{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the most present protein domains per biological function?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from intermine.webservice import Service\n",
    "import numpy as np\n",
    "import scipy.io\n",
    "import seaborn as sns\n",
    "from scipy import stats, optimize, interpolate\n",
    "import pandas as pd\n",
    "from collections import defaultdict \n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm, lognorm\n",
    "from scipy import stats\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.mlab as mlab\n",
    "import os, fnmatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting the modules to evaluate the protein domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_dir = os.path.dirname('__file__') #<-- absolute dir the script is in\n",
    "rel_path_domains=\"datasets/proteins-domains-from-Pfam.xlsx\"\n",
    "\n",
    "abs_file_path_domains = os.path.join(script_dir, rel_path_domains)\n",
    "\n",
    "# os.chdir('../') #<-- for binder os.chdir('../')\n",
    "my_path_domains=abs_file_path_domains\n",
    "data_domains=pd.read_excel(my_path_domains,header=0,index_col='Unnamed: 0')\n",
    "data_domains=data_domains.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>domain-name</th>\n",
       "      <th>domain-descrip</th>\n",
       "      <th>domain-start</th>\n",
       "      <th>domain-end</th>\n",
       "      <th>domain-method</th>\n",
       "      <th>domain-id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAC1</td>\n",
       "      <td>PF00153</td>\n",
       "      <td>Mito_carr; Mitochondrial substrate/solute carrier</td>\n",
       "      <td>10</td>\n",
       "      <td>106</td>\n",
       "      <td>Pfam</td>\n",
       "      <td>2056375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAC1</td>\n",
       "      <td>PF00153</td>\n",
       "      <td>Mito_carr; Mitochondrial substrate/solute carrier</td>\n",
       "      <td>116</td>\n",
       "      <td>210</td>\n",
       "      <td>Pfam</td>\n",
       "      <td>2056376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAC1</td>\n",
       "      <td>PF00153</td>\n",
       "      <td>Mito_carr; Mitochondrial substrate/solute carrier</td>\n",
       "      <td>217</td>\n",
       "      <td>304</td>\n",
       "      <td>Pfam</td>\n",
       "      <td>2056377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAC3</td>\n",
       "      <td>PF00153</td>\n",
       "      <td>Mito_carr; Mitochondrial substrate/solute carrier</td>\n",
       "      <td>9</td>\n",
       "      <td>105</td>\n",
       "      <td>Pfam</td>\n",
       "      <td>2062638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAC3</td>\n",
       "      <td>PF00153</td>\n",
       "      <td>Mito_carr; Mitochondrial substrate/solute carrier</td>\n",
       "      <td>115</td>\n",
       "      <td>208</td>\n",
       "      <td>Pfam</td>\n",
       "      <td>2062639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name domain-name                                     domain-descrip  \\\n",
       "0  AAC1     PF00153  Mito_carr; Mitochondrial substrate/solute carrier   \n",
       "1  AAC1     PF00153  Mito_carr; Mitochondrial substrate/solute carrier   \n",
       "2  AAC1     PF00153  Mito_carr; Mitochondrial substrate/solute carrier   \n",
       "3  AAC3     PF00153  Mito_carr; Mitochondrial substrate/solute carrier   \n",
       "4  AAC3     PF00153  Mito_carr; Mitochondrial substrate/solute carrier   \n",
       "\n",
       "   domain-start  domain-end domain-method  domain-id  \n",
       "0            10         106          Pfam    2056375  \n",
       "1           116         210          Pfam    2056376  \n",
       "2           217         304          Pfam    2056377  \n",
       "3             9         105          Pfam    2062638  \n",
       "4           115         208          Pfam    2062639  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_domains.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathways=[ 'galactose degradation','phospholipid biosynthesis', \"ethanol degradation\"]\n",
    "biological_processes_slim=[\"cell budding\",\"lipid binding\",\"cytokinesis\"]\n",
    "biological_processes_goterm=[\"cell morphogenesis involved in conjugation with cellular fusion\",\"budding cell apical bud growth\",\"establishment of cell polarity\",\"cytoskeleton organization\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def from_go_to_genes(go,label):\n",
    "    #label=[\"GOTerm\" or \"GOSlimTerm\"]\n",
    "    service = Service('https://yeastmine.yeastgenome.org/yeastmine/service', token = 'K1P7y5Oeb608Hcu06e44')\n",
    "    query = service.new_query(\"Gene\")\n",
    "    query.add_constraint(\"goAnnotation.ontologyTerm.parents\", label)\n",
    "    query.add_view(\n",
    "        \"symbol\", \"goAnnotation.evidence.code.annotType\",\n",
    "        \"goAnnotation.ontologyTerm.parents.name\"\n",
    "    )\n",
    "    query.add_constraint(\"goAnnotation.qualifier\", \"!=\", \"NOT\", code = \"C\")\n",
    "    query.add_constraint(\"goAnnotation.qualifier\", \"IS NULL\", code = \"D\")\n",
    "    query.add_constraint(\"goAnnotation.evidence.code.annotType\", \"=\", \"manually curated\", code = \"F\")\n",
    "    query.add_constraint(\"goAnnotation.ontologyTerm.parents.name\", \"=\", go, code = \"G\")\n",
    "    query.set_logic(\"(C or D) and F and G\")\n",
    "\n",
    "    data_toy=defaultdict(dict)\n",
    "\n",
    "    for row,counter in zip(query.rows(),np.arange(0,len(query.rows()))):\n",
    "\n",
    "        data_toy['gene-name'][counter]=row[\"symbol\"]\n",
    "        data_toy['evidence'][counter]=row[\"goAnnotation.evidence.code.annotType\"]\n",
    "        data_toy['annotation'][counter]=row[\"goAnnotation.ontologyTerm.parents.name\"]\n",
    "\n",
    "\n",
    "    data_toy_pd=pd.DataFrame(data_toy)\n",
    "    data=data_toy_pd.drop_duplicates()\n",
    "\n",
    "    data.index=np.arange(0,len(data))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "go=biological_processes_goterm[2]\n",
    "data=from_go_to_genes(go,label='GOTerm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_data=[]\n",
    "\n",
    "\n",
    "for i in np.arange(0,len(biological_processes_goterm)):\n",
    "    go=biological_processes_goterm[i]\n",
    "    data=from_go_to_genes(go,label='GOTerm')\n",
    "    big_data.append(data['gene-name'].tolist())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "flat_list = [item for sublist in big_data for item in sublist]\n",
    "res = [i for i in flat_list if i] \n",
    "flat_list_unique=np.unique(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the meaningful columns in the respective dataset\n",
    "domain_id_list=data_domains['domain-name']\n",
    "#query_gene=data['gene-name']\n",
    "query_gene=flat_list_unique\n",
    "\n",
    "# Initialising the arrays\n",
    "protein_a_list=[]\n",
    "\n",
    "population = np.arange(0,len(query_gene))\n",
    "\n",
    "\n",
    "for m in population:\n",
    "\n",
    "    protein_a=data_domains[data_domains['name']==query_gene[m]]\n",
    "    protein_a_list.append(protein_a['domain-name'].tolist())\n",
    "    \n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_empty_domains(protein_list_search):\n",
    "    index=[]\n",
    "    for i in np.arange(0,len(protein_list_search)):\n",
    "        if protein_list_search[i]==[]:\n",
    "            index.append(i) ## index of empty values for the protein_a_list meaning they dont have any annotated domain\n",
    "\n",
    "    y=[x for x in np.arange(0,len(protein_list_search)) if x not in index] # a list with non empty values from protein_a list\n",
    "\n",
    "    protein_list_search_new=[]\n",
    "    \n",
    "    for i in y:\n",
    "        protein_list_search_new.append(protein_list_search[i])\n",
    "        \n",
    "    return protein_list_search_new\n",
    "\n",
    "## evaluating the function\n",
    "\n",
    "protein_a_list_new=remove_empty_domains(protein_a_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The empty domain in the data were: 39 out of 287 domains\n"
     ]
    }
   ],
   "source": [
    "print('The empty domain in the data were:', len(protein_a_list)-len(protein_a_list_new), 'out of', len(protein_a_list),'domains')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "protein_module=[]\n",
    "\n",
    "for i in np.arange(0,len(protein_a_list_new)):\n",
    "    protein_module.append(np.unique(protein_a_list_new[i]))# just taking the unique domains per protein in the module \n",
    "protein_module=np.concatenate(protein_module, axis=0 )\n",
    "unique_domains,index_domains,index_counts=np.unique(protein_module,return_index=True,return_counts=True) # counting how many proteins have the same domain per module "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats_module=pd.DataFrame()\n",
    "stats_module['unique-domains']=unique_domains\n",
    "stats_module['counts-each-domains']=index_counts\n",
    "stats_module['index-for-original-domains-array']=index_domains\n",
    "stats_module['ratio-of-sharing']=(index_counts-1)/len(unique_domains)\n",
    "\n",
    "stats_module.set_index=np.arange(0,len(stats_module))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting the dataframe by the ratio of sharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\linigodelacruz\\appdata\\local\\continuum\\anaconda3\\envs\\wintest\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "c:\\users\\linigodelacruz\\appdata\\local\\continuum\\anaconda3\\envs\\wintest\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique-domains</th>\n",
       "      <th>counts-each-domains</th>\n",
       "      <th>index-for-original-domains-array</th>\n",
       "      <th>ratio-of-sharing</th>\n",
       "      <th>domain-descrip</th>\n",
       "      <th>protein-name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PF00069</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>0.074510</td>\n",
       "      <td>Pkinase; Protein kinase domain</td>\n",
       "      <td>[CLA4, SPS1, CDC5, PBS2, SSK2, KCC4, BCK1, ARK...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PF00018</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.054902</td>\n",
       "      <td>SH3_1; SH3 domain</td>\n",
       "      <td>[YSC84, LSB1, RVS167, MYO3, BZZ1, BEM1, BOI1, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PF00169</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>0.027451</td>\n",
       "      <td>PH; Pleckstrin homology domain</td>\n",
       "      <td>[BEM3, BUD4, CLA4, SLM1, BOI1, SLM2, BEM2, BOI2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PF00071</td>\n",
       "      <td>7</td>\n",
       "      <td>91</td>\n",
       "      <td>0.023529</td>\n",
       "      <td>Ras; Small GTPase superfamily</td>\n",
       "      <td>[RHO3, RHO2, TEM1, CDC42, RHO1, RSR1, RHO4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PF00134</td>\n",
       "      <td>6</td>\n",
       "      <td>116</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>Cyclin_N; Cyclin, N-terminal</td>\n",
       "      <td>[PCL1, CLB4, CLB3, PCL2, CLB1, CLB5]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique-domains  counts-each-domains  index-for-original-domains-array  \\\n",
       "8         PF00069                   20                                18   \n",
       "3         PF00018                   15                                 0   \n",
       "17        PF00169                    8                                39   \n",
       "9         PF00071                    7                                91   \n",
       "14        PF00134                    6                               116   \n",
       "\n",
       "    ratio-of-sharing                  domain-descrip  \\\n",
       "8           0.074510  Pkinase; Protein kinase domain   \n",
       "3           0.054902               SH3_1; SH3 domain   \n",
       "17          0.027451  PH; Pleckstrin homology domain   \n",
       "9           0.023529   Ras; Small GTPase superfamily   \n",
       "14          0.019608    Cyclin_N; Cyclin, N-terminal   \n",
       "\n",
       "                                         protein-name  \n",
       "8   [CLA4, SPS1, CDC5, PBS2, SSK2, KCC4, BCK1, ARK...  \n",
       "3   [YSC84, LSB1, RVS167, MYO3, BZZ1, BEM1, BOI1, ...  \n",
       "17   [BEM3, BUD4, CLA4, SLM1, BOI1, SLM2, BEM2, BOI2]  \n",
       "9         [RHO3, RHO2, TEM1, CDC42, RHO1, RSR1, RHO4]  \n",
       "14               [PCL1, CLB4, CLB3, PCL2, CLB1, CLB5]  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_module['domain-descrip']=np.zeros_like(unique_domains)\n",
    "stats_module['protein-name']=np.zeros_like(unique_domains)\n",
    "#stats_module['module-name']=np.zeros_like(unique_domains)\n",
    "for i in np.arange(0,len(unique_domains)):\n",
    "    stats_module['domain-descrip'][i]=data_domains[data_domains['domain-name']==unique_domains[i]]['domain-descrip'].tolist()[0]\n",
    "    names=data_domains[data_domains['domain-name']==unique_domains[i]]['name'].tolist()\n",
    "    inter=set(names).intersection(query_gene)\n",
    "    stats_module['protein-name'][i]=list(inter) # print the intresection of all proteins with those domains with the protein from the module \n",
    "    #stats_module['module-name'][i]=go\n",
    "stats_module_sorted=stats_module.sort_values(by=['ratio-of-sharing'],ascending=False)\n",
    "stats_module_sorted.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Module:cytoskeleton organization')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "C:\\Users\\linigodelacruz\\Documents\\PhD_2018\\Documentation\\jupyter-book-deployment\\jupyter-book\\mini_book\\docs\\_build\\jupyter_execute\\evaluating-protein-domains-per-module_17_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats_module_sorted_diff_zero=stats_module_sorted[stats_module_sorted['ratio-of-sharing']!=0]\n",
    "plt.bar(x=stats_module_sorted_diff_zero['domain-descrip'],height=stats_module_sorted_diff_zero['ratio-of-sharing']);\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('ratio of sharing of domains')\n",
    "plt.title('Module:' + go)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "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": "3.7.8"
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 "nbformat": 4,
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}