Disease signatures are robust across tissues and experiments

Authors

  • Joel T Dudley,

    1. Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
    2. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
    3. Lucile Packard Children's Hospital, Palo Alto, CA, USA
    Search for more papers by this author
  • Robert Tibshirani,

    1. Department of Health Research and Policy, Stanford University, Stanford, CA, USA
    2. Department of Statistics, Stanford University, Stanford, CA, USA
    Search for more papers by this author
  • Tarangini Deshpande,

    1. NuMedii Inc., Menlo Park, CA, USA
    Search for more papers by this author
  • Atul J Butte

    Corresponding author
    1. Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
    2. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
    3. Lucile Packard Children's Hospital, Palo Alto, CA, USA
    • Corresponding author. Department of Pediatrics and Medicine, Stanford University, 251 Campus Drive, Room X-215 MS-5479, Stanford, CA 94305-5479, USA. Tel.: +1 650 723 3465; Fax: +1 650 723 7070; E-mail: abutte@stanford.edu

    Search for more papers by this author

Abstract

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.

Ancillary