Slim-TPCA 0.1.1

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

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! [e-mail]