Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection Onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing Online publication date: Sat, 24-Jan-2015
by Stan Pounds, Xueyuan Cao, Cheng Cheng, Jun J. Yang, Dario Campana, Ching-Hon Pui, William E. Evans, Mary V. Relling
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 5, No. 2, 2011
Abstract: We recently developed the Projection Onto the Most Interesting Statistical Evidence (PROMISE) procedure that uses prior biological knowledge to guide an integrated analysis of gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to the integrated analysis of pharmacologic, clinical and genome-wide genotype data. An efficient permutation-testing algorithm is introduced so that PROMISE is computationally feasible in this higher-dimension setting. In the analysis of a paediatric leukaemia data set, PROMISE effectively identifies genomic features that exhibit a biologically meaningful pattern of association with multiple endpoint variables.
Online publication date: Sat, 24-Jan-2015
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