.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/biology/plot_antibody_clone.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_antibody_clone.py>`
        to download the full example code.

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

.. _sphx_glr_gallery_biology_plot_antibody_clone.py:


Antibody clones
===============

This example demonstrates how to visualise the antibodies of a large repertoire clone with ``iplotx``.

This particular example uses igraph to load and process the network data, but you can also use networkx, the internal
data structures of ``iplotx``, or any other library you prefer.

Data source: Horns et al., Quake. et al. (2016): https://elifesciences.org/articles/16578.

.. GENERATED FROM PYTHON SOURCE LINES 12-78

.. code-block:: Python


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


    # The original data format is a JSON file with source: {target1: distance1, target2: distance2, ...}
    # We convert it into a DataFrame for igraph
    with open("data/80201010000000001.mst") as handle:
        data = json.load(handle)
    edge_data = {"source": [], "target": [], "distance": []}
    for source, target_dict in data.items():
        for target, distance in target_dict.items():
            edge_data["source"].append(source)
            edge_data["target"].append(target)
            edge_data["distance"].append(distance)
    edge_data = pd.DataFrame(edge_data)
    edge_data["weight"] = 1 / edge_data["distance"]  # Invert distance to get some kind of weight

    # NOTE: This particular format is a directed graph, from germline antibody to hypermutated antibody
    g = ig.Graph.DataFrame(edge_data, directed=True, use_vids=False)

    # Color nodes by distance from the germline antibody
    germline = "8031,NA,germline,NA"
    depths = {germline: 0.0}
    to_visit = [germline]
    while to_visit:
        node = to_visit.pop()
        for child, dist in data.get(node, {}).items():
            depths[child] = depths[node] + dist
            to_visit.append(child)
    depth_max = max(depths.values())
    colors = [depths[name] for name in g.vs["name"]]

    # Compute bipartite layout
    layout = g.layout_fruchterman_reingold()

    fig, ax = plt.subplots(figsize=(8, 7))
    artist = ipx.network(
        g,
        layout=layout,
        ax=ax,
        vertex_facecolor=colors,
        vertex_cmap=plt.cm.copper,
        vertex_alpha=0.5,
        vertex_size=5,
        edge_alpha=0.2,
        edge_arrow_width=2,
    )[0]
    fig.colorbar(
        artist.get_vertices(),
        ax=ax,
        label="Distance from germline\n[# mutations]",
        aspect=10,
        shrink=0.5,
    )

    # Label the germline antibody for clarity
    coords_germline = layout[g.vs["name"].index(germline)]
    ax.scatter([coords_germline[0]], [coords_germline[1]], color="tomato", s=80, marker="*", label="Germline")
    ax.legend()
    fig.tight_layout()





.. image-sg:: /gallery/biology/images/sphx_glr_plot_antibody_clone_001.png
   :alt: plot antibody clone
   :srcset: /gallery/biology/images/sphx_glr_plot_antibody_clone_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 79-81

This graph turns out to be a tree, so we can revisualise the same data using a tree layout. As an example,
we use our internal tree data structure, which is a glorified dictionary:

.. GENERATED FROM PYTHON SOURCE LINES 81-112

.. code-block:: Python


    tree = {
        "children": [],
        "name": germline,
        "branch_length": 0.0,
    }
    to_visit = [(tree, germline)]
    while to_visit:
        node, key = to_visit.pop()
        for child, dist in data.get(key, {}).items():
            child_node = {"children": [], "branch_length": dist, "name": child}
            node["children"].append(child_node)
            to_visit.append((child_node, child))
    tree = ipx.ingest.providers.tree.simple.SimpleTree.from_dict(tree)

    fig, ax = plt.subplots(figsize=(5, 10))
    artist = ipx.tree(
        tree,
        ax=ax,
        edge_color="branch_length",
        edge_cmap=plt.cm.plasma,
    )
    fig.colorbar(
        artist.get_edges(),
        ax=ax,
        label="# mutations\non branch",
        fraction=0.07,
        aspect=10,
        shrink=0.5,
    )
    fig.tight_layout()



.. image-sg:: /gallery/biology/images/sphx_glr_plot_antibody_clone_002.png
   :alt: plot antibody clone
   :srcset: /gallery/biology/images/sphx_glr_plot_antibody_clone_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 21.212 seconds)


.. _sphx_glr_download_gallery_biology_plot_antibody_clone.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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