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

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# 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)]( can be found in a [separate repository](

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

## Getting started

### Installation

The recommended way to install Monet is to first install most of its dependencies using [conda](, and to then install Monet and other dependencies that are not available through conda using [pip](

#### 1. Installing Miniconda

If you are new to conda, please [install Miniconda](

#### 2. Create a new conda environment for installing Monet

Create a new conda environment named “monet” with Python 3.8 as follows (commands are for Linux/Ubuntu):

`sh $ conda create -n monet python=3.8 `

#### 3. Use conda to install most of Monet’s dependencies

Activate the new environment and install the following packages:

`sh $ conda activate monet (monet) $ conda install scikit-learn pandas cython plotly seaborn statsmodels numba pytables networkx click `

#### 4. Use pip to install the remaining dependencies and Monet itself

Make sure your conda environment is still activated. Then install the following packages:

`sh (monet) $ pip install leidenalg scanpy monet `

### Tutorials (v0.2.2)

The following tutorials were developed using Monet v0.2.2. They demonstrate how to use Monet to perform various basic and advanced analysis tasks. The Jupyter electronic notebooks can be [downloaded from GitHub](

#### Basics 1. [Loading and saving expression data]( 2. [Importing/exporting data from/to Scanpy]( 3. [Visualizing data with t-SNE](

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

#### Denoising 1. [Denoising data with ENHANCE](

#### Data integration 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 and License

Copyright (c) 2020-2021 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]( and Dr. Matthias Stahl ([@h_i_g_s_c_h]( for providing these references.

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