cartoGRAPHs_ManuscriptFigure_Extended6B.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "corresponding-alert",
   "metadata": {},
   "source": [
    "-----\n",
    "# cartoGRAPHs - FIGURE 6B\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61d2efb9-8305-42a7-97e4-d88276d0f506",
   "metadata": {},
   "outputs": [],
   "source": [
    "from cartoGRAPHs import * "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "necessary-malawi",
   "metadata": {
    "tags": []
   },
   "source": [
    "_____ \n",
    "# HUMAN NETWORK\n",
    "_____"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "mineral-pharmacy",
   "metadata": {},
   "outputs": [],
   "source": [
    "organism = 'human'\n",
    "\n",
    "G = load_graph(organism) \n",
    "\n",
    "d_gene_sym = load_genesymbols(G, organism)\n",
    "l_features = list(d_gene_sym.values())\n",
    "\n",
    "d_gene_do = pickle.load( open( \"input/d_gene_do.pkl\", \"rb\" ) )\n",
    "d_do_genes = pickle.load( open( \"input/d_do_genes.pkl\", \"rb\" ) )\n",
    "d_do_names = pickle.load( open( \"input/DO_names.pkl\", \"rb\" ) )\n",
    "d_names_do = {y:x for x,y in d_do_names.items()}\n",
    "\n",
    "d_centralities = load_centralities(G, organism)\n",
    "df_centralities = pd.DataFrame(d_centralities).T\n",
    "df_centralities.columns = ['degree','closeness', 'betweeness', 'eigenvector']\n",
    "essential_genes,non_ess_genes,notdefined_genes = load_essentiality(G, organism)\n",
    "\n",
    "# get Layout basis --> global layout matrix \n",
    "DF_structural = load_datamatrix(G,organism, 'global')\n",
    "df_max = DF_structural.max()\n",
    "l_max_visprob = max(list(df_max.values))\n",
    "\n",
    "# get Disease Feature Matrix\n",
    "FM_disease = pd.read_pickle('input/Features_Disease_Dataframe_'+organism+'.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "comparative-cisco",
   "metadata": {
    "tags": []
   },
   "source": [
    "---\n",
    "# VECTOR : NF1 GENE + DISEASE MODULE (8712 - neurofibromatosis)\n",
    "---\n",
    "e.g.\n",
    "+ DOID:8712 / Neurofibromatosis\n",
    "+ DOID:962 / Neurofibroma\n",
    "+ DOID:0050736 / Autosomal Dominant Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "collective-ballot",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genes associated to DOID 8712 : 12\n",
      "Number of NF1 neighbor genes 8712 : 49\n"
     ]
    }
   ],
   "source": [
    "path_nf = 'input/neurofibromatosis/'\n",
    "\n",
    "# gene of interest\n",
    "nf_goi_ = str(int(np.loadtxt(path_nf+'nf_gene_of_interest.txt', unpack=False)))\n",
    "nf_goi = list(nf_goi_.split(\" \"))\n",
    "nf_goi_str = ' '.join([str(item) for item in nf_goi])\n",
    "\n",
    "nf_neighbors = []\n",
    "for edge in G.edges():\n",
    "    if edge[0] == nf_goi_str:\n",
    "        nf_neighbors.append(edge[1])\n",
    "    elif edge[1] == nf_goi_str: \n",
    "        nf_neighbors.append(edge[0])\n",
    "\n",
    "nf_all = nf_neighbors + nf_goi\n",
    "\n",
    "# get disease module genes \n",
    "num_doid = '8712' \n",
    "\n",
    "# define specific DOID \n",
    "dismod = FM_disease.loc[:,'DOID:'+num_doid]\n",
    "dismod.index = FM_disease.index\n",
    "dismod_id = 'DOID'+num_doid\n",
    "\n",
    "dismod_genes = [] \n",
    "for i,v in enumerate(dismod.values):\n",
    "    if v == 1:\n",
    "        dismod_genes.append(dismod.index[i])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "l_genes_nf = [str(i) for i in dismod_genes]\n",
    "print('Number of Genes associated to DOID', num_doid,':',len(l_genes_nf))\n",
    "l_genes_neighbors = [str(i) for i in nf_neighbors]\n",
    "print('Number of NF1 neighbor genes', num_doid,':',len(l_genes_neighbors))\n",
    "\n",
    "# create a dataframe (sorted by DF global) and fill 1 when disease-assoc. gene\n",
    "DF_dismod = pd.DataFrame(dismod.reindex(DF_structural.index.copy() ,fill_value=0))\n",
    "DF_dismod_withneigh = pd.DataFrame(dismod.reindex(DF_structural.index.copy() ,fill_value=0))\n",
    "\n",
    "for i in DF_dismod.index:\n",
    "    if i in l_genes_nf:\n",
    "        DF_dismod.loc[i] = 1\n",
    "        DF_dismod_withneigh.loc[i] = 1\n",
    "    elif i in l_genes_neighbors:\n",
    "        DF_dismod_withneigh.loc[i] = 1\n",
    "    else: \n",
    "        pass\n",
    "\n",
    "DF_dismod.columns = ['Neurofib 8712']\n",
    "DF_dismod_withneigh.columns = ['Neurofib 8712+neigh']\n",
    "\n",
    "DF_NFneighbors = pd.DataFrame(dismod.reindex(DF_structural.index.copy() ,fill_value=0))\n",
    "for i in DF_NFneighbors.index:\n",
    "    if i in l_genes_neighbors:# and i != nf_goi_str:\n",
    "        DF_NFneighbors.loc[i] = 1\n",
    "    \n",
    "DF_NFneighbors.columns = ['NF1 neigh']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compound-congress",
   "metadata": {},
   "source": [
    "---\n",
    "# VECTORS : Disease Context \n",
    "---\n",
    "+ DOID:3165 - Skin benign neoplasm\n",
    "+ DOID:10534 - Stomach Cancer\n",
    "+ DOID:3620 - Central Nervous System Cancer\n",
    "+ DOID:0060089 - Endocrine organ benign neoplasm\n",
    "+ resources: https://disease-ontology.org/ https://en.wikipedia.org/wiki/Neurofibromatosis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cubic-hypothetical",
   "metadata": {
    "tags": []
   },
   "source": [
    "### First Disease Vector | DOID:3165 - Skin benign neoplasm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "light-corpus",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genes associated to DOID 3165 : 163\n",
      "Number of Genes associated to DOID with overlap of NF neighbors 3165 : 165\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "num_doid_1 = '3165'\n",
    "# -----------------------------\n",
    "\n",
    "# define specific DOID \n",
    "dismod_1 = FM_disease.loc[:,'DOID:'+num_doid_1]\n",
    "dismod_1.index = FM_disease.index\n",
    "\n",
    "# get all genes associated to disease\n",
    "dismod_genes_1 = [] \n",
    "for i,v in enumerate(dismod_1.values):\n",
    "    if v == 1:\n",
    "        dismod_genes_1.append(dismod_1.index[i])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "l_genes_dismod_1 = [str(i) for i in dismod_genes_1]\n",
    "l_genes_nfdismod_overlap_1 = []\n",
    "for i in nf_neighbors:\n",
    "    if i in l_genes_dismod_1:\n",
    "        l_genes_nfdismod_overlap_1.append(i)\n",
    "l_genes_dismod_1_all = l_genes_dismod_1 + l_genes_nfdismod_overlap_1\n",
    "print('Number of Genes associated to DOID', num_doid_1,':',len(l_genes_dismod_1))\n",
    "print('Number of Genes associated to DOID with overlap of NF neighbors', num_doid_1,':',len(l_genes_dismod_1_all))\n",
    "\n",
    "# create a dataframe (sorted by DF global) and fill 1 when disease-assoc. gene\n",
    "DF_dismod_1 = pd.DataFrame(dismod_1.reindex(DF_structural.index ,fill_value=0))\n",
    "for i in DF_dismod_1.index:\n",
    "    if i in l_genes_dismod_1_all: #l_genes_dismod_1:\n",
    "        DF_dismod_1.loc[i] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "connected-clearing",
   "metadata": {},
   "source": [
    "### Second Disease Vector | DOID:10534 - Stomach Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "further-concept",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genes associated to DOID 10534 : 303\n",
      "Number of Genes associated to DOID with overlap of NF neighbors 10534 : 307\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "num_doid_2 = '10534' #'3883' #'1612' #'3490'\n",
    "# -----------------------------\n",
    "\n",
    "# define specific DOID \n",
    "dismod_2 = FM_disease.loc[:,'DOID:'+num_doid_2]\n",
    "dismod_2.index = FM_disease.index\n",
    "\n",
    "# get all genes associated to disease\n",
    "dismod_genes_2 = [] \n",
    "for i,v in enumerate(dismod_2.values):\n",
    "    if v == 1:\n",
    "        dismod_genes_2.append(dismod_2.index[i])\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "l_genes_dismod_2 = [str(i) for i in dismod_genes_2]\n",
    "l_genes_nfdismod_overlap_2 = []\n",
    "for i in nf_neighbors:\n",
    "    if i in l_genes_dismod_2:\n",
    "        l_genes_nfdismod_overlap_2.append(i)\n",
    "l_genes_dismod_2_all = l_genes_dismod_2 + l_genes_nfdismod_overlap_2\n",
    "print('Number of Genes associated to DOID', num_doid_2,':',len(l_genes_dismod_2)) \n",
    "print('Number of Genes associated to DOID with overlap of NF neighbors', num_doid_2,':',len(l_genes_dismod_2_all))\n",
    "\n",
    "# create a dataframe (sorted by DF global) and fill 1 when disease-assoc. gene\n",
    "DF_dismod_2 = pd.DataFrame(dismod_2.reindex(DF_structural.index ,fill_value=0))\n",
    "for i in DF_dismod_2.index:\n",
    "    if i in l_genes_dismod_2_all:\n",
    "        DF_dismod_2.loc[i] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "terminal-garage",
   "metadata": {},
   "source": [
    "### Third Disease Vector | DOID:3620 - Central Nervous System Cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "sophisticated-sector",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genes associated to DOID 3620 : 173\n",
      "Number of Genes associated to DOID with overlap of NF neighbors 3620 : 174\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "num_doid_3 = '3620' #'0060115' \n",
    "# -----------------------------\n",
    "\n",
    "# define specific DOID \n",
    "dismod_3 = FM_disease.loc[:,'DOID:'+num_doid_3]\n",
    "dismod_3.index = FM_disease.index\n",
    "\n",
    "dismod_genes_3 = [] \n",
    "for i,v in enumerate(dismod_3.values):\n",
    "    if v == 1:\n",
    "        dismod_genes_3.append(dismod_3.index[i])\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "l_genes_dismod_3 = [str(i) for i in dismod_genes_3]\n",
    "l_genes_nfdismod_overlap_3 = []\n",
    "for i in nf_neighbors:\n",
    "    if i in l_genes_dismod_3:\n",
    "        l_genes_nfdismod_overlap_3.append(i)\n",
    "l_genes_dismod_3_all = l_genes_dismod_3 + l_genes_nfdismod_overlap_3\n",
    "print('Number of Genes associated to DOID', num_doid_3,':',len(l_genes_dismod_3))\n",
    "print('Number of Genes associated to DOID with overlap of NF neighbors', num_doid_3,':',len(l_genes_dismod_3_all))\n",
    "\n",
    "# create a dataframe (sorted by DF global) and fill 1 when disease-assoc. gene\n",
    "DF_dismod_3 = pd.DataFrame(dismod_3.reindex(DF_structural.index ,fill_value=0))\n",
    "for i in DF_dismod_3.index:\n",
    "    if i in l_genes_dismod_3_all:\n",
    "        DF_dismod_3.loc[i] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "original-romantic",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Fourth Disease Vector | DOID:0060089 - Endocrine Organ benign neoplasm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "comic-bobby",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Genes associated to DOID 0060089 : 122\n",
      "Number of Genes associated to DOID with overlap of NF neighbors 0060089 : 123\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "num_doid_4 = '0060089'\n",
    "# -----------------------------\n",
    "\n",
    "# define specific DOID \n",
    "dismod_4 = FM_disease.loc[:,'DOID:'+num_doid_4]\n",
    "dismod_4.index = FM_disease.index\n",
    "dismod_id_4 = 'DOID'+num_doid_4\n",
    "\n",
    "dismod_genes_4 = [] \n",
    "for i,v in enumerate(dismod_4.values):\n",
    "    if v == 1:\n",
    "        dismod_genes_4.append(dismod_4.index[i])\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "l_genes_dismod_4 = [str(i) for i in dismod_genes_4]\n",
    "l_genes_nfdismod_overlap_4 = []\n",
    "for i in nf_neighbors:\n",
    "    if i in l_genes_dismod_4:\n",
    "        l_genes_nfdismod_overlap_4.append(i)\n",
    "l_genes_dismod_4_all = l_genes_dismod_4 + l_genes_nfdismod_overlap_4\n",
    "print('Number of Genes associated to DOID', num_doid_4,':',len(l_genes_dismod_4))\n",
    "print('Number of Genes associated to DOID with overlap of NF neighbors', num_doid_4,':',len(l_genes_dismod_4_all))\n",
    "\n",
    "# create a dataframe (sorted by DF global) and fill 1 when disease-assoc. gene\n",
    "DF_dismod_4 = pd.DataFrame(dismod_4.reindex(DF_structural.index ,fill_value=0))\n",
    "for i in DF_dismod_4.index:\n",
    "    if i in l_genes_dismod_4_all:\n",
    "        DF_dismod_4.loc[i] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "located-detector",
   "metadata": {},
   "source": [
    "---\n",
    "# ALL DISEASE VECTORS / MATRIX PREP \n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e60abdf1-4ca8-42c8-bbfb-6a041cea3a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENHANCING FACTOR Disease Context :  0.187514\n"
     ]
    }
   ],
   "source": [
    "# --------------------------------------\n",
    "#\n",
    "scalar_val = 2 \n",
    "#\n",
    "# -------------------------------------- \n",
    "enhance_factor_discontext = round((1-l_max_visprob)*scalar_val,10)\n",
    "print('ENHANCING FACTOR Disease Context : ', enhance_factor_discontext)\n",
    "\n",
    "# Vector of NF1 Neurofibromatosis\n",
    "DF_dismod_scal = DF_dismod * enhance_factor_discontext\n",
    "DF_dismod_withneigh_scal = DF_dismod_withneigh * enhance_factor_discontext\n",
    "\n",
    "# Vector of NF1 neighbor genes \n",
    "DF_NFneighbors_scal = DF_NFneighbors * enhance_factor_discontext \n",
    "\n",
    "DF_dismod_1_scal = DF_dismod_1 * enhance_factor_discontext\n",
    "DF_dismod_2_scal = DF_dismod_2 * enhance_factor_discontext\n",
    "DF_dismod_3_scal = DF_dismod_3 * enhance_factor_discontext\n",
    "DF_dismod_4_scal = DF_dismod_4 * enhance_factor_discontext\n",
    "\n",
    "# merge into one dataframe \n",
    "DF_merge_discontext = pd.concat([\n",
    "                                DF_structural, # original global matrix \n",
    "                                \n",
    "                                DF_dismod_scal,  # Disease Module Neurofibromatosis\n",
    "                                #DF_dismod_withneigh_scal, # Disease Module Neurofibromatosis WITH NEIGHBORS of NF1 gene\n",
    "                                #DF_NFneighbors_scal, #_scal, # Neighbors of NF1\n",
    "                        \n",
    "                                DF_dismod_1_scal, # Disease Context Vectors 1-4\n",
    "                                DF_dismod_2_scal,\n",
    "                                DF_dismod_3_scal,\n",
    "                                DF_dismod_4_scal,\n",
    "    \n",
    "                                ],\n",
    "                                axis=1\n",
    "                                )\n",
    "\n",
    "#DF_merge_discontext"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8d4dca-c0bf-4249-b78e-5d7f0ef0650b",
   "metadata": {
    "tags": []
   },
   "source": [
    "_____ \n",
    "# SPATIAL EMBEDDING\n",
    "_____"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "failing-spare",
   "metadata": {},
   "source": [
    "### 2D PORTRAIT POSITIONING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "dedicated-mission",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_n = 8 #6 #20\n",
    "spr = 1.0\n",
    "md = 0.0  #.4\n",
    "metr = 'cosine'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1145de3f-48fc-4411-a5ce-202a9cf8f1c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 19s, sys: 7.81 s, total: 1min 27s\n",
      "Wall time: 43.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "posG_umap2D = layout_portrait_umap(G,DF_merge_discontext, 2, \n",
    "                                   n_neighbors = n_n, \n",
    "                                   spread = spr, \n",
    "                                   min_dist = md, \n",
    "                                   metric = metr) \n",
    "posG = posG_umap2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1f534a82-8edc-48ba-a5df-2ebc654855fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%%time \n",
    "#posG_umap3D = layout_portrait_umap(G,DF_merge_discontext, 3, \n",
    "#                                   n_neighbors = n_n, \n",
    "#                                   spread = spr, \n",
    "#                                   min_dist = md, \n",
    "#                                   metric = metr) \n",
    "#posG = posG_umap3D"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2015b5-625b-476d-a619-5ac1f551c10e",
   "metadata": {
    "tags": []
   },
   "source": [
    "____________\n",
    "# VISUAL SETTINGS + PLOT PREPARATION\n",
    "____________"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807a5984-ccb5-46b4-a7cd-aad24163ed53",
   "metadata": {
    "tags": []
   },
   "source": [
    "### DISEASE SPECIFIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "0de0c5e1-50d1-4577-bfce-568c04e8d6e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes within radius around Center - Disease 1: 91\n",
      "Number of links in circle buffer - Disease 1: 105\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# DISEASE 1 | ORANGE\n",
    "#\n",
    "\n",
    "# color nodes\n",
    "node_col_1 = '#FF7001' # (orange) #'#B00160' \n",
    "\n",
    "center_dis_1_sym = 'TYR' #'GLI2' #'AIRE' # 'KRAS' #'PTEN' \n",
    "\n",
    "# get nodeID of gene symbol \n",
    "for ix, sym in d_gene_sym.items():\n",
    "    if sym == center_dis_1_sym:\n",
    "        center_dis_1_id = ix\n",
    "\n",
    "# get coordinates from nodeID\n",
    "for node, coords in posG.items():\n",
    "    if node == center_dis_1_id:\n",
    "        center_dis_1_coords = coords\n",
    "\n",
    "# make shapely point from defined center of cluster \n",
    "point_1 = geometry.Point(center_dis_1_coords[0], center_dis_1_coords[1])\n",
    "\n",
    "# create your circle buffer from one of the points\n",
    "distance = 0.05\n",
    "\n",
    "circle_buffer = point_1.buffer(distance)\n",
    "\n",
    "# get list of genes that are within radius around the center \n",
    "l_genes_circlebuffer_1 = []\n",
    "for node,coords in posG.items():\n",
    "    point = geometry.Point(coords[0], coords[1])\n",
    "    if point.within(circle_buffer) and node in l_genes_dismod_1: \n",
    "        l_genes_circlebuffer_1.append(node)\n",
    "        \n",
    "print('Number of genes within radius around Center - Disease 1:' ,len(l_genes_circlebuffer_1))\n",
    "\n",
    "# color edges within circle buffer     \n",
    "d_edge_col_circlebuffer_1 = color_edges_from_nodelist_specific(G, l_genes_circlebuffer_1, node_col_1)\n",
    "print('Number of links in circle buffer - Disease 1:', len(d_edge_col_circlebuffer_1))\n",
    "\n",
    "# color all dismod edges    \n",
    "d_edge_col_1 = color_edges_from_nodelist_specific(G, l_genes_dismod_1, node_col_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5801dabe-9069-4b68-a2d9-1f6f12b63706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes within radius around Center - Disease 2: 194\n",
      "Number of links in circle buffer - Disease 2: 165\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# DISEASE 2 | YELLOW\n",
    "#\n",
    "\n",
    "# color nodes\n",
    "node_col_2 = '#FFC433' #(yellow)\n",
    "\n",
    "center_dis_2_sym = 'ACTC1' #'HIKESHI' # 'ZNF160' #'TBX3' \n",
    "# get nodeID of gene symbol \n",
    "for ix, sym in d_gene_sym.items():\n",
    "    if sym == center_dis_2_sym:\n",
    "        center_dis_2_id = ix\n",
    "\n",
    "# get coordinates from nodeID\n",
    "for node, coords in posG.items():\n",
    "    if node == center_dis_2_id:\n",
    "        center_dis_2_coords = coords\n",
    "\n",
    "# make shapely point from defined center of cluster \n",
    "point_2 = geometry.Point(center_dis_2_coords[0], center_dis_2_coords[1])\n",
    "\n",
    "# create your circle buffer from one of the points\n",
    "distance = 0.05\n",
    "circle_buffer = point_2.buffer(distance)\n",
    "\n",
    "# get list of genes that are within radius around the center \n",
    "l_genes_circlebuffer_2 = []\n",
    "for node,coords in posG.items():\n",
    "    point = geometry.Point(coords[0], coords[1])\n",
    "    if point.within(circle_buffer) and node in l_genes_dismod_2: \n",
    "        l_genes_circlebuffer_2.append(node)\n",
    "        \n",
    "print('Number of genes within radius around Center - Disease 2:' ,len(l_genes_circlebuffer_2))\n",
    "\n",
    "# color edges within circle buffer     \n",
    "d_edge_col_circlebuffer_2 = color_edges_from_nodelist_specific(G, l_genes_circlebuffer_2, node_col_2)\n",
    "print('Number of links in circle buffer - Disease 2:', len(d_edge_col_circlebuffer_2))\n",
    "\n",
    "# color all dismod edges    \n",
    "d_edge_col_2 = color_edges_from_nodelist_specific(G, l_genes_dismod_2, node_col_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8c2a3bf1-b882-4349-882c-e7e8e3b6fb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes within radius around Center - Disease 3: 78\n",
      "Number of links in circle buffer - Disease 3: 71\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# DISEASE 3 | GREEN\n",
    "#\n",
    "\n",
    "# color nodes\n",
    "node_col_3 = '#BEDC00' # (yellowgreen)\n",
    "\n",
    "center_dis_3_sym = 'FLI1' #'MTR' \n",
    "# get nodeID of gene symbol \n",
    "for ix, sym in d_gene_sym.items():\n",
    "    if sym == center_dis_3_sym:\n",
    "        center_dis_3_id = ix\n",
    "\n",
    "# get coordinates from nodeID\n",
    "for node, coords in posG.items():\n",
    "    if node == center_dis_3_id:\n",
    "        center_dis_3_coords = coords\n",
    "\n",
    "# make shapely point from defined center of cluster \n",
    "point_3 = geometry.Point(center_dis_3_coords[0], center_dis_3_coords[1])\n",
    "\n",
    "# create your circle buffer from one of the points\n",
    "distance = 0.06\n",
    "circle_buffer = point_3.buffer(distance)\n",
    "\n",
    "# get list of genes that are within radius around the center \n",
    "l_genes_circlebuffer_3 = []\n",
    "for node,coords in posG.items():\n",
    "    point = geometry.Point(coords[0], coords[1])\n",
    "    if point.within(circle_buffer) and node in l_genes_dismod_3: \n",
    "        l_genes_circlebuffer_3.append(node)\n",
    "        \n",
    "print('Number of genes within radius around Center - Disease 3:' ,len(l_genes_circlebuffer_3))\n",
    "\n",
    "# color edges within circle buffer     \n",
    "d_edge_col_circlebuffer_3 = color_edges_from_nodelist_specific(G, l_genes_circlebuffer_3, node_col_3)\n",
    "print('Number of links in circle buffer - Disease 3:', len(d_edge_col_circlebuffer_3))\n",
    "\n",
    "# color all dismod edges    \n",
    "d_edge_col_3 = color_edges_from_nodelist_specific(G, l_genes_dismod_3, node_col_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7c0f0b72-bfe5-4e4a-9f8b-f266762a38fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of genes within radius around Center - Disease 4: 99\n",
      "Number of links in circle buffer - Disease 4: 43\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# DISEASE 4 | DARK RED\n",
    "#\n",
    "\n",
    "# color nodes\n",
    "node_col_4 = '#992121' # '#A60C0C' # (darkred)\n",
    "\n",
    "center_dis_4_sym = 'SLC26A3' #'SCNN1G'\n",
    "# get nodeID of gene symbol \n",
    "for ix, sym in d_gene_sym.items():\n",
    "    if sym == center_dis_4_sym:\n",
    "        center_dis_4_id = ix\n",
    "\n",
    "# get coordinates from nodeID\n",
    "for node, coords in posG.items():\n",
    "    if node == center_dis_4_id:\n",
    "        center_dis_4_coords = coords\n",
    "\n",
    "# make shapely point from defined center of cluster \n",
    "point_4 = geometry.Point(center_dis_4_coords[0], center_dis_4_coords[1])\n",
    "\n",
    "# create your circle buffer from one of the points\n",
    "distance = 0.15\n",
    "circle_buffer = point_4.buffer(distance)\n",
    "\n",
    "# get list of genes that are within radius around the center \n",
    "l_genes_circlebuffer_4 = []\n",
    "for node,coords in posG.items():\n",
    "    point = geometry.Point(coords[0], coords[1])\n",
    "    if point.within(circle_buffer) and node in l_genes_dismod_4: \n",
    "        l_genes_circlebuffer_4.append(node)\n",
    "        \n",
    "print('Number of genes within radius around Center - Disease 4:' ,len(l_genes_circlebuffer_4))\n",
    "\n",
    "# color edges within circle buffer     \n",
    "d_edge_col_circlebuffer_4 = color_edges_from_nodelist_specific(G, l_genes_circlebuffer_4, node_col_4)\n",
    "print('Number of links in circle buffer - Disease 4:', len(d_edge_col_circlebuffer_4))\n",
    "\n",
    "# color all dismod edges    \n",
    "d_edge_col_4 = color_edges_from_nodelist_specific(G, l_genes_dismod_4, node_col_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4615fc-b189-4e09-8f5c-88c7c01b85d5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### NF SPECIFIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "301cbd71-0679-416b-aa59-a9342ce9b96a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of links of NF1 overlap with disease modules genes (total): 690\n",
      "Number of links of NF1 neighbors to filtered disease module genes: 185\n"
     ]
    }
   ],
   "source": [
    "# -------------------------\n",
    "#\n",
    "# Links of NF1 to all its neighbors\n",
    "#\n",
    "# -------------------------\n",
    "node_col_nf = '#0068FF'\n",
    "\n",
    "d_edge_col_nf1_to_neighbors = color_edges_from_node(G, nf_goi_str, node_col_nf)\n",
    "#print('Number of links of NF1 neighbors:', len(d_edge_col_nf1_to_neighbors))\n",
    "\n",
    "\n",
    "# -------------------------\n",
    "#\n",
    "# Links of Neurofibromatosis genes to disease module genes \n",
    "#\n",
    "# -------------------------\n",
    "\n",
    "node_col_nf_diseasemodule_overlap = '#89BBD4'\n",
    "dismod_all = l_genes_dismod_1 + l_genes_dismod_2 + l_genes_dismod_3 + l_genes_dismod_4\n",
    "\n",
    "l_genes_nf_diseasemodule_overlap = []\n",
    "for edge in G.edges():\n",
    "    if edge[0] in dismod_all and edge[1] in l_genes_nf:\n",
    "        l_genes_nf_diseasemodule_overlap.append(edge)\n",
    "    elif edge[1] in dismod_all and edge[0] in l_genes_nf:\n",
    "        l_genes_nf_diseasemodule_overlap.append(edge)\n",
    "        \n",
    "l_selection = []\n",
    "for edge in l_genes_nf_diseasemodule_overlap:\n",
    "    for i in edge:\n",
    "        if i in l_genes_nf or i in dismod_all:\n",
    "            l_selection.append(i)\n",
    "        else:\n",
    "            pass \n",
    "        \n",
    "d_edge_col_nf_diseasemodule_overlap = color_edges_from_nodelist_specific(G, set(l_selection), node_col_nf_diseasemodule_overlap)\n",
    "print('Number of links of NF1 overlap with disease modules genes (total):', len(d_edge_col_nf_diseasemodule_overlap))\n",
    "\n",
    "# W I T H  C I R C L E B U F F E R \n",
    "center_nf_sym = 'NF1' \n",
    "\n",
    "# get nodeID of gene symbol \n",
    "for ix, sym in d_gene_sym.items():\n",
    "    if sym == center_nf_sym:\n",
    "        center_nf_id = ix\n",
    "        \n",
    "# get coordinates from nodeID\n",
    "for node, coords in posG.items():\n",
    "    if node == center_nf_id:\n",
    "        center_nf_coords = coords\n",
    "        \n",
    "# make shapely point from defined center of cluster \n",
    "point_nf = geometry.Point(center_nf_coords[0], center_nf_coords[1])\n",
    "\n",
    "# create your circle buffer from one of the points\n",
    "distance = 0.08\n",
    "circle_buffer= point_nf.buffer(distance)\n",
    "\n",
    "# get list of genes that are within radius around the centerpoint\n",
    "l_genes_circlebuffer_nf_diseasemodule_overlap = []\n",
    "for node,coords in posG.items():\n",
    "    point = geometry.Point(coords[0], coords[1])\n",
    "    if point.within(circle_buffer) and node in l_selection: \n",
    "        l_genes_circlebuffer_nf_diseasemodule_overlap.append(node)\n",
    "\n",
    "d_edge_col_nf_diseasemodule_overlap_buffer = color_edges_from_nodelist_specific(G, l_genes_circlebuffer_nf_diseasemodule_overlap, node_col_nf_diseasemodule_overlap)\n",
    "print('Number of links of NF1 neighbors to filtered disease module genes:', len(d_edge_col_nf_diseasemodule_overlap_buffer))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8fdf15-473a-4747-a223-e5153d74e851",
   "metadata": {},
   "source": [
    "---\n",
    "## PLOT \n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "substantial-angola",
   "metadata": {},
   "source": [
    "#### MERGE NODE COLORS > 4 diseases = 4 different colors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "wooden-circulation",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge all node colors (and sort like G.nodes)\n",
    "\n",
    "col_param = list(df_centralities['closeness'].values)\n",
    "d_node_colors = dict(zip(list(G.nodes()), col_param))\n",
    "\n",
    "node_col_shared = '#AFC8E8' \n",
    "\n",
    "c1='#E6E6E6' \n",
    "c2='#9B9B9B' \n",
    "n=len(set(col_param))\n",
    "\n",
    "colors_for_palette = []\n",
    "for x in range(n+1):\n",
    "    colors_for_palette.append(colorFader(c1,c2,x/n))    \n",
    "\n",
    "customPalette = sns.set_palette(sns.color_palette(colors_for_palette))\n",
    "d_colors = color_nodes_from_dict(G, d_node_colors, palette = customPalette)\n",
    "\n",
    "d_col_all = {}\n",
    "for k,v in d_colors.items():\n",
    "    \n",
    "    # color for all NF1 associated genes (i.e. neighbors, NF1 disease module) \n",
    "    if k in l_genes_nf:# or k in nf_neighbors:\n",
    "        d_col_all[k] = node_col_nf\n",
    "   \n",
    "    #shared genes 1+2\n",
    "    elif k in l_genes_dismod_1 and k in l_genes_dismod_2:# and k in l_genes_dismod_3 and k in l_genes_dismod_4:\n",
    "        d_col_all[k] = node_col_shared\n",
    "        \n",
    "    #shared genes 1+3\n",
    "    elif k in l_genes_dismod_1 and k in l_genes_dismod_3:\n",
    "        d_col_all[k] = node_col_shared\n",
    "        \n",
    "    #shared genes 1+4\n",
    "    elif k in l_genes_dismod_1 and k in l_genes_dismod_4:\n",
    "        d_col_all[k] = node_col_shared \n",
    "        \n",
    "    #shared genes 2+3\n",
    "    elif k in l_genes_dismod_2 and k in l_genes_dismod_3:\n",
    "        d_col_all[k] = node_col_shared\n",
    "        \n",
    "    #shared genes 2+4\n",
    "    elif k in l_genes_dismod_2 and k in l_genes_dismod_4:\n",
    "        d_col_all[k] = node_col_shared \n",
    "        \n",
    "    #shared genes 3+4\n",
    "    elif k in l_genes_dismod_3 and k in l_genes_dismod_4:\n",
    "        d_col_all[k] = node_col_shared \n",
    "    \n",
    "    \n",
    "    # disease module colors \n",
    "    elif k in l_genes_dismod_1:\n",
    "        d_col_all[k] = node_col_1\n",
    "        \n",
    "    elif k in l_genes_dismod_2:\n",
    "        d_col_all[k] = node_col_2\n",
    "        \n",
    "    elif k in l_genes_dismod_3:\n",
    "        d_col_all[k] = node_col_3\n",
    "    \n",
    "    elif k in l_genes_dismod_4:\n",
    "        d_col_all[k] = node_col_4\n",
    "    \n",
    "    else:\n",
    "        d_col_all[k]=v\n",
    "        \n",
    "colors = list(d_col_all.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8aecdcee-6ccc-4080-b4bb-ec495cb28f37",
   "metadata": {},
   "source": [
    "---\n",
    "## 2D PLOT\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "90ce2ec0-b8a8-4d79-8a6e-d714e049df16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'output_plots/2Dlayouts/2Dportrait_DOIDs3165_10534_3620_0060089_enh2_nn8_spr1.0_md0.0_cosine_human_dismod01linksonly.html'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------\n",
    "# G E N E R A L S E T T I N G S\n",
    "# --------------------------------------------\n",
    "\n",
    "opacity_nodes = 0.8\n",
    "node_edge_col = '#696969' \n",
    "node_linewidth = 0.5\n",
    "scale_factor = 0.55\n",
    "\n",
    "# --------------------------------------------\n",
    "# plot nodes based on focus \n",
    "# > separate to background/foreground \n",
    "# --------------------------------------------\n",
    "l_genes_all = l_genes_nf + dismod_all + l_genes_neighbors\n",
    "\n",
    "posG_foreground = {}\n",
    "posG_background = {}\n",
    "for k,v in posG.items():\n",
    "    if k in l_genes_all:\n",
    "        posG_foreground[k]=v\n",
    "    else: \n",
    "        posG_background[k]=v\n",
    "    \n",
    "d_colors_foreground = {}\n",
    "d_colors_background = {}\n",
    "for i,c in d_col_all.items():\n",
    "    if i in posG_foreground.keys():\n",
    "        d_colors_foreground[i]=c\n",
    "    else: \n",
    "        d_colors_background[i]=c\n",
    "\n",
    "colors_foreground = list(d_colors_foreground.values())\n",
    "colors_background = list(d_colors_background.values())\n",
    "\n",
    "d_feat_foreground = {}\n",
    "d_feat_background = {}\n",
    "for k,v in d_gene_sym.items():\n",
    "    if k in posG_foreground: \n",
    "        d_feat_foreground[k]=v\n",
    "    else:\n",
    "        d_feat_background[k]=v\n",
    "        \n",
    "feat_foreground = list(d_feat_foreground.values())\n",
    "feat_background = list(d_feat_background.values())\n",
    "\n",
    "d_size_plotly = draw_node_degree_3D(G, scale_factor/80 ) # 50) #100\n",
    "d_size_plotly_foreground = {}\n",
    "d_size_plotly_background = {}\n",
    "for k,v in d_size_plotly.items():\n",
    "    if k in posG_foreground.keys():\n",
    "        d_size_plotly_foreground[k]=v\n",
    "    else:\n",
    "        d_size_plotly_background[k]=v\n",
    "        \n",
    "size_plotly_foreground = list(d_size_plotly_foreground.values())\n",
    "size_plotly_background = list(d_size_plotly_background.values())\n",
    "\n",
    "# --------------------------------------------\n",
    "# N O D E S \n",
    "# --------------------------------------------\n",
    "umap_nodes_foreground = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, size_plotly_foreground, \n",
    "                                           node_linewidth, 0.8)\n",
    "umap_nodes_foreground_ = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, 3, #4, #3,\n",
    "                                           None, 0.35)\n",
    "umap_nodes_background = get_trace_nodes_2D(posG_background, feat_background, colors_background, size_plotly_background, \n",
    "                                           None,0.5) \n",
    "umap_nodes_background_ = get_trace_nodes_2D(posG_background, feat_background, colors_background, 2, #2\n",
    "                                           None,0.5)\n",
    "\n",
    "# --------------------------------------------\n",
    "# E D G E S \n",
    "# --------------------------------------------\n",
    "opac_shine = 0.08\n",
    "lw_shine = 3.0\n",
    "lw_buffer = 0.25\n",
    "opac_buffer = 0.5 \n",
    "umap_edges_circlebuffer_1 = get_trace_edges_specific2D(d_edge_col_circlebuffer_1,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_1_shine = get_trace_edges_specific2D(d_edge_col_circlebuffer_1, posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_2 = get_trace_edges_specific2D(d_edge_col_circlebuffer_2,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_2_shine = get_trace_edges_specific2D(d_edge_col_circlebuffer_2,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_3 = get_trace_edges_specific2D(d_edge_col_circlebuffer_3,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_3_shine = get_trace_edges_specific2D(d_edge_col_circlebuffer_3,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_4 = get_trace_edges_specific2D(d_edge_col_circlebuffer_4,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_4_shine = get_trace_edges_specific2D(d_edge_col_circlebuffer_4,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_nf_diseasemodule_overlap = get_trace_edges_specific2D(d_edge_col_nf_diseasemodule_overlap_buffer,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_nf_diseasemodule_overlap_shine = get_trace_edges_specific2D(d_edge_col_nf_diseasemodule_overlap_buffer,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "\n",
    "opacity_edges = 0.3\n",
    "width_edges = 0.25\n",
    "umap_edges_1 = get_trace_edges_specific2D(d_edge_col_1, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_2 = get_trace_edges_specific2D(d_edge_col_2, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_3 = get_trace_edges_specific2D(d_edge_col_3, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_4 = get_trace_edges_specific2D(d_edge_col_4, posG, linew=width_edges, opac=opacity_edges)\n",
    "                  \n",
    "    \n",
    "data = [\n",
    "    \n",
    "        ######################\n",
    "        # nodes background \n",
    "        ######################\n",
    "    \n",
    "        umap_nodes_background_, \n",
    "        umap_nodes_background, \n",
    "    \n",
    "        ######################\n",
    "        # all edges \n",
    "        ######################\n",
    "        \n",
    "        #umap_edges_1, \n",
    "        #umap_edges_2,\n",
    "        #umap_edges_3,\n",
    "        #umap_edges_4,\n",
    "        \n",
    "        ######################\n",
    "        # edges within radius\n",
    "        ######################\n",
    "        \n",
    "        umap_edges_circlebuffer_1_shine,\n",
    "        umap_edges_circlebuffer_2_shine,\n",
    "        umap_edges_circlebuffer_3_shine,\n",
    "        umap_edges_circlebuffer_4_shine,\n",
    "    \n",
    "        umap_edges_circlebuffer_1, \n",
    "        umap_edges_circlebuffer_2, \n",
    "        umap_edges_circlebuffer_3, \n",
    "        umap_edges_circlebuffer_4, \n",
    "    \n",
    "        umap_edges_nf_diseasemodule_overlap,\n",
    "        umap_edges_nf_diseasemodule_overlap_shine,\n",
    "        \n",
    "        ######################\n",
    "        # nodes foreground \n",
    "        ######################\n",
    "        umap_nodes_foreground_,\n",
    "        umap_nodes_foreground]\n",
    "\n",
    "path = 'output_plots/2Dlayouts/'\n",
    "schema = 'light' #'dark' \n",
    "fname = '2Dportrait_'+'DOIDs'+num_doid_1+'_'+num_doid_2+'_'+num_doid_3+'_'+num_doid_4+'_enh'+str(scalar_val)+'_nn'+str(n_n)+'_spr'+str(spr)+'_md'+str(md)+'_'+str(metr)+'_'+organism+'_dismod01linksonly'\n",
    "#fname = ' test'\n",
    "\n",
    "plot_2D(data,path,fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d04a910-76e6-4f1f-ac3d-aff6e833510f",
   "metadata": {},
   "source": [
    "---\n",
    "## 3D PLOT\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a88d2d9b-f323-49ed-88c1-c9fc0a5385ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'output_plots/2Dlayouts/3Dportrait_DOIDs3165_10534_3620_0060089_enh2_nn8_spr0.9_md0.0_cosine_human.html'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --------------------------------------------\n",
    "# G E N E R A L S E T T I N G S\n",
    "# --------------------------------------------\n",
    "\n",
    "opacity_nodes = 0.8\n",
    "node_edge_col = '#696969' \n",
    "node_linewidth = 0.5\n",
    "scale_factor = 0.55\n",
    "\n",
    "\n",
    "# --------------------------------------------\n",
    "# plot nodes based on focus \n",
    "# > separate to background/foreground \n",
    "# --------------------------------------------\n",
    "l_genes_all = l_genes_nf + dismod_all + l_genes_neighbors\n",
    "\n",
    "posG_foreground = {}\n",
    "posG_background = {}\n",
    "for k,v in posG.items():\n",
    "    if k in l_genes_all:\n",
    "        posG_foreground[k]=v\n",
    "    else: \n",
    "        posG_background[k]=v\n",
    "    \n",
    "d_colors_foreground = {}\n",
    "d_colors_background = {}\n",
    "for i,c in d_col_all.items():\n",
    "    if i in posG_foreground.keys():\n",
    "        d_colors_foreground[i]=c\n",
    "    else: \n",
    "        d_colors_background[i]=c\n",
    "\n",
    "colors_foreground = list(d_colors_foreground.values())\n",
    "colors_background = list(d_colors_background.values())\n",
    "\n",
    "d_feat_foreground = {}\n",
    "d_feat_background = {}\n",
    "for k,v in d_gene_sym.items():\n",
    "    if k in posG_foreground: \n",
    "        d_feat_foreground[k]=v\n",
    "    else:\n",
    "        d_feat_background[k]=v\n",
    "        \n",
    "feat_foreground = list(d_feat_foreground.values())\n",
    "feat_background = list(d_feat_background.values())\n",
    "\n",
    "d_size_plotly = draw_node_degree(G, scale_factor/50 ) # 50) #100\n",
    "d_size_plotly_foreground = {}\n",
    "d_size_plotly_background = {}\n",
    "for k,v in d_size_plotly.items():\n",
    "    if k in posG_foreground.keys():\n",
    "        d_size_plotly_foreground[k]=v\n",
    "    else:\n",
    "        d_size_plotly_background[k]=v\n",
    "        \n",
    "size_plotly_foreground = list(d_size_plotly_foreground.values())\n",
    "size_plotly_background = list(d_size_plotly_background.values())\n",
    "\n",
    "# --------------------------------------------\n",
    "# N O D E S \n",
    "# --------------------------------------------\n",
    "umap_nodes_foreground = get_trace_nodes_3D(posG_foreground, feat_foreground, colors_foreground, size_plotly_foreground, \n",
    "                                           node_linewidth, 0.8)\n",
    "umap_nodes_foreground_ = get_trace_nodes_3D(posG_foreground, feat_foreground, colors_foreground, 3, #4, #3,\n",
    "                                           None, 0.35)\n",
    "umap_nodes_background = get_trace_nodes_3D(posG_background, feat_background, colors_background, size_plotly_background, \n",
    "                                           None,0.5) \n",
    "umap_nodes_background_ = get_trace_nodes_3D(posG_background, feat_background, colors_background, 2, #2\n",
    "                                           None,0.5)\n",
    "\n",
    "# --------------------------------------------\n",
    "# E D G E S \n",
    "# --------------------------------------------\n",
    "opac_shine = 0.05\n",
    "lw_shine = 20.0\n",
    "\n",
    "lw_buffer = 0.5\n",
    "opac_buffer = 0.8 \n",
    "\n",
    "umap_edges_circlebuffer_1 = get_trace_edges_specific3D(d_edge_col_circlebuffer_1,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_1_shine = get_trace_edges_specific3D(d_edge_col_circlebuffer_1, posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_2 = get_trace_edges_specific3D(d_edge_col_circlebuffer_2,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_2_shine = get_trace_edges_specific3D(d_edge_col_circlebuffer_2,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_3 = get_trace_edges_specific3D(d_edge_col_circlebuffer_3,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_3_shine = get_trace_edges_specific3D(d_edge_col_circlebuffer_3,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_circlebuffer_4 = get_trace_edges_specific3D(d_edge_col_circlebuffer_4,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_circlebuffer_4_shine = get_trace_edges_specific3D(d_edge_col_circlebuffer_4,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "umap_edges_nf_diseasemodule_overlap = get_trace_edges_specific3D(d_edge_col_nf_diseasemodule_overlap_buffer,posG, linew=lw_buffer, opac=opac_buffer)\n",
    "umap_edges_nf_diseasemodule_overlap_shine = get_trace_edges_specific3D(d_edge_col_nf_diseasemodule_overlap_buffer,posG, linew=lw_shine, opac=opac_shine)\n",
    "\n",
    "\n",
    "opacity_edges = 0.3\n",
    "width_edges = 0.25\n",
    "umap_edges_1 = get_trace_edges_specific3D(d_edge_col_1, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_2 = get_trace_edges_specific3D(d_edge_col_2, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_3 = get_trace_edges_specific3D(d_edge_col_3, posG, linew=width_edges, opac=opacity_edges)\n",
    "umap_edges_4 = get_trace_edges_specific3D(d_edge_col_4, posG, linew=width_edges, opac=opacity_edges)\n",
    "                  \n",
    "    \n",
    "data = [\n",
    "    \n",
    "        ######################\n",
    "        # nodes background \n",
    "        ######################\n",
    "    \n",
    "        umap_nodes_background_, \n",
    "        umap_nodes_background, \n",
    "    \n",
    "        ######################\n",
    "        # all edges \n",
    "        ######################\n",
    "        \n",
    "        #umap_edges_1, \n",
    "        #umap_edges_2,\n",
    "        #umap_edges_3,\n",
    "        #umap_edges_4,\n",
    "        \n",
    "        ######################\n",
    "        # edges within radius\n",
    "        ######################\n",
    "        \n",
    "        umap_edges_circlebuffer_1_shine,\n",
    "        umap_edges_circlebuffer_2_shine,\n",
    "        umap_edges_circlebuffer_3_shine,\n",
    "        umap_edges_circlebuffer_4_shine,\n",
    "    \n",
    "        umap_edges_circlebuffer_1, \n",
    "        umap_edges_circlebuffer_2, \n",
    "        umap_edges_circlebuffer_3, \n",
    "        umap_edges_circlebuffer_4, \n",
    "    \n",
    "        umap_edges_nf_diseasemodule_overlap,\n",
    "        umap_edges_nf_diseasemodule_overlap_shine,\n",
    "        \n",
    "        ######################\n",
    "        # nodes foreground \n",
    "        ######################\n",
    "        umap_nodes_foreground_,\n",
    "        umap_nodes_foreground]\n",
    "\n",
    "\n",
    "\n",
    "path = 'output_plots/2Dlayouts/'\n",
    "schema = 'light' #'dark' \n",
    "fname = '3Dportrait_'+'DOIDs'+num_doid_1+'_'+num_doid_2+'_'+num_doid_3+'_'+num_doid_4+'_enh'+str(scalar_val)+'_nn'+str(n_n)+'_spr'+str(spr)+'_md'+str(md)+'_'+str(metr)+'_'+organism\n",
    "#fname = ' test'\n",
    "\n",
    "plot_3D(data,path,fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8acad7-cae3-40e6-a728-6bc211808185",
   "metadata": {
    "tags": []
   },
   "source": [
    "### EXPORT POSITIONS >  VRNETZER Format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "723f7864-5da7-433f-9a97-59f1f3d103ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'output_plots/VRlayouts/' \n",
    "layout_nmspce = '2Dportrait_NF1'\n",
    "cols = colors\n",
    "export_to_csv2D(path, layout_nmspce, posG, cols)\n",
    "#export_to_csv3D(path, layout_nmspce, posG, cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "voluntary-freeware",
   "metadata": {},
   "source": [
    "---\n",
    "# ADDITIONAL | NF1 + 8712 + NEIGHBORS OF NF1\n",
    "---\n",
    "highlighting Neurofibromatosis + neighbors of gene NF1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dffe1537-3f66-4ca3-b422-072eefb7e676",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge all node colors (and sort like G.nodes)\n",
    "\n",
    "col_param = list(df_centralities['closeness'].values)\n",
    "d_node_colors = dict(zip(list(G.nodes()), col_param))\n",
    "\n",
    "c1='#E6E6E6' \n",
    "c2='#9B9B9B' \n",
    "n=len(set(col_param))\n",
    "\n",
    "colors_for_palette = []\n",
    "for x in range(n+1):\n",
    "    colors_for_palette.append(colorFader(c1,c2,x/n))    \n",
    "\n",
    "customPalette = sns.set_palette(sns.color_palette(colors_for_palette))\n",
    "d_colors = color_nodes_from_dict(G, d_node_colors, palette = customPalette)\n",
    "\n",
    "d_col_all = {}\n",
    "for k,v in d_colors.items():\n",
    "    \n",
    "    if k in l_genes_dismod:\n",
    "        d_col_all[k] = node_col_dismod\n",
    "\n",
    "    elif k in nf_neighbors: \n",
    "        d_col_all[k] = node_col_dismod\n",
    "    \n",
    "    else:\n",
    "        d_col_all[k]=v\n",
    "        \n",
    "colors = list(d_col_all.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "reduced-singapore",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot nodes based on focus > separate to background/foreground \n",
    "\n",
    "posG_foreground = {}\n",
    "posG_background = {}\n",
    "for k,v in posG.items():\n",
    "    if k in l_genes_dismod:\n",
    "        posG_foreground[k]=v\n",
    "    else: \n",
    "        posG_background[k]=v\n",
    "    \n",
    "d_colors_foreground = {}\n",
    "d_colors_background = {}\n",
    "for i,c in d_col_all.items():\n",
    "    if i in posG_foreground.keys():\n",
    "        d_colors_foreground[i]=c\n",
    "    else: \n",
    "        d_colors_background[i]=c\n",
    "\n",
    "colors_foreground = list(d_colors_foreground.values())\n",
    "colors_background = list(d_colors_background.values())\n",
    "\n",
    "d_feat_foreground = {}\n",
    "d_feat_background = {}\n",
    "for k,v in d_gene_sym.items():\n",
    "    if k in posG_foreground: \n",
    "        d_feat_foreground[k]=v\n",
    "    else:\n",
    "        d_feat_background[k]=v\n",
    "        \n",
    "feat_foreground = list(d_feat_foreground.values())\n",
    "feat_background = list(d_feat_background.values())\n",
    "\n",
    "d_size_plotly = draw_node_degree(G, scale_factor/100)\n",
    "d_size_plotly_foreground = {}\n",
    "d_size_plotly_background = {}\n",
    "for k,v in d_size_plotly.items():\n",
    "    if k in posG_foreground.keys():\n",
    "        d_size_plotly_foreground[k]=v\n",
    "    else:\n",
    "        d_size_plotly_background[k]=v\n",
    "        \n",
    "size_plotly_foreground = list(d_size_plotly_foreground.values())\n",
    "size_plotly_background = list(d_size_plotly_background.values())\n",
    "\n",
    "\n",
    "umap_nodes_foreground = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, size_plotly_foreground, \n",
    "                                           node_linewidth*0.25, 0.8)\n",
    "umap_nodes_foreground_ = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, 5,\n",
    "                                           None, 0.2)\n",
    "umap_nodes_background = get_trace_nodes_2D(posG_background, feat_background, colors_background, size_plotly_background, \n",
    "                                           None,0.5) #node_linewidth*0.05, 0.5)\n",
    "umap_nodes_background_ = get_trace_nodes_2D(posG_background, feat_background, colors_background, 2, \n",
    "                                            None,0.5)\n",
    "\n",
    "umap_edges_dismod = get_trace_edges_specific2D(d_edge_col_dismod, posG, linew=width_edges, \n",
    "                                               opac=0.5)# opacity_edges)\n",
    "umap_edges_dismod_neigh = get_trace_edges_specific2D(d_edge_col_neigh, posG, linew=width_edges, \n",
    "                                                     opac=0.5) #opacity_edges)\n",
    "\n",
    "data = [\n",
    "        umap_edges_dismod,\n",
    "        umap_edges_dismod_neigh,\n",
    "        umap_nodes_background_, \n",
    "        umap_nodes_background, \n",
    "        umap_nodes_foreground_,\n",
    "        umap_nodes_foreground]\n",
    "\n",
    "path = 'output_plots/2Dlayouts/'\n",
    "schema = 'light' #'dark' \n",
    "fname_NF1links = '2Dportrait_NF8712_'+'DOIDs'+num_doid_1+'_'+num_doid_2+'_'+num_doid_3+'_'+num_doid_4+'_enhDismod'+str(scalar_val_dismod)+'_enhContext'+str(scalar_val_discontext)+'_nn'+str(n_n)+'_spr'+str(spr)+'_md'+str(md)+'_'+str(metr)+'_'+organism+'_NF1links'\n",
    "\n",
    "plot_2D(data,path,fname_NF1links)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "essential-angel",
   "metadata": {},
   "source": [
    "---\n",
    "# ADDITIONAL | ONLY NF1 + 8712\n",
    "---\n",
    "highlighting Neurofibromatosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "textile-teaching",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_param = list(df_centralities['closeness'].values)\n",
    "d_node_colors = dict(zip(list(G.nodes()), col_param))\n",
    "\n",
    "c1='#E6E6E6' \n",
    "c2='#9B9B9B' \n",
    "n=len(set(col_param))\n",
    "\n",
    "colors_for_palette = []\n",
    "for x in range(n+1):\n",
    "    colors_for_palette.append(colorFader(c1,c2,x/n))    \n",
    "\n",
    "customPalette = sns.set_palette(sns.color_palette(colors_for_palette))\n",
    "d_colors = color_nodes_from_dict(G, d_node_colors, palette = customPalette)\n",
    "\n",
    "d_col_all = {}\n",
    "for k,v in d_colors.items():\n",
    "    if k in l_genes_dismod:\n",
    "        d_col_all[k] = node_col_dismod\n",
    "    \n",
    "    else:\n",
    "        d_col_all[k]=v\n",
    "        \n",
    "colors = list(d_col_all.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "assumed-tongue",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot nodes based on focus > separate to background/foreground \n",
    "\n",
    "posG_foreground = {}\n",
    "posG_background = {}\n",
    "for k,v in posG.items():\n",
    "    if k in l_genes_dismod:\n",
    "        posG_foreground[k]=v\n",
    "    else: \n",
    "        posG_background[k]=v\n",
    "    \n",
    "d_colors_foreground = {}\n",
    "d_colors_background = {}\n",
    "for i,c in d_col_all.items():\n",
    "    if i in posG_foreground.keys():\n",
    "        d_colors_foreground[i]=c\n",
    "    else: \n",
    "        d_colors_background[i]=c\n",
    "\n",
    "colors_foreground = list(d_colors_foreground.values())\n",
    "colors_background = list(d_colors_background.values())\n",
    "\n",
    "d_feat_foreground = {}\n",
    "d_feat_background = {}\n",
    "for k,v in d_gene_sym.items():\n",
    "    if k in posG_foreground: \n",
    "        d_feat_foreground[k]=v\n",
    "    else:\n",
    "        d_feat_background[k]=v\n",
    "        \n",
    "feat_foreground = list(d_feat_foreground.values())\n",
    "feat_background = list(d_feat_background.values())\n",
    "\n",
    "d_size_plotly = draw_node_degree(G, scale_factor/100)\n",
    "d_size_plotly_foreground = {}\n",
    "d_size_plotly_background = {}\n",
    "for k,v in d_size_plotly.items():\n",
    "    if k in posG_foreground.keys():\n",
    "        d_size_plotly_foreground[k]=v\n",
    "    else:\n",
    "        d_size_plotly_background[k]=v\n",
    "        \n",
    "size_plotly_foreground = list(d_size_plotly_foreground.values())\n",
    "size_plotly_background = list(d_size_plotly_background.values())\n",
    "\n",
    "\n",
    "umap_nodes_foreground = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, size_plotly_foreground, \n",
    "                                           node_linewidth*0.25, 0.8)\n",
    "umap_nodes_foreground_ = get_trace_nodes_2D(posG_foreground, feat_foreground, colors_foreground, 5,\n",
    "                                           None, 0.2)\n",
    "umap_nodes_background = get_trace_nodes_2D(posG_background, feat_background, colors_background, size_plotly_background, \n",
    "                                           None,0.5) #node_linewidth*0.05, 0.5)\n",
    "umap_nodes_background_ = get_trace_nodes_2D(posG_background, feat_background, colors_background, 2, \n",
    "                                            None,0.5)\n",
    "\n",
    "umap_edges_dismod = get_trace_edges_specific2D(d_edge_col_dismod, posG, linew=0.5,#width_edges, \n",
    "                                               opac=0.9) #opacity_edges)\n",
    "\n",
    "data = [\n",
    "        #umap_edges_dismod,\n",
    "        umap_nodes_background_, \n",
    "        umap_nodes_background, \n",
    "        umap_nodes_foreground_,\n",
    "        umap_nodes_foreground]\n",
    "\n",
    "path = 'output_plots/2Dlayouts/'\n",
    "schema = 'light' #'dark' \n",
    "fname_NF1module = '2Dportrait_NF8712_'+'DOIDs'+num_doid_1+'_'+num_doid_2+'_'+num_doid_3+'_'+num_doid_4+'_enhDismod'+str(scalar_val_dismod)+'_enhContext'+str(scalar_val_discontext)+'_nn'+str(n_n)+'_spr'+str(spr)+'_md'+str(md)+'_'+str(metr)+'_'+organism+'_NF1module_nolinks'\n",
    "\n",
    "plot_2D(data,path,fname_NF1module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "graduate-release",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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