Exp_fin_4_Bipartite.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b680532f-17a7-47bf-a130-dad85105e3df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pickle as pk\n",
    "import matplotlib.pyplot as plt \n",
    "from collections import Counter\n",
    "from scipy import stats\n",
    "import gseapy as gp\n",
    "from community import community_louvain\n",
    "import random as rd\n",
    "import itertools as it\n",
    "from networkx.algorithms import bipartite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b026eb3f-da51-49a3-a994-9855dd3d9ff8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def overlap_jaccard(list1,list2):\n",
    "    intersction_term= len(set(list1) & set(list2))\n",
    "    denominator = len(set(list1).union(set(list2)))\n",
    "    overlap_jaccard_coeff = intersction_term/denominator\n",
    "    return overlap_jaccard_coeff\n",
    "\n",
    "#Here, we define the fisher test that we will use\n",
    "def overlap_set(setA, setB,background):    \n",
    "    \"\"\"\n",
    "    Accepts to lists\n",
    "    M is the population size (previously N)\n",
    "    n is the number of successes in the population\n",
    "    N is the sample size (previously n)\n",
    "    x is still the number of drawn “successes”\n",
    "    \"\"\"\n",
    "    M= background #total number of exposures in chem_gene_cleaned\n",
    "    n= len(setA)\n",
    "    N= len(setB)\n",
    "    x= len(setA.intersection(setB))\n",
    "    return str(stats.hypergeom.sf(x-1, M, n, N))\n",
    "\n",
    "#Here we define the adjustment\n",
    "from statsmodels.sandbox.stats.multicomp import multipletests\n",
    "def fdr_adjustment(list_of_pvals,alpha):    \n",
    "    return multipletests(list_of_pvals,alpha=alpha,method='fdr_bh')[1] #the benjamin hochberg method is used\n",
    "\n",
    "def read_file_as_lists(file_path):\n",
    "    \"\"\"\n",
    "    Reads a file and converts its content into lists.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the file to be read.\n",
    "\n",
    "    Returns:\n",
    "        list of lists: A list where each element is a list of items from each line.\n",
    "    \"\"\"\n",
    "    lists = []\n",
    "    try:\n",
    "        with open(file_path, 'r') as file:\n",
    "            for line in file:\n",
    "                # Remove leading/trailing whitespace and split by whitespace or commas\n",
    "                elements = line.strip().replace(\",\", \" \").split()\n",
    "                lists.append(elements)\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: The file at {file_path} was not found.\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "    \n",
    "    return lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54e909a8-b244-455f-a3b4-6dd1fd0a3547",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of exposures is 9887\n",
      "The total number of perturbed genes is 25580\n"
     ]
    }
   ],
   "source": [
    "#Let's import the chemical-gene interactions from CTD (downloaded on 5th April 2021)\n",
    "chem_gene_df = pd.read_csv(\"input/CTD/CTD_chem_gene_ixns.tsv\",delimiter= '\\t', skipinitialspace=True)\n",
    "#Here, we filter for only the interactions that regards the H. Sapiens\n",
    "chem_homo = chem_gene_df[(chem_gene_df['Organism'] == 'Homo sapiens')]\n",
    "\n",
    "#Here, we create a dictionary out of the dataframe that has as a key the exposures and as \n",
    "#values the set of the genes that they perturb\n",
    "chem_gene = {}\n",
    "for i,v in chem_homo.iterrows():\n",
    "    try:\n",
    "        chem_gene[v[\"ChemicalID\"]] |= {v[\"GeneSymbol\"]}\n",
    "    except KeyError as e:\n",
    "        chem_gene[v[\"ChemicalID\"]] = set([v[\"GeneSymbol\"]])\n",
    "\n",
    "#Here, we remove the elements that do not perturb any genes:       \n",
    "chem_gene_cleaned = {}\n",
    "tot_gene_list=[]\n",
    "for k,v in chem_gene.items():\n",
    "    if len(v)>0:\n",
    "        chem_gene_cleaned[k]=v\n",
    "        for gene in v:\n",
    "            tot_gene_list.append(gene)           \n",
    "    else:\n",
    "        pass\n",
    " \n",
    "print(\"The number of exposures is %s\" %len(chem_gene_cleaned))\n",
    "print(\"The total number of perturbed genes is %s\" %len(set(tot_gene_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3f330830-842f-43b6-b3b4-eac605946f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bipartite_G = nx.Graph()\n",
    "exposures_list = []\n",
    "genes_list = []\n",
    "for k,v in chem_gene_cleaned.items():\n",
    "    if str(k)!='nan':\n",
    "        exposures_list.append(k)\n",
    "        for gene in v:\n",
    "            if str(gene)!='nan':\n",
    "                genes_list.append(gene)   \n",
    "for gene in set(genes_list):\n",
    "    bipartite_G.add_node(gene,s=\"^\", bipartite='gene')   \n",
    "for exposure in set(exposures_list):\n",
    "    bipartite_G.add_node(exposure,s=\"o\", bipartite='exposure')\n",
    "    for genes in [i for i in chem_gene_cleaned[exposure] if str(i)!='nan']:\n",
    "        bipartite_G.add_edges_from([(exposure,genes)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "7d1ce335-47d8-4b7c-b265-cdba841f4614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25579"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(genes_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1566611c-9cdf-4b34-88ff-4574aa6399f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "bipartite_clustering_coefficient = bipartite.clustering(bipartite_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4be0bb2-06f3-49aa-bde1-8bbe4719f696",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output/Communities/bipartite_clustering_coefficient.pickle', 'wb') as handle:\n",
    "    pk.dump(bipartite_clustering_coefficient, handle, protocol=pk.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f852f304-1d73-4116-b022-2b52dd5e8960",
   "metadata": {},
   "source": [
    "### Louvain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d633d73-5c9a-47d6-9eef-f7253b26cb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Louvain with Barber modularity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b765af5a-653c-4088-8ec8-9659e920f656",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Resolution: 0.8999999999999999\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sknetwork.clustering import Louvain\n",
    "from sknetwork.data import from_edge_list\n",
    "\n",
    "#Let's see whether we can use Louvain to cluster the bipartite network\n",
    "#We have used this approach\n",
    "#https://medium.com/eni-digitalks/uncovering-hidden-communities-in-bipartite-graphs-8a1fc518a04a\n",
    "#https://scikit-network.readthedocs.io/en/latest/tutorials/clustering/louvain.html\n",
    "\n",
    "#For bipartite graphs, the algorithm maximizes Barber’s modularity by default.\n",
    "\n",
    "def compute_modularity(G, partition):\n",
    "    \"\"\"\n",
    "    Compute the modularity of a given partition.\n",
    "    \"\"\"\n",
    "    communities = {}\n",
    "    for node, community in partition.items():\n",
    "        if community not in communities:\n",
    "            communities[community] = []\n",
    "        communities[community].append(node)\n",
    "    \n",
    "    modularity = 0\n",
    "    m = G.size(weight='weight')\n",
    "    for community in communities.values():\n",
    "        subgraph = G.subgraph(community)\n",
    "        lc = subgraph.size(weight='weight')\n",
    "        dc = sum([G.degree(node, weight='weight') for node in community])\n",
    "        modularity += lc / m - (dc / (2 * m)) ** 2\n",
    "    \n",
    "    return modularity\n",
    "    \n",
    "#Here, we find the resolution parameter that optimizes Barber's modularity\n",
    "def optimal_resolution_with_diagnostics(G, resolution_list):\n",
    "    edge_list=[(e[0],e[1]) for e in G.edges(data=True)]\n",
    "    bgraph = from_edge_list(edge_list, bipartite=True)\n",
    "    names = bgraph.names\n",
    "    names_row = bgraph.names_row\n",
    "    names_col = bgraph.names_col\n",
    "\n",
    "    biadjacency = bgraph.biadjacency\n",
    "    \n",
    "    max_modularity = -1\n",
    "    best_partition = {}\n",
    "    best_resolution = None\n",
    "    modularity_scores = []\n",
    "    \n",
    "    for resolution in resolution_list:\n",
    "        louvain = Louvain(resolution=resolution)\n",
    "        louvain.fit(biadjacency,force_bipartite=True)\n",
    "        labels_row = louvain.labels_row_\n",
    "        labels_col = louvain.labels_col_\n",
    "\n",
    "        partition = {}\n",
    "        for i, n_r in enumerate(names_row):\n",
    "            partition[n_r] = labels_row[i]\n",
    "        for i, n_c in enumerate(names_col):\n",
    "            partition[n_c] = labels_col[i]\n",
    "\n",
    "        modularity = compute_modularity(G, partition)\n",
    "        modularity_scores.append((resolution, modularity))\n",
    "        \n",
    "        if modularity > max_modularity:\n",
    "            max_modularity = modularity\n",
    "            best_partition = partition\n",
    "            best_resolution = resolution\n",
    "    \n",
    "    return best_partition, best_resolution, max_modularity, modularity_scores\n",
    "\n",
    "\n",
    "resolution_list = np.linspace(0.1, 2, 20) \n",
    "best_partition, best_resolution, max_modularity, modularity_scores = optimal_resolution_with_diagnostics(bipartite_G, resolution_list)\n",
    "\n",
    "# Plot the modularity versus resolution\n",
    "resolutions, modularities = zip(*modularity_scores)\n",
    "plt.plot(resolutions, modularities, marker='o')\n",
    "plt.xlabel('Resolution')\n",
    "plt.ylabel('Modularity')\n",
    "plt.title('Modularity vs. Resolution')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Best Resolution:\", best_resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d39bee22-3ffc-44b4-9ba8-8910bfdf3905",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n"
     ]
    }
   ],
   "source": [
    "\n",
    "edge_list=[(e[0],e[1]) for e in bipartite_G.edges(data=True)]\n",
    "bgraph = from_edge_list(edge_list, bipartite=True)\n",
    "names = bgraph.names\n",
    "names_row = bgraph.names_row\n",
    "names_col = bgraph.names_col\n",
    "\n",
    "biadjacency = bgraph.biadjacency\n",
    "\n",
    "louvain = Louvain(resolution=0.9)\n",
    "louvain.fit(biadjacency,force_bipartite=True)\n",
    "labels_row = louvain.labels_row_\n",
    "labels_col = louvain.labels_col_\n",
    "\n",
    "partition={}\n",
    "for i,n_r in enumerate(names_row):\n",
    "    partition[n_r]=labels_row[i]\n",
    "for i,n_c in enumerate(names_col):\n",
    "    partition[n_c]=labels_col[i]\n",
    "\n",
    "partition_rev = {}\n",
    "for com_partition in set(partition.values()):\n",
    "    node_list = []\n",
    "    for node,part in partition.items():   \n",
    "        if com_partition==part:\n",
    "            node_list.append(node)\n",
    "    partition_rev[com_partition] = node_list\n",
    "\n",
    "print(len(partition_rev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a5135e0-0b3d-41ba-a51b-d7d097da541a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('intermediate/bipartite_G_edges_float_Louvain_initial_comm.txt', 'w') as f:\n",
    "    for com, nodelist in partition_rev.items():\n",
    "        for i in range(len(nodelist)):\n",
    "            if i!=len(nodelist)-1:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]) +\",\")\n",
    "            else:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]))\n",
    "        f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ec69eee9-bb93-4653-979b-173cb86bd6db",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Let's try to see whether the genes that belong to the same community\n",
    "#are the same genes that are shared between the exposures that belong to the same community\n",
    "#we will do this by overlapping the shared components with the genes by using the jaccard index\n",
    "\n",
    "partition_rev_ji_dict = {}\n",
    "\n",
    "for com,el_list in partition_rev.items():\n",
    "    explist = set(el_list)&set(exposures_list)\n",
    "    genelist = set(el_list)&set(genes_list)\n",
    "    shared_genes = []\n",
    "    pairwise_exp = list(it.combinations(explist, 2))\n",
    "    for exp1,exp2 in pairwise_exp:\n",
    "        shared_genes.extend(list(set(chem_gene_cleaned[exp1])&set(chem_gene_cleaned[exp2])))\n",
    "    ji = overlap_jaccard(genelist,list(set(shared_genes)))\n",
    "    partition_rev_ji_dict[com] = ji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "863cc66b-1ae6-4070-9f42-6c8442dc995a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5015367053038285"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(list(partition_rev_ji_dict.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "eab2f3e4-a25a-42a9-9df3-e8af1f24648d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n"
     ]
    }
   ],
   "source": [
    "#Let's compute a p-value of overlap\n",
    "    \n",
    "partition_rev_pval_dict = {}\n",
    "partition_rev_fdr_dict = {}\n",
    "\n",
    "for com,el_list in partition_rev.items():\n",
    "    explist = set(el_list)&set(exposures_list)\n",
    "    genelist = set(el_list)&set(genes_list)\n",
    "    shared_genes = []\n",
    "    pairwise_exp = list(it.combinations(explist, 2))\n",
    "    for exp1,exp2 in pairwise_exp:\n",
    "        shared_genes.extend(list(set(chem_gene_cleaned[exp1])&set(chem_gene_cleaned[exp2])))\n",
    "    pval = overlap_set(set(genelist),set(shared_genes),len(set(genes_list)))\n",
    "    partition_rev_pval_dict[com] = float(pval)\n",
    "\n",
    "adj_pval=fdr_adjustment(list(partition_rev_pval_dict.values()),0.05)\n",
    "com_list=list(partition_rev_pval_dict.keys())\n",
    "for i in range(len(adj_pval)):\n",
    "    partition_rev_fdr_dict[com_list[i]] = adj_pval[i]\n",
    "\n",
    "sig_partition_rev_fdr_dict = [ i for i in list(partition_rev_fdr_dict.values()) if i<0.05]\n",
    "print(len(sig_partition_rev_fdr_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eef0eb01-69db-4102-aff3-a9fae641a99c",
   "metadata": {},
   "source": [
    "### Infomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "255f0a28-042a-42c6-b8f1-1351f217e59f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from this https://mapequation.github.io/infomap/python/infomap.html#infomap.Infomap.add_multilayer_link\n",
    "#Let's create a mapping\n",
    "bipartite_G_node_list = list(bipartite_G.nodes())\n",
    "\n",
    "mapping_bipartite_G_node = {}\n",
    "for el in range(len(bipartite_G_node_list)):\n",
    "    mapping_bipartite_G_node[bipartite_G_node_list[el]] = el\n",
    "\n",
    "bipartite_G_edges_list = list(bipartite_G.edges())\n",
    "\n",
    "bipartite_G_edges_mapped_list = []\n",
    "for e in bipartite_G_edges_list:\n",
    "    bipartite_G_edges_mapped_list.append((mapping_bipartite_G_node[e[0]],mapping_bipartite_G_node[e[1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "52d9b181-5217-40ff-9304-1beb3072b04a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from infomap import Infomap\n",
    "im = Infomap(silent=True, num_trials=10)\n",
    "\n",
    "for old_n,new_n in mapping_bipartite_G_node.items():\n",
    "    im.add_node(new_n, old_n)\n",
    "for new_e in bipartite_G_edges_mapped_list:\n",
    "    im.add_link(new_e[0],new_e[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6d6c74aa-22a6-49ef-93cd-b51155f72681",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "im.run(bipartite_teleportation=True)\n",
    "for modules in sorted(im.get_multilevel_modules().values()):\n",
    "    print(modules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "442c8d83-f19a-46a8-aacc-54465283f4da",
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping_bipartite_G_node_rev = {}\n",
    "\n",
    "for old_node,new_node in mapping_bipartite_G_node.items():\n",
    "    mapping_bipartite_G_node_rev[new_node]=old_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ac652d5-dcb7-4f02-a6e5-260024151311",
   "metadata": {},
   "outputs": [],
   "source": [
    "bipartite_node_com_dict={}\n",
    "for node,modules in im.get_multilevel_modules(states=True).items():\n",
    "    bipartite_node_com_dict[mapping_bipartite_G_node_rev[node]]=list(modules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "95b71175-f963-449d-a99d-26a8b590da4b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "174\n",
      "In 1 there are 1399 exposures and 23672 genes\n",
      "In 2 there are 3139 exposures and 349 genes\n",
      "In 3 there are 564 exposures and 22 genes\n",
      "In 4 there are 573 exposures and 18 genes\n",
      "In 5 there are 226 exposures and 8 genes\n",
      "In 6 there are 123 exposures and 17 genes\n",
      "In 7 there are 123 exposures and 20 genes\n",
      "In 8 there are 132 exposures and 13 genes\n",
      "In 9 there are 34 exposures and 20 genes\n",
      "In 10 there are 45 exposures and 33 genes\n",
      "In 11 there are 2 exposures and 200 genes\n",
      "In 12 there are 63 exposures and 16 genes\n",
      "In 13 there are 127 exposures and 11 genes\n",
      "In 14 there are 55 exposures and 7 genes\n",
      "In 15 there are 16 exposures and 62 genes\n",
      "In 16 there are 109 exposures and 6 genes\n",
      "In 17 there are 82 exposures and 16 genes\n",
      "In 18 there are 161 exposures and 5 genes\n",
      "In 19 there are 78 exposures and 16 genes\n",
      "In 20 there are 38 exposures and 10 genes\n",
      "In 21 there are 60 exposures and 13 genes\n",
      "In 22 there are 37 exposures and 13 genes\n",
      "In 23 there are 29 exposures and 12 genes\n",
      "In 24 there are 17 exposures and 22 genes\n",
      "In 25 there are 27 exposures and 9 genes\n",
      "In 26 there are 58 exposures and 9 genes\n",
      "In 27 there are 2 exposures and 113 genes\n",
      "In 28 there are 56 exposures and 11 genes\n",
      "In 29 there are 176 exposures and 3 genes\n",
      "In 30 there are 95 exposures and 1 genes\n",
      "In 31 there are 54 exposures and 9 genes\n",
      "In 32 there are 30 exposures and 10 genes\n",
      "In 33 there are 6 exposures and 66 genes\n",
      "In 34 there are 4 exposures and 28 genes\n",
      "In 35 there are 89 exposures and 6 genes\n",
      "In 36 there are 49 exposures and 13 genes\n",
      "In 37 there are 17 exposures and 14 genes\n",
      "In 38 there are 69 exposures and 6 genes\n",
      "In 39 there are 33 exposures and 7 genes\n",
      "In 40 there are 5 exposures and 34 genes\n",
      "In 41 there are 34 exposures and 4 genes\n",
      "In 42 there are 35 exposures and 4 genes\n",
      "In 43 there are 38 exposures and 8 genes\n",
      "In 44 there are 69 exposures and 2 genes\n",
      "In 45 there are 43 exposures and 2 genes\n",
      "In 46 there are 41 exposures and 5 genes\n",
      "In 47 there are 15 exposures and 9 genes\n",
      "In 48 there are 43 exposures and 5 genes\n",
      "In 49 there are 21 exposures and 3 genes\n",
      "In 50 there are 2 exposures and 50 genes\n",
      "In 51 there are 18 exposures and 6 genes\n",
      "In 52 there are 30 exposures and 4 genes\n",
      "In 53 there are 31 exposures and 8 genes\n",
      "In 54 there are 31 exposures and 3 genes\n",
      "In 55 there are 30 exposures and 7 genes\n",
      "In 56 there are 41 exposures and 5 genes\n",
      "In 57 there are 26 exposures and 4 genes\n",
      "In 58 there are 36 exposures and 1 genes\n",
      "In 59 there are 26 exposures and 8 genes\n",
      "In 60 there are 27 exposures and 3 genes\n",
      "In 61 there are 31 exposures and 2 genes\n",
      "In 62 there are 21 exposures and 4 genes\n",
      "In 63 there are 1 exposures and 39 genes\n",
      "In 64 there are 18 exposures and 8 genes\n",
      "In 65 there are 18 exposures and 5 genes\n",
      "In 66 there are 48 exposures and 4 genes\n",
      "In 67 there are 42 exposures and 4 genes\n",
      "In 68 there are 24 exposures and 3 genes\n",
      "In 69 there are 20 exposures and 6 genes\n",
      "In 70 there are 23 exposures and 2 genes\n",
      "In 71 there are 22 exposures and 5 genes\n",
      "In 72 there are 18 exposures and 4 genes\n",
      "In 73 there are 20 exposures and 2 genes\n",
      "In 74 there are 10 exposures and 7 genes\n",
      "In 75 there are 14 exposures and 7 genes\n",
      "In 76 there are 30 exposures and 5 genes\n",
      "In 77 there are 2 exposures and 11 genes\n",
      "In 78 there are 32 exposures and 2 genes\n",
      "In 79 there are 24 exposures and 3 genes\n",
      "In 80 there are 6 exposures and 31 genes\n",
      "In 81 there are 16 exposures and 3 genes\n",
      "In 82 there are 23 exposures and 7 genes\n",
      "In 83 there are 13 exposures and 4 genes\n",
      "In 84 there are 22 exposures and 4 genes\n",
      "In 85 there are 11 exposures and 7 genes\n",
      "In 86 there are 13 exposures and 10 genes\n",
      "In 87 there are 38 exposures and 1 genes\n",
      "In 88 there are 4 exposures and 20 genes\n",
      "In 89 there are 24 exposures and 1 genes\n",
      "In 90 there are 21 exposures and 3 genes\n",
      "In 91 there are 21 exposures and 5 genes\n",
      "In 92 there are 36 exposures and 3 genes\n",
      "In 93 there are 4 exposures and 12 genes\n",
      "In 94 there are 15 exposures and 2 genes\n",
      "In 95 there are 15 exposures and 9 genes\n",
      "In 96 there are 19 exposures and 11 genes\n",
      "In 97 there are 12 exposures and 4 genes\n",
      "In 98 there are 5 exposures and 18 genes\n",
      "In 99 there are 2 exposures and 7 genes\n",
      "In 100 there are 26 exposures and 4 genes\n",
      "In 101 there are 13 exposures and 2 genes\n",
      "In 102 there are 29 exposures and 1 genes\n",
      "In 103 there are 15 exposures and 4 genes\n",
      "In 104 there are 29 exposures and 3 genes\n",
      "In 105 there are 10 exposures and 8 genes\n",
      "In 106 there are 10 exposures and 7 genes\n",
      "In 107 there are 21 exposures and 4 genes\n",
      "In 108 there are 2 exposures and 37 genes\n",
      "In 109 there are 12 exposures and 4 genes\n",
      "In 110 there are 16 exposures and 5 genes\n",
      "In 111 there are 16 exposures and 1 genes\n",
      "In 112 there are 11 exposures and 2 genes\n",
      "In 113 there are 1 exposures and 17 genes\n",
      "In 114 there are 7 exposures and 3 genes\n",
      "In 115 there are 10 exposures and 4 genes\n",
      "In 116 there are 16 exposures and 4 genes\n",
      "In 117 there are 11 exposures and 2 genes\n",
      "In 118 there are 10 exposures and 1 genes\n",
      "In 119 there are 1 exposures and 15 genes\n",
      "In 120 there are 3 exposures and 7 genes\n",
      "In 121 there are 17 exposures and 3 genes\n",
      "In 122 there are 10 exposures and 4 genes\n",
      "In 123 there are 17 exposures and 1 genes\n",
      "In 124 there are 10 exposures and 2 genes\n",
      "In 125 there are 1 exposures and 20 genes\n",
      "In 126 there are 10 exposures and 2 genes\n",
      "In 127 there are 15 exposures and 1 genes\n",
      "In 128 there are 6 exposures and 1 genes\n",
      "In 129 there are 9 exposures and 2 genes\n",
      "In 130 there are 8 exposures and 3 genes\n",
      "In 131 there are 7 exposures and 2 genes\n",
      "In 132 there are 5 exposures and 1 genes\n",
      "In 133 there are 7 exposures and 2 genes\n",
      "In 134 there are 6 exposures and 1 genes\n",
      "In 135 there are 4 exposures and 1 genes\n",
      "In 136 there are 5 exposures and 2 genes\n",
      "In 137 there are 5 exposures and 2 genes\n",
      "In 138 there are 12 exposures and 1 genes\n",
      "In 139 there are 12 exposures and 2 genes\n",
      "In 140 there are 7 exposures and 1 genes\n",
      "In 141 there are 5 exposures and 1 genes\n",
      "In 142 there are 1 exposures and 8 genes\n",
      "In 143 there are 3 exposures and 3 genes\n",
      "In 144 there are 8 exposures and 1 genes\n",
      "In 145 there are 6 exposures and 1 genes\n",
      "In 146 there are 5 exposures and 1 genes\n",
      "In 147 there are 3 exposures and 1 genes\n",
      "In 148 there are 2 exposures and 1 genes\n",
      "In 149 there are 3 exposures and 1 genes\n",
      "In 150 there are 3 exposures and 1 genes\n",
      "In 151 there are 2 exposures and 1 genes\n",
      "In 152 there are 1 exposures and 1 genes\n",
      "In 153 there are 1 exposures and 1 genes\n",
      "In 154 there are 1 exposures and 2 genes\n",
      "In 155 there are 3 exposures and 1 genes\n",
      "In 156 there are 2 exposures and 1 genes\n",
      "In 157 there are 1 exposures and 1 genes\n",
      "In 158 there are 1 exposures and 1 genes\n",
      "In 159 there are 2 exposures and 1 genes\n",
      "In 160 there are 1 exposures and 1 genes\n",
      "In 161 there are 1 exposures and 1 genes\n",
      "In 162 there are 1 exposures and 1 genes\n",
      "In 163 there are 1 exposures and 1 genes\n",
      "In 164 there are 1 exposures and 1 genes\n",
      "In 165 there are 1 exposures and 1 genes\n",
      "In 166 there are 1 exposures and 1 genes\n",
      "In 167 there are 1 exposures and 1 genes\n",
      "In 168 there are 1 exposures and 1 genes\n",
      "In 169 there are 1 exposures and 1 genes\n",
      "In 170 there are 1 exposures and 1 genes\n",
      "In 171 there are 1 exposures and 1 genes\n",
      "In 172 there are 1 exposures and 1 genes\n",
      "In 173 there are 1 exposures and 1 genes\n",
      "In 174 there are 1 exposures and 1 genes\n"
     ]
    }
   ],
   "source": [
    "tot_com_list=[]\n",
    "for node,com_list in bipartite_node_com_dict.items():\n",
    "    tot_com_list.extend(com_list)\n",
    "print(len(set(tot_com_list)))  #This is the total number of communities\n",
    "\n",
    "bipartite_node_com_up_dict={}\n",
    "for com in set(tot_com_list):\n",
    "    node_list=[]\n",
    "    for node,com_list in bipartite_node_com_dict.items():\n",
    "        if com in com_list:\n",
    "            node_list.append(node)\n",
    "    bipartite_node_com_up_dict[com]=node_list\n",
    "        \n",
    "for com,node_list in bipartite_node_com_up_dict.items():\n",
    "    n_genes = len(set(node_list)&set(genes_list))\n",
    "    n_exposures = len(set(node_list)&set(exposures_list))\n",
    "    print(\"In %s there are %s exposures and %s genes\" %(com,n_exposures,n_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2503161c-7c1b-45cb-bab3-2c532c29acf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('intermediate/bipartite_G_edges_float_Infomap_initial_comm.txt', 'w') as f:\n",
    "    for com, nodelist in bipartite_node_com_up_dict.items():\n",
    "        for i in range(len(nodelist)):\n",
    "            if i!=len(nodelist)-1:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]) +\",\")\n",
    "            else:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]))\n",
    "        f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "07468731-204c-4ec9-9e6b-ea3c712dc15e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#Let's try to see whether the genes that belong to the same community\n",
    "#are the same genes that are shared between the exposures that belong to the same community\n",
    "#we will do this by overlapping the shared components with the genes by using the jaccard index\n",
    "\n",
    "bipartite_node_com_up_ji_dict = {}\n",
    "\n",
    "for com,el_list in bipartite_node_com_up_dict.items():\n",
    "    explist = set(el_list)&set(exposures_list)\n",
    "    genelist = set(el_list)&set(genes_list)\n",
    "    shared_genes = []\n",
    "    pairwise_exp = list(it.combinations(explist, 2))\n",
    "    for exp1,exp2 in pairwise_exp:\n",
    "        shared_genes.extend(list(set(chem_gene_cleaned[exp1])&set(chem_gene_cleaned[exp2])))\n",
    "    ji = overlap_jaccard(genelist,list(set(shared_genes)))\n",
    "    bipartite_node_com_up_ji_dict[com] = ji"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e16ef4d7-bca4-4a9c-9e63-fbafda6233dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149\n"
     ]
    }
   ],
   "source": [
    "#Let's compute a p-value of overlap\n",
    "    \n",
    "bipartite_node_com_up_pval_dict = {}\n",
    "bipartite_node_com_up_fdr_dict = {}\n",
    "\n",
    "for com,el_list in bipartite_node_com_up_dict.items():\n",
    "    explist = set(el_list)&set(exposures_list)\n",
    "    genelist = set(el_list)&set(genes_list)\n",
    "    shared_genes = []\n",
    "    pairwise_exp = list(it.combinations(explist, 2))\n",
    "    for exp1,exp2 in pairwise_exp:\n",
    "        shared_genes.extend(list(set(chem_gene_cleaned[exp1])&set(chem_gene_cleaned[exp2])))\n",
    "    pval = overlap_set(set(genelist),set(shared_genes),len(set(genes_list)))\n",
    "    bipartite_node_com_up_pval_dict[com] = float(pval)\n",
    "\n",
    "adj_pval=fdr_adjustment(list(bipartite_node_com_up_pval_dict.values()),0.05)\n",
    "com_list=list(bipartite_node_com_up_pval_dict.keys())\n",
    "for i in range(len(adj_pval)):\n",
    "    bipartite_node_com_up_fdr_dict[com_list[i]] = adj_pval[i]\n",
    "\n",
    "sig_bipartite_node_com_up_fdr_dict = [ i for i in list(bipartite_node_com_up_fdr_dict.values()) if i<0.05]\n",
    "print(len(sig_bipartite_node_com_up_fdr_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82319c21-54d3-48cd-93d3-d4b108d2951c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7071b63e-c6a7-4846-a6b3-7b4382d1f597",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d0d14e79-9757-47cc-8912-be8a479af532",
   "metadata": {},
   "source": [
    "### Bilouvain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "236c846b-0879-4f1f-9f1e-9a0583f76788",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bipartite Louvain\n",
    "#As initial conditions, we have used the communities identified in the bipartite network by using Louvain\n",
    "#There are 19 initial communities, with an initial Murata+ Modularity of 0.25\n",
    "#After having run biLouvain algorithm, we obtained only 2 communities, with a Murata+ Modularity of 0.25 (no modularity change)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7c945d6f-1823-4950-96eb-e283ed085bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filename = 'intermediate/biLouvain/bipartite_G_edges_float_BipLouvain_ResultsCommunities.txt'\n",
    "result = read_file_as_lists(filename)\n",
    "\n",
    "BipLouvain_communities_dict = {}\n",
    "with open(filename, 'r') as file:\n",
    "    for line in file:\n",
    "        if 'Community' in line:\n",
    "            com_name = line.split(\":\")[0]\n",
    "            com_el = line.split(\":\")[1]\n",
    "            BipLouvain_communities_dict[com_name] = com_el\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "88740a01-48ed-4675-a6f8-d828c259b4cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BipLouvain_communities_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1191a82d-e1cb-4944-bfbe-c62d83a35749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Community 0[V2]', 'Community 1[V2]'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BipLouvain_communities_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cf64ceb5-6054-4dcd-bd89-d6b695db65d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35466"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BipLouvain_communities_dict['Community 0[V2]'].split(\",\")) + len(BipLouvain_communities_dict['Community 1[V2]'].split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5bea5f06-8f45-4c93-8871-06df1fa5bec5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bipartite Infomap\n",
    "\n",
    "\n",
    "\n",
    "#As initial conditions, we have used the communities identified in the bipartite network by using Infomap\n",
    "#There are 174 initial communities, with an initial Murata+ Modularity of 0.25\n",
    "#After having run biLouvain algorithm, we obtained only 2 communities, with a Murata+ Modularity of 0.36\n",
    "\n",
    "filename = 'intermediate/biLouvain/bipartite_G_edges_float_BipInfomap_ResultsCommunities.txt'\n",
    "result = read_file_as_lists(filename)\n",
    "\n",
    "BipInfomap_communities_dict = {}\n",
    "with open(filename, 'r') as file:\n",
    "    for line in file:\n",
    "        if 'Community' in line:\n",
    "            com_name = line.split(\":\")[0]\n",
    "            com_el = line.split(\":\")[1]\n",
    "            BipInfomap_communities_dict[com_name] = com_el\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c88b153-5840-43a2-8875-4a8a91855348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BipInfomap_communities_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fdea03d9-7cf0-4391-89c1-8f24b3a855e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35466"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BipInfomap_communities_dict['Community 0[V2]'].split(\",\")) + len(BipInfomap_communities_dict['Community 1[V2]'].split(\",\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19be23f-0741-4ae9-8d39-43d551bfa500",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2dba0a30-ae91-48ed-b158-7587827c602d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Infomap Fine-grained Communities\n",
    "\n",
    "\n",
    "#As initial conditions, we have used the intermediate communities identified in the EEN (n=461) and the GGN (n=458) by using Infomap\n",
    "#There are 919 initial communities, with an initial Murata+ Modularity of 0.09\n",
    "#After having run biLouvain algorithm, we obtained 2 communities, with a Murata+ Modularity of 0.49\n",
    "\n",
    "filename = 'intermediate/biLouvain/bipartite_G_edges_float_FineInfomap_ResultsCommunities.txt'\n",
    "result = read_file_as_lists(filename)\n",
    "\n",
    "FineInfomap_communities_dict = {}\n",
    "with open(filename, 'r') as file:\n",
    "    for line in file:\n",
    "        if 'Community' in line:\n",
    "            com_name = line.split(\":\")[0]\n",
    "            com_el = line.split(\":\")[1]\n",
    "            FineInfomap_communities_dict[com_name] = com_el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "191e0b4e-1af9-450b-b1d1-2e95d658c2bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(FineInfomap_communities_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89d7898e-0db5-47b2-b544-e609f8102090",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd2fbe0-85bc-4bf5-b179-4549e38c6b10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "962bdc6c-f247-4071-9875-9309867d2399",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Louvain  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b549cd46-fe46-4c94-8d9b-597de30636f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the networks\n",
    "filtered_weighted_gene_graph_significant = nx.read_weighted_edgelist(\"output/backbone_gene_graph_significant_weighted.edgelist\")\n",
    "filtered_weighted_exp_graph_significant = nx.read_weighted_edgelist(\"output/backbone_exp_graph_significant_weighted.edgelist\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61f2bc49-ce22-4829-b901-87509b03c463",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_weighted_exp_graph_significant_best_partition = community_louvain.best_partition(\n",
    "    filtered_weighted_exp_graph_significant,\n",
    "    resolution=0.9,\n",
    "    weight='weight',\n",
    "    random_state=5495\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26bc2930-4ef3-48ef-9d5f-667497fa9eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "125\n"
     ]
    }
   ],
   "source": [
    "filtered_weighted_exp_graph_significant_best_partition_rev = {}\n",
    "for com_partition in set(filtered_weighted_exp_graph_significant_best_partition.values()):\n",
    "    node_list = []\n",
    "    for node,part in filtered_weighted_exp_graph_significant_best_partition.items():   \n",
    "        if com_partition==part:\n",
    "            node_list.append(node)\n",
    "    filtered_weighted_exp_graph_significant_best_partition_rev[com_partition] = node_list\n",
    "\n",
    "print(len(filtered_weighted_exp_graph_significant_best_partition_rev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ac00309c-4e91-4778-8422-f3683717c559",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_weighted_gene_graph_significant_best_partition = community_louvain.best_partition(\n",
    "    filtered_weighted_gene_graph_significant,\n",
    "    resolution=0.95,\n",
    "    weight='weight',\n",
    "    random_state=5495\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0745b8c6-6ce0-4b10-89c2-c47dbdb1752c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81\n"
     ]
    }
   ],
   "source": [
    "filtered_weighted_gene_graph_significant_best_partition_rev = {}\n",
    "for com_partition in set(filtered_weighted_gene_graph_significant_best_partition.values()):\n",
    "    node_list = []\n",
    "    for node,part in filtered_weighted_gene_graph_significant_best_partition.items():   \n",
    "        if com_partition==part:\n",
    "            node_list.append(node)\n",
    "    filtered_weighted_gene_graph_significant_best_partition_rev[com_partition] = node_list\n",
    "\n",
    "print(len(filtered_weighted_gene_graph_significant_best_partition_rev))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "de13bdb8-bb66-45d3-b9a7-63a3c2990d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output/bipartite_G_edges_float_Louvain_initial_comm_both_best.txt', 'w') as f:\n",
    "    for com, nodeset in filtered_weighted_gene_graph_significant_best_partition_rev.items():\n",
    "        nodelist = list(nodeset)\n",
    "        for i in range(len(nodelist)):\n",
    "            if i!=len(nodelist)-1:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]) +\",\")\n",
    "            else:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]))\n",
    "        f.write(\"\\n\")\n",
    "    for com, nodelist in filtered_weighted_exp_graph_significant_best_partition_rev.items():\n",
    "        for i in range(len(nodelist)):\n",
    "            if i!=len(nodelist)-1:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]) +\",\")\n",
    "            else:\n",
    "                f.write(str(mapping_bipartite_G_node[nodelist[i]]))\n",
    "        f.write(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab49f881-055f-4859-b339-3b769ad35ae2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Louvain Best Communities\n",
    "\n",
    "\n",
    "#As initial conditions, we have used the communities identified in the EEN (n=125) and the GGN (n=81) by using Louvain\n",
    "#There are 206 initial communities, with an initial Murata+ Modularity of 0.09\n",
    "#After having run biLouvain algorithm, we obtained 3 communities, with a Murata+ Modularity of 0.22\n",
    "\n",
    "filename = 'intermediate/biLouvain/bipartite_G_edges_float_BestLouvain_ResultsCommunities.txt'\n",
    "result = read_file_as_lists(filename)\n",
    "\n",
    "BestLouvain_communities_dict = {}\n",
    "with open(filename, 'r') as file:\n",
    "    for line in file:\n",
    "        if 'Community' in line:\n",
    "            com_name = line.split(\":\")[0]\n",
    "            com_el = line.split(\":\")[1]\n",
    "            BestLouvain_communities_dict[com_name] = com_el"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db69e547-4a95-478f-ad8d-9c05bd6698d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(BestLouvain_communities_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "706289f4-82db-4c6f-ab3f-d36f290e1aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Community 0[V1]', 'Community 1[V2]', 'Community 2[V2]'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BestLouvain_communities_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d799af6-3c03-4bf3-84f4-661cd088978b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Data for the stacked bar\n",
    "data = [174, 19, 2, 2, 2,3]\n",
    "infomap_stacked_data = [149, 25]\n",
    "louvain_stacked_data = [16, 3]\n",
    "\n",
    "# Labels for the x-axis\n",
    "labels = ['Infomap', 'Louvain', 'BiLouvain \\n Infomap \\n Bipartite', \n",
    "          'BiLouvain \\n Louvain \\n Bipartite', \n",
    "          'BiLouvain \\n Infomap \\n Fine-grained',\n",
    "          'BiLouvain \\n Louvain \\n Best Partition'\n",
    "         ]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "\n",
    "# Create the stacked bar for the first element\n",
    "ax.bar(labels[0], infomap_stacked_data[0], color='red')  # Add the bottom part (red)\n",
    "ax.bar(labels[0], infomap_stacked_data[1], bottom=infomap_stacked_data[0], color='blue')  # Add the top part (blue)\n",
    "\n",
    "ax.bar(labels[1], louvain_stacked_data[0], color='red')  # Add the bottom part (red)\n",
    "ax.bar(labels[1], louvain_stacked_data[1], bottom=louvain_stacked_data[0], color='blue')  # Add the top part (blue)\n",
    "\n",
    "# Create the rest of the bars\n",
    "ax.bar(labels[2:], data[2:], color='black')\n",
    "\n",
    "# Add titles and labels\n",
    "plt.xlabel('Method')\n",
    "plt.ylabel('Number of communities')\n",
    "plt.title('Number of communities clustering the CEN')\n",
    "\n",
    "plt.savefig('output/Figures/SF2A_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6369e9e0-4f71-4332-81bb-99545ed989ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04eaa171-a99a-4f1b-b772-b6cf33a823c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96bd662a-2cc4-4abc-99c2-5024d80d4349",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65129f63-a347-4799-b11d-589e35fcd7ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}