Slim-TPCA: a python package to expediate functional characterization of existing and newly identified protein complexes by optimizing existing TPCA algorithm implementations
Project description
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
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
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