fickludd / pyprophet

python reimplementation of mProphet

pyprophet

python reimplementation of mProphet algorithm.

In short, the algorithm can take targeted proteomics data, learn a linear separation between true signal and the noise signal and then compute a q-value (false discovery rate) to achieve experiment-wide cutoffs. The original algorithm as also extended with more options for classifiers, cross-validation, null-models and statistics calculations. For more information, see the following publications:

Reiter L, Rinner O, Picotti P, Hüttenhain R, Beck M, Brusniak MY, Hengartner MO, Aebersold R. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods. 2011 May;8(5):430-5. doi: 10.1038/nmeth.1584. Epub 2011 Mar 20.

Johan Teleman, Hannes Röst, George Rosenberger, Uwe Schmitt, Lars Malmström, Johan Malmström and Fredrik Levander. DIANA - algorithmic improvements for analysis of complex peptide sample data-independent acquisition MS data. Bioinformatics 2015 Feb 15;31(4):555-62. doi:10.1093/bioinformatics/btu686 Epub 2014 Oct 27.

Installation

Install pyprophet from Python package index:

    $ pip install numpy
    $ pip install pyprophet

or:

   $ easy_install numpy
   $ easy_install pyprophet

Running pyprophet

pyoprophet is not only a Python package, but also a command line tool:

   $ pyprophet --help

or:

   $ pyprophet --delim=tab tests/test_data.txt

Running tests

The pyprophet tests are best executed using py.test, to run the tests use:

    $ pip install pytest
    $ py.test tests