Skip to main content

Monet: An open-source Python package for analyzing and integrating single-cell RNA-Seq data using PCA-based latent spaces.

Project description

[![Version][version-shield]][version-url] [![Python versions][python-shield]][python-url] [![License][license-shield]][license-url]

![Logo][logo]

# Monet

Note: This repository contains the scRNA-Seq analysis software. For other tools named Monet, see [Disambiguation](#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)](https://www.biorxiv.org/content/10.1101/2020.06.08.140673v2) can be found in a [separate repository](https://github.com/flo-compbio/monet-paper).

For questions and requests, please create an “issue” on GitHub. For a version history, see [CHANGES](CHANGES.md).

## Getting started

### Installation

To install Monet, please first use [conda](https://docs.conda.io/en/latest/) to install the packages pandas, scipy, scikit-learn, and plotly. If you are new to conda, you can either [install Anaconda](https://docs.anaconda.com/anaconda/install/), which includes all of the aforementioned packages, or you can [install miniconda](https://docs.conda.io/en/latest/miniconda.html) and then manually install these packages. I also recommend using [Jupyter electronic notebooks](https://jupyter.org/) 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:

`sh $ pip install monet `

### Tutorials

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](https://github.com/flo-compbio/monet-tutorials).

#### Basics 1. [Loading and saving expression data](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/010%20-%20Loading%20and%20saving%20expression%20data.ipynb) 2. [Importing/exporting data from/to Scanpy](https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/i30w4g0egkhjt5o/020%20-%20Importing%20data%20from%20Scanpy%20and%20exporting%20data%20to%20Scanpy.ipynb) 3. [Visualizing data with t-SNE](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/030%20-%20Visualizing%20data%20with%20t-SNE.ipynb)

#### Clustering 1. [Clustering data with Galapagos (t-SNE + DBSCAN)](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/040%20-%20Clustering%20data%20with%20Galapagos%20%28t-SNE%20plus%20DBSCAN%29.ipynb) 2. Annotating clusters with cell types (coming soon)

#### Denoising 1. [Denoising data with ENHANCE](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/060%20-%20Denoising%20data%20with%20ENHANCE.ipynb)

#### Data integration 1. [Training a Monet model (for integrative anlayses)](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/070%20-%20Train%20a%20Monet%20model%20%28for%20integrative%20analyses%29.ipynb) 2. [Plotting a batch-corrected t-SNE using mutual nearest neighbors (Haghverdi et al.%2C 2018)](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/080%20-%20Plot%20a%20batch-corrected%20t-SNE%20using%20mutual%20nearest%20neighbors%20%28Haghverdi%20et%20al.%2C%202018%29.ipynb) 3. [Transferring labels between datasets using K-nearest neighbor classification](https://nbviewer.jupyter.org/github/flo-compbio/monet-tutorials/blob/master/090%20-%20Label%20transfer%20using%20K-nearest%20neighbor%20classification.ipynb)

## Copyright and License

Copyright (c) 2020 Florian Wagner

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

## Disambiguation

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

Thanks to Michał Krassowski ([@krassowski_m](https://twitter.com/krassowski_m)) and Dr. Matthias Stahl ([@h_i_g_s_c_h](https://twitter.com/h_i_g_s_c_h)) for providing these references.

<!– MARKDOWN LINKS & IMAGES –> <!– https://www.markdownguide.org/basic-syntax/#reference-style-links –> [version-shield]: https://img.shields.io/pypi/v/monet.svg [version-url]: https://pypi.python.org/pypi/monet [python-shield]: https://img.shields.io/pypi/pyversions/monet.svg [python-url]: https://pypi.python.org/pypi/monet [license-shield]: https://img.shields.io/pypi/l/monet.svg [license-url]: https://github.com/flo-compbio/monet/blob/master/LICENSE [logo]: images/monet_logo_25perc.jpg

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for monet, version 0.2.2
Filename, size File type Python version Upload date Hashes
Filename, size monet-0.2.2-py3-none-any.whl (27.3 MB) File type Wheel Python version py3 Upload date Hashes View
Filename, size monet-0.2.2.tar.gz (27.5 MB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page