DeSide is a DEep-learning and SIngle-cell based DEconvolution method for solid tumors, which can be used to infer cellular proportions of different cell types from bulk RNA-seq data.
DeSide consists of the following four parts (see figure below):
- DNN Model
- Single Cell Dataset Integration
- Cell Proportion Generation
- Bulk Tumor Synthesis
In this repository, we provide the code for implementing these four parts and visualizing the results.
DeSide requires Python 3.8 or higher. It has been tested on Linux and MacOS, but should work on Windows as well.
- tensorflow>=2.11.1
- scikit-learn==0.24.2
- anndata>=0.8.0
- scanpy==1.8.0
- umap-learn==0.5.1
- pandas==1.5.3
- numpy>=1.22
- matplotlib
- seaborn>=0.11.2
- bbknn==1.5.1
- SciencePlots
- matplotlib<3.7
pip should work out of the box:
# creating a virtual environment is recommended
conda create -n deside python=3.8
conda activate deside
# update pip
python3 -m pip install --upgrade pip
# install deside
pip install desideUsage examples can be found: DeSide_mini_example
Three examples are provided:
- Using pre-trained model
- Training a model from scratch
- Generating a synthetic dataset
For all detailed documentation, please check https://deside.readthedocs.io/. The documentation will demonstrate the usage of DeSide from the following aspects:
- Installation in a virtual environment
- Usage examples
- Datasets used in DeSide
- Functions and classes in DeSide
DeSide can be used under the terms of the MIT License.
Any questions or suggestions about DeSide are welcomed! Please report it on issues, or contact Xin Xiong (onlybelter@outlook.com) or Xuefei Li (xuefei.li@siat.ac.cn).
@article {Xiong2023.05.11.540466,
author = {Xin Xiong and Yerong Liu and Dandan Pu and Zhu Yang and Zedong Bi and Liang Tian and Xuefei Li},
title = {DeSide: A unified deep learning approach for cellular decomposition of bulk tumors based on limited scRNA-seq data},
elocation-id = {2023.05.11.540466},
year = {2023},
doi = {10.1101/2023.05.11.540466},
URL = {https://www.biorxiv.org/content/early/2023/05/14/2023.05.11.540466},
eprint = {https://www.biorxiv.org/content/early/2023/05/14/2023.05.11.540466.full.pdf},
journal = {bioRxiv}
}