Statistical Inference Methods for Detecting Altered Gene Associations

Sang-Heon Yoon (Purist21@catholic.ac.kr)
Je-Suk Kim (rion01@catholic.ac.kr)
Hae-Hiang Song (hhsong@catholic.ac.kr)

Department of Biostatistics, Medical College, The Catholic University of Korea, Seoul 137-701, Korea


Abstract

The higher incidence of liver disease in the Asian population raises a great concern to clinicians. To understand the gene functions involved in different stages of the disease, microarray expression data of histological progressive grades, starting from the dysplastic nodule in cirrhotic liver to hepatocellular carcinoma Edmonson grade III are analyzed. The statistical procedures are divided into two parts: First, microarray data are suitably normalized, including a method of analysis of variance (ANOVA). There are great differences of opinion regarding the currently used normalization methods. In order to proceed to the second part of statistical analyses of gene-pair associations, these normalization methods need first to be compared. Based on the assumption that a union set of significant genes from these normalization methods includes sufficiently general and well-defined, differentially expressed genes, one must carry out the second part of statistical analyses of searching for evidence of altered gene-gene relationships with progression of the disease. Significantly altered gene-pair associations are identified with the ratio of gene-pair correlations. The methods are illustrated with replicated microarray expression data.

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Japanese Society for Bioinformatics