ArticleBMC bioinformatics2009
A statistical framework for integrating two microarray data sets in differential expression analysis.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An order-preserving batch-effect correction method based on a monotonic deep learning framework.Briefings in bioinformatics · 2025Article
- An efficient concordant integrative analysis of multiple large-scale two-sample expression data sets.Bioinformatics (Oxford, England) · 2017Article
- Detecting discordance enrichment among a series of two-sample genome-wide expression data sets.BMC genomics · 2017Article
- Diagnostic biases in translational bioinformatics.BMC medical genomics · 2015Article
- Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets.BMC genomics · 2014Article
- Discovery and analysis of consistent active sub-networks in cancers.BMC bioinformatics · 2013Article
- Meta-analysis of gene expression microarrays with missing replicates.BMC bioinformatics · 2011Article
- The identification of informative genes from multiple datasets with increasing complexity.BMC bioinformatics · 2010Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundDifferent 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.
resultsBefore 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.
conclusionThe 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.
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