https://github.com/mfitzp/padua

Proteomic Data Analysis with Python (pandas, scikit-learn, numpy, scipy)
https://github.com/mfitzp/padua

Last synced: 16 days ago
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Proteomic Data Analysis with Python (pandas, scikit-learn, numpy, scipy)

• Host: GitHub
• URL: https://github.com/mfitzp/padua
• Owner: mfitzp
• License: bsd-2-clause
• Created: 2016-08-16T09:46:26.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:23:06.000Z (over 1 year ago)
• Last Synced: 2024-10-15T04:32:52.668Z (29 days ago)
• Language: Python
• Size: 1.91 MB
• Stars: 23
• Watchers: 5
• Forks: 10
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-proteomics - PaDuA - python - proteomics and phosphoproteomics data analysis - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.8b00576) (6. Stastical Analysis / Table of Contents)

README

        
![PaDuA](PaDuA.png)

A Python package for Proteomic Data Analysis, offering processing and analysis of the output of proteomics software [MaxQuant](http://maxquant.org).

# Installation

PaDuA is available via the Python package index at [PyPi](http://pypi.org) and can be installed in the usual way with:

    pip install padua

    

Once installed the package is available for import using:

    import padua

    

The package is organised into multiple submodules for different purposes, eg.

1. `io` for reading and writing both MaxQuant and Perseus format files (input/output)

1. `filters` for filtering data by quality and features

1. `process` incorporating experimental design, labels to index, expand-side-table (Perseus) and more

1. `normalization` for performing normalisation methods, e.g. remove column median

1. `annotations` adding annotation metadata for quantified proteins

1. `analysis` performing simple analyses, e.g. column correlations

1. `plots` standard plot outputs for overviews of data

# What is it for?

The goal is to provide a simple scripting approach to replicate many of the common steps for interacting with the output

of MaxQuant. Many of the steps implemented are based on similar steps used in the MaxQuant sister software 

[Perseus](http://141.61.102.17/perseus_doku/). While currently Perseus has more features, it has stability issues with

the larger datasets we are currently using. Having the processing steps implemented in Python allows for simple 

processing workflow scripts to be created and re-used.

# Examples

An example Phosphoproteomic label-free-quantification workflow would be as follows:

    import padua

    df = padua.io.read_maxquant('Phospho (STY)Sites.txt')

    df = padua.filter.filter_localization_probability(df)

    df = padua.filter.remove_reverse(df)

    df = padua.filter.remove_only_identified_by_site(df)

    df = padua.filter.remove_potential_contaminants(df)

    # Use standard Pandas dataframe manipulations to set an index

    df.set_index('Proteins', inplace=True)

    df = df.filter(regex='Intensity ')

    df = df.process.expand_side_table(df)

    # Remove the multiplicity column

    df = df.filter(regex='Intensity ')

    df = padua.process.apply_experimental_design(df, 'experimentalDesignTable.txt')

    # The result of this step will be a multilevel index Class, Replicate

    # built by matching sample labels using regex

    indices = [

        ('Class': '^(.*)_',

        ('Replicate': '_(\d)', 

    ]

    df = padua.process.build_index_from_labels(df, indices)

# Future

Provided functions are based on our current requirements, but will be expanded in future. 

# License

PaDuA is open source software and available under the BSD 2-clause (Simplified) license.