Combining multiple microarray studies and modeling interstudy variation

Cited 331 time in scopus
Metadata Downloads
Combining multiple microarray studies and modeling interstudy variation
Jung Kyoon Choi; Ung Sik Yu; Sang Soo Kim; O J Yoo
Bibliographic Citation
Bioinformatics, vol. 19, no. S, pp. i84-i90
Publication Year
We have established a method for systematic integration of multiple microarray datasets. The method was applied to two different sets of cancer profiling studies. The change of gene expression in cancer was expressed as 'effect size', a standardized index measuring the magnitude of a treatment or covariate effect. The effect sizes were combined to obtain the estimate of the overall mean. The statistical significance was determined by a permutation test extended to multiple datasets. It was shown that the data integration promotes the discovery of small but consistent expression changes with increased sensitivity and reliability. The effect size methods provided the efficient modeling framework for addressing interstudy variation as well. Based on the result of homogeneity tests, a fixed effects model was adopted for one set of datasets that had been created in controlled experimental conditions. By contrast, a random effects model was shown to be appropriate for the other set of datasets that had been published by independent groups. We also developed an alternative modeling procedure based on a Bayesian approach, which would offer flexibility and robustness compared to the classical procedure.
Bayesian meta-analysisEffect sizeMeta-analysisMicroarray
Oxford Univ Press
Appears in Collections:
1. Journal Articles > Journal Articles
Files in This Item:
  • There are no files associated with this item.

Items in OpenAccess@KRIBB are protected by copyright, with all rights reserved, unless otherwise indicated.