mssuite

The mssuite python package provides a framework for streamlined data analysis of TMT based shotgun proteomics data. The package is of modular nature and besides already implemented pipelines you can easily build your one data analysis pipeline, including automated plots. The package defaults to PSM/Peptide output files from ProteomeDiscoverer Software. However, it is easily customizable to your input files, by changing a few parameters.

Important

Patch Notes 1.4:

All classes accept a Defaults object during initialisation. Otherwise a native Defaults object is created with the default values. If you change the class variables in an initilized object, pass it to the other classes during init.

mssuite 1.4 now contains a peptide_based_lmm_multicore method. This method uses multiprocessing to speed up the data analysis significantly. You can set the number of used cores manually or the function will detect the number of available cores automatically.

Examples

In the filder "Examples" in this repository you will find Jupyter notebooks showing basic workflows using the mssuite package. It also contains example data to reproduce the example workflows.

Install

Using pip

mssuite has been uploaded to the PyPi repository and can be easily installed using the pip package manager:

pip install mssuite

Alternatively you can download the binaries and install locally using the following command:

!cd /PATH/TO/PACKAGE
pip install .

Compile from source

To compile from source, please download the package files and compile using python:

!cd /PATH/TO/PACKAGE
python3 -m build

Quickstart

Export your proteomics experiment on PSM or Peptide level from ProteomeDiscoverer Software as a tab-delimited text file. mssuite works with pandas dataframes, so you need to load your data as a pandas dataframe:

import pandas as pd

psms = pd.read_csv("PATH/TO/FILE.txt",sep='\t',header=0)

To calculate the proper statistics you need to specify the experimental conditions in the order they appear in your input file:

    conditions = ['Control','Control','Control','Treatment','Treatment','Treatment']

Lastly, you need to specify a working directory, where your output files are written:

    wd = 'YOUR/PATH'

To start the analysis you initialize the Pipelines module from the mssuite package and run the analysis:

pipe = Pipelines()
results = pipe.singlefile_lmm(psms, conditions, wd=wd)

The pipeline will now filter your input for contaminants and razor peptides, normalize the data, calculate differential expression analysis for all condition pairs by using a peptide-based linear mixed regression model and create automated plots (Clustermap, Abundances and Volcano plots). All output will be written to your specified working directory. The resulting dataframe will be written to a csv file and also returned to the results variable (see code above) for further use if intended.

Credits and Citation

Link to Publication

License

MIT License

Copyright (c) 2021 Kevin Klann

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 copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.