Disease signatures are robust across tissues and experiments.
Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data.
Dudley
Joel T
JT
Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Tibshirani
Robert
R
Deshpande
Tarangini
T
Butte
Atul J
AJ
D000704:Analysis of Variance; D015415:Biomarkers / Q000378:metabolism; D016208:Databases, Factual; D004194:Disease / Q000235:genetics*; D006651:Histocytochemistry; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010336:Pathology / Q000379:methods*; D049490:Systems Biology / Q000379:methods*
D015415:Biomarkers