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Updated: Jul 20, 2026
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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
A rapid method for microarray cross platform comparisons using gene expression signatures
Chris Cheadle1, Kevin G Becker, Yoon S Cho-Chung
1Genomics Core, Division of Allergy and Clinical Immunology, School of Medicine, Johns Hopkins University, Mason Lord Bldg., Center Tower, Rm. 664, 5200 Eastern Avenue, Baltimore, MD 21224, USA. ccheadl1@jhmi.edu
Molecular and Cellular Probes
|September 20, 2006
Summary
Comparing microarray gene expression data across platforms like Affymetrix, Agilent, and Illumina is challenging. Advanced analysis methods reveal profound relatedness between platforms, overcoming low direct gene list concordance.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Microarray technology is crucial for global gene expression analysis.
- Comparing data across different microarray platforms presents significant challenges.
- Ensuring consistent and reliable results between studies and databases is essential.
Purpose of the Study:
- To compare gene expression data concordance across three major commercial microarray platforms: Affymetrix, Agilent, and Illumina.
- To evaluate methods for straightforward data comparison between different microarray platforms.
- To assess the impact of normalization and statistical analysis on cross-platform data comparability.
Main Methods:
- Data normalization was applied to each microarray platform's dataset.
- Gene lists were generated using a common significance threshold across all platforms.
- Probes were mapped to Human Gene Organization (HUGO) gene names for concordance estimation.
- Statistical tests, including gene set enrichment analysis (GSEA) and parametric analysis of gene enrichment (PAGE), were employed.
Main Results:
- Direct comparison of gene lists showed low concordance (average 22.8%) between platforms.
- Utilizing gene set enrichment analysis (GSEA) and parametric analysis of gene enrichment (PAGE) revealed significant and profound relatedness.
- These advanced statistical methods aligned gene lists with continuous differential gene expression measures, highlighting cross-platform consistency.
Conclusions:
- Direct comparison of gene lists is insufficient for assessing cross-platform microarray data concordance.
- Advanced statistical methods like GSEA and PAGE are effective in demonstrating the underlying relatedness of gene expression profiles across different microarray platforms.
- These findings support the integration and comparison of gene expression data from diverse microarray sources when appropriate analytical techniques are applied.
