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Disease signatures are robust across tissues and experiments
Joel T Dudley1, Robert Tibshirani, Tarangini Deshpande
1Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Molecular Systems Biology
|September 17, 2009
Summary
Meta-analyses of gene expression microarray data reveal consistent disease signatures across studies. This suggests public data quality supports disease commonalities research and multi-tissue models.
Area of Science:
- Genomics
- Bioinformatics
- Systems Biology
Background:
- Gene expression microarray meta-analyses can uncover disease insights missed by individual studies.
- Technical reproducibility of microarrays is known, but pathophysiological reproducibility across experiments requires validation.
Purpose of the Study:
- To evaluate the pathophysiological concordance of disease-associated gene expression microarray experiments.
- To assess the quality of public microarray data for large-scale meta-analysis.
Main Methods:
- Conducted a large-scale analysis of 429 disease-associated experiments from NCBI GEO.
- Included 238 diseases, 122 tissues, and 8435 microarrays.
- Evaluated concordance across diverse diseases and tissue types.
Main Results:
- Found general pathophysiological concordance between experiments for the same disease.
- Observed that disease molecular signatures across tissues are more prominent than tissue expression signatures across diseases.
- Demonstrated the utility of public microarray data for disease research.
Conclusions:
- Public microarray data exhibits pathophysiological concordance, supporting its use in meta-analysis.
- Results advocate for characterizing disease commonalities irrespective of tissue type.
- Supports the creation of multi-tissue systems biology models for disease pathology using public data.
