https://github.com/theislab/scgen
Single cell perturbation prediction
https://github.com/theislab/scgen
bioinformatics deep-learning generative-model scrna-seq single-cell single-cell-genomics transcriptomics
Last synced: 4 months ago
JSON representation
Single cell perturbation prediction
- Host: GitHub
- URL: https://github.com/theislab/scgen
- Owner: theislab
- License: gpl-3.0
- Created: 2018-11-28T17:59:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-12-05T12:08:14.000Z (about 1 year ago)
- Last Synced: 2025-09-29T13:01:45.999Z (4 months ago)
- Topics: bioinformatics, deep-learning, generative-model, scrna-seq, single-cell, single-cell-genomics, transcriptomics
- Language: Python
- Homepage: https://scgen.readthedocs.io
- Size: 69.3 MB
- Stars: 315
- Watchers: 8
- Forks: 65
- Open Issues: 31
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# scGen [](https://badge.fury.io/py/scgen) [](https://travis-ci.com/theislab/scGen) [](https://scgen.readthedocs.io/en/latest/?badge=latest) [](https://pepy.tech/project/scgen)
## Introduction
scGen is a generative model to predict single-cell perturbation response across cell types, studies and species
[(Nature Methods, 2019)](https://www.nature.com/articles/s41592-019-0494-8). scGen is implemented using the [scvi-tools framework](https://scvi-tools.org/).
## Getting Started
What you can do with scGen:
- Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type
which you only have in one condition. This scenario can be extended to multiple species where you want to predict
the effect of a specific species using another or all the species.
- Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset
with similar genes.
- Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to
the method. Note that `batch_removal` does not require all cell types to be present in all datasets (batches). If
you have dataset specific cell type it will preserved as before.
- We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with
your data and predict the perturbation for the cell type/species of interest.
- We recommend to use normalized data for the training. A simple example for normalization pipeline using scanpy:
```python
import scanpy as sc
adata = sc.read(data)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
```
- We further recommend to use highly variable genes (HVG). For the most examples in the paper we used top ~7000
HVG. However, this is optional and highly depend on your application and computational power.
## Installation
### Installation with pip
To install the latest version scGen via pip:
```bash
pip install scgen
```
or install the development version via pip:
```bash
pip install git+https://github.com/theislab/scgen.git
```
## Examples
See examples at our [documentation site](https://scgen.readthedocs.io/).
## Reproducing paper results
In order to reproduce paper results visit [here](https://github.com/M0hammadL/scGen_reproducibility).
## References
Lotfollahi, Mohammad and Wolf, F. Alexander and Theis, Fabian J.
**"scGen predicts single-cell perturbation responses."**
Nature Methods, 2019. [pdf](https://rdcu.be/bMlbD)