Unsupervised Outlier Profile Analysis: Supplement Issue: Array Platform Modeling and Analysis (A). (January 2014)

• Record Type: Journal Article
• Authors: Ghosh, Debashis; Li, Song
• Is Part Of: Cancer informatics. Volume 13(2014)Supplement 4
• Publication Date: 2014-01
• Subjects: biomarkers -- genomic data integration -- heterogeneity -- microarray -- mixture model -- tumor subtypes; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.4137/CIN.S13969
• Languages: English
• ISSNs: 1176-9351

Abstract

In much of the analysis of high-throughput genomic data, "interesting" genes have been selected based on assessment of differential expression between two groups or generalizations thereof. Most of the literature focuses on changes in mean expression or the entire distribution. In this article, we explore the use of C (α) tests, which have been applied in other genomic data settings. Their use for the outlier expression problem, in particular with continuous data, is problematic but nevertheless motivates new statistics that give an unsupervised analog to previously developed outlier profile analysis approaches. Some simulation studies are used to evaluate the proposal. A bivariate extension is described that can accommodate data from two platforms on matched samples. The proposed methods are applied to data from a prostate cancer study.