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 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
Google Scholar
Right arrow Articles by Tanay, A.
Right arrow Articles by Shamir, R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Tanay, A.
Right arrow Articles by Shamir, R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Bioinformatics Vol. 18 no. 90001 2002
Pages S136-S144
© 2002 Oxford University Press

Discovering statistically significant biclusters in gene expression data

Amos Tanay *, Roded Sharan * and Ron Shamir

School Of Computer Science, Tel-Aviv University, Ramat-Aviv, Tel-Aviv, 69978, Israel

Received on January 24, 2002 ; revised on March 31, 2002 ; accepted on March 31, 2002

In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).

Contact: amos{at}tau.ac.il; roded{at}tau.ac.il; rshamir{at}tau.ac.il

Availability:

* These authors contributed equally to this work.


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
 BioinformaticsHome page
I. G. Costa, S. Roepcke, C. Hafemeister, and A. Schliep
Inferring differentiation pathways from gene expression
Bioinformatics, July 1, 2008; 24(13): i156 - i164.
[Abstract] [PDF]

Home page
 Nucleic Acids ResHome page
A. Pati, Y. Jin, K. Klage, R. F. Helm, L. S. Heath, and N. Ramakrishnan
CMGSDB: integrating heterogeneous Caenorhabditis elegans data sources using compositional data mining
Nucleic Acids Res., January 11, 2008; 36(suppl_1): D69 - D76.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. A. Hibbs, D. C. Hess, C. L. Myers, C. Huttenhower, K. Li, and O. G. Troyanskaya
Exploring the functional landscape of gene expression: directed search of large microarray compendia
Bioinformatics, October 15, 2007; 23(20): 2692 - 2699.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Buness, R. Kuner, M. Ruschhaupt, A. Poustka, H. Sultmann, and A. Tresch
Identification of aberrant chromosomal regions from gene expression microarray studies applied to human breast cancer
Bioinformatics, September 1, 2007; 23(17): 2273 - 2280.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
Y. Lu, X. He, and S. Zhong
Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer's disease
Nucleic Acids Res., July 13, 2007; 35(suppl_2): W105 - W114.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Shi, M. Klustein, I. Simon, T. Mitchell, and Z. Bar-Joseph
Continuous hidden process model for time series expression experiments
Bioinformatics, July 1, 2007; 23(13): i459 - i467.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
N. Yosef, Z. Yakhini, A. Tsalenko, V. Kristensen, A.-L. Borresen-Dale, E. Ruppin, and R. Sharan
A supervised approach for identifying discriminating genotype patterns and its application to breast cancer data
Bioinformatics, January 15, 2007; 23(2): e91 - e98.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Buhlmann, W. Gruissem, L. Hennig, L. Thiele, and E. Zitzler
A systematic comparison and evaluation of biclustering methods for gene expression data
Bioinformatics, May 1, 2006; 22(9): 1122 - 1129.
[Abstract] [Full Text] [PDF]

Home page
 Genome Res.Home page
K. Dolinski and D. Botstein
Changing perspectives in yeast research nearly a decade after the genome sequence
Genome Res., December 1, 2005; 15(12): 1611 - 1619.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
P. Flaherty, G. Giaever, J. Kumm, M. I. Jordan, and A. P. Arkin
A latent variable model for chemogenomic profiling
Bioinformatics, August 1, 2005; 21(15): 3286 - 3293.
[Abstract] [Full Text] [PDF]

Home page
 Proc. Natl. Acad. Sci. USAHome page
A. Tanay, R. Sharan, M. Kupiec, and R. Shamir
Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data
PNAS, March 2, 2004; 101(9): 2981 - 2986.
[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.