api.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:08.616209Z",
     "start_time": "2024-08-07T21:01:08.583566Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "                          disease.name                    lipid.name  \\\n0                   Insulin Resistance                 Palmitic acid   \n1                              Obesity                 Palmitic acid   \n2                          Fatty Liver                 Palmitic acid   \n3    Non-alcoholic Fatty Liver Disease                 Palmitic acid   \n4            Diabetes Mellitus, Type 2                 Palmitic acid   \n..                                 ...                           ...   \n856                            Fatigue  PC(20:4(8Z,11Z,14Z,17Z)/0:0)   \n857          Fatigue Syndrome, Chronic           9-Decenoylcarnitine   \n858           Irritable Bowel Syndrome           9-Decenoylcarnitine   \n859               Sleep Wake Disorders           9-Decenoylcarnitine   \n860                            Fatigue           9-Decenoylcarnitine   \n\n     id.pubchem.full                              lipidisease.citations  \\\n0                985  7752913, 8138059, 9392480, 9805648, 10446195, ...   \n1                985  1356939, 1864970, 1985095, 2111005, 2552844, 3...   \n2                985  1640634, 7128823, 9441722, 9799807, 10068949, ...   \n3                985  20014489, 21042596, 21147505, 21206006, 214880...   \n4                985  1634190, 1666753, 2035624, 8024729, 8257420, 8...   \n..               ...                                                ...   \n856         53480469                                           29968805   \n857         53481651                                           29968805   \n858         53481651                                           29968805   \n859         53481651                                           29968805   \n860         53481651                                           29968805   \n\n     lipidisease.pval  lipidisease.fdr  \n0        0.000000e+00     0.000000e+00  \n1       3.273250e-150    1.167941e-149  \n2       1.803147e-145    6.368138e-145  \n3       6.135278e-124    2.070283e-123  \n4        1.715283e-96     5.383524e-96  \n..                ...              ...  \n856      1.530831e-03     2.372922e-03  \n857      3.552205e-04     5.643585e-04  \n858      3.564287e-04     5.662091e-04  \n859      7.926008e-04     1.243086e-03  \n860      1.530831e-03     2.372922e-03  \n\n[861 rows x 6 columns]",
      "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>disease.name</th>\n      <th>lipid.name</th>\n      <th>id.pubchem.full</th>\n      <th>lipidisease.citations</th>\n      <th>lipidisease.pval</th>\n      <th>lipidisease.fdr</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Insulin Resistance</td>\n      <td>Palmitic acid</td>\n      <td>985</td>\n      <td>7752913, 8138059, 9392480, 9805648, 10446195, ...</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Obesity</td>\n      <td>Palmitic acid</td>\n      <td>985</td>\n      <td>1356939, 1864970, 1985095, 2111005, 2552844, 3...</td>\n      <td>3.273250e-150</td>\n      <td>1.167941e-149</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Fatty Liver</td>\n      <td>Palmitic acid</td>\n      <td>985</td>\n      <td>1640634, 7128823, 9441722, 9799807, 10068949, ...</td>\n      <td>1.803147e-145</td>\n      <td>6.368138e-145</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Non-alcoholic Fatty Liver Disease</td>\n      <td>Palmitic acid</td>\n      <td>985</td>\n      <td>20014489, 21042596, 21147505, 21206006, 214880...</td>\n      <td>6.135278e-124</td>\n      <td>2.070283e-123</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Diabetes Mellitus, Type 2</td>\n      <td>Palmitic acid</td>\n      <td>985</td>\n      <td>1634190, 1666753, 2035624, 8024729, 8257420, 8...</td>\n      <td>1.715283e-96</td>\n      <td>5.383524e-96</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>856</th>\n      <td>Fatigue</td>\n      <td>PC(20:4(8Z,11Z,14Z,17Z)/0:0)</td>\n      <td>53480469</td>\n      <td>29968805</td>\n      <td>1.530831e-03</td>\n      <td>2.372922e-03</td>\n    </tr>\n    <tr>\n      <th>857</th>\n      <td>Fatigue Syndrome, Chronic</td>\n      <td>9-Decenoylcarnitine</td>\n      <td>53481651</td>\n      <td>29968805</td>\n      <td>3.552205e-04</td>\n      <td>5.643585e-04</td>\n    </tr>\n    <tr>\n      <th>858</th>\n      <td>Irritable Bowel Syndrome</td>\n      <td>9-Decenoylcarnitine</td>\n      <td>53481651</td>\n      <td>29968805</td>\n      <td>3.564287e-04</td>\n      <td>5.662091e-04</td>\n    </tr>\n    <tr>\n      <th>859</th>\n      <td>Sleep Wake Disorders</td>\n      <td>9-Decenoylcarnitine</td>\n      <td>53481651</td>\n      <td>29968805</td>\n      <td>7.926008e-04</td>\n      <td>1.243086e-03</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>Fatigue</td>\n      <td>9-Decenoylcarnitine</td>\n      <td>53481651</td>\n      <td>29968805</td>\n      <td>1.530831e-03</td>\n      <td>2.372922e-03</td>\n    </tr>\n  </tbody>\n</table>\n<p>861 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diffexp_newmodel_df = pd.read_excel('diffexp_newmodel.xlsx')\n",
    "common_protein_df = pd.read_excel('lr_protein_common_df.xlsx')\n",
    "\n",
    "###Import protein-disease association dataframes\n",
    "#Text Mining Data\n",
    "disease_tm_df = pd.read_csv('human_disease_textmining_filtered.tsv', sep='\\t', header=None)\n",
    "#Knowledge Data\n",
    "disease_k_df = pd.read_csv('human_disease_knowledge_filtered.tsv', sep='\\t', header=None)\n",
    "#Experiment Data\n",
    "disease_e_df = pd.read_csv('human_disease_experiments_filtered.tsv', sep='\\t', header=None)\n",
    "\n",
    "###Import lipid-disease association dataframes\n",
    "lipidisease_df = pd.read_table('lipidisease_output.tsv',sep='\\t')\n",
    "lipidisease_df = lipidisease_df.rename(columns={'Disease':'disease.name','Lipid':'lipid.name','Lipid_pubchem_cid':'id.pubchem.full','Citations':'lipidisease.citations','p-value':'lipidisease.pval','FDR':'lipidisease.fdr'})\n",
    "lipidisease_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:10.571339Z",
     "start_time": "2024-08-07T21:01:08.617601Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "52931203\n"
     ]
    }
   ],
   "source": [
    "# Extract pubchem ids\n",
    "for i in diffexp_newmodel_df[diffexp_newmodel_df['id.pubchem.full'].notna()]['id.pubchem.full']:\n",
    "    if ',' in i:\n",
    "        sep = i.split(',')\n",
    "        for j in sep:\n",
    "            print(j)\n",
    "    else:\n",
    "        print(i)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:10.574351Z",
     "start_time": "2024-08-07T21:01:10.570989Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "#Rename columns of each dataframe for clarity\n",
    "disease_tm_df = disease_tm_df.rename(columns={0:'id.string', 1:'gene.name', 2:'disease.id', 3:'disease.name',4:'zscore',5:'confidence',6:'url'})\n",
    "disease_k_df = disease_k_df.rename(columns={0:'id.string', 1:'gene.name', 2:'disease.id', 3:'disease.name',4:'source.db',5:'evidence.type',6:'confidence'})\n",
    "disease_e_df = disease_e_df.rename(columns={0:'id.string', 1:'gene.name', 2:'disease.id', 3:'disease.name',4:'source.db',5:'source.score',6:'confidence'})"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:10.592238Z",
     "start_time": "2024-08-07T21:01:10.575570Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Retrieve analyte name from uniprot id\n",
    "def get_name_from_uniprot_id(accession):\n",
    "    if not accession:  # Check if the accession is empty or None\n",
    "        return 'invalid accession' # or return '' if you prefer an empty string\n",
    "    try:\n",
    "        response = requests.get(f\"https://rest.uniprot.org/uniprotkb/{accession}.json\")\n",
    "        if response and response.status_code == 200:\n",
    "            data = response.json()\n",
    "            if 'uniProtkbId' in data:\n",
    "                return data.get('uniProtkbId')\n",
    "        else:\n",
    "            return \"Error: Response not successful\"\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        return f\"Error: {e}\"\n",
    "\n",
    "# Retrieve string protein id from uniprot id\n",
    "def get_string_from_uniprot_id(accession):\n",
    "    if not accession:  # Check if the accession is empty or None\n",
    "        return 'i' # or return '' if you prefer an empty string\n",
    "    try:\n",
    "        response = requests.get(f\"https://rest.uniprot.org/uniprotkb/{accession}.json\")\n",
    "        if response and response.status_code == 200:\n",
    "            data = response.json()\n",
    "            if 'uniProtKBCrossReferences' in data:\n",
    "                uniprot_crossref =  data.get('uniProtKBCrossReferences')\n",
    "                for i in uniprot_crossref:\n",
    "                    if i.get('database') == 'STRING':\n",
    "                        string_id = i.get('id')\n",
    "                        string_id = string_id.split('.')[1]\n",
    "                        print(string_id)\n",
    "                        return string_id\n",
    "        else:\n",
    "            return ''\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        return ''\n",
    "\n",
    "# Retrieve disease associations from uniprot\n",
    "def get_uniprot_disease(accession):\n",
    "    if not accession:  # Check if the accession is empty or None\n",
    "        return 'invalid accession' # or return '' if you prefer an empty string\n",
    "    try:\n",
    "        response = requests.get(f\"https://rest.uniprot.org/uniprotkb/{accession}.json\")\n",
    "        if response and response.status_code == 200:\n",
    "            output = []\n",
    "            data = response.json()\n",
    "            protein_desc = data.get('proteinDescription').get('recommendedName')\n",
    "            protein_full_name = protein_desc.get('fullName').get('value') if protein_desc else ''\n",
    "            if 'comments' in data:\n",
    "                protein_name = data.get('uniProtkbId')\n",
    "                protein_id = data.get('primaryAccession')\n",
    "                for comment in data['comments']:\n",
    "                    if comment.get('commentType') == 'DISEASE':\n",
    "                        disease_info = comment.get('disease')\n",
    "                        if disease_info:\n",
    "                            id = disease_info.get('diseaseId')\n",
    "                            acro = disease_info.get('acronym')\n",
    "                            desc = disease_info.get('description')\n",
    "                            evidence = disease_info.get('evidences')\n",
    "                            ev_ids = []\n",
    "                            if disease_info.get('evidences'):\n",
    "                                for ev in evidence:\n",
    "                                    source = ev.get('source')\n",
    "                                    source_id = ev.get('id')\n",
    "                                    ev_ids.append(f'{source} ID: {source_id}')\n",
    "                            output.append({'id.name':protein_name, 'id.full':protein_full_name,'id.uniprot':protein_id, 'disease.name':id, 'disease.acronym':acro,'disease.description':desc,'disease.evidence':ev_ids})\n",
    "                return output\n",
    "        else:\n",
    "            return \"Error: Response not successful\"\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        return f\"Error: {e}\"\n",
    "\n",
    "# Retrieve GO annotations from uniprot id\n",
    "def get_go_annotations(accession):\n",
    "    if not accession:  # Check if the accession is empty or None\n",
    "        return 'invalid accession' # or return '' if you prefer an empty string\n",
    "    try:\n",
    "        uniprot_name = get_name_from_uniprot_id(accession)\n",
    "        response = requests.get(f\"https://www.ebi.ac.uk/QuickGO/services/annotation/search?taxonId=9606&geneProductId={accession}\")\n",
    "        if response and response.status_code == 200:\n",
    "            output = []\n",
    "            data = response.json()\n",
    "            if 'results' in data:\n",
    "                for result in data['results']:\n",
    "                    ref = result.get('reference')\n",
    "                    goid = result.get('goId')\n",
    "                    qual = result.get('qualifier')\n",
    "                    goid_desc = get_goid_name(goid)\n",
    "                    output.append({'id.name':uniprot_name,'id.uniprot':accession,'goID':goid,'qualifier':qual,'reference.id':ref,'function.name':goid_desc[0]['function.name'],'function.definition':goid_desc[0]['function.definition']})\n",
    "                return output\n",
    "        else:\n",
    "            return \"Error: Response not successful\"\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        return f\"Error: {e}\"\n",
    "\n",
    "\n",
    "# Retrieve GOID name from go id\n",
    "def get_goid_name(goid):\n",
    "    if not goid:  # Check if the accession is empty or None\n",
    "        return 'invalid accession' # or return '' if you prefer an empty string\n",
    "    try:\n",
    "        goid_new = goid.replace(':','%3A')\n",
    "        response = requests.get(f\"https://www.ebi.ac.uk/QuickGO/services/ontology/go/terms/{goid_new}\")\n",
    "        if response and response.status_code == 200:\n",
    "            data = response.json()\n",
    "            if 'results' in data:\n",
    "                for result in data['results']:\n",
    "                    if result.get('id') == goid:\n",
    "                        name = result.get('name')\n",
    "                        definition = ''\n",
    "                        if 'definition' in result:\n",
    "                            output = []\n",
    "                            definition = result.get('definition').get('text')\n",
    "                            output.append({'goID': goid, 'function.name': name, 'function.definition': definition})\n",
    "                            return output\n",
    "        else:\n",
    "            return \"Error: Response not successful\"\n",
    "    except requests.exceptions.RequestException as e:\n",
    "        return f\"Error: {e}\"\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:10.629227Z",
     "start_time": "2024-08-07T21:01:10.598937Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "lipid_dgc_df = diffexp_newmodel_df[(diffexp_newmodel_df['lm1.groupDMEK.bh.pval']<0.01)&(diffexp_newmodel_df['lm1.groupDG.bh.pval']<0.01)&(diffexp_newmodel_df['id.type']=='lipid')].copy()\n",
    "\n",
    "# Convert 'id.pubchem.full' to string and apply string methods in both DataFrames\n",
    "lipid_dgc_df['id.pubchem.full'] = lipid_dgc_df['id.pubchem.full'].astype(str).str.strip().str.lower()\n",
    "lipidisease_df['id.pubchem.full'] = lipidisease_df['id.pubchem.full'].astype(str).str.strip().str.lower()\n",
    "\n",
    "# Initialize an empty list to store the lipid information\n",
    "lipids = []\n",
    "\n",
    "# Iterate through each row of the DataFrame\n",
    "for index, row in lipid_dgc_df.iterrows():\n",
    "    # Split the 'id.pubchem.full' by comma if necessary\n",
    "    pubchem_ids = row['id.pubchem.full'].split(',') if ',' in row['id.pubchem.full'] else [row['id.pubchem.full']]\n",
    "\n",
    "    # For each pubchem ID, create a dictionary with the lipid information\n",
    "    for pubchem_id in pubchem_ids:\n",
    "        lipid_info = {\n",
    "            'id.pubchem.full': pubchem_id.strip().lower(),  # Ensure ID is stripped and lowercase\n",
    "            'id.name': row['id.name'],\n",
    "            'id.description': row['id.description'],\n",
    "            'lm1.groupDMEK.bh.pval': row['lm1.groupDMEK.bh.pval'],\n",
    "            'lm1.groupDMEK.coef': row['lm1.groupDMEK.coef'],\n",
    "            'lm1.groupDG.bh.pval': row['lm1.groupDG.bh.pval'],\n",
    "            'lm1.groupDG.coef': row['lm1.groupDG.coef'],\n",
    "            'lm1.groupDGC.holm.pval': row['lm1.groupDGC.holm.pval'],\n",
    "            'lm1.groupDGC.coef': row['lm1.groupDGC.coef']\n",
    "        }\n",
    "        lipids.append(lipid_info)\n",
    "\n",
    "# Convert the list of dictionaries to a DataFrame\n",
    "lipid_df = pd.DataFrame(lipids)\n",
    "# Filter lipidisease_df for matching entries\n",
    "lipid_common_df = lipidisease_df[lipidisease_df['id.pubchem.full'].isin(lipids[0])]\n",
    "lipid_merged_df = lipidisease_df.merge(lipid_df,how='right',on='id.pubchem.full')\n",
    "lipid_merged_df = lipid_merged_df[lipid_merged_df['lipidisease.citations'].notna()]\n",
    "lipid_merged_df.to_excel('lipidisease_assocations.xlsx',index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:01:10.660260Z",
     "start_time": "2024-08-07T21:01:10.610553Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "ENSP00000376287\n"
     ]
    }
   ],
   "source": [
    "diffexp_newmodel_df['id.string'] = diffexp_newmodel_df['id.uniprot'].apply(get_string_from_uniprot_id)\n",
    "diffexp_newmodel_df.to_excel('diffexp_newmodel.xlsx',index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:08:18.392079Z",
     "start_time": "2024-08-07T21:01:10.631153Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1721\n",
      "89\n",
      "67\n",
      "1842\n",
      "36\n"
     ]
    }
   ],
   "source": [
    "# Extract list of all pvalues and coefficients of proteins with string id\n",
    "dgc_df = diffexp_newmodel_df[(diffexp_newmodel_df['lm1.groupDG.bh.pval']<0.01)&(diffexp_newmodel_df['lm1.groupDMEK.bh.pval']<0.01)&(diffexp_newmodel_df['id.type']=='protein')].copy()\n",
    "string_ids = {\n",
    "    'id.string':dgc_df['id.string'].tolist(),\n",
    "    'lm1.groupDGC.holm.pval':dgc_df['lm1.groupDGC.holm.pval'].tolist(),\n",
    "    'lm1.groupDGC.bh.pval':dgc_df['lm1.groupDGC.bh.pval'].tolist(),\n",
    "    'lm1.groupDGC.coef':dgc_df['lm1.groupDGC.coef'].tolist(),\n",
    "    'lm1.groupDMEK.holm.pval':dgc_df['lm1.groupDMEK.holm.pval'].tolist(),\n",
    "    'lm1.groupDMEK.bh.pval':dgc_df['lm1.groupDMEK.bh.pval'].tolist(),\n",
    "    'lm1.groupDMEK.coef':dgc_df['lm1.groupDMEK.coef'].tolist(),\n",
    "    'lm1.groupDG.holm.pval':dgc_df['lm1.groupDG.holm.pval'].tolist(),\n",
    "    'lm1.groupDG.bh.pval':dgc_df['lm1.groupDG.bh.pval'].tolist(),\n",
    "    'lm1.groupDG.coef':dgc_df['lm1.groupDG.coef'].tolist(),\n",
    "    'id.uniprot':dgc_df['id.uniprot'].tolist(),\n",
    "    'id.name':dgc_df['id.name'].tolist()\n",
    "}\n",
    "\n",
    "string_ids_df = pd.DataFrame(string_ids,index=None)\n",
    "\n",
    "# organize disease associations by evidence\n",
    "disease_dgc_tm_df = disease_tm_df[disease_tm_df['id.string'].isin(string_ids['id.string'])]\n",
    "disease_dgc_tm_df = disease_dgc_tm_df.assign(channel = 'TM')\n",
    "disease_dgc_tm_merged_df = disease_dgc_tm_df.merge(string_ids_df,how='right',on='id.string')\n",
    "\n",
    "disease_dgc_k_df = disease_k_df[disease_k_df['id.string'].isin(string_ids['id.string'])]\n",
    "disease_dgc_k_df = disease_dgc_k_df.assign(channel = 'K')\n",
    "disease_dgc_k_merged_df = disease_dgc_k_df.merge(string_ids_df,how='right',on='id.string')\n",
    "\n",
    "disease_dgc_e_df = disease_e_df[disease_e_df['id.string'].isin(string_ids['id.string'])]\n",
    "disease_dgc_e_df = disease_dgc_e_df.assign(channel = 'E')\n",
    "disease_dgc_e_merged_df = disease_dgc_e_df.merge(string_ids_df,how='right',on='id.string')\n",
    "\n",
    "disease_dgc_all_df = pd.concat([disease_dgc_tm_df,disease_dgc_k_df,disease_dgc_e_df])\n",
    "disease_dgc_all_merged_df = disease_dgc_all_df.merge(string_ids_df,how='right',on='id.string')\n",
    "\n",
    "print(len(disease_dgc_tm_merged_df))\n",
    "print(len(disease_dgc_k_merged_df))\n",
    "print(len(disease_dgc_e_merged_df))\n",
    "print(len(disease_dgc_all_df))\n",
    "\n",
    "disease_dgc_all_merged_df.to_excel('disease_dgc_all.xlsx',index=False)\n",
    "sorted_disease_df = disease_dgc_all_merged_df.sort_values('lm1.groupDGC.coef',ascending=False)\n",
    "sorted_disease_df.to_excel('disease_dgc_all_sorted.xlsx', index=False)\n",
    "print(len(disease_dgc_all_merged_df['id.string'].unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:08:19.070203Z",
     "start_time": "2024-08-07T21:08:18.396323Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2160x1872 with 1 Axes>",
      "image/png": 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     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2160x3600 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all number of disease associations\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import rcParams\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rc('font', size=22)\n",
    "figwidth = 30\n",
    "scalar = .7\n",
    "pad = 10\n",
    "counts = 2\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']>0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'TM')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "max_length = len(disease_all_plot_filtered_df)\n",
    "\n",
    "dynamic_scalar = max_length / 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(figwidth,dynamic_scalar))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlim([0,15])\n",
    "plt.title('Number of Proteins Identified for Each Disease Association (DMEK vs. Glaucoma+Control: coef > 0, adjusted p-val < 0.01)')\n",
    "plt.xlabel('Number of Proteins')\n",
    "plt.ylabel('Disease')\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']>0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'K')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']>0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'E')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "plt.legend(['Text Mining','Knowledge','Experiments'])\n",
    "plt.savefig('disease_dgc_g0.png')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "######\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']<0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'TM')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "max_length = len(disease_all_plot_filtered_df)\n",
    "\n",
    "dynamic_scalar = max_length / 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(figwidth,dynamic_scalar))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlim([0,15])\n",
    "plt.title('Number of Proteins Identified for Each Disease Association (DMEK vs. Glaucoma+Control: coef < 0, adjusted p-val < 0.01)')\n",
    "plt.xlabel('Number of Proteins')\n",
    "plt.ylabel('Disease')\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']>0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'K')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "diseases = disease_dgc_all_merged_df[(disease_dgc_all_merged_df['lm1.groupDGC.coef']>0)&(disease_dgc_all_merged_df['confidence']>2)&(disease_dgc_all_merged_df['channel'] == 'E')]['disease.name'].value_counts()\n",
    "disease_all_plot_df = pd.DataFrame(diseases).reset_index()\n",
    "disease_all_plot_df.columns = ['disease.name', 'count']  # Renaming columns for clarity\n",
    "disease_all_plot_filtered_df = disease_all_plot_df[disease_all_plot_df['count'] > counts]\n",
    "\n",
    "x = disease_all_plot_filtered_df['disease.name']\n",
    "y = disease_all_plot_filtered_df['count']\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "plt.savefig('disease_dgc_l0.png')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:08:21.154136Z",
     "start_time": "2024-08-07T21:08:19.070393Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "disease_dgc_all_merged_df.to_excel('protein_disease_associations.xlsx',index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:08:22.156899Z",
     "start_time": "2024-08-07T21:08:21.156700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "       id.name             id.full id.uniprot  \\\n0  APOA1_HUMAN  Apolipoprotein A-I     P02647   \n1  APOA1_HUMAN  Apolipoprotein A-I     P02647   \n2  APOA1_HUMAN  Apolipoprotein A-I     P02647   \n3  APOA1_HUMAN  Apolipoprotein A-I     P02647   \n\n                                        disease.name disease.acronym  \\\n0               Hypoalphalipoproteinemia, primary, 2            FHA2   \n1  Hypoalphalipoproteinemia, primary, 2, intermed...           FHA2I   \n2                                      Amyloidosis 8           AMYL8   \n3  Familial apolipoprotein gene cluster deletion ...          FAPLDS   \n\n                                 disease.description  \\\n0  An autosomal recessive disorder of lipoprotein...   \n1  An autosomal dominant disorder of lipoprotein ...   \n2  A form of hereditary generalized amyloidosis. ...   \n3  An autosomal dominant disorder of lipoprotein ...   \n\n                                    disease.evidence  \n0  [PubMed ID: 1898657, PubMed ID: 1901417, PubMe...  \n1                               [PubMed ID: 8240372]  \n2  [PubMed ID: 10198255, PubMed ID: 10487826, Pub...  \n3                               [PubMed ID: 6800349]  ",
      "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>id.name</th>\n      <th>id.full</th>\n      <th>id.uniprot</th>\n      <th>disease.name</th>\n      <th>disease.acronym</th>\n      <th>disease.description</th>\n      <th>disease.evidence</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>APOA1_HUMAN</td>\n      <td>Apolipoprotein A-I</td>\n      <td>P02647</td>\n      <td>Hypoalphalipoproteinemia, primary, 2</td>\n      <td>FHA2</td>\n      <td>An autosomal recessive disorder of lipoprotein...</td>\n      <td>[PubMed ID: 1898657, PubMed ID: 1901417, PubMe...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>APOA1_HUMAN</td>\n      <td>Apolipoprotein A-I</td>\n      <td>P02647</td>\n      <td>Hypoalphalipoproteinemia, primary, 2, intermed...</td>\n      <td>FHA2I</td>\n      <td>An autosomal dominant disorder of lipoprotein ...</td>\n      <td>[PubMed ID: 8240372]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>APOA1_HUMAN</td>\n      <td>Apolipoprotein A-I</td>\n      <td>P02647</td>\n      <td>Amyloidosis 8</td>\n      <td>AMYL8</td>\n      <td>A form of hereditary generalized amyloidosis. ...</td>\n      <td>[PubMed ID: 10198255, PubMed ID: 10487826, Pub...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>APOA1_HUMAN</td>\n      <td>Apolipoprotein A-I</td>\n      <td>P02647</td>\n      <td>Familial apolipoprotein gene cluster deletion ...</td>\n      <td>FAPLDS</td>\n      <td>An autosomal dominant disorder of lipoprotein ...</td>\n      <td>[PubMed ID: 6800349]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(get_uniprot_disease('P02647'))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:08:22.227940Z",
     "start_time": "2024-08-07T21:08:21.457496Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P06396\n",
      "Q9UBM4\n",
      "P07339\n",
      "P02766\n",
      "P16870\n",
      "O43505\n",
      "P10645\n",
      "Q01995\n",
      "P07225\n",
      "P02656\n",
      "Q08629\n",
      "P05156\n",
      "P24592\n",
      "Q8IZJ3\n",
      "Q12841\n",
      "P05067\n",
      "P01034\n",
      "Q96KN2\n",
      "P10745\n",
      "P22352\n",
      "P02655\n",
      "Q15904\n",
      "P35443\n",
      "P19022\n",
      "Q96JP9\n",
      "Q14624\n",
      "O94985\n",
      "Q17R60\n",
      "P06733\n",
      "P08571\n",
      "P02671\n",
      "Q9NQ79\n",
      "Q06481\n",
      "P02647\n",
      "Q9BQT9\n",
      "P62328\n",
      "Q99519\n",
      "Q96NZ9\n",
      "P41222\n",
      "Q14126\n",
      "Q9HCB6\n",
      "Q9BZV3\n",
      "P04114\n",
      "P02765\n",
      "Q14515\n",
      "P02768\n",
      "P09871\n",
      "P0DJI8\n",
      "P01042\n",
      "P36955\n",
      "Q9GZZ8\n",
      "Q9Y646\n",
      "P10451\n",
      "P81605\n",
      "Q9UBP4\n",
      "O60938\n",
      "B4E1Z4\n",
      "Q86UD1\n",
      "P55058\n",
      "P29622\n",
      "Q96FE7\n",
      "P07357\n",
      "P02511\n",
      "P0DP23\n",
      "Q9Y5W5\n",
      "Q16610\n",
      "P59665\n",
      "P12109\n",
      "P14618\n",
      "P00760\n",
      "Q14574\n",
      "Q86UX2\n",
      "Q8NBJ4\n",
      "Q14767\n",
      "Q9UHG2\n",
      "Q15582\n",
      "P01160\n",
      "P40967\n",
      "P78509\n",
      "P15291\n",
      "P51884\n",
      "P08697\n",
      "Q16661\n",
      "P06727\n",
      "P09486\n",
      "P39060\n",
      "P00450\n",
      "Q14393\n",
      "P02489\n",
      "P68104\n",
      "P51888\n",
      "P01023\n",
      "P08670\n",
      "P04196\n",
      "P98160\n",
      "P02652\n",
      "Q02818\n",
      "P05408\n",
      "Q9HCQ7\n",
      "P53674\n",
      "P01031\n",
      "A0A0B4J1X5\n",
      "P02787\n",
      "Q08174\n",
      "A0A096LPE2\n",
      "O43491\n",
      "P33908\n",
      "Q8IUX7\n",
      "Q5FWE3\n",
      "O14791\n",
      "O00461\n",
      "O60664\n",
      "Q12797\n",
      "Q9BRK5\n",
      "P0C0L4\n",
      "P04264\n",
      "P05813\n",
      "A0A494C0G5\n",
      "P02748\n",
      "P19823\n",
      "O15354\n",
      "P01008\n",
      "P05060\n",
      "P00736\n",
      "P53673\n",
      "P13647\n",
      "P60709\n",
      "O00391\n",
      "Q86X29\n",
      "P24593\n",
      "Q9UN70\n",
      "A0A0G2JPD3\n",
      "P16519\n",
      "P01011\n",
      "Q9Y240\n",
      "Q14112\n",
      "P52565\n",
      "Q8N475\n",
      "P35555\n",
      "P49257\n",
      "Q8WXD2\n",
      "P43320\n",
      "Q9BXP8\n",
      "P21291\n",
      "Q99972\n",
      "P80108\n",
      "P14543\n",
      "P01009\n",
      "P07360\n",
      "P06454\n",
      "P01210\n",
      "P07996\n",
      "P16152\n",
      "P01024\n",
      "Q01469\n",
      "P08727\n",
      "P04217\n",
      "Q8N3Z0\n",
      "Q9H3G5\n",
      "Q9ULI3\n",
      "P19021\n",
      "P04792\n",
      "P27169\n",
      "P52209\n",
      "P63261\n",
      "Q8IWU5\n",
      "P08493\n",
      "P00738\n",
      "P04004\n",
      "P10909\n",
      "P0C0L5\n",
      "O95428\n",
      "P02675\n",
      "P02649\n",
      "Q9UJJ9\n",
      "P04406\n",
      "P35527\n",
      "Q16568\n",
      "P12259\n",
      "P00734\n",
      "Q9UHI8\n",
      "P47755\n",
      "P30838\n",
      "Q16378\n",
      "P13521\n",
      "P05155\n",
      "P11021\n",
      "Q06033\n",
      "P22792\n",
      "P00740\n",
      "Q9NRN5\n",
      "P68363\n",
      "P01019\n",
      "P08253\n",
      "P51693\n",
      "Q92743\n",
      "P68871\n",
      "Q6ZMP0\n",
      "P31025\n",
      "P69905\n",
      "P22692\n",
      "O15240\n",
      "P22914\n"
     ]
    }
   ],
   "source": [
    "protein_disease = []\n",
    "protein_df = diffexp_newmodel_df[diffexp_newmodel_df['id.uniprot'].notnull()]\n",
    "uniprot_ids = protein_df['id.uniprot'].tolist()\n",
    "\n",
    "for i in uniprot_ids:\n",
    "    print(i)\n",
    "    protein_disease.extend(get_uniprot_disease(i))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:10:49.339378Z",
     "start_time": "2024-08-07T21:08:22.224626Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P06396\n",
      "P07339\n",
      "P02766\n",
      "P16870\n",
      "O43505\n",
      "P10645\n",
      "Q01995\n",
      "Q08629\n",
      "Q12841\n",
      "Q96KN2\n",
      "P06733\n",
      "P08571\n",
      "P62328\n",
      "P04114\n",
      "Q9GZZ8\n",
      "Q14767\n"
     ]
    }
   ],
   "source": [
    "protein_function = []\n",
    "uniprot_ids = common_protein_df['id.uniprot'].tolist()\n",
    "\n",
    "for i in uniprot_ids:\n",
    "    print(i)\n",
    "    protein_function.extend(get_go_annotations(i))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:15:07.814404Z",
     "start_time": "2024-08-07T21:10:49.345680Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "protein_disease_df = pd.DataFrame(protein_disease)\n",
    "protein_disease_df\n",
    "protein_disease_df.to_excel('protein_disease.xlsx', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:15:07.855273Z",
     "start_time": "2024-08-07T21:15:07.819148Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "         id.name id.uniprot        goID         qualifier    reference.id  \\\n0     GELS_HUMAN     P06396  GO:0022617   NOT|involved_in   PMID:24236012   \n1     GELS_HUMAN     P06396  GO:0005634  colocalizes_with   PMID:23729654   \n2     GELS_HUMAN     P06396  GO:0030864  colocalizes_with   PMID:23575248   \n3     GELS_HUMAN     P06396  GO:0003779           enables  GO_REF:0000043   \n4     GELS_HUMAN     P06396  GO:0003779           enables   PMID:18266911   \n..           ...        ...         ...               ...             ...   \n364  LTBP2_HUMAN     Q14767  GO:0031012        located_in    PMID:7798248   \n365  LTBP2_HUMAN     Q14767  GO:0062023        located_in   PMID:27559042   \n366  LTBP2_HUMAN     Q14767  GO:0062023        located_in   PMID:28327460   \n367  LTBP2_HUMAN     Q14767  GO:0070062        located_in   PMID:21362503   \n368  LTBP2_HUMAN     Q14767  GO:0070062        located_in   PMID:23533145   \n\n                                function.name  \\\n0            extracellular matrix disassembly   \n1                                     nucleus   \n2                 cortical actin cytoskeleton   \n3                               actin binding   \n4                               actin binding   \n..                                        ...   \n364                      extracellular matrix   \n365  collagen-containing extracellular matrix   \n366  collagen-containing extracellular matrix   \n367                     extracellular exosome   \n368                     extracellular exosome   \n\n                                   function.definition  \n0    A process that results in the breakdown of the...  \n1    A membrane-bounded organelle of eukaryotic cel...  \n2    The portion of the actin cytoskeleton, compris...  \n3    Binding to monomeric or multimeric forms of ac...  \n4    Binding to monomeric or multimeric forms of ac...  \n..                                                 ...  \n364  A structure lying external to one or more cell...  \n365  An extracellular matrix consisting mainly of p...  \n366  An extracellular matrix consisting mainly of p...  \n367  A vesicle that is released into the extracellu...  \n368  A vesicle that is released into the extracellu...  \n\n[369 rows x 7 columns]",
      "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>id.name</th>\n      <th>id.uniprot</th>\n      <th>goID</th>\n      <th>qualifier</th>\n      <th>reference.id</th>\n      <th>function.name</th>\n      <th>function.definition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0022617</td>\n      <td>NOT|involved_in</td>\n      <td>PMID:24236012</td>\n      <td>extracellular matrix disassembly</td>\n      <td>A process that results in the breakdown of the...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0005634</td>\n      <td>colocalizes_with</td>\n      <td>PMID:23729654</td>\n      <td>nucleus</td>\n      <td>A membrane-bounded organelle of eukaryotic cel...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0030864</td>\n      <td>colocalizes_with</td>\n      <td>PMID:23575248</td>\n      <td>cortical actin cytoskeleton</td>\n      <td>The portion of the actin cytoskeleton, compris...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0003779</td>\n      <td>enables</td>\n      <td>GO_REF:0000043</td>\n      <td>actin binding</td>\n      <td>Binding to monomeric or multimeric forms of ac...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0003779</td>\n      <td>enables</td>\n      <td>PMID:18266911</td>\n      <td>actin binding</td>\n      <td>Binding to monomeric or multimeric forms of ac...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>364</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0031012</td>\n      <td>located_in</td>\n      <td>PMID:7798248</td>\n      <td>extracellular matrix</td>\n      <td>A structure lying external to one or more cell...</td>\n    </tr>\n    <tr>\n      <th>365</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0062023</td>\n      <td>located_in</td>\n      <td>PMID:27559042</td>\n      <td>collagen-containing extracellular matrix</td>\n      <td>An extracellular matrix consisting mainly of p...</td>\n    </tr>\n    <tr>\n      <th>366</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0062023</td>\n      <td>located_in</td>\n      <td>PMID:28327460</td>\n      <td>collagen-containing extracellular matrix</td>\n      <td>An extracellular matrix consisting mainly of p...</td>\n    </tr>\n    <tr>\n      <th>367</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0070062</td>\n      <td>located_in</td>\n      <td>PMID:21362503</td>\n      <td>extracellular exosome</td>\n      <td>A vesicle that is released into the extracellu...</td>\n    </tr>\n    <tr>\n      <th>368</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0070062</td>\n      <td>located_in</td>\n      <td>PMID:23533145</td>\n      <td>extracellular exosome</td>\n      <td>A vesicle that is released into the extracellu...</td>\n    </tr>\n  </tbody>\n</table>\n<p>369 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protein_function_df = pd.DataFrame(protein_function)\n",
    "protein_function_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:15:07.860082Z",
     "start_time": "2024-08-07T21:15:07.854831Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-0.1716740348357557\n",
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     "data": {
      "text/plain": "         id.name id.uniprot        goID         qualifier    reference.id  \\\n0     GELS_HUMAN     P06396  GO:0022617   NOT|involved_in   PMID:24236012   \n1     GELS_HUMAN     P06396  GO:0005634  colocalizes_with   PMID:23729654   \n2     GELS_HUMAN     P06396  GO:0030864  colocalizes_with   PMID:23575248   \n3     GELS_HUMAN     P06396  GO:0003779           enables  GO_REF:0000043   \n4     GELS_HUMAN     P06396  GO:0003779           enables   PMID:18266911   \n..           ...        ...         ...               ...             ...   \n364  LTBP2_HUMAN     Q14767  GO:0031012        located_in    PMID:7798248   \n365  LTBP2_HUMAN     Q14767  GO:0062023        located_in   PMID:27559042   \n366  LTBP2_HUMAN     Q14767  GO:0062023        located_in   PMID:28327460   \n367  LTBP2_HUMAN     Q14767  GO:0070062        located_in   PMID:21362503   \n368  LTBP2_HUMAN     Q14767  GO:0070062        located_in   PMID:23533145   \n\n                                function.name  \\\n0            extracellular matrix disassembly   \n1                                     nucleus   \n2                 cortical actin cytoskeleton   \n3                               actin binding   \n4                               actin binding   \n..                                        ...   \n364                      extracellular matrix   \n365  collagen-containing extracellular matrix   \n366  collagen-containing extracellular matrix   \n367                     extracellular exosome   \n368                     extracellular exosome   \n\n                                   function.definition  \\\n0    A process that results in the breakdown of the...   \n1    A membrane-bounded organelle of eukaryotic cel...   \n2    The portion of the actin cytoskeleton, compris...   \n3    Binding to monomeric or multimeric forms of ac...   \n4    Binding to monomeric or multimeric forms of ac...   \n..                                                 ...   \n364  A structure lying external to one or more cell...   \n365  An extracellular matrix consisting mainly of p...   \n366  An extracellular matrix consisting mainly of p...   \n367  A vesicle that is released into the extracellu...   \n368  A vesicle that is released into the extracellu...   \n\n                                      reference.id.url  lr.DMEK.coef  \n0    =HYPERLINK(\"https://europepmc.org/article/MED/...      -0.16209  \n1    =HYPERLINK(\"https://europepmc.org/article/MED/...      -0.16209  \n2    =HYPERLINK(\"https://europepmc.org/article/MED/...      -0.16209  \n3    =HYPERLINK(\"https://github.com/geneontology/go...      -0.16209  \n4    =HYPERLINK(\"https://europepmc.org/article/MED/...      -0.16209  \n..                                                 ...           ...  \n364  =HYPERLINK(\"https://europepmc.org/article/MED/...       0.26905  \n365  =HYPERLINK(\"https://europepmc.org/article/MED/...       0.26905  \n366  =HYPERLINK(\"https://europepmc.org/article/MED/...       0.26905  \n367  =HYPERLINK(\"https://europepmc.org/article/MED/...       0.26905  \n368  =HYPERLINK(\"https://europepmc.org/article/MED/...       0.26905  \n\n[369 rows x 9 columns]",
      "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>id.name</th>\n      <th>id.uniprot</th>\n      <th>goID</th>\n      <th>qualifier</th>\n      <th>reference.id</th>\n      <th>function.name</th>\n      <th>function.definition</th>\n      <th>reference.id.url</th>\n      <th>lr.DMEK.coef</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0022617</td>\n      <td>NOT|involved_in</td>\n      <td>PMID:24236012</td>\n      <td>extracellular matrix disassembly</td>\n      <td>A process that results in the breakdown of the...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>-0.16209</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0005634</td>\n      <td>colocalizes_with</td>\n      <td>PMID:23729654</td>\n      <td>nucleus</td>\n      <td>A membrane-bounded organelle of eukaryotic cel...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>-0.16209</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0030864</td>\n      <td>colocalizes_with</td>\n      <td>PMID:23575248</td>\n      <td>cortical actin cytoskeleton</td>\n      <td>The portion of the actin cytoskeleton, compris...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>-0.16209</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0003779</td>\n      <td>enables</td>\n      <td>GO_REF:0000043</td>\n      <td>actin binding</td>\n      <td>Binding to monomeric or multimeric forms of ac...</td>\n      <td>=HYPERLINK(\"https://github.com/geneontology/go...</td>\n      <td>-0.16209</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GELS_HUMAN</td>\n      <td>P06396</td>\n      <td>GO:0003779</td>\n      <td>enables</td>\n      <td>PMID:18266911</td>\n      <td>actin binding</td>\n      <td>Binding to monomeric or multimeric forms of ac...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>-0.16209</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>364</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0031012</td>\n      <td>located_in</td>\n      <td>PMID:7798248</td>\n      <td>extracellular matrix</td>\n      <td>A structure lying external to one or more cell...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>0.26905</td>\n    </tr>\n    <tr>\n      <th>365</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0062023</td>\n      <td>located_in</td>\n      <td>PMID:27559042</td>\n      <td>collagen-containing extracellular matrix</td>\n      <td>An extracellular matrix consisting mainly of p...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>0.26905</td>\n    </tr>\n    <tr>\n      <th>366</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0062023</td>\n      <td>located_in</td>\n      <td>PMID:28327460</td>\n      <td>collagen-containing extracellular matrix</td>\n      <td>An extracellular matrix consisting mainly of p...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>0.26905</td>\n    </tr>\n    <tr>\n      <th>367</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0070062</td>\n      <td>located_in</td>\n      <td>PMID:21362503</td>\n      <td>extracellular exosome</td>\n      <td>A vesicle that is released into the extracellu...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>0.26905</td>\n    </tr>\n    <tr>\n      <th>368</th>\n      <td>LTBP2_HUMAN</td>\n      <td>Q14767</td>\n      <td>GO:0070062</td>\n      <td>located_in</td>\n      <td>PMID:23533145</td>\n      <td>extracellular exosome</td>\n      <td>A vesicle that is released into the extracellu...</td>\n      <td>=HYPERLINK(\"https://europepmc.org/article/MED/...</td>\n      <td>0.26905</td>\n    </tr>\n  </tbody>\n</table>\n<p>369 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export protein functions\n",
    "\n",
    "def make_clickable(val):\n",
    "    # Check if the value is a string to avoid errors\n",
    "    if isinstance(val, str):\n",
    "        if 'PMID:' in val:\n",
    "            return f'https://europepmc.org/article/MED/{val.split(\":\")[1]}'\n",
    "        elif 'GO_REF:' in val:\n",
    "            return f'https://github.com/geneontology/go-site/blob/master/metadata/gorefs/goref-{val.split(\":\")[1]}.md'\n",
    "        elif 'Reactome:' in val:\n",
    "            return f'https://www.reactome.org/content/detail/{val.split(\":\")[1]}'\n",
    "    # Return the original value if it doesn't match the conditions\n",
    "    return val\n",
    "\n",
    "def hyperlink(val):\n",
    "    url = make_clickable(val)\n",
    "    return f'=HYPERLINK(\"{url}\",\"{val}\")'\n",
    "\n",
    "# Apply the make_clickable function to the 'reference.id.url' column for display\n",
    "protein_function_df['reference.id.url'] = protein_function_df['reference.id'].apply(hyperlink)\n",
    "\n",
    "coefs = []\n",
    "for ids in protein_function_df['id.name']:\n",
    "    print(common_protein_df[common_protein_df['id.name'] == ids]['lr.DMEK.coef'].item())\n",
    "    coefs.append(common_protein_df[common_protein_df['id.name'] == ids]['lr.DMEK.coef'].item())\n",
    "\n",
    "protein_function_df['lr.DMEK.coef'] = coefs\n",
    "\n",
    "protein_function_df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:15:08.166923Z",
     "start_time": "2024-08-07T21:15:07.862700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "protein_function_df.to_excel('protein_function.xlsx', index=False)\n",
    "protein_function_df.sort_values('lr.DMEK.coef',ascending=False).to_excel('protein_function_sorted.xlsx', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:15:08.167530Z",
     "start_time": "2024-08-07T21:15:07.930258Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P06396\n",
      "Q9UBM4\n",
      "P07339\n",
      "P02766\n",
      "P16870\n",
      "O43505\n",
      "P10645\n",
      "Q01995\n",
      "P07225\n",
      "P02656\n",
      "Q08629\n",
      "P05156\n",
      "P24592\n",
      "Q8IZJ3\n",
      "Q12841\n",
      "P05067\n",
      "P01034\n",
      "Q96KN2\n",
      "P10745\n",
      "P22352\n",
      "Q15904\n",
      "P35443\n",
      "P19022\n",
      "Q96JP9\n",
      "Q14624\n",
      "O94985\n",
      "Q17R60\n",
      "P06733\n",
      "P08571\n",
      "P02671\n",
      "Q9NQ79\n",
      "Q06481\n",
      "P02647\n",
      "Q9BQT9\n",
      "Q99519\n",
      "Q96NZ9\n",
      "Q9HCB6\n",
      "P04114\n",
      "Q14515\n",
      "Q9GZZ8\n"
     ]
    }
   ],
   "source": [
    "# Retrieve all go annotations\n",
    "protein_DGC_function = []\n",
    "uniprot_ids = diffexp_newmodel_df[(diffexp_newmodel_df['lm1.groupDGC.holm.pval']<0.01)&(diffexp_newmodel_df['id.type']=='protein')]['id.uniprot'].tolist()\n",
    "\n",
    "for i in uniprot_ids:\n",
    "    print(i)\n",
    "    protein_DGC_function.extend(get_go_annotations(i))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:25:37.354131Z",
     "start_time": "2024-08-07T21:15:08.110306Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "protein_DGC_function_df = pd.DataFrame(protein_DGC_function)\n",
    "protein_DGC_function_df.to_excel('protein_DGC_function.xlsx',index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:25:37.477983Z",
     "start_time": "2024-08-07T21:25:37.423766Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "protein_DGC_function_df = pd.read_excel('protein_DGC_function.xlsx')\n",
    "\n",
    "coefs = []\n",
    "pvals = []\n",
    "for ids in protein_DGC_function_df['id.name']:\n",
    "    coefs.append(diffexp_newmodel_df[diffexp_newmodel_df['id.name'] == ids]['lm1.groupDGC.coef'].item())\n",
    "    pvals.append(diffexp_newmodel_df[diffexp_newmodel_df['id.name'] == ids]['lm1.groupDGC.holm.pval'].item())\n",
    "\n",
    "protein_DGC_function_df['lm1.groupDGC.coef'] = coefs\n",
    "protein_DGC_function_df['lm1.groupDGC.holm.pval'] = pvals\n",
    "protein_DGC_function_df['reference.id.url'] = protein_DGC_function_df['reference.id'].apply(hyperlink)\n",
    "\n",
    "\n",
    "protein_DGC_function_df.to_excel('protein_function.xlsx',index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:25:37.830777Z",
     "start_time": "2024-08-07T21:25:37.478791Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2160x576 with 1 Axes>",
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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2160x2160 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot all protein functions\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams.update({'font.size': 20})\n",
    "\n",
    "functions_positive = protein_DGC_function_df[\n",
    "    # (protein_DGC_function_df['qualifier'] == 'involved_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'NOT|enables') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'NOT|involved_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'located_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'colocalizes_with') &\n",
    "    (protein_DGC_function_df['function.name'] != 'protein binding') &\n",
    "    (protein_DGC_function_df['lm1.groupDGC.coef'] > 0)\n",
    "].groupby('function.name')['id.name'].nunique()\n",
    "\n",
    "# For functions with negative coefficients\n",
    "functions_negative = protein_DGC_function_df[\n",
    "    # (protein_DGC_function_df['qualifier'] == 'involved_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'NOT|enables') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'NOT|involved_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'located_in') &\n",
    "    (protein_DGC_function_df['qualifier'] != 'colocalizes_with') &\n",
    "    (protein_DGC_function_df['function.name'] != 'protein binding') &\n",
    "    (protein_DGC_function_df['lm1.groupDGC.coef'] < 0)\n",
    "].groupby('function.name')['id.name'].nunique()\n",
    "\n",
    "# Convert the Series to DataFrame for easier manipulation and renaming\n",
    "functions_positive_df = functions_positive.reset_index(name='unique_proteins_count')\n",
    "functions_positive_df.columns = ['function.name', 'count']\n",
    "functions_negative_df = functions_negative.reset_index(name='unique_proteins_count')\n",
    "functions_negative_df.columns = ['function.name', 'count']\n",
    "\n",
    "functions_positive_df = functions_positive_df[functions_positive_df['count'] > 1].sort_values('count',ascending=False)\n",
    "functions_negative_df = functions_negative_df[functions_negative_df['count'] > 1].sort_values('count',ascending=False)\n",
    "\n",
    "length = len(functions_positive_df)\n",
    "\n",
    "max_length = len(functions_positive_df)\n",
    "\n",
    "dynamic_scalar = max_length / 1\n",
    "\n",
    "x = functions_positive_df['function.name']\n",
    "y = functions_positive_df['count']\n",
    "\n",
    "xmax = 11\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(figwidth,dynamic_scalar))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlim([0,xmax])\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "plt.savefig('function_dgc_g0.png')\n",
    "plt.show()\n",
    "\n",
    "length = len(functions_negative_df)\n",
    "\n",
    "max_length = len(functions_negative_df)\n",
    "\n",
    "dynamic_scalar = max_length / 1\n",
    "\n",
    "x = functions_negative_df['function.name']\n",
    "y = functions_negative_df['count']\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(figwidth,dynamic_scalar))\n",
    "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "plt.xlim([0,xmax])\n",
    "ax.barh(x, width = y)\n",
    "ax.invert_yaxis()\n",
    "plt.savefig('function_dgc_l0.png')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:25:38.819369Z",
     "start_time": "2024-08-07T21:25:37.835467Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "indices = range(1, len(diffexp_newmodel_df) + 1)\n",
    "diffexp_newmodel_df.to_excel('diffexp_newmodel.xlsx', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T21:25:40.745905Z",
     "start_time": "2024-08-07T21:25:38.818977Z"
    }
   }
  }
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