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Extracting expression modules from perturbational gene expression compendia

Steven Maere UGent, Patrick Van Dijck and Martin Kuiper UGent (2008) BMC SYSTEMS BIOLOGY. 2.
abstract
Background: Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes. Results: We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these ( often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions. Conclusion: It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MICROARRAY DATA, SYSTEMS BIOLOGY, REGULATORY NETWORKS, SACCHAROMYCES-CEREVISIAE, GENOME, YEAST, ORGANIZATION, MODULARITY, DISCOVERY, MAP
journal title
BMC SYSTEMS BIOLOGY
BMC Syst. Biol.
volume
2
article number
33
pages
16 pages
Web of Science type
Article
Web of Science id
000256213900001
ISSN
1752-0509
DOI
10.1186/1752-0509-2-33
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
440989
handle
http://hdl.handle.net/1854/LU-440989
date created
2008-11-18 08:08:00
date last changed
2016-12-21 15:42:15
@article{440989,
  abstract     = {Background: Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes. 
Results: We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these ( often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions. 
Conclusion: It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.},
  articleno    = {33},
  author       = {Maere, Steven and Van Dijck, Patrick and Kuiper, Martin},
  issn         = {1752-0509},
  journal      = {BMC SYSTEMS BIOLOGY},
  keyword      = {MICROARRAY DATA,SYSTEMS BIOLOGY,REGULATORY NETWORKS,SACCHAROMYCES-CEREVISIAE,GENOME,YEAST,ORGANIZATION,MODULARITY,DISCOVERY,MAP},
  language     = {eng},
  pages        = {16},
  title        = {Extracting expression modules from perturbational gene expression compendia},
  url          = {http://dx.doi.org/10.1186/1752-0509-2-33},
  volume       = {2},
  year         = {2008},
}

Chicago
Maere, Steven, Patrick Van Dijck, and Martin Kuiper. 2008. “Extracting Expression Modules from Perturbational Gene Expression Compendia.” Bmc Systems Biology 2.
APA
Maere, S., Van Dijck, P., & Kuiper, M. (2008). Extracting expression modules from perturbational gene expression compendia. BMC SYSTEMS BIOLOGY, 2.
Vancouver
1.
Maere S, Van Dijck P, Kuiper M. Extracting expression modules from perturbational gene expression compendia. BMC SYSTEMS BIOLOGY. 2008;2.
MLA
Maere, Steven, Patrick Van Dijck, and Martin Kuiper. “Extracting Expression Modules from Perturbational Gene Expression Compendia.” BMC SYSTEMS BIOLOGY 2 (2008): n. pag. Print.