ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions

By Jie Tan, J. Hammond, Deborah A. Hogan, Casey S. Greene. 2016

108 citations recorded by the source corpus.

The quantity and breadth of genome-scale data sets that examine RNA expression in diverse bacterial and eukaryotic species are increasing more rapidly than for curated knowledge. Our ADAGE method integrates such data without requiring gene function, gene pathway, or experiment labeling, making practical its application to any large gene expression compendium. We built a Pseudomonas aeruginosa ADAGE model from a diverse set of publicly available experiments without any prespecified biological knowledge, and this model was accurate and predictive. We provide ADAGE results for the complete P. aeruginosa GeneChip compendium for use by researchers studying P. aeruginosa and source code that facilitates ADAGE’s application to other species and data types. ABSTRACT The increasing number of genome-wide assays of gene expression available from public databases presents opportunities for computational methods that facilitate hypothesis generation and biological interpretation of these data. We present an unsupervised machine learning approach, ADAGE (analysis using denoising autoencoders of gene expression), and apply it to the publicly available gene expression data compendium for Pseudomonas aeruginosa. In this approach, the machine-learned ADAGE model contained 50 nodes which we predicted would correspond to gene expression patterns across the gene expression compendium. While no biological knowledge was used during model construction, cooperonic genes had similar weights across nodes, and genes with similar weights across nodes were significantly more likely to share KEGG pathways. By analyzing newly generated and previously published microarray and transcriptome sequencing data, the ADAGE model identified differences between strains, modeled the cellular response to low oxygen, and predicted the involvement of biological processes based on low-level gene expression differences. ADAGE compared favorably with traditional principal component analysis and independent component analysis approaches in its ability to extract validated patterns, and based on our analyses, we propose that these approaches differ in the types of patterns they preferentially identify. We provide the ADAGE model with analysis of all publicly available P. aeruginosa GeneChip experiments and open source code for use with other species and settings. Extraction of consistent patterns across large-scale collections of genomic data using methods like ADAGE provides the opportunity to identify general principles and biologically important patterns in microbial biology. This approach will be particularly useful in less-well-studied microbial species. IMPORTANCE The quantity and breadth of genome-scale data sets that examine RNA expression in diverse bacterial and eukaryotic species are increasing more rapidly than for curated knowledge. Our ADAGE method integrates such data without requiring gene function, gene pathway, or experiment labeling, making practical its application to any large gene expression compendium. We built a Pseudomonas aeruginosa ADAGE model from a diverse set of publicly available experiments without any prespecified biological knowledge, and this model was accurate and predictive. We provide ADAGE results for the complete P. aeruginosa GeneChip compendium for use by researchers studying P. aeruginosa and source code that facilitates ADAGE’s application to other species and data types. Author Video: An author video summary of this article is available.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, background): “Shallow (one hidden layer) denoising autoencoders have also been fruitful in extracting biological insight from thousands of Pseudomonas aeruginosa experiments [144,145] and in aggregating features relevant to specific breast cancer subtypes [24].”
Unsupervised extraction of functional gene expression signatures in the bacterial pathogen Pseudomonas aeruginosa with eADAGE (2016, methodology): “We constructed ADAGE models as described in (Tan et al , 2016b).”
Applications of machine learning in drug discovery and development (2019, methodology): “For example, researchers applied unsupervised DL models to extract meaningful representations of gene modules or sample clusters 80 .”
Learning the Structural Vocabulary of a Network (2017, unknown): “…al., 2012), speech (Hinton et al., 2012), genomic (Alipanahi, Delong, Weirauch, & Frey, 2015; Zhou & Troyanskaya, 2015), and gene expression data (Tan, Hammond, Hogan, & Greene, 2016; Tan, Ung, Cheng, & Greene, 2015) that have improved on manually designed features (LeCun, Bengio, & Hinton,…”
Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, background): “Notably, autoencoder transformation tends to aggregate genes sharing similar pathways (27), therefore making it appealing to interpret the biological functions.”
Using neural networks for reducing the dimensions of single-cell RNA-Seq data (2017, background): “(18) used denoising autoencoders (DAEs) to reconstruct bulk gene expression data.”