Note: This repository contains the scRNA-Seq analysis software. For other tools named Monet, see Disambiguation

Monet is an open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces.

Datasets from the Monet paper (Wagner, 2020) can be found in a separate repository.

Additional documentation is in the works! For questions and requests, please create an "issue" on GitHub.

To install Monet, please first use conda to install the packages pandas, scipy, scikit-learn, and plotly. If you are new to conda, you can either install Anaconda, which includes all of the aforementioned packages, or you can install miniconda and then manually install these packages. I also recommend using Jupyter electronic notebooks to analyze scRNA-Seq data, which requires installation of the jupyter package (also with conda).

Once these prerequisites are installed, you can install Monet using pip:

$ pip install monet

The following tutorials demonstrate how to use Monet to perform various basic and advanced analysis tasks. The Jupyter electronic notebooks can be downloaded from GitHub.

1. Loading and saving expression data
2. Importing data from scanpy (coming soon)
3. Visualizing data with t-SNE

1. Clustering data with Galapagos (t-SNE + DBSCAN)
2. Annotating clusters with cell types (coming soon)

1. Denoising data with ENHANCE

1. Training a Monet model (for integrative anlayses)
2. Plotting a batch-corrected t-SNE using mutual nearest neighbors (Haghverdi et al.%2C 2018)
3. Transferring labels between datasets using K-nearest neighbor classification

Copyright (c) 2020 Florian Wagner

Monet is licensed under an OSI-compliant 3-clause BSD license. For details, see LICENSE.

The following other tools have been named Monet (styled either MONET or MONet):

• Overview of the Model and Observation Evaluation Toolkit (MONET) (Baker and Pan, 2017) [github]
• MONet: Unsupervised Scene Decomposition and Representation (Burgess et al., 2019) [github]
• MONET: a toolbox integrating top-performing methods for network modularization (Tomasoni et al., 2020) [preprint] [github]
• Multi-Objective Cellular Evolutionary Algorithm (MONET) (García-Nieto et al., 2019) [github]
• MONET: Multi-omic patient module detection by omic selection (Rappoport et al., 2020) [github]

Thanks to Michał Krassowski (@krassowski_m) and Dr. Matthias Stahl (@h_i_g_s_c_h) for providing these references.