wgapl / adage

This is the repository for ADAGE (Analysis using Denoising Autoencoders for 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 analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions" (submitted).

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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 [email protected] if you have questions.