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Multi-group cancer outlier differential gene expression detection

Paper ID Volume ID Publish Year Pages File Format Full-Text
15486 1417 2007 7 PDF Available
Title
Multi-group cancer outlier differential gene expression detection
Abstract

It has recently been shown that cancer genes (oncogenes) tend to have heterogeneous expressions across disease samples. So it is reasonable to assume that in a microarray data only a subset of disease samples will be activated (often referred to as outliers), which presents some new challenges for statistical analysis. In this paper, we study the multi-class cancer outlier differential gene expression detection. Statistical methods will be proposed to take into account the expression heterogeneity. Through simulation studies and application to public microarray data, we will show that the proposed methods could provide more comprehensive analysis results and improve upon the traditional differential gene expression detection methods, which often ignore the expression heterogeneity and may loss power. Supplementary information can be found at http://www.biostat.umn.edu/∼baolin/research/orf.html.

Keywords
Cancer gene activation heterogeneity; Differential gene expression detection; False discovery rate; Microarray; Outlier; Robust regression
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 31, Issue 2, April 2007, Pages 65–71
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us