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ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions

Jie Tan, John H. Hammond, View ORCID ProfileDeborah A. Hogan, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/030650
Jie Tan
1Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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John H. Hammond
2Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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Deborah A. Hogan
2Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
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Casey S. Greene
1Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
3Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania. Philadelphia PA 19104, USA.
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  • For correspondence: csgreene@upenn.edu
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Abstract

The growth in genome-scale assays of gene expression for different species in publicly available databases presents new opportunities for computational methods that aid in hypothesis generation and biological interpretation of these data. Here, we present an unsupervised machine-learning approach, ADAGE (Analysis using Denoising Autoencoders of Gene Expression) and apply it to the interpretation of all of the publicly available gene expression data for Pseudomonas aeruginosa, an important opportunistic bacterial pathogen. In post-hoc positive control analyses using curated knowledge, the P. aeruginosa ADAGE model found that co-operonic genes often participated in similar processes and accurately predicted which genes had similar functions. By analyzing newly generated data and previously published microarray and RNA-seq data, the ADAGE model identified gene expression differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes despite low level expression differences in directly involved genes. Comparison of ADAGE with PCA and ICA revealed that ADAGE extracts distinct signals. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments, and we provide open source code for use in other species and settings.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 05, 2015.
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ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions
Jie Tan, John H. Hammond, Deborah A. Hogan, Casey S. Greene
bioRxiv 030650; doi: https://doi.org/10.1101/030650
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ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions
Jie Tan, John H. Hammond, Deborah A. Hogan, Casey S. Greene
bioRxiv 030650; doi: https://doi.org/10.1101/030650

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