igraph_cx_experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b01f5fc2-3390-4c06-8880-6b1bceab62b3",
   "metadata": {},
   "source": [
    "## igraph-CX experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "356a3579-9dc9-47ad-b40a-4cc356f28016",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ndex2\n",
    "import matplotlib.pyplot as plt\n",
    "import igraph\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "456cdd68-4076-43ca-92e3-58ba163c9a42",
   "metadata": {},
   "source": [
    "## Get a test network from NDEx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c27559f4-1e5c-47b1-a06c-6be89fd7aff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Sonic Hedgehog\n",
      "Nodes: 18\n",
      "Edges: 45\n",
      "Node Attributes: 36\n",
      "Edge Attributes: 450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "NETWORK_UUID = \"cfd3528f-a7a4-11eb-9e72-0ac135e8bacf\"\n",
    "SERVER = \"https://ndexbio.org\"\n",
    "test_network = ndex2.create_nice_cx_from_server(SERVER, uuid=NETWORK_UUID)\n",
    "test_network.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302703c9-8e55-4d36-9a7e-8fd7e8e6bb2a",
   "metadata": {},
   "source": [
    "## Make an igraph Graph object from the NiceCX object\n",
    "- make a Graph with the same number of nodes\n",
    "- set the Graph attributes to the CX network attributes\n",
    "- iterate over the CX node list, incrementing the node index for the Graph\n",
    " - set a special \"cx_id\" attribute with the CX permanent ID\n",
    " - set the other node attributes to the CX node attributes\n",
    " - *right now,* I rely on Python default type assignment, not looking at the CX type declaration\n",
    "- iterate over the CX edge list, incrementing an index for the Graph edge.\n",
    " - find the Graph node ids for the source and target CX ids \n",
    " - add an edge to the Graph and set its attributes\n",
    " - get the CX style aspect and add it to the Graph as a network attribute\n",
    " - TODO: add the other opaque aspects, especially the style aspect\n",
    " \n",
    "## TODO: CX2\n",
    "Maybe optimization of this function to (1) avoid making a NiceCX object and (2) use streaming could wait for CX2.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6b221c40-824d-4d7a-b5b7-c8cd22933486",
   "metadata": {},
   "outputs": [],
   "source": [
    "def igraph_from_nice_cx(nice_cx):\n",
    "    g = igraph.Graph(len(nice_cx.get_nodes()))\n",
    "    # network attributes\n",
    "    for name in nice_cx.get_network_attribute_names():\n",
    "        value = nice_cx.get_network_attribute(name)[\"v\"]\n",
    "        #print(value)\n",
    "        g[name]=value      \n",
    "    # nodes and attributes and coordinates\n",
    "    # make a node_id list\n",
    "    node_id_list = []\n",
    "    n_id = 0\n",
    "    for node_id, node in nice_cx.get_nodes():\n",
    "        g.vs[n_id][\"cx_id\"] = node_id\n",
    "        if node.get(\"n\"):\n",
    "            g.vs[n_id][\"name\"] = node[\"n\"]\n",
    "        if node.get(\"r\"):\n",
    "            g.vs[n_id][\"represents\"] = node[\"r\"]\n",
    "        atts = nice_cx.get_node_attributes(node_id)\n",
    "        for att in atts:\n",
    "            att_name = att[\"n\"]\n",
    "            att_value = att[\"v\"]\n",
    "            g.vs[n_id][att_name] = att_value\n",
    "        n_id += 1\n",
    "        \n",
    "    # edges and attributes\n",
    "    for edge_id, edge in nice_cx.get_edges():\n",
    "        cx_source_id = edge[\"s\"]\n",
    "        cx_target_id = edge[\"t\"]\n",
    "        g_source = g.vs.find(cx_id=cx_source_id)\n",
    "        g_target = g.vs.find(cx_id=cx_target_id)\n",
    "        g.add_edges([(g_source, g_target)])\n",
    "        e_id = g.get_eid(g_source, g_target)\n",
    "        g.es[e_id][\"cx_id\"] = edge_id\n",
    "        atts = nice_cx.get_edge_attributes(node_id)\n",
    "        for att in atts:\n",
    "            att_name = att[\"n\"]\n",
    "            att_value = att[\"v\"]\n",
    "            g.es[e_id][att_name] = att_value\n",
    "\n",
    "    # cartesian coordinates\n",
    "    g[\"cartesian_layout\"] = nice_cx.get_opaque_aspect(ndex2.constants.CARTESIAN_LAYOUT_ASPECT)\n",
    "    #visual styles\n",
    "    # bypasses\n",
    "    # rules\n",
    "    return g\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b1afca7-89ed-405d-a8bf-9d3d7fcd658e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IGRAPH UN-T 18 45 -- Sonic Hedgehog\n",
      "+ attr: @context (g), __normalizationversion (g), cartesian_layout (g), description (g), labels (g), name (g), networkType (g), organism (g), prov:wasDerivedFrom (g), prov:wasGeneratedBy (g), reference (g), rights (g), rightsHolder (g), version (g), cx_id (v), location (v), name (v), represents (v), type (v), annotator (e), cell_data (e), citation (e), cx_id (e), direct (e), mechanism (e), notes (e), residue (e), sentence (e), sequence (e), tissue_data (e)\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 0, {'cx_id': 0, 'name': 'GLI1', 'represents': 'uniprot:P08151', 'type': 'protein', 'location': 'factor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 1, {'cx_id': 1, 'name': 'Cell_growth', 'represents': 'signor:SIGNOR-PH33', 'type': 'phenotype', 'location': ''})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 2, {'cx_id': 2, 'name': 'ADCY1', 'represents': 'uniprot:Q08828', 'type': 'protein', 'location': 'receptor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 3, {'cx_id': 3, 'name': \"3',5'-cyclic AMP\", 'represents': 'CHEBI:17489', 'type': 'smallmolecule', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 4, {'cx_id': 4, 'name': 'GNAI2', 'represents': 'uniprot:P04899', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 5, {'cx_id': 5, 'name': 'SUFU', 'represents': 'uniprot:Q9UMX1', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 6, {'cx_id': 6, 'name': 'GLI3', 'represents': 'uniprot:P10071', 'type': 'protein', 'location': 'factor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 7, {'cx_id': 7, 'name': 'GSK3B', 'represents': 'uniprot:P49841', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 8, {'cx_id': 8, 'name': 'PRKACA', 'represents': 'uniprot:P17612', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 9, {'cx_id': 9, 'name': 'KIF7', 'represents': 'uniprot:Q2M1P5', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 10, {'cx_id': 10, 'name': 'SMO', 'represents': 'uniprot:Q99835', 'type': 'protein', 'location': 'receptor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 11, {'cx_id': 11, 'name': 'GLI2', 'represents': 'uniprot:P10070', 'type': 'protein', 'location': 'factor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 12, {'cx_id': 12, 'name': 'HHIP', 'represents': 'uniprot:Q96QV1', 'type': 'protein', 'location': 'extracellular'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 13, {'cx_id': 13, 'name': 'IHH', 'represents': 'uniprot:Q14623', 'type': 'protein', 'location': 'extracellular'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 14, {'cx_id': 14, 'name': 'SHH', 'represents': 'uniprot:Q15465', 'type': 'protein', 'location': 'extracellular'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 15, {'cx_id': 15, 'name': 'PTCH1', 'represents': 'uniprot:Q13635', 'type': 'protein', 'location': 'receptor'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 16, {'cx_id': 16, 'name': 'DISP1', 'represents': 'uniprot:Q96F81', 'type': 'protein', 'location': 'cytoplasm'})\n",
      "igraph.Vertex(<igraph.Graph object at 0x11cbdc040>, 17, {'cx_id': 17, 'name': 'Cyclopamine', 'represents': 'CHEBI:4021', 'type': 'chemical', 'location': 'cytoplasm'})\n"
     ]
    }
   ],
   "source": [
    "g = igraph_from_nice_cx(test_network)\n",
    "igraph.summary(g)\n",
    "for v in g.vs:\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c22ba358-6711-4154-81d3-3d737ba9cc81",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Each time you plot, you have to create a fresh layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f8334f8c-a023-4e43-bc29-522a841dfafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Layout with 18 vertices and 2 dimensions>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def igraph_layout_from_cx_cartesian_layout(g):\n",
    "    cx_layout = g[\"cartesian_layout\"]\n",
    "    igraph_layout = [(0,0)] * len(g.vs)\n",
    "    for node in cx_layout:\n",
    "        igraph_id = g.vs.find(node[\"node\"]).index\n",
    "        igraph_layout[igraph_id] = (node[\"x\"],node[\"y\"])\n",
    "    return igraph.Layout(igraph_layout)\n",
    "\n",
    "l = igraph_layout_from_cx_cartesian_layout(g)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6184b214-6ff7-4be3-93d6-bc5a67c88c5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = igraph_layout_from_cx_cartesian_layout(g)\n",
    "fig, ax = plt.subplots()\n",
    "igraph.plot(g, layout=layout, target=ax, vertex_label=g.vs[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4524386e-c8a3-4d7e-ae52-4bea2c3db61e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8df6f0ae-ab9f-4983-9e3c-9154ffefa11d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6090e3c9-84cf-49d2-9bb4-36628f8a6213",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}