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Discovering Statistically Significant Biclusters in Gene Expression Data (2002)
| Venue: | In Proceedings of ISMB 2002 |
| Citations: | 302 - 4 self |
BibTeX
@INPROCEEDINGS{Tanay02discoveringstatistically,
author = {Amos Tanay and Roded Sharan and Ron Shamir},
title = {Discovering Statistically Significant Biclusters in Gene Expression Data},
booktitle = {In Proceedings of ISMB 2002},
year = {2002},
pages = {136--144}
}
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Abstract
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).
Keyphrases
gene expression data statistically significant biclusters significant biclusters human cancer dataset cross validation result high specificity tissue type new method new concrete biological association cancer data plausible assumption uncharacterized yeast gene yeast expression profile consistent pattern large expression datasets statistical modelling
