Cross-platform microarray data integration combining meta-analysis and gene set enrichment analysis

Jian Yu, Yajun Yi, Jun Wu, Yu Shyr, Miaoxin Li, Lu Xie

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Integrative analysis of microarray data has been proven as a more reliable approach to deciphering molecular mechanisms underlying biological studies. Traditional integration such as meta-analysis is usually gene-centered. Recently, gene set enrichment analysis (GSEA) has been widely applied to bring gene-level interpretation to pathway-level. GSEA is an algorithm focusing on whether an a priori defined set of genes shows statistically significant differences between two biological states. However, GSEA does not support integrating multiple microarray datasets generated from different studies. To overcome this, the improved version of GSEA, ASSESS, is more applicable, after necessary modifications. By making proper combined use of meta-analysis, GSEA, and modified ASSESS, this chapter reports two workflow pipelines to extract consistent expression pattern change at pathway-level, from multiple microarray datasets generated by the same or different microarray production platforms, respectively. Such strategies amplify the advantage and overcome the disadvantage than if using each method individually, and may achieve a more comprehensive interpretation towards a biological theme based on an increased sample size. With further network analysis, it may also allow an overview of cross-talking pathways based on statistical integration of multiple gene expression studies. A web server where one of the pipelines is implemented is available at: http://lifecenter.sgst.cn/mgsea//home.htm.

Original languageEnglish
Title of host publicationBioinformatics
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages570-587
Number of pages18
Volume1
ISBN (Electronic)9781466636057
ISBN (Print)1466636041, 9781466636040
DOIs
Publication statusPublished - 2013 Mar 31

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Medicine(all)

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