Integrated analysis of pharmacologic, clinical and SNP microarray data using Projection Onto the Most Interesting Statistical Evidence with Adaptive Permutation Testing. (25th March 2011)

• Record Type: Journal Article
• Authors: Pounds, Stan; Cao, Xueyuan; Cheng, Cheng; Yang, Jun J.; Campana, Dario; Pui, Ching-Hon; Evans, William; Relling, Mary
• Is Part Of: International journal of data mining and bioinformatics. Volume 5:Number 2(2011)
• Page Start: 143
• Page End: 157
• Publication Date: 2011-03-25
• Subjects: PROMISE -- integrated statistical analysis -- genomic dates -- adaptive permutation testing -- biologically meaningful -- correlation patterns -- statistical genomics -- computational genomics -- linear combinations -- association statistics -- bioinformatics -- prior knowledge -- data analysis -- paediatric leukaemia -- clinical data -- pharmacological data -- SNP microarray data -- gene expression data; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.