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 Systjournal_title
Cell systemsauthors
Tan J,Doing G,Lewis KA,Price CE,Chen KM,Cady KC,Perchuk B,Laub MT,Hogan DA,Greene CSdoi
10.1016/j.cels.2017.06.003subject
Has Abstractpub_date
2017-07-26 00:00:00pages
63-71.e6issue
1eissn
2405-4712issn
2405-4720pii
S2405-4712(17)30231-4journal_volume
5pub_type
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