A statistical framework for integrating two microarray data sets in differential expression analysis.
Abstract
BACKGROUND: Different microarray data sets can be collected for studying the same or similar diseases. We expect to achieve a more efficient analysis of differential expression if an efficient statistical method can be developed for integrating different microarray data sets. Although many statistical methods have been proposed for data integration, the genome-wide concordance of different data sets has not been well considered in the analysis. RESULTS: Before considering data integration, it is necessary to evaluate the genome-wide concordance so that misleading results can be avoided. Based on the test results, different subsequent actions are suggested. The evaluation of genome-wide concordance and the data integration can be achieved based on the normal distribution based mixture models. CONCLUSION: The results from our simulation study suggest that misleading results can be generated if the genome-wide concordance issue is not appropriately considered. Our method provides a rigorous parametric solution. The results also show that our method is robust to certain model misspecification and is practically useful for the integrative analysis of differential expression.Citation
BMC Bioinformatics. 2009 Jan 30; 10(Suppl 1):S23ae974a485f413a2113503eed53cd6c53
10.1186/1471-2105-10-S1-S23
Scopus Count
Related articles
- Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.
- Authors: Lai Y, Zhang F, Nayak TK, Modarres R, Lee NH, McCaffrey TA
- Issue date: 2014
- PAGE: parametric analysis of gene set enrichment.
- Authors: Kim SY, Volsky DJ
- Issue date: 2005 Jun 8
- Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.
- Authors: Lai Y, Zhang F, Nayak TK, Modarres R, Lee NH, McCaffrey TA
- Issue date: 2017 Jan 25
- Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways.
- Authors: Wu MC, Lin X
- Issue date: 2009 Dec
- A GMM-IG framework for selecting genes as expression panel biomarkers.
- Authors: Wang M, Chen JY
- Issue date: 2010 Feb-Mar