Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

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.

journal_name

Cell Syst

journal_title

Cell systems

authors

Tan J,Doing G,Lewis KA,Price CE,Chen KM,Cady KC,Perchuk B,Laub MT,Hogan DA,Greene CS

doi

10.1016/j.cels.2017.06.003

subject

Has Abstract

pub_date

2017-07-26 00:00:00

pages

63-71.e6

issue

1

eissn

2405-4712

issn

2405-4720

pii

S2405-4712(17)30231-4

journal_volume

5

pub_type

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