https://github.com/greenelab/adage
Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016
https://github.com/greenelab/adage
autoencoders data dataset denoising-autoencoders gene-expression machine-learning manuscript methodology neural-networks paper pseudomonas-aeruginosa research supplement
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Data and code related to the paper "ADAGE-Based Integration of Publicly Available Pseudomonas aeruginosa..." Jie Tan, et al · mSystems · 2016
- Host: GitHub
- URL: https://github.com/greenelab/adage
- Owner: greenelab
- License: bsd-3-clause
- Created: 2015-11-03T15:56:31.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-05-26T02:15:39.000Z (over 9 years ago)
- Last Synced: 2023-10-25T19:22:43.716Z (almost 2 years ago)
- Topics: autoencoders, data, dataset, denoising-autoencoders, gene-expression, machine-learning, manuscript, methodology, neural-networks, paper, pseudomonas-aeruginosa, research, supplement
- Language: Python
- Homepage:
- Size: 79.1 MB
- Stars: 61
- Watchers: 15
- Forks: 30
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# adage
[](https://zenodo.org/badge/latestdoi/19060/greenelab/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](http://msystems.asm.org/content/1/1/e00025-15). 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.