Supper, Jochen and Strauch, Martin and Wanke, Dierk and Harter, Klaus and Zell, Andreas

EDISA: extracting biclusters from multiple time-series of gene expression profiles

BMC Bioinformatics vol. 8 (2007), no. 1, pp. 334


Abstract

Background: Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional gene-condition-time dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for gene-condition-time datasets. Results: In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters. Conclusion: We successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.


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BibTeX

@article{2007_5,
  author = {Supper, Jochen and Strauch, Martin and Wanke, Dierk and Harter, Klaus
	and Zell, Andreas},
  title = {{EDISA}: extracting biclusters from multiple time-series of gene
	expression profiles},
  journal = {BMC Bioinformatics},
  year = {2007},
  volume = {8},
  pages = {334},
  number = {1},
  abstract = {Background: Cells dynamically adapt their gene expression patterns
	in response to various stimuli. This response is orchestrated into
	a number of gene expression modules consisting of co-regulated genes.
	A growing pool of publicly available microarray datasets allows the
	identification of modules by monitoring expression changes over time.
	These time-series datasets can be searched for gene expression modules
	by one of the many clustering methods published to date. For an integrative
	analysis, several time-series datasets can be joined into a three-dimensional
	gene-condition-time dataset, to which standard clustering or biclustering
	methods are, however, not applicable. We thus devise a probabilistic
	clustering algorithm for gene-condition-time datasets. Results: In
	this work, we present the EDISA (Extended Dimension Iterative Signature
	Algorithm), a novel probabilistic clustering approach for 3D gene-condition-time
	datasets. Based on mathematical definitions of gene expression modules,
	the EDISA samples initial modules from the dataset which are then
	refined by removing genes and conditions until they comply with the
	module definition. A subsequent extension step ensures gene and condition
	maximality. We applied the algorithm to a synthetic dataset and were
	able to successfully recover the implanted modules over a range of
	background noise intensities. Analysis of microarray datasets has
	lead us to define three biologically relevant module types: 1) We
	found modules with independent response profiles to be the most prevalent
	ones. These modules comprise genes which are co-regulated under several
	conditions, yet with a different response pattern under each condition.
	2) Coherent modules with similar responses under all conditions occurred
	frequently, too, and were often contained within these modules. 3)
	A third module type, which covers a response specific to a single
	condition was also detected, but rarely. All of these modules are
	essentially different types of biclusters. Conclusion: We successfully
	applied the EDISA to different 3D datasets. While previous studies
	were mostly aimed at detecting coherent modules only, our results
	show that coherent responses are often part of a more general module
	type with independent response profiles under different conditions.
	Our approach thus allows for a more comprehensive view of the gene
	expression response. After subsequent analysis of the resulting modules,
	the EDISA helped to shed light on the global organization of transcriptional
	control. An implementation of the algorithm is available at \url{http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/}.},
  doi = {10.1186/1471-2105-8-334},
  issn = {1471-2105},
  url = {http://www.biomedcentral.com/1471-2105/8/334}
}