Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks
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Author(s)
Tan, Jie
Doing, Georgia
Lewis, Kimberley A.
Price, Courtney E.
Chen, Kathleen M.
Hogan, Deborah A.
Greene, Casey S.
Cady, Kyle
Perchuk, Barrett
Laub, Michael T
Date Issued
July 2017
Journal
Cell Systems
Publisher
Elsevier BV
Citation
Tan, Jie, Georgia Doing, Kimberley A. Lewis, Courtney E. Price, Kathleen M. Chen, Kyle C. Cady, Barret Perchuk, Michael T. Laub, Deborah A. Hogan, and Casey S. Greene. “Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.” Cell Systems 5, no. 1 (July 2017): 63–71.
Version
Final published version
Abstract
Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1016/J.CELS.2017.06.003
