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Modeling transcriptional profiles of gene perturbation with deep neural network

View ORCID ProfileWenke Liu, Xuya Wang, View ORCID ProfileD R Mani, View ORCID ProfileDavid Fenyö
doi: https://doi.org/10.1101/2021.07.15.452534
Wenke Liu
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, 10016, USA
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
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  • ORCID record for Wenke Liu
Xuya Wang
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, 10016, USA
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
3Translational Medicine Bioinformatics, Informatics & Predictive Sciences, Bristol-Myers Squibb, Princeton, NJ, 08540, USA
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D R Mani
4Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
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  • For correspondence: david.fenyo@nyulangone.org
David Fenyö
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY, 10016, USA
2Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY, 10016, USA
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  • For correspondence: david.fenyo@nyulangone.org
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Abstract

Background Cell line perturbation data could be utilized as a reference for inferring underlying molecular processes in new gene expression profiles. It is important to develop accurate and computationally efficient algorithms to exploit biological knowledge in the growing compendium of existing perturbation data and harness these for new predictions.

Results We reframed the problem of inferring possible gene perturbation based on a reference perturbation database into a classification task and evaluated the application of deep neural network models to address this problem. Our results showed that a fully-connected multi-layer neural network was able to achieve up to 74.9% accuracy in a holdout test set, but the model generalizability was limited by consistency between training and testing data.

Conclusion Capacity and flexibility enables neural network models to efficiently represent transcriptomic features associated with single gene knockdown perturbations. With consistent signals between training and testing sets, neural networks may be trained to classify new samples to experimentally confirmed molecular phenotypes.

Competing Interest Statement

The authors have declared no competing interest.

  • List of Abbreviations

    CMap
    Connectivity Map
    DNN
    Deep Neural Network
    shRNA
    short hairpin RNA
    CRISPR
    Clustered Regularly Interspaced Short Palindromic Repeats
    CGS
    Consensus Gene Signatures
    ES
    Enrichment Score
    WTCS
    Weighted Connectivity Score
    ELU
    Exponential Linear Unit
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    Posted July 16, 2021.
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    Modeling transcriptional profiles of gene perturbation with deep neural network
    Wenke Liu, Xuya Wang, D R Mani, David Fenyö
    bioRxiv 2021.07.15.452534; doi: https://doi.org/10.1101/2021.07.15.452534
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    Modeling transcriptional profiles of gene perturbation with deep neural network
    Wenke Liu, Xuya Wang, D R Mani, David Fenyö
    bioRxiv 2021.07.15.452534; doi: https://doi.org/10.1101/2021.07.15.452534

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