Skip Navigation



Biostatistics Advance Access published online on October 4, 2006
Biostatistics, doi:10.1093/biostatistics/kxl029
This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
8/3/566    most recent
kxl029v2
kxl029v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Wu, B.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wu, B.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received June 15, 2006
Revised September 11, 2006
Accepted September 29, 2006

Article

Cancer outlier differential gene expression detection

Baolin Wu 1 *

1 Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN, 55455, USA

* To whom correspondence should be addressed.
Baolin Wu, E-mail: baolin{at}biostat.umn.edu


  Abstract

We study statistical methods to detect cancer genes that are over- or down-expressed in some but not all samples in a disease group. This has proven useful in cancer studies where oncogenes are activated only in a small subset of samples. We propose the outlier robust t-statistic, which is intuitively motivated from the t-statistic, the most commonly used differential gene expression detection method. Using real and simulation studies, we compare the outlier robust t-statistic to the recently proposed COPA (Tomlins et al. (2005) and the outlier sum statistic of Tibshirani and Hastie (2006). The proposed method often has more detection power and smaller false discovery rates. Supplementary information can be found at http://www.biostat.umn.edu/~baolin/research/ort.html.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Brief BioinformHome page
B. Andreopoulos, A. An, X. Wang, and M. Schroeder
A roadmap of clustering algorithms: finding a match for a biomedical application
Brief Bioinform, May 1, 2009; 10(3): 297 - 314.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
D. Ghosh and A. M. Chinnaiyan
Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation
Biostat., January 1, 2009; 10(1): 60 - 69.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Hu
Cancer outlier detection based on likelihood ratio test
Bioinformatics, October 1, 2008; 24(19): 2193 - 2199.
[Abstract] [Full Text] [PDF]

Home page
 BiostatisticsHome page
H. Lian
MOST: detecting cancer differential gene expression
Biostat., July 1, 2008; 9(3): 411 - 418.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.