Moonstone

Moonstone aims to provide a way of performing analysis of metagenomics counts from raw data to statistical analysis and visualization of the results.

Thus, in moonstone you will find:

• parsers for common file types for metagenomics counts
• modules for cleaning and filtering your data
• normalization modules
• analysis modules
• plot modules

The main idea is to keep track of every steps applied to a raw data to be able to easily share the analysis and reproduce it.

Please check our documentation for more information.

Install moonstone

You can install moonstone either as a user (to use the tool as-is) or as a developer (to work directly on the code). We recommend using Mamba (a faster drop-in replacement for Conda) to manage environments.

🧪 User Installation

If you only want to use moonstone and don't need to edit the source code:

# Create and activate a Python 3.10 environment
mamba create -n moonstone python=3.10 -y
conda activate moonstone

# Install the latest published version of moonstone from PyPI
pip install moonstone

🛠️ Developer Installation

To work directly with the code and have your changes reflected automatically, follow these steps:

# Clone the repository**  
git clone git@github.com:motleystate/moonstone.git
cd moonstone

# Create a development environment
mamba create -n moonstone-dev python=3.10 -y
conda activate moonstone-dev

# Install Moonstone in editable mode
pip install -e .

You can also install dependencies required for development : pip install -r requirements-dev.txt.

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Quickstart with moonstone on the command-line

More detailed documentation is available on the documentation.

Moonstone is directly callable from your terminal for available built-in analysis scripts:

(moonstone) $ moonstone --help
usage: moonstone [-h] [-f filtering] [-p] [-k clusters] [-s variable]
                 [-sr variable] [-sc variable] [-rf variable]
                 countfile metadata

Microbiota Analysis Scripts for Machine LEarning

positional arguments:
  countfile     Normalized count file input
  metadata      Clinical data input file

optional arguments:
  -h, --help    show this help message and exit
  -f filtering  Minimum mean reads per variable: use a float >0
  -p            Generates PCA plot of data
  -k clusters   Runs K-Means clustering. Provide number of clusters
  -s variable   Run SVMachine Classification
  -sr variable  SVM Classifier with ROC Analysis
  -sc variable  SVM and output Classifier Components
  -rf variable  Random Forest Analysis, using supplied variable

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Development guide

To work on a new feature, related or not to an issue, we open a new branch from the development (currently master) branch to work on it.

If an issue is opened, we recommend to name your branch with the issue number at the begining, e.g. 5-my-new-feature

Once done, work is reviewed through a pull request.