Microbe-metabolite interactions through neural networks
Project description
# rhapsody
Neural networks for estimating microbe-metabolite co-occurence probabilities.
# Installation
```
conda create -n mae python=3.5 tensorflow numpy scipy pandas scikit-bio tqdm pip
conda install -n mae biom-format -c conda-forge
source activate mae
pip install h5py git+https://github.com/mortonjt/rhapsody.git
```
If you are getting errors, it is likely because you have garbage channels under your .condarc. Make sure to delete your .condarc -- you shouldn't need it.
# Getting started
To get started you can run a quick example as follows. This will generate
microbe-metabolite conditional probabilities that are accurate up to rank.
```
rhapsody autoencoder \
--otu-file data/otu.biom \
--metabolite-file data/ms.biom \
--summary-dir summary \
--results-file cv-results.csv \
--ranks-file ranks.csv
```
While this is running, you can open up another session and run `tensorboard --logdir .` for diagnosis.
See the following url for a more complete tutorial with real datasets.
https://github.com/knightlab-analyses/multiomic-cooccurences
More information can found under `rhapsody --help`
# Qiime2 plugin
If you want to make this qiime2 compatible, install this in your
qiime2 conda environment and run the following
```
qiime dev refresh-cache
```
This should allow your q2 environment to recognize rhapsody. To test run
the qiime2 plugin, run the following commands
```
qiime tools import \
--input-path data/otu.biom \
--output-path otu.qza \
--type FeatureTable[Frequency]
qiime tools import \
--input-path data/ms.biom \
--output-path ms.qza \
--type FeatureTable[Frequency]
qiime rhapsody autoencoder \
--i-microbes otu.qza \
--i-metabolites ms.qza \
--o-conditional-ranks ranks.qza
```
More information can found under `qiime rhapsody --help`
Neural networks for estimating microbe-metabolite co-occurence probabilities.
# Installation
```
conda create -n mae python=3.5 tensorflow numpy scipy pandas scikit-bio tqdm pip
conda install -n mae biom-format -c conda-forge
source activate mae
pip install h5py git+https://github.com/mortonjt/rhapsody.git
```
If you are getting errors, it is likely because you have garbage channels under your .condarc. Make sure to delete your .condarc -- you shouldn't need it.
# Getting started
To get started you can run a quick example as follows. This will generate
microbe-metabolite conditional probabilities that are accurate up to rank.
```
rhapsody autoencoder \
--otu-file data/otu.biom \
--metabolite-file data/ms.biom \
--summary-dir summary \
--results-file cv-results.csv \
--ranks-file ranks.csv
```
While this is running, you can open up another session and run `tensorboard --logdir .` for diagnosis.
See the following url for a more complete tutorial with real datasets.
https://github.com/knightlab-analyses/multiomic-cooccurences
More information can found under `rhapsody --help`
# Qiime2 plugin
If you want to make this qiime2 compatible, install this in your
qiime2 conda environment and run the following
```
qiime dev refresh-cache
```
This should allow your q2 environment to recognize rhapsody. To test run
the qiime2 plugin, run the following commands
```
qiime tools import \
--input-path data/otu.biom \
--output-path otu.qza \
--type FeatureTable[Frequency]
qiime tools import \
--input-path data/ms.biom \
--output-path ms.qza \
--type FeatureTable[Frequency]
qiime rhapsody autoencoder \
--i-microbes otu.qza \
--i-metabolites ms.qza \
--o-conditional-ranks ranks.qza
```
More information can found under `qiime rhapsody --help`
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