Discovering statistically significant pathways in expression profiling studies.
Accurate and rapid identification of perturbed pathways through the analysis of genome-wide expression profiles facilitates the generation of biological hypotheses. We propose a statistical framework for determining whether a specified group of genes for a pathway has a coordinated association with a phenotype of interest. Several issues on proper hypothesis-testing procedures are clarified. In particular, it is shown that the differences in the correlation structure of each set of genes can lead to a biased comparison among gene sets unless a normalization procedure is applied. We propose statistical tests for two important but different aspects of association for each group of genes. This approach has more statistical power than currently available methods and can result in the discovery of statistically significant pathways that are not detected by other methods. This method is applied to data sets involving diabetes, inflammatory myopathies, and Alzheimer's disease, using gene sets we compiled from various public databases. In the case of inflammatory myopathies, we have correctly identified the known cytotoxic T lymphocyte-mediated autoimmunity in inclusion body myositis. Furthermore, we predicted the presence of dendritic cells in inclusion body myositis and of an IFN-alpha/beta response in dermatomyositis, neither of which was previously described. These predictions have been subsequently corroborated by immunohistochemistry.
Tian
Lu
L
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 North Lake Shore Drive, Chicago, IL 60611, USA.
Greenberg
Steven A
SA
Kong
Sek Won
SW
Altschuler
Josiah
J
Kohane
Isaac S
IS
Park
Peter J
PJ
D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism; D000818:Animals; D015551:Autoimmunity / Q000235:genetics / Q000502:physiology; D030541:Databases, Genetic; D003882:Dermatomyositis / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D016898:Interferon-alpha / Q000235:genetics / Q000378:metabolism; D016899:Interferon-beta / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D009220:Myositis / Q000235:genetics / Q000378:metabolism; D018979:Myositis, Inclusion Body / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011237:Predictive Value of Tests; D013601:T-Lymphocytes / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism
D016898:Interferon-alpha; D014157:Transcription Factors; D016899:Interferon-beta