Adage
Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016
This is the repository for ADAGE (Analysis using Denoising Autoencoders for Gene Expression) The project is written primarily in Python, distributed under the BSD 3-Clause "New" or "Revised" License license, first published in 2015. Key topics include: autoencoders, data, dataset, denoising-autoencoders, gene-expression.
adage
This is the repository for ADAGE (Analysis using Denoising Autoencoders for Gene Expression)
This repository provides the source code in support of the manuscript: ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa Gene Expression Data with Denoising Autoencoders Illuminates Microbe-Host Interactions. J Tan, JH Hammond, DA Hogan, CS Greene. mSystems, 00025-15.
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To set up ADAGE, first clone the repository. This is a short summary. Detailed instructions and steps to generate the model and reproduce analyses used in the manuscript are in pseudomonas_autoencoder.sh
Building an ADAGE model requires installing python packages Theano and Docopt
Instructions for Theano: http://deeplearning.net/software/theano/install.html
Instructions for docopt: https://pypi.python.org/pypi/docopt
We provide a gene expression compendium of Pseudomonas aeruginosa that contains datasets available before 02.22.2014. To get an up-to-date compendium, follow the instructions in Section One in pseudomonas_autoencoder.sh
Before training, first 0-1 normalize the compendium, run
python Data_collection_processing/zero_one_normalization.py Data_collection_processing/Pa_compendium_02.22.2014.pcl Train_test_DAs/train_set_normalized.pcl None
To train a denoising autoencoders, run
python Train_test_DAs/SdA_train.py Train_test_DAs/train_set_normalized.pcl --parameters
To test a dataset on an ADAGE model, run
python Train_test_DAs/SdA_test.py Train_test_DAs/Genome-hybs_normalized.pcl --parameters
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Please email jie.tan.gr@dartmouth.edu if you have questions.
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