| Name | DeSide JSON |
| Version |
1.2.2
JSON |
| download |
| home_page | |
| Summary | A DEep-learning and SIngle-cell based DEconvolution method for solid tumors |
| upload_time | 2024-02-03 13:44:37 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2022 Xin Xiong Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
deside
rna-seq
deconvolution
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# DeSide: Cellular Deconvolution of Bulk RNA-seq
<img src="https://raw.githubusercontent.com/OnlyBelter/DeSide/main/docs/_static/logo.png" width="300">
## What is DeSide?
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
<img src="https://raw.githubusercontent.com/OnlyBelter/DeSide/main/Fig.1a_b.svg" width="800" alt="Overview of DeSide">
In this repository, we provide the code for implementing these four parts and visualizing the results.
## Requirements
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
## Installation
pip should work out of the box:
```shell
# 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 deside
```
## Usage Examples
Usage examples can be found: [DeSide_mini_example](https://github.com/OnlyBelter/DeSide_mini_example)
Three examples are provided:
- Using pre-trained model
- Training a model from scratch
- Generating a synthetic dataset
## Documentation
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
## License
DeSide can be used under the terms of the MIT License.
## Contact
Any questions or suggestions about DeSide are welcomed! Please report it on [issues](https://github.com/OnlyBelter/DeSide/issues), or contact Xin Xiong (onlybelter@outlook.com) or Xuefei Li (xuefei.li@siat.ac.cn).
## Manuscript
```text
@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}
}
```
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