Metadata-Version: 2.1
Name: pcser
Version: 0.0.1
Summary: protein corona stealth effect prediction
License: GPL-3.0
Author: Jianfeng Sun
Author-email: jianfeng.sunmt@gmail.com
Requires-Python: >=3.11,<4.0
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: click (>=8.1.7,<9.0.0)
Requires-Dist: llvmlite (==0.40.1)
Requires-Dist: matplotlib (>=3.8.3,<4.0.0)
Requires-Dist: numpy (==1.24.1)
Requires-Dist: pyfiglet (>=1.0.2,<2.0.0)
Requires-Dist: scikit-learn (>=1.4.1.post1,<2.0.0)
Requires-Dist: seaborn (>=0.13.2,<0.14.0)
Requires-Dist: shap (>=0.44.1,<0.45.0)
Description-Content-Type: text/markdown

<h1 align="center">
    <img src="https://github.com/2003100127/pcser/blob/main/img/pcser-logo.png?raw=true" width="200" height="70">
    <br>
</h1>


![PyPI](https://img.shields.io/pypi/v/pcser?logo=PyPI)
![](https://img.shields.io/github/stars/cribbslab/pcser?logo=GitHub&color=blue)
[![Downloads](https://pepy.tech/badge/pcser)](https://pepy.tech/project/pcser)

<hr>


![Python](https://img.shields.io/badge/-Python-000?&logo=Python)
![PyPI](https://img.shields.io/badge/-PyPI-000?&logo=PyPI)

###### tags: `protein corona` `nanoparticles` `stealth effect` `machine learning`

## Overview
PCSER is a computational tool for predicting protein corona stealth effects. It was built using the random forest machine learning approach.

## 📔 Documentation
Please check https://2003100127.github.io/pcser for how to use PCSER.

## 🛠️ Installation

PCSER can be installed in the following ways.

* ![PyPI](https://img.shields.io/badge/-PyPI-000?&logo=PyPI) (https://pypi.org/project/pcser)

  ```bash
  conda create --name pcser python=3.11
      
  conda activate pcser
  
  pip install pcser --upgrade
  ```

* ![Github](https://img.shields.io/badge/-Github-000?&logo=Github)

  ```bash
  conda create --name pcser python=3.11
    
  conda activate pcser
  
  git clone https://github.com/2003100127/pcser.git
  
  cd pcser
  
  pip install .
  ```

## 🚀 Quick start

```python
import pcser as pcs

pcs.load.evaluate(
    data_ref_fpn='./Proteomics_07262023_rv_C57BL6_spl54.xlsx',
    sv_fp='./',  # None to('data/')
    input_fpn='./example.xlsx',
    model_fpn='./best_cv.joblib',
    sheet_name='a', # a b
    # mfi_ref=[10271.33333, 10747, 10303.33333, 9663.333333, 10056],
    mfi_ref=[3606.333333, 3606.333333, 3606.333333, 3606.333333],

    # is_norm=True,
    # norm_met='minmax',  # minmax std maxabs
    # mode='compo',  # compo annot
    # mark='spl54',  # spl54 spl63
    # version='extended',  # extended old
)
```

Then, it outputs what is shown below.

```python
# You are using extended sheets.
# You have selected the minmax normalization method.
# Data summary:
# Number of samples: 54
# Number of features: 419
# You have the samples: ['HuApoA1', 'MoApoA1', 'HuClusterin', 'MoClusterin']
# PCSER predictions: 
#              stealth_effect          MFI
# HuApoA1            0.670762  3099.790003
# MoApoA1            0.662108  3189.458730
# HuClusterin        0.634621  3474.270396
# MoClusterin        0.633914  3481.599008
# stealth_effect	MFI
# HuApoA1	0.670762	3099.790003
# MoApoA1	0.662108	3189.458730
# HuClusterin	0.634621	3474.270396
# MoClusterin	0.633914	3481.599008
```

## 📄 Citation
```angular2html
@article{PCSER,
    title = {PCSER},
    author = {Jianfeng Sun},
    doi = {xxx},
    url = {https://github.com/2003100127/pcser},
    journal = {xxx}
    year = {2024},
}
```

## 🏠 Homepage
[📍Oxford University](https://www.ndorms.ox.ac.uk/team/jianfeng-sun) 

## 📧 Reach us
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