cnmfsns 1.8.2
cNMF Solution Network Space
Requires Python: >=3.9


![cNMF-SNS logo](logo.png)

-----------------

# cNMF-SNS: powerful factorization-based multi-omics integration toolkit

![version badge](https://img.shields.io/badge/version-1.8.2-blue)
[![PyPI Latest Release](https://img.shields.io/pypi/v/cnmfsns.svg)](https://pypi.org/project/cnmfsns/)
[![Conda Latest Release](https://img.shields.io/conda/vn/conda-forge/cnmfsns)](https://anaconda.org/conda-forge/cnmfsns/)
[![Documentation status](https://readthedocs.org/projects/cnmf-sns/badge/?version=latest&style=flat)]()
[![Downloads](https://static.pepy.tech/badge/cnmfsns)](https://pepy.tech/project/cnmfsns)
[![License](https://img.shields.io/pypi/l/cnmfsns.svg)](https://github.com/MorrissyLab/cNMF-SNS/blob/main/LICENSE)

Authors: [Ted Verhey](https://github.com/verheytb), [Heewon Seo](https://github.com/lootpiz), [Sorana Morrissy](https://github.com/ancasorana)

**cNMF-SNS** (consensus Non-negative Matrix Factorization Solution Network Space) is a Python package enabling mosaic integration of bulk, single-cell, and
spatial expression data between and within datasets. Datasets can have partially overlapping features (eg. genes) as well as non-overlapping features. cNMF provides a **robust, 
unsupervised** deconvolution of each dataset into gene expression programs (GEPs).
**Network-based integration** of GEPs enables flexible integration of many datasets
across assays (eg. Protein, RNA-Seq, scRNA-Seq, spatial expression) and patient cohorts.

Communities with GEPs from multiple datasets can be annotated with dataset-specific
annotations to facilitate interpretation.

## ⚡Main Features

Here are just a few of the things that cNMF-SNS does well:

- Identifies interpretable, non-negative programs at multiple resolutions
- Mosaic integration does not require subsetting features/genes to
  a shared or overdispersed subset
- Ideal for incremental integration (adding datasets one at a time) since
  deconvolution is performed independently on each dataset
- Integration performs well even when the datasets have mismatched features
  (eg. Microarray, RNA-Seq, Proteomics) or sparsity (eg single-cell vs bulk RNA-Seq and ATAC-Seq)
- Two interfaces: command-line interface for rapid data exploration and python
  interface for extensibility and flexibility

## 🔧 Install

### ☁️ Public Release

Install the package with conda (in an isolated conda environment)
```bash
conda create -n cnmfsns -c conda-forge cnmfsns
conda activate cnmfsns
```

## 📖 Documentation

### 🗐 Data guidelines

cNMF-SNS can factorize a wide variety of datasets, but will work optimally in these conditions:
  - Use untransformed (raw) data where possible, and avoid log-transformed data.
  - For single-cell or spatial RNA-Seq data, the best data to use is feature counts, then TPM-normalized values, then RPKM/FPKM-normalized values.

### 📓 Python interface

To get started, sample proteomics datasets and a Jupyter notebook tutorial is available [here](/tutorial/tutorial.ipynb).

Detailed API reference can be found on [ReadTheDocs](https://cnmf-sns.readthedocs.io/).


### ⌨️ Command line interface

See the [command line interface documentation](/CLI.md).

## 💭 Getting Help

For errors arising during use of cNMF-SNS, create and browse issues in the [GitHub "issues" tab](https://github.com/MorrissyLab/cNMF-SNS/issues).