Format

Send to

Choose Destination
Biostatistics. 2007 Jan;8(1):2-8. Epub 2006 May 15.

Outlier sums for differential gene expression analysis.

Author information

1
Department of Health Research and Policy, Stanford University, Stanford, CA 94305, USA. tibs@stat.stanford.edu

Abstract

We propose a method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples. This can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples. In real and simulated examples, the new method often exhibits lower false discovery rates than simple t-statistic thresholding. We also compare our approach to the recent cancer profile outlier analysis proposal of Tomlins and others (2005).

PMID:
16702229
DOI:
10.1093/biostatistics/kxl005
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center