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

.. only:: html

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

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

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

.. _sphx_glr_gallery_biology_plot_ppi.py:


Protein Protein Interactions
============================

This example demonstrates how to visualise Protein Protein Interaction (PPI) networks using ``iplotx``.

We use ``igraph`` to download the physical PPI network of *E. coli* from STRING database. The same thing could be done with ``networkx``.
We also subsample the network to just 300 random interactions to keep it reasonably quick, but different filtering strategies are possible.

.. GENERATED FROM PYTHON SOURCE LINES 10-48



.. image-sg:: /gallery/biology/images/sphx_glr_plot_ppi_001.png
   :alt: plot ppi
   :srcset: /gallery/biology/images/sphx_glr_plot_ppi_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import igraph as ig
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import iplotx as ipx


    # Load the data with the adjacency matrix. 1 means that pollinator j visits plant i.
    edge_data = pd.read_csv("https://stringdb-downloads.org/download/protein.physical.links.v12.0/511145.protein.physical.links.v12.0.txt.gz", sep=" ")
    edge_data["weight"] = 1.0 * edge_data["combined_score"] / edge_data["combined_score"].max()

    # Subset just to keep it quick as an example
    edge_data = edge_data.sample(300, random_state=42)

    # Convert it into a DataFrame for igraph
    g = ig.Graph.DataFrame(edge_data, directed=False, use_vids=False)

    # Compute layout
    layout = g.layout_fruchterman_reingold()

    # Highlight hubs
    degrees = np.array(g.vs.degree()).astype(float)
    vertex_size = 3 + 27 * degrees / degrees.max()
    vertex_alpha = np.clip(0.2 + 0.8 * degrees / degrees.max(), 0, 1)
    vertex_color = ["red" if d > degrees.max() / 2 else "black" for d in degrees]

    # Visualise
    fig, ax = plt.subplots(figsize=(7, 7))
    ipx.network(
        g,
        layout=layout,
        ax=ax,
        vertex_size=vertex_size,
        vertex_alpha=vertex_alpha,
        vertex_facecolor=vertex_color,
    )
    fig.tight_layout()


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

   **Total running time of the script:** (0 minutes 1.456 seconds)


.. _sphx_glr_download_gallery_biology_plot_ppi.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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