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A comprehensive evaluation of module detection methods for gene expression data

Wouter Saelens (UGent) , Robrecht Cannoodt (UGent) and Yvan Saeys (UGent)
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Abstract
A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.
Keywords
NETWORK INFERENCE METHODS, TRANSCRIPTION FACTORS, REGULATORY NETWORKS, MUTUAL INFORMATION, CLUSTERING METHODS, MICROARRAY DATA, VISUALIZATION, ARCHITECTURE, PERFORMANCE, REPERTOIRE

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Citation

Please use this url to cite or link to this publication:

Chicago
Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. 2018. “A Comprehensive Evaluation of Module Detection Methods for Gene Expression Data.” Nature Communications 9.
APA
Saelens, W., Cannoodt, R., & Saeys, Y. (2018). A comprehensive evaluation of module detection methods for gene expression data. NATURE COMMUNICATIONS, 9.
Vancouver
1.
Saelens W, Cannoodt R, Saeys Y. A comprehensive evaluation of module detection methods for gene expression data. NATURE COMMUNICATIONS. 2018;9.
MLA
Saelens, Wouter, Robrecht Cannoodt, and Yvan Saeys. “A Comprehensive Evaluation of Module Detection Methods for Gene Expression Data.” NATURE COMMUNICATIONS 9 (2018): n. pag. Print.
@article{8558915,
  abstract     = {A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods.},
  articleno    = {1090},
  author       = {Saelens, Wouter and Cannoodt, Robrecht and Saeys, Yvan},
  issn         = {2041-1723},
  journal      = {NATURE COMMUNICATIONS},
  keywords     = {NETWORK INFERENCE METHODS,TRANSCRIPTION FACTORS,REGULATORY NETWORKS,MUTUAL INFORMATION,CLUSTERING METHODS,MICROARRAY DATA,VISUALIZATION,ARCHITECTURE,PERFORMANCE,REPERTOIRE},
  language     = {eng},
  pages        = {12},
  title        = {A comprehensive evaluation of module detection methods for gene expression data},
  url          = {http://dx.doi.org/10.1038/s41467-018-03424-4},
  volume       = {9},
  year         = {2018},
}

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