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A comprehensive evaluation of module detection methods for gene expression data

Author

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  • Wouter Saelens

    (VIB Center for Inflammation Research
    Ghent University)

  • Robrecht Cannoodt

    (VIB Center for Inflammation Research
    Ghent University Hospital)

  • Yvan Saeys

    (VIB Center for Inflammation Research
    Ghent University)

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.

Suggested Citation

  • Wouter Saelens & Robrecht Cannoodt & Yvan Saeys, 2018. "A comprehensive evaluation of module detection methods for gene expression data," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03424-4
    DOI: 10.1038/s41467-018-03424-4
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    Cited by:

    1. Xinrui Zhou & Wan Yi Seow & Norbert Ha & Teh How Cheng & Lingfan Jiang & Jeeranan Boonruangkan & Jolene Jie Lin Goh & Shyam Prabhakar & Nigel Chou & Kok Hao Chen, 2024. "Highly sensitive spatial transcriptomics using FISHnCHIPs of multiple co-expressed genes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    2. Donghui Choe & Connor A. Olson & Richard Szubin & Hannah Yang & Jaemin Sung & Adam M. Feist & Bernhard O. Palsson, 2024. "Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Arjun Patel & Dominic McGrosso & Ying Hefner & Anaamika Campeau & Anand V. Sastry & Svetlana Maurya & Kevin Rychel & David J. Gonzalez & Bernhard O. Palsson, 2024. "Proteome allocation is linked to transcriptional regulation through a modularized transcriptome," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Leonardo Sportelli & Daniel P. Eisenberg & Roberta Passiatore & Enrico D’Ambrosio & Linda A. Antonucci & Jasmine S. Bettina & Qiang Chen & Aaron L. Goldman & Michael D. Gregory & Kira Griffiths & Thom, 2024. "Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    5. Matthew A. Lawlor & Weihuan Cao & Christopher E. Ellison, 2021. "A transposon expression burst accompanies the activation of Y-chromosome fertility genes during Drosophila spermatogenesis," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    6. Brandon Monier & Adam McDermaid & Cankun Wang & Jing Zhao & Allison Miller & Anne Fennell & Qin Ma, 2019. "IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-15, February.
    7. Allen W. Lynch & Myles Brown & Clifford A. Meyer, 2023. "Multi-batch single-cell comparative atlas construction by deep learning disentanglement," Nature Communications, Nature, vol. 14(1), pages 1-22, December.

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