MOTIVATION: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions. RESULTS: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.
MOTIVATION: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions. RESULTS: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.
Authors: Amy Holleman; Meyling H Cheok; Monique L den Boer; Wenjian Yang; Anjo J P Veerman; Karin M Kazemier; Deqing Pei; Cheng Cheng; Ching-Hon Pui; Mary V Relling; Gritta E Janka-Schaub; Rob Pieters; William E Evans Journal: N Engl J Med Date: 2004-08-05 Impact factor: 91.245
Authors: Marc Weeber; Bob J Schijvenaars; Erik M Van Mulligen; Barend Mons; Rob Jelier; Christian C Van Der Eijk; Jan A Kors Journal: AMIA Annu Symp Proc Date: 2003
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Authors: G Joshi-Tope; M Gillespie; I Vastrik; P D'Eustachio; E Schmidt; B de Bono; B Jassal; G R Gopinath; G R Wu; L Matthews; S Lewis; E Birney; L Stein Journal: Nucleic Acids Res Date: 2005-01-01 Impact factor: 16.971
Authors: Pengyi Yang; Ellis Patrick; Shi-Xiong Tan; Daniel J Fazakerley; James Burchfield; Christopher Gribben; Matthew J Prior; David E James; Yee Hwa Yang Journal: Bioinformatics Date: 2013-10-27 Impact factor: 6.937
Authors: Irina Dinu; John D Potter; Thomas Mueller; Qi Liu; Adeniyi J Adewale; Gian S Jhangri; Gunilla Einecke; Konrad S Famulski; Philip Halloran; Yutaka Yasui Journal: Brief Bioinform Date: 2008-10-04 Impact factor: 11.622