.
authors.affiliation: Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China. jzhu@zju.edu.cn
authors.forename: Hai-Yan
authors.initials: HY
authors.lastname: Pan
authors.forename: Jun
authors.initials: J
authors.lastname: Zhu
authors.forename: Dan-Fu
authors.initials: DF
authors.lastname: Han
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D006801:Humans; D015233:Models, Statistical; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D012680:Sensitivity and Specificity; D013172:Spores, Fungal
chemical_list: D029701:Saccharomyces cerevisiae Proteins