Merge pull request #17 from wfondrie/0.6.0rc

Final updates for the 0.6.0 release

README.md

@@ -27,9 +27,10 @@ For more information, check out our
 ## Citing  
 If you use mokapot in your work, please cite:  
 
-> Fondrie, W. E. & Noble, W. S. mokapot: Fast and flexible semi-supervised
-> learning for peptide detection. *bioRxiv* (2020)
-> doi:10.1101/2020.12.01.407270.
+> Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised
+> Learning for Peptide Detection. J Proteome Res (2021) doi:
+> 10.1021/acs.jproteome.0c01010. PMID: 33596079.
+> [Link](https://doi.org/10.1021/acs.jproteome.0c01010)
 
 ## Installation  
 
@@ -76,6 +77,3 @@ interpreter and affords greater flexibility:
 
 Check out our [documentation](https://mokapot.readthedocs.io) for more details
 and examples of mokapot in action.
-
-
-[![Built with Spacemacs](https://cdn.rawgit.com/syl20bnr/spacemacs/442d025779da2f62fc86c2082703697714db6514/assets/spacemacs-badge.svg)](http://spacemacs.org)  

docs/source/api/dataset.rst

@@ -1,5 +1,5 @@
-Datasets
-========
+Collections of PSMs
+===================
 .. automodule:: mokapot.dataset
    :members:
    :inherited-members:

docs/source/api/functions.rst

@@ -6,6 +6,7 @@ Functions
 Primary Functions
 -----------------
 .. autofunction:: read_pin
+.. autofunction:: read_pepxml
 .. autofunction:: brew
 
 

docs/source/api/index.rst

@@ -1,18 +1,26 @@
 Python API
 ==========
 
-The Python API enables maximum flexibility using mokapot. It also aids in
+The Python API enables maximum flexibility when using mokapot. It also aids in
 making analyses reproducible by easily integrating into Jupyter notebooks and
 Python scripts.
 
-Basic analyses with mokapot can be conducted using only the :ref:`Primary
-Functions`. PSMs not saved in the Percolator tab-delimited format can be loaded
-from a :py:class:`~pandas.DataFrame` using the :ref:`Dataset` classes. Finally,
-custom models can be created using the :ref:`Model` class.
+Read PSMs using the :py:func:`~mokapot.read_pin()` or
+:py:func:`~mokapot.read_pepxml()` functions for files in the Percolator
+tab-delimited format or PepXML format, respectively. Once a collection of PSMs
+has been read, the :py:func:`~mokapot.brew()` function will apply the mokapot
+algorithm to learn models from the PSMs and assign confidence estimates based on
+their new scores. Alternatively, the
+:py:meth:`~mokapot.dataset.LinearPsmDataset.assign_confidence()` method will
+assign confidence estimates to PSMs based on the best feature, which is often
+the primary score from the database search engine.
 
-Using :py:func:`mokapot.brew()` or the
-:py:meth:`~mokapot.datasets.LinearPsmDataset.assign_confidence()` method return
-objects that contain :ref:`Confidence Estimates`.
+Alternatively, PSMs that are already represented in a
+:py:class:`pandas.DataFrame` can be directly used to create a
+:py:class:`~mokapot.dataset.LinearPsmDataset`.
+
+Finally, custom machine learning models can be created using the
+:py:class:`mokapot.model.Model` class.
 
 .. toctree::
    :maxdepth: 1
@@ -35,6 +43,7 @@ Primary Functions
    :nosignatures:
 
    read_pin
+   read_pepxml
    brew
 
 Utility Functions
@@ -50,26 +59,41 @@ Utility Functions
    digest
 
 
-Model
------
+Machine Learning Models
+-----------------------
+
+Use a model that emulates the Linear support vector machine used by Percolator
+or create a custom model from anything with a Scikit-Learn interface.
+
 .. currentmodule:: mokapot.model
 .. autosummary::
    :nosignatures:
 
-   Model
    PercolatorModel
+   Model
+
+
+Collections of PSMs
+-------------------
+
+PSMs can be parsed from Percolator tab-delimited files, PepXML files, or
+directly from a :py:class:`pandas.DataFrame`.
 
-Dataset
--------
 .. currentmodule:: mokapot.dataset
 .. autosummary::
    :nosignatures:
 
    LinearPsmDataset
    .. CrossLinkedPsmDataset
 
+
 Confidence Estimates
 --------------------
+
+An analysis with mokapot yields two forms of confidence estimates---q-values and
+posterior error probabilities (PEPs)---at various levels: PSMs, peptides, and
+optionally, proteins.
+
 .. currentmodule:: mokapot.confidence
 .. autosummary::
    :nosignatures:

docs/source/api/model.rst

@@ -1,4 +1,4 @@
-Model
-=====
+Machine Learning Models
+=======================
 .. automodule:: mokapot.model
 

docs/source/conf.py

@@ -67,7 +67,7 @@
 intersphinx_mapping = {
     "pandas": ("https://pandas.pydata.org/docs", None),
     "sklearn": ("https://scikit-learn.org/stable", None),
-    "numpy": ("https://docs.scipy.org/doc/numpy", None),
+    "numpy": ("https://numpy.org/doc/stable", None),
     "matplotlib": ("https://matplotlib.org", None),
 }
 

docs/source/index.rst

@@ -30,10 +30,10 @@ Citing
 If you use mokapot in your work, please cite:
 
 .. epigraph::
-   Fondrie, W. E. & Noble, W. S. mokapot: Fast and flexible semi-supervised
-   learning for peptide detection. bioRxiv 2020.12.01.407270 (2020)
-   doi:10.1101/2020.12.01.407270. `[Link]
-   <https://www.biorxiv.org/content/10.1101/2020.12.01.407270v2>`_
+   Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised
+   Learning for Peptide Detection. J Proteome Res (2021) doi:
+   10.1021/acs.jproteome.0c01010. PMID: 33596079. `[Link]
+   <https://doi.org/10.1021/acs.jproteome.0c01010>`_
 
 
 Introduction
@@ -86,6 +86,7 @@ mokapot also depends on several Python packages:
 - `scikit-learn <https://scikit-learn.org/stable/>`_
 - `Numba <http://numba.pydata.org/>`_
 - `triqler <https://github.com/statisticalbiotechnology/triqler>`_
+- `lxml <https://lxml.de/>`_
 
 
 We recommend using `conda` to install mokapot. Missing dependencies will also
@@ -106,19 +107,19 @@ Basic Usage
 -----------
 
 Before you can use mokapot, you need PSMs assigned by a search engine available
-in the `Percolator tab-delimited file format
+in either the `Percolator tab-delimited file format
 <https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format>`_
-(often referred to as the Percolator input, or "PIN", file format). These files can
-be generated from various search engines, such as `Comet
-<http://comet-ms.sourceforge.net/>`_ or `Tide
+(often referred to as the Percolator input, or "PIN", file format) or as a
+PepXML file. These files can be generated from various search engines, such as
+`Comet <http://comet-ms.sourceforge.net/>`_ or `Tide
 <http://crux.ms/commands/tide-search.html>`_ (which is part of the `Crux mass
 spectrometry toolkit <http://crux.ms>`_).
 
 If you need an example file to get started with, a selection of PSMs from
-Hogrebe et al. [1]_ is available to download from the mokapot
-repository, `phospho_rep1.pin
-<https://github.com/wfondrie/mokapot/raw/master/tests/data/phoshpo_rep1.pin>`_. This
-is the file we'll use in the examples below.
+Hogrebe et al. [1]_ is available to download from the mokapot repository,
+`phospho_rep1.pin
+<https://github.com/wfondrie/mokapot/raw/master/tests/data/phoshpo_rep1.pin>`_.
+This is the file we'll use in the examples below.
 
 
 Run **mokapot** from the command line
@@ -155,7 +156,7 @@ Then conduct your mokapot analysis:
    >>> results.to_txt()
 
 This is great for when your upstream and/or downstream analyses are also
-conducted in Python. Additionally, a great deal more flexibility is available
+conducted in Python. Additionally, a good deal more flexibility is available
 when using mokapot from the Python interpreter. For more details, see the
 :doc:`Python API <api/index>`.