Metadata-Version: 2.1
Name: DeSide
Version: 1.0.1
Summary: A DEep-learning and SIngle-cell based DEconvolution method for solid tumors
Home-page: 
Author: Xin (Belter) Xiong
Author-email: OnlyBelter <onlybelter@outlook.com>
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
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/OnlyBelter/DeSide
Project-URL: Source, https://github.com/OnlyBelter/DeSide
Project-URL: Tracker, https://github.com/OnlyBelter/DeSide/issues
Keywords: DeSide,RNA-seq,deconvolution
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Typing :: Typed
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: full
License-File: LICENSE

# DeSide <img src="https://raw.githubusercontent.com/OnlyBelter/DeSide/main/docs/_static/logo.png" width="50">

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>=2.8.0
- 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

### Installation

pip should work out of the box:
```
# create a virtual environment if necessary
conda create -n deside python=3.8
conda activate deside
pip install deside
```

### Documentation
Documentation is available either in the source tree (doc/), or online. (will be available soon)


### 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

### License
DeSide can be used under the terms of the MIT License.
