Network_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = pd.read_csv('data/cell_lines_corr-above0-5-0-323.net', sep='\\t')\n",
    "node_lookup = pd.read_csv('data/cell_lines_corr-above0-5-0-323.nodes', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Node_ID</th>\n",
       "      <th>DepMap_ID</th>\n",
       "      <th>Primary Disease</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Cancer_subtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>ACH-000012</td>\n",
       "      <td>Lung Cancer</td>\n",
       "      <td>Female</td>\n",
       "      <td>NSCLC-Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>ACH-000019</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Female</td>\n",
       "      <td>Carcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13</td>\n",
       "      <td>ACH-000021</td>\n",
       "      <td>Lung Cancer</td>\n",
       "      <td>Female</td>\n",
       "      <td>NSCLC-Adenocarcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>ACH-000028</td>\n",
       "      <td>Breast Cancer</td>\n",
       "      <td>Female</td>\n",
       "      <td>Carcinoma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>ACH-000029</td>\n",
       "      <td>Lung Cancer</td>\n",
       "      <td>-1</td>\n",
       "      <td>NSCLC-Adenocarcinoma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Node_ID   DepMap_ID Primary Disease  Gender        Cancer_subtype\n",
       "0        6  ACH-000012     Lung Cancer  Female  NSCLC-Adenocarcinoma\n",
       "1       12  ACH-000019   Breast Cancer  Female             Carcinoma\n",
       "2       13  ACH-000021     Lung Cancer  Female  NSCLC-Adenocarcinoma\n",
       "3       16  ACH-000028   Breast Cancer  Female             Carcinoma\n",
       "4       17  ACH-000029     Lung Cancer      -1  NSCLC-Adenocarcinoma"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_lookup.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_positions(thresh_stack):\n",
    "\n",
    "  # Create a networkx graph from the list of pairs\n",
    "  G=nx.from_pandas_edgelist(thresh_stack, '#SOURCE', '#TARGET', ['#WEIGHT'])\n",
    "\n",
    "  # Generate position data for each node:\n",
    "  #pos=layout(G)\n",
    "  # if weighted:\n",
    "  pos=nx.spring_layout(G, weight='#WEIGHT')\n",
    "      \n",
    "  # Save x, y locations of each edge\n",
    "  edge_x = []\n",
    "  edge_y = []\n",
    "\n",
    "  # Calculate x,y positions of an edge's 'start' (x0,y0) and 'end' (x1,y1) points\n",
    "  for edge in G.edges():\n",
    "      x0, y0 = pos[edge[0]]\n",
    "      x1, y1 = pos[edge[1]]\n",
    "      edge_x.append(x0)\n",
    "      edge_x.append(x1)\n",
    "      edge_y.append(y0)\n",
    "      edge_y.append(y1)\n",
    "\n",
    "  # Bundle it all up in a dict:\n",
    "  edges = dict(x=edge_x,y=edge_y)\n",
    "\n",
    "  # Save x, y locations of each node\n",
    "  node_x = []\n",
    "  node_y = []\n",
    "\n",
    "  # Save node stats for annotation\n",
    "  node_name = []\n",
    "  node_adjacencies = []\n",
    "  node_centralities = []\n",
    "\n",
    "  # Calculate x,y positions of nodes\n",
    "  for node in G.nodes():\n",
    "      node_name.append(node)# Save node names\n",
    "      x, y = pos[node]\n",
    "      node_x.append(x)\n",
    "      node_y.append(y)\n",
    "\n",
    "  for node, adjacencies in enumerate(G.adjacency()):\n",
    "      node_adjacencies.append(len(adjacencies[1]))\n",
    "\n",
    "  for n in G.nodes():\n",
    "      node_centralities.append(nx.degree_centrality(G)[n])\n",
    "\n",
    "  # Bundle it all up in a dict:\n",
    "  nodes = dict(x=node_x,y=node_y,name=node_name,adjacencies=node_adjacencies,centralities=node_centralities)\n",
    "\n",
    "  return edges,nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "[edges,nodes] = calculate_positions(network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormap = dict(zip(list(set(node_lookup['Primary Disease'])),px.colors.qualitative.Dark24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Lymphoma': '#2E91E5',\n",
       " 'Lung Cancer': '#E15F99',\n",
       " 'Eye Cancer': '#1CA71C',\n",
       " 'Skin Cancer': '#FB0D0D',\n",
       " 'Head and Neck Cancer': '#DA16FF',\n",
       " 'Ovarian Cancer': '#222A2A',\n",
       " 'Kidney Cancer': '#B68100',\n",
       " 'Liver Cancer': '#750D86',\n",
       " 'Sarcoma': '#EB663B',\n",
       " 'Leukemia': '#511CFB',\n",
       " 'Colon/Colorectal Cancer': '#00A08B',\n",
       " 'Rhabdoid': '#FB00D1',\n",
       " 'Endometrial/Uterine Cancer': '#FC0080',\n",
       " 'Breast Cancer': '#B2828D',\n",
       " 'Pancreatic Cancer': '#6C7C32',\n",
       " 'Esophageal Cancer': '#778AAE',\n",
       " 'Bone Cancer': '#862A16',\n",
       " 'Brain Cancer': '#A777F1',\n",
       " 'Neuroblastoma': '#620042',\n",
       " 'Bladder Cancer': '#1616A7'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes['color']=[colormap[list(node_lookup[node_lookup['Node_ID']==x]['Primary Disease'])[0]] for x in nodes['name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_graph(edges,nodes,title,**kwargs):\n",
    "\n",
    "  # Draw edges\n",
    "  edge_trace = go.Scatter(\n",
    "      x=edges['x'], y=edges['y'],\n",
    "      line=dict(width=0.5, color='#888'),\n",
    "      mode='lines+markers',\n",
    "      hoverinfo='text')\n",
    "\n",
    "  # Draw nodes\n",
    "  node_trace = go.Scatter(\n",
    "      x=nodes['x'],\n",
    "      y=nodes['y'],\n",
    "      # Optional: Add labels to points *without* hovering (can get a little messy)\n",
    "      #mode='markers+text',\n",
    "      # ...or, just add markers (no text)\n",
    "      mode='markers',\n",
    "      text=[list(node_lookup[node_lookup['Node_ID']==x]['Primary Disease'])[0] for x in nodes['name']],\n",
    "      hoverinfo='text')\n",
    "  filename=title.lower().replace(\" \",\"_\")\n",
    "\n",
    "  # Color the node by its number of connections\n",
    "  #node_trace.marker.color = nodes['adjacencies']\n",
    "  node_trace.marker.color = nodes['color']\n",
    "  \n",
    "  # Draw figure\n",
    "  fig = go.Figure(data=[edge_trace,node_trace],\n",
    "            layout=go.Layout(\n",
    "              title=title,\n",
    "              height=600,\n",
    "              titlefont_size=16,\n",
    "              showlegend=False,\n",
    "              hovermode='closest',\n",
    "              margin=dict(b=20,l=5,r=5,t=120),\n",
    "              xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
    "              yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
    "              template='plotly_white')\n",
    "              )\n",
    "  \n",
    "  fig.update_traces(textposition='top center')\n",
    "  # Show figure\n",
    "  fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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