pyprophet

python reimplementation of mProphet algorithm. For more information, see the following publication:

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.

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.

Installation

Install pyprophet from Python package index:

    $ pip install numpy
    $ pip 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 and pytest-regtest plugin, to run the tests use:

    $ pip install pytest
    $ pip install pytest-regtest
    $ py.test tests