.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery_output/FunctionEnrichment/plot_specific_annotation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_output_FunctionEnrichment_plot_specific_annotation.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_output_FunctionEnrichment_plot_specific_annotation.py:


Assessing enriched PTM functions
=======================================================

As described in Running PTM-POSE section, PTM-POSE provides various options for annotating functional information for PTMs, coming from various databases. Often, we will want to dig deeper into the specific functions, processes, interactions, etc. associated with the proteins in our dataset. First, we can look at the annotations currently available for analysis, based on annotations that have been appended using the annotate module:

.. GENERATED FROM PYTHON SOURCE LINES 7-20

.. code-block:: Python


    from ptm_pose import helpers
    from ptm_pose.analyze import annotations
    import matplotlib.pyplot as plt
    import warnings
    warnings.filterwarnings("ignore")

    spliced_ptms, altered_flanks = helpers.load_example_data(spliced_ptms = True, altered_flanks = True)


    annot_categories = annotations.get_available_annotations(spliced_ptms)
    annot_categories






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Database</th>
          <th>Annotation Type</th>
          <th>Appended to PTM data?</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>iKiP</td>
          <td>Enzyme</td>
          <td>No</td>
        </tr>
        <tr>
          <th>1</th>
          <td>PhosphoSitePlus</td>
          <td>Enzyme</td>
          <td>No</td>
        </tr>
        <tr>
          <th>2</th>
          <td>PhosphoSitePlus</td>
          <td>Disease</td>
          <td>No</td>
        </tr>
        <tr>
          <th>3</th>
          <td>PhosphoSitePlus</td>
          <td>Interactions</td>
          <td>No</td>
        </tr>
        <tr>
          <th>4</th>
          <td>PhosphoSitePlus</td>
          <td>Function</td>
          <td>No</td>
        </tr>
        <tr>
          <th>5</th>
          <td>PhosphoSitePlus</td>
          <td>Process</td>
          <td>No</td>
        </tr>
        <tr>
          <th>6</th>
          <td>PhosphoSitePlus</td>
          <td>Perturbation</td>
          <td>No</td>
        </tr>
        <tr>
          <th>7</th>
          <td>PTMsigDB</td>
          <td>Perturbation-DIA</td>
          <td>No</td>
        </tr>
        <tr>
          <th>8</th>
          <td>PTMsigDB</td>
          <td>Perturbation-DIA2</td>
          <td>No</td>
        </tr>
        <tr>
          <th>9</th>
          <td>PTMsigDB</td>
          <td>Pathway-NetPath</td>
          <td>No</td>
        </tr>
        <tr>
          <th>10</th>
          <td>PTMsigDB</td>
          <td>Pathway-WikiPathways</td>
          <td>No</td>
        </tr>
        <tr>
          <th>11</th>
          <td>PTMsigDB</td>
          <td>Perturbation-PRM</td>
          <td>No</td>
        </tr>
        <tr>
          <th>12</th>
          <td>PTMsigDB</td>
          <td>Pathway-BI</td>
          <td>No</td>
        </tr>
        <tr>
          <th>13</th>
          <td>DEPOD</td>
          <td>Enzyme</td>
          <td>No</td>
        </tr>
        <tr>
          <th>14</th>
          <td>RegPhos</td>
          <td>Enzyme</td>
          <td>No</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 21-22

This will tell us what database information is available and the types of information from that database. Let's take a closer look at the biological process information from PhosphoSitePlus:

.. GENERATED FROM PYTHON SOURCE LINES 22-26

.. code-block:: Python

    ptms_with_annotation, annotation_counts = annotations.get_ptm_annotations(spliced_ptms, database = "PhosphoSitePlus", annotation_type = 'Process')
    print('Specific PTMs with annotation:')
    ptms_with_annotation





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    3 PTMs removed due to insignificant splice event (p < 0.05, dpsi >= 0.1): (33.33%)
    Final number of PTMs to be assessed: 6
    Specific PTMs with annotation:


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Gene</th>
          <th>UniProtKB Accession</th>
          <th>Residue</th>
          <th>PTM Position in Isoform</th>
          <th>Modification Class</th>
          <th>PhosphoSitePlus:Function</th>
          <th>dPSI</th>
          <th>Significance</th>
          <th>Impact</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>CEACAM1</td>
          <td>P13688</td>
          <td>S</td>
          <td>461.0</td>
          <td>Phosphorylation</td>
          <td>activity, inhibited</td>
          <td>0.525</td>
          <td>1.73943268451e-09</td>
          <td>Included</td>
        </tr>
        <tr>
          <th>1</th>
          <td>SPHK2</td>
          <td>Q9NRA0</td>
          <td>S</td>
          <td>419.0</td>
          <td>Phosphorylation</td>
          <td>intracellular localization</td>
          <td>0.253</td>
          <td>0.0129400018182</td>
          <td>Included</td>
        </tr>
        <tr>
          <th>2</th>
          <td>SPHK2</td>
          <td>Q9NRA0</td>
          <td>S</td>
          <td>421.0</td>
          <td>Phosphorylation</td>
          <td>intracellular localization</td>
          <td>0.253</td>
          <td>0.0129400018182</td>
          <td>Included</td>
        </tr>
        <tr>
          <th>3</th>
          <td>TSC2</td>
          <td>P49815</td>
          <td>S</td>
          <td>981.0</td>
          <td>Phosphorylation</td>
          <td>activity, inhibited;intracellular localization...</td>
          <td>-0.219</td>
          <td>4.18472157275e-05</td>
          <td>Excluded</td>
        </tr>
        <tr>
          <th>4</th>
          <td>YAP1</td>
          <td>P46937</td>
          <td>K</td>
          <td>342.0</td>
          <td>Ubiquitination</td>
          <td>protein degradation</td>
          <td>-0.188;-0.161</td>
          <td>0.000211254197372;4.17884655686e-07</td>
          <td>Excluded</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 27-28

From this, we note a total of 9 impacted PTMs from 7 genes that have biological process information available. While we could manually look through to look for common processes, we can also inspect the annotation counts object to see the most common processes, including a breakdown by the type of impact (included [dPSI > 0], excluded [dPSI < 0], or altered flanking sequence). We can also look at the number of PTMs associated with each annotation:

.. GENERATED FROM PYTHON SOURCE LINES 28-31

.. code-block:: Python

    print('Number of PTMs associated with each annotation:')
    annotation_counts





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Number of PTMs associated with each annotation:


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>All Impacted</th>
          <th>Included</th>
          <th>Excluded</th>
        </tr>
        <tr>
          <th>PhosphoSitePlus:Function</th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>intracellular localization</th>
          <td>3</td>
          <td>2.0</td>
          <td>1</td>
        </tr>
        <tr>
          <th>activity, inhibited</th>
          <td>2</td>
          <td>1.0</td>
          <td>1</td>
        </tr>
        <tr>
          <th>molecular association, regulation</th>
          <td>1</td>
          <td>0.0</td>
          <td>1</td>
        </tr>
        <tr>
          <th>protein degradation</th>
          <td>1</td>
          <td>0.0</td>
          <td>1</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 32-33

To better visualize the enriched functions, we can also plot the number of PTMs associated with each function:

.. GENERATED FROM PYTHON SOURCE LINES 33-44

.. code-block:: Python


    #plot annotatinos when not collapsing them
    fig, ax = plt.subplots(ncols = 2, figsize = (6, 3))
    fig.subplots_adjust(wspace = 2)
    annotations.plot_annotation_counts(spliced_ptms =spliced_ptms, altered_flanks = altered_flanks, ax = ax[0], collapse_on_similar = False, database = 'PhosphoSitePlus', annot_type = 'Process', top_terms = 10)
    ax[0].set_title('Full Annotation')
    #plot annotations when collapsing them
    annotations.plot_annotation_counts(altered_flanks = altered_flanks, ax = ax[1], collapse_on_similar = True, database = 'PhosphoSitePlus', annot_type = 'Process', top_terms = 10)
    ax[1].set_title('Collapsed Annotation')





.. image-sg:: /gallery_output/FunctionEnrichment/images/sphx_glr_plot_specific_annotation_001.png
   :alt: Full Annotation, Collapsed Annotation
   :srcset: /gallery_output/FunctionEnrichment/images/sphx_glr_plot_specific_annotation_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Text(0.5, 1.0, 'Collapsed Annotation')



.. GENERATED FROM PYTHON SOURCE LINES 45-46

We can also perform enrichment analysis to identify if any of the functions are more likely to be present then expected, using collapsed annotations to increase likelihood of finding enriched terms:

.. GENERATED FROM PYTHON SOURCE LINES 46-53

.. code-block:: Python


    enrichment = annotations.annotation_enrichment(spliced_ptms, database = 'PhosphoSitePlus', annotation_type = 'Function', collapse_on_similar=True)
    enrichment.head()









.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Fraction Impacted</th>
          <th>p-value</th>
          <th>Adjusted p-value</th>
          <th>PTM</th>
        </tr>
        <tr>
          <th>PhosphoSitePlus:Function</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>intracellular localization</th>
          <td>3/3006</td>
          <td>0.394168</td>
          <td>1.0</td>
          <td>SPHK2_S419;SPHK2_S421;TSC2_S981</td>
        </tr>
        <tr>
          <th>protein degradation</th>
          <td>1/1645</td>
          <td>0.710863</td>
          <td>1.0</td>
          <td>YAP1_K342</td>
        </tr>
        <tr>
          <th>activity</th>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>CEACAM1_S461;TSC2_S981</td>
        </tr>
        <tr>
          <th>molecular association</th>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>TSC2_S981</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.462 seconds)


.. _sphx_glr_download_gallery_output_FunctionEnrichment_plot_specific_annotation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_specific_annotation.ipynb <plot_specific_annotation.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_specific_annotation.py <plot_specific_annotation.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_specific_annotation.zip <plot_specific_annotation.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_