semvec/pypi-packages huggingface dataset:
Slim-TPCA 0.1.3
Slim-TPCA: a python package to expediate functional characterization of existing and newly identified protein complexes by optimizing existing TPCA algorithm implementations
Requires Python: >=3.7

[![Documentation Status](https://readthedocs.org/projects/slim_tpca/badge/?version=latest)](https://slim_tpca.readthedocs.io/en/latest/?badge=latest)
[![Build Status](https://app.travis-ci.com/mailhexu/slim_tpca.svg?branch=master)](https://app.travis-ci.com/mailhexu/slim_tpca)
[![Downloads](https://pepy.tech/badge/slim_tpca)](https://pepy.tech/project/slim_tpca)

## Description

Slim-TPCA package is a python package which requires python version higher than 3.7 to work. Slim-TPCA has been optimised based on the TPCA method published in 2018. By using fewer temperature points, Slim-TPCA can reduce the volume of samples required, eliminate the batch effect in multiplex mass spectrometry experiments, and greatly shorten the calculation time required. In the Slim-TPCA package, users can perform data pre-processing, graph ROC plots to determine the ability of the data to predict protein interactions, calculate the TPCA signatures of the complexes and dynamic modulations of the complexes.

The features include:
 - Calculates soluble fraction at each temperature.
 - Calculates distance between every two proteins
 - Based on the protein pair interaction Database, look for protein pairs where both proteins appear in the data.
 - Calculate parameters of the ROC curve.
 - Draw ROC plot based on parameters.
 - Look for complexes that meet the requirements of the analysis.
 - Calculate average distance between the subunit proteins of the complex.
 - Sample virtual random complexes for calculation.
 - Calculate TPCA signatures of complexes by sampling. 
 - Calculate TPCA signatures of complexes by fitting a beta distribution to random complexes.
 - Multiple sets of data may identify different proteins and align them here.
 - Calculate TPCA dynamic modulation signatures of complexes by sampling and absolute distance.
 - Calculate TPCA dynamic modulation signatures of complexes by sampling and relative distance.
 - Calculate TPCA dynamic modulation signatures of complexes by Beta distribution fitting and absolute distance.
 - Calculate TPCA dynamic modulation signatures of complexes by Beta distribution fitting and relative distance.

For more information, see the documentation on
 <https://slim-tpca.readthedocs.io/en/latest/index.html>

## Dependencies
* python (tested for ver 3.7)
* numpy 
* pandas 
* matplotlib 
* scipy 
* sklearn
* random 
* seaborn 
* copy
* re

## Installation
pip install Slim-TPCA 

from Slim_TPCA import Slim_TPCA 

## Message:
- We welcome contributions. If you would like to add the interface to other codes, or extend the capability of Slim-TPCA, please contact us! <11930100@mail.sustech.edu.cn>