Prep_edge_table.ipynb

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    "#!/usr/bin/env python3\n",
    "\n",
    "##################################################################\n",
    "## For the given list of proteins print out only the interactions\n",
    "## between these protein which have medium or higher confidence\n",
    "## experimental score\n",
    "##\n",
    "## Requires requests module:\n",
    "## type \"python -m pip install requests\" in command line (win)\n",
    "## or terminal (mac/linux) to install the module\n",
    "##################################################################\n",
    "\n",
    "import requests ## python -m pip install requests\n",
    "import pandas as pd\n",
    "import sys\n",
    "import os\n",
    "\n",
    "#path = os.path.dirname(os.path.dirname(os.path.abspath('__file__')))+'\\\\Prepared_tables\\\\'\n",
    "path = os.path.dirname(os.path.abspath('__file__'))+'\\\\Prepared_tables\\\\'\n",
    "\n",
    "\n",
    "string_api_url = \"https://string-db.org/api\"\n",
    "output_format = \"tsv-no-header\"\n",
    "method = \"network\"\n",
    "header = ['stringId_A', 'stringId_B', 'preferredName_A', 'preferredName_B', 'ncbiTaxonId', 'score', 'nscore', 'fscore', 'pscore', 'ascore', 'escore', 'dscore', 'tscore']\n",
    "StringID = pd.DataFrame(columns= header)\n",
    "##\n",
    "## Construct URL\n",
    "##\n",
    "\n",
    "request_url = \"/\".join([string_api_url, output_format, method])\n",
    "\n",
    "##\n",
    "## Set parameters\n",
    "##\n",
    "\n",
    "#df_prot = pd.read_csv((path+ 'Prep_ProteinGroups_allproteins_75.csv'))\n",
    "\n",
    "#df_prot = pd.read_csv((path+ 'Prep_Protein Groups_mito.csv'))\n",
    "df_prot = pd.read_csv((path+ 'Prep_Protein Groups_cut05_notimuted.csv'))\n",
    "df_prot = df_prot.iloc[[i for i in range(len(df_prot)) if str(df_prot[\"Pathways\"][i]) != \"nan\"]].reset_index(drop= True)\n",
    "df_prot['StringID'] = [list(set(str(df_prot['StringID'][i]).split(';'))) for i in range(len(df_prot))]\n",
    "flatList = [ item for elem in list(df_prot['StringID']) for item in elem]\n",
    "my_genes = sorted(list(set(flatList)))[1:-1]\n",
    "\n",
    "\n",
    "params = {\n",
    "\n",
    "    \"identifiers\" : \"%0d\".join(my_genes), # your protein\n",
    "    \"species\" : 10090, # species NCBI identifier \n",
    "    \"caller_identity\" : \"www.awesome_app.org\" # your app name\n",
    "\n",
    "}\n",
    "\n",
    "##\n",
    "## Call STRING\n",
    "##\n",
    "\n",
    "response = requests.post(request_url, data=params)\n",
    "\n",
    "for line in response.text.strip().split(\"\\n\"):\n",
    "\n",
    "    l = line.strip().split(\"\\t\")\n",
    "    p1, p2 = l[2], l[3]\n",
    "    \n",
    "    # filter the interaction according to experimental score\n",
    "    experimental_score = float(l[10])\n",
    "    score = float(l[5])\n",
    "    if score >= 0:\n",
    "        StringID.loc[len(StringID)]=l\n",
    "\n",
    "\n",
    "\n",
    "StringID = StringID.drop_duplicates()        \n",
    "StringID.to_csv(path+'EdgeTable.csv', index= False)"
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