pyprophet 0.13.2

Python reimplementation of mProphet peak scoring

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.](http://dx.doi.org/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
```

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
```