DeSide 1.0.2

A DEep-learning and SIngle-cell based DEconvolution method for solid tumors

DeSide: Cellular Deconvolution of Bulk RNA-seq

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

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>=2.8.0
• scikit-learn==0.24.0
• anndata>=0.8.0
• scanpy==1.8.0
• pandas==1.2.5
• numpy<1.22
• matplotlib
• seaborn>=0.11.2
• bbknn==1.5.1
• SciencePlots
• matplotlib<3.7

Installation

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 deside

Usage Examples

Usage examples can be found: 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, or contact Xin Xiong ([e-mail]) or Xuefei Li ([e-mail]).

Manuscript

@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}
}