Skip Navigation

This Article
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow FREE Full Text (Screen PDF)
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 ISI Web of Science
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 arrow Search for citing articles in:
ISI Web of Science (15)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Baggerly, K. A.
Right arrow Articles by Aldaz, C. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Baggerly, K. A.
Right arrow Articles by Aldaz, C. M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 19 no. 12 2003
Pages 1477-1483
© 2003 Oxford University Press

Differential expression in SAGE: accounting for normal between-library variation

Keith A. Baggerly 1,*, Li Deng 2, Jeffrey S. Morris 1 and C. Marcelo Aldaz 3

1 Department of Biostatistics, UT M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 447, Houston, TX 77030-4009, USA
2 Department of Statistics, Rice University, Houston TX 77005, USA
3 Department of Carcinogenesis, UT M.D. Anderson Cancer Center, 1515 Holcombe Blvd, Box 447, Houston, TX 77030-4009, USA

Received on December 24, 2002 ; revised on January 30, 2003 ; accepted on February 16, 2003

Motivation: In contrasting levels of gene expression between groups of SAGE libraries, the libraries within each group are often combined and the counts for the tag of interest summed, and inference is made on the basis of these larger ‘pseudolibraries’. While this captures the sampling variability inherent in the procedure, it fails to allow for normal variation in levels of the gene between individuals within the same group, and can consequently overstate the significance of the results. The effect is not slight: between-library variation can be hundreds of times the within-library variation.

Results: We introduce a beta-binomial sampling model that correctly incorporates both sources of variation. We show how to fit the parameters of this model, and introduce a test statistic for differential expression similar to a two-sample t-test.

Contact: kabagg{at}mdanderson.org

Supplementary information http://bioinformatics.mdanderson.org/ Includes Matlab and R code for fitting the model.

* To whom correspondence should be addressed.


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
 Mol Cancer ResHome page
M. C. Abba, H. Sun, K. A. Hawkins, J. A. Drake, Y. Hu, M. I. Nunez, S. Gaddis, T. Shi, S. Horvath, A. Sahin, et al.
Breast Cancer Molecular Signatures as Determined by SAGE: Correlation with Lymph Node Status
Mol. Cancer Res., September 1, 2007; 5(9): 881 - 890.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
S. Lee, J. Chen, G. Zhou, R. Z. Shi, G. G. Bouffard, M. Kocherginsky, X. Ge, M. Sun, N. Jayathilaka, Y. C. Kim, et al.
Gene expression profiles in acute myeloid leukemia with common translocations using SAGE
PNAS, January 24, 2006; 103(4): 1030 - 1035.
[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.