STRING_for_labelled_data_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/bioinformatics/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 13.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the screen data\n",
    "# Bear in mind that for certain columns, the data type has to be\n",
    "# manually specified\n",
    "dtype_dict = {\n",
    "    \"Ensembl_ID_OnTarget_Ensembl_GRCh38_release_87\": str,\n",
    "    \"Ensembl_ID_OnTarget_NCBI_HeLa_phs000643_v3_p1_c1_HMB\": str,\n",
    "    \"Gene_Description\": str,\n",
    "    \"ID\": str,\n",
    "    \"ID_OnTarget_Ensembl_GRCh38_release_87\": str,\n",
    "    \"ID_OnTarget_Merge\": str,\n",
    "    \"ID_OnTarget_NCBI_HeLa_phs000643_v3_p1_c1_HMB\": str,\n",
    "    \"ID_OnTarget_RefSeq_20170215\": str,\n",
    "    \"ID_manufacturer\": str,\n",
    "    \"Name_alternatives\": str,\n",
    "    \"PLATE_QUALITY_DESCRIPTION\": str,\n",
    "    \"RefSeq_ID_OnTarget_RefSeq_20170215\": str,\n",
    "    \"Seed_sequence_common\": str,\n",
    "    \"WELL_QUALITY_DESCRIPTION\": str,\n",
    "    \"siRNA_error\": str,\n",
    "    \"siRNA_number\": str,\n",
    "    \"Precursor_Name\": str\n",
    "}\n",
    "\n",
    "main_csv_df = dd.read_csv(\n",
    "    \"VacciniaReport_20170223-0958_ZScored_conc_and_NaN_adjusted.csv\",\n",
    "    sep=\"\\t\",\n",
    "    dtype=dtype_dict\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# According to the feature \"WellType\", eight different types of\n",
    "# experiments have been conducted, which are the following:\n",
    "# 1.) COMPOUND: refers to small molecules, i.e. low-molecular weight\n",
    "# organic compounds; note that for small molecules, the feature \"Name\"\n",
    "# represents the name of the small compound, whereas the proteins\n",
    "# targeted by the small molecules are specified by the feature\n",
    "# \"Name_alternatives\"\n",
    "# 2.) CONTROL\n",
    "# 3.) ESIRNA, i.e. endoribonuclease-prepared siRNA, which differs from\n",
    "# the conventional siRNA in that it is not chemically synthesised, but\n",
    "# generated by enzymatic digestion of long dsRNA molecules\n",
    "# 4.) MIRNA_INHIBITOR\n",
    "# 5.) MIRNA_MIMIC\n",
    "# 6.) Not available\n",
    "# 7.) POOLED_SIRNA, i.e. transfection of mammalian cells has been\n",
    "# performed with an ensemble of four different siRNAs\n",
    "# 8.) SIRNA, i.e. transfection of mammalian cells has been performed\n",
    "# with only one single type of siRNA molecules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract target genes knocked down via single siRNAs, pooled siRNAs and\n",
    "# esiRNAs\n",
    "# Bear in mind that due operator precedence, i.e. \"|\" (logical or)\n",
    "# having precedence over equality checks, the equality checks must be\n",
    "# surrounded by parentheses\n",
    "target_name_series_single_pooled_siRNA_and_esiRNA = main_csv_df.loc[\n",
    "    (main_csv_df[\"WellType\"] == \"SIRNA\")\n",
    "    |\n",
    "    (main_csv_df[\"WellType\"] == \"POOLED_SIRNA\")\n",
    "    |\n",
    "    (main_csv_df[\"WellType\"] == \"ESIRNA\")\n",
    "][\"Name\"]\n",
    "\n",
    "# Some entries of the feature \"Name\" are \"Not available\", those need to\n",
    "# be filtered out\n",
    "# As the list is later converted into a set and sets do not allow\n",
    "# duplicates anyway, applying np.unique() is not necessary\n",
    "target_names_single_pooled_siRNA_and_esiRNA = [\n",
    "    name for name in target_name_series_single_pooled_siRNA_and_esiRNA\n",
    "    if name != \"Not available\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract target genes knocked down by low-molecular weight organic\n",
    "# compounds\n",
    "target_name_series_compounds = main_csv_df.loc[\n",
    "    main_csv_df[\"WellType\"] == \"COMPOUND\"\n",
    "][\"Name_alternatives\"]\n",
    "\n",
    "# The names of genes targeted by small molecules require special\n",
    "# processing for three reasons: Firstly, some entries encompass multiple\n",
    "# names separated by commata or the ampersand sign. Secondly, in case of\n",
    "# proteins belonging to the same family, such as HER1 and HER2, or\n",
    "# otherwise related proteins, e.g. Aurora kinase and CDK, the individual\n",
    "# proteins are separated from each other via forward slashes. Thirdly,\n",
    "# in the case of TGF-beta, a question mark occurs in lieu of the\n",
    "# corresponding Greek letter. Hence, the question mark has to be\n",
    "# replaced with the word \"beta\".\n",
    "# Unfortunately, iterable unpacking cannot be used in conjunction with\n",
    "# list comprehensions, which is why for loops must be resorted to\n",
    "target_names_compounds = []\n",
    "\n",
    "for name in target_name_series_compounds:\n",
    "    if \"TGF\" in name:\n",
    "        name = name.replace(\"?\", \"beta\")\n",
    "    # The forward slash only occurs in two situations, the first of\n",
    "    # which is \"HER1/2\" and the second if which is \"Aurora / CDK\"\n",
    "    if \"1/2\" in name:\n",
    "        name = name.replace(\"HER1/2\", \"HER1,HER2\")\n",
    "    if \" / \" in name:\n",
    "        name = name.replace(\" / \", \",\")\n",
    "    if \",\" in name:\n",
    "        target_names_compounds += name.split(\",\")\n",
    "    elif \"&\" in name:\n",
    "        target_names_compounds += name.split(\"&\")\n",
    "    else:\n",
    "        target_names_compounds.append(name)\n",
    "\n",
    "# As the list is later converted into a set and sets do not allow\n",
    "# duplicates anyway, applying np.unique() is not necessary\n",
    "#target_names_compounds = np.unique(target_names_compounds).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, determine the union of both lists\n",
    "# Creating the union does not include duplicates\n",
    "target_names_siRNA_and_compounds = list(\n",
    "    set(target_names_single_pooled_siRNA_and_esiRNA)\n",
    "    |\n",
    "    set(target_names_compounds)\n",
    ")\n",
    "\n",
    "# Also determine unique target names for single/pooled siRNA and esiRNA\n",
    "# separately\n",
    "# Again, this can be accomplished by converting the list into a set as\n",
    "# sets do not include duplicate values\n",
    "target_names_siRNA = list(\n",
    "    set(target_names_single_pooled_siRNA_and_esiRNA)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "string_api_url = \"https://version-12-0.string-db.org/api\"\n",
    "output_format = \"tsv\"\n",
    "method = \"get_string_ids\"\n",
    "\n",
    "# Set parameters\n",
    "params = {\n",
    "    \"identifiers\": \"\\r\".join(target_names_siRNA_and_compounds), # Protein list\n",
    "    \"species\": 9606, # Species NCBI identifier, in this case human\n",
    "    \"limit\": 1, # Only one (best) identifier per input protein\n",
    "    \"echo_query\": 1, # See your input identifiers in the output\n",
    "    \"caller_identity\": \"Elvis Jagger Abdul-Jabbar\"\n",
    "}\n",
    "\n",
    "# Construct the URL\n",
    "request_url = \"/\".join([string_api_url, output_format, method])\n",
    "\n",
    "# Call STRING\n",
    "results = requests.post(request_url, data=params)\n",
    "\n",
    "assert results.status_code == 200, \"The request was not successful!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the result to a CSV file\n",
    "# with statements are preferred for the purpose of working with files as\n",
    "# they automatically take care of closing files, even in case of errors/\n",
    "# exceptions\n",
    "with open(\"siRNA_and_compounds_STRING_IDs.csv\", \"w\") as f:\n",
    "    # By using a tab in lieu of a comma as delimiter, a TSV instead of a\n",
    "    # CSV is generated, technically speaking\n",
    "    csv_writer = csv.writer(f, delimiter=\"\\t\")\n",
    "\n",
    "    for i, line in enumerate(results.text.strip().split(\"\\n\")):\n",
    "        split_line = line.split(\"\\t\")\n",
    "        input_identifier, string_identifier = split_line[0], split_line[2]\n",
    "        csv_writer.writerow([input_identifier, string_identifier])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map the target names to STRING IDs (Ensembl IDs) for single/pooled\n",
    "# siRNAs and esiRNAs separately\n",
    "params = {\n",
    "    \"identifiers\": \"\\r\".join(target_names_siRNA), # Protein list\n",
    "    \"species\": 9606, # Species NCBI identifier, in this case human\n",
    "    \"limit\": 1, # Only one (best) identifier per input protein\n",
    "    \"echo_query\": 1, # See your input identifiers in the output\n",
    "    \"caller_identity\": \"Elvis Jagger Abdul-Jabbar\"\n",
    "}\n",
    "\n",
    "# Construct the URL\n",
    "request_url = \"/\".join([string_api_url, output_format, method])\n",
    "\n",
    "# Call STRING\n",
    "results = requests.post(request_url, data=params)\n",
    "\n",
    "assert results.status_code == 200, \"The request was not successful!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Also save the results for single/pooled siRNAs and esiRNAs to a CSV\n",
    "# file\n",
    "with open(\"single_pooled_siRNA_and_esiRNA_STRING_IDs.csv\", \"w\") as f:\n",
    "    csv_writer = csv.writer(f, delimiter=\"\\t\")\n",
    "\n",
    "    for i, line in enumerate(results.text.strip().split(\"\\n\")):\n",
    "        split_line = line.split(\"\\t\")\n",
    "        input_identifier, string_identifier = split_line[0], split_line[2]\n",
    "        csv_writer.writerow([input_identifier, string_identifier])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_per_row_list = []\n",
    "\n",
    "# Load the STRING identifiers from the csv file\n",
    "with open(\"siRNA_and_compounds_STRING_IDs.csv\", \"r\") as f:\n",
    "    csv_reader = csv.reader(f, delimiter=\"\\t\")\n",
    "    # Skip the header row as it does not contain any actual data\n",
    "    next(csv_reader)\n",
    "\n",
    "    for row in csv_reader:\n",
    "        # Each row in the CSV file is retrieved as a list the elements\n",
    "        # of which are the tab-separated values\n",
    "        # Thus, appending the list of each row to `csv_per_row_list`\n",
    "        # results in a list of lists, i.e. a nested list\n",
    "        # Employing a trick involving the unpacking operator (asterisk)\n",
    "        # in conjunction with the zip() function, the two columns are\n",
    "        # restored in an elegant way\n",
    "        # (https://realpython.com/python-zip-function/ and\n",
    "        # https://realpython.com/python-kwargs-and-args/#unpacking-with-the-asterisk-operators)\n",
    "        csv_per_row_list.append(row)\n",
    "\n",
    "_, siRNA_and_compounds_string_IDs = zip(*csv_per_row_list)\n",
    "\n",
    "# As the zip() function returns an iterator of tuples, the tuple is\n",
    "# converted into a list\n",
    "siRNA_and_compounds_string_IDs = list(siRNA_and_compounds_string_IDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conveniently enough, in addition to the ordinary STRING database\n",
    "# focusing on intraspecies protein-protein interactions, the\n",
    "# Viruses.STRING database was introduced in October 2018\n",
    "# As its name already suggests, it focuses on virus-virus and virus-host\n",
    "# protein-protein interactions (PPI)\n",
    "# However, contrary to the claim on the website, this resource cannot be\n",
    "# accessed via a REST API\n",
    "# Hence, the result file is downloaded from the website and loaded into\n",
    "# the script\n",
    "cols_to_use = [\"node1_external_id\", \"node2_external_id\"]\n",
    "\n",
    "string_interaction_VACV_human_df = pd.read_csv(\n",
    "    \"string_interactions_VACV_homo_sapiens.tsv\",\n",
    "    delimiter=\"\\t\",\n",
    "    usecols=cols_to_use\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "9606.ENSP00000376943\n",
      "9606.ENSP00000376943\n",
      "\n",
      "10\n",
      "9606.ENSP00000256458\n",
      "9606.ENSP00000256458\n",
      "\n",
      "18\n",
      "9606.ENSP00000415941\n",
      "9606.ENSP00000415941\n",
      "\n",
      "19\n",
      "9606.ENSP00000419425\n",
      "9606.ENSP00000419425\n",
      "\n",
      "21\n",
      "9606.ENSP00000400175\n",
      "9606.ENSP00000400175\n",
      "\n",
      "22\n",
      "9606.ENSP00000393312\n",
      "9606.ENSP00000393312\n",
      "\n",
      "23\n",
      "9606.ENSP00000425561\n",
      "9606.ENSP00000425561\n",
      "\n",
      "79\n",
      "9606.ENSP00000376943\n",
      "9606.ENSP00000376943\n",
      "\n",
      "81\n",
      "9606.ENSP00000256458\n",
      "9606.ENSP00000256458\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialise a list the elements of which are zeros\n",
    "# The amount of zeros in the list corresponds to the amount of\n",
    "# individual (pooled) siRNAs and compounds, i.e. to the length of the\n",
    "# set `siRNA_and_compounds_string_IDs`\n",
    "int_with_VACV_siRNA_and_compounds = [0] * len(siRNA_and_compounds_string_IDs)\n",
    "\n",
    "# Iterate over the data frame rows using the iterrows() method\n",
    "for i, row in string_interaction_VACV_human_df.iterrows():\n",
    "    int_partner_1 = row[\"node1_external_id\"]\n",
    "    int_partner_2 = row[\"node2_external_id\"]\n",
    "    # The if-elif-statement below checks whether the first or the second\n",
    "    # interaction partner also occurs in the list of human proteins\n",
    "    # targeted by siRNAs and compounds\n",
    "    # If this is the case, the other interaction partner has to belong\n",
    "    # to vaccinia virus in order for the criterion of host-pathogen PPI\n",
    "    # to be met\n",
    "    if int_partner_1 in siRNA_and_compounds_string_IDs:\n",
    "        # The NCBI identifier of vaccinia virus is 10245; it is\n",
    "        # prepended to the identifiers of VACV proteins\n",
    "        if \"10245\" in int_partner_2:\n",
    "            print(i)\n",
    "            print(int_partner_1)\n",
    "            print(siRNA_and_compounds_string_IDs[siRNA_and_compounds_string_IDs.index(int_partner_1)])\n",
    "            print()\n",
    "            # Determine the position index of the human protein\n",
    "            # interacting with VACV in `siRNA_and_compounds_string_IDs`\n",
    "            # and alter the entry of `int_with_VACV_siRNA_and_compounds`\n",
    "            # at the respective position from 0 to 1\n",
    "            human_prot_idx = siRNA_and_compounds_string_IDs.index(\n",
    "                int_partner_1\n",
    "            )\n",
    "            int_with_VACV_siRNA_and_compounds[human_prot_idx] = 1\n",
    "    elif int_partner_2 in siRNA_and_compounds_string_IDs:\n",
    "        if \"10245\" in int_partner_1:\n",
    "            print(i)\n",
    "            print(int_partner_2)\n",
    "            print(siRNA_and_compounds_string_IDs[siRNA_and_compounds_string_IDs.index(int_partner_2)])\n",
    "            print()\n",
    "            human_prot_idx = siRNA_and_compounds_string_IDs.index(\n",
    "                int_partner_2\n",
    "            )\n",
    "            int_with_VACV_siRNA_and_compounds[human_prot_idx] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of human proteins involved in interspecies PPIs recorded by Viruses.STRING: 7\n"
     ]
    }
   ],
   "source": [
    "n_recorded_interactions = np.count_nonzero(\n",
    "    int_with_VACV_siRNA_and_compounds\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Amount of human proteins involved in interspecies PPIs recorded \"\n",
    "    \"by Viruses.STRING: \"\n",
    "    f\"{n_recorded_interactions}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In total, 13 virus-host PPIs between VACV and Homo sapiens are\n",
    "# recorded\n",
    "# I only checked whether the targeted genes in the VACV screen are\n",
    "# involved in interspecies PPI at all; thus, multiple interactions\n",
    "# involving one and the same human protein are not captured\n",
    "# It is therefore investigated whether there indeed is a discrepancy\n",
    "# between the human proteins involved in interspecies PPI according to\n",
    "# Viruses.STRING and the VACV screen or whether all human interaction\n",
    "# partners captured by Viruses.STRING also occur in the VACV screen, but\n",
    "# are involved in multiple interactions\n",
    "# The TSV file from Viruses.STRING harbours both intra- and interspecies\n",
    "# PPI; since we are only interested in intraspecies PPI occurring\n",
    "# between VACV and Homo sapiens, we extract them from the TSV file\n",
    "cols_to_use = [\n",
    "    \"#node1\",\n",
    "    \"node2\",\n",
    "    \"node1_external_id\",\n",
    "    \"node2_external_id\"\n",
    "]\n",
    "\n",
    "string_interaction_VACV_human_df = pd.read_csv(\n",
    "    \"string_interactions_VACV_homo_sapiens.tsv\",\n",
    "    delimiter=\"\\t\",\n",
    "    usecols=cols_to_use\n",
    ")\n",
    "\n",
    "string_interspecies_interactions = string_interaction_VACV_human_df.loc[\n",
    "    # The criterion for an interspecies PPI is met when one interaction\n",
    "    # partner originates from VACV, whereas the other originates from\n",
    "    # Homo sapiens\n",
    "    # The NCBI identifier of VACV is 10245, while that of Homo sapiens\n",
    "    # is 9606\n",
    "    # Bear in mind that due to operator precedence, parentheses have to\n",
    "    # be used at the appropriate locations\n",
    "    (\n",
    "        string_interaction_VACV_human_df[\"node1_external_id\"].str\n",
    "        .contains(\"9606\")\n",
    "        &\n",
    "        string_interaction_VACV_human_df[\"node2_external_id\"].str\n",
    "        .contains(\"10245\")\n",
    "    )\n",
    "    |\n",
    "    (\n",
    "        string_interaction_VACV_human_df[\"node1_external_id\"].str\n",
    "        .contains(\"10245\")\n",
    "        &\n",
    "        string_interaction_VACV_human_df[\"node2_external_id\"].str\n",
    "        .contains(\"9606\")\n",
    "    )\n",
    "]\n",
    "\n",
    "assert len(string_interspecies_interactions) == 13, (\n",
    "    \"Row extraction of the Pandas DataFrame has not been successful!\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      #node1    node2 node1_external_id     node2_external_id\n",
      "9   VACWR040  BCL2L11    10245.F1_VACCW  9606.ENSP00000376943\n",
      "10  VACWR039    IRAK2    10245.K7_VACCW  9606.ENSP00000256458\n",
      "11  VACWR040     BAK1    10245.F1_VACCW  9606.ENSP00000353878\n",
      "12  VACWR178    TRAF6   10245.A52_VACCW  9606.ENSP00000337853\n",
      "13  VACWR039    DDX3X    10245.K7_VACCW  9606.ENSP00000382840\n",
      "18        C3      C4B   10245.VCP_VACCW  9606.ENSP00000415941\n",
      "19  VACWR030     PPIA    10245.M1_VACCW  9606.ENSP00000419425\n",
      "21  VACWR050     RHOA   10245.F11_VACCW  9606.ENSP00000400175\n",
      "22  VACWR101    FGFR1    10245.H3_VACCW  9606.ENSP00000393312\n",
      "23  VACWR085    EIF4E    10245.G7_VACCW  9606.ENSP00000425561\n",
      "79  VACWR028  BCL2L11    10245.N1_VACCW  9606.ENSP00000376943\n",
      "80  VACWR039    TRAF6    10245.K7_VACCW  9606.ENSP00000337853\n",
      "81  VACWR178    IRAK2   10245.A52_VACCW  9606.ENSP00000256458\n"
     ]
    }
   ],
   "source": [
    "print(string_interspecies_interactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following human interactions partners involved in interspecies PPI\n",
    "# are also comprised in the VACV screen:\n",
    "# BCL2L11 (reliable measurement)\n",
    "# IRAK2 (reliable measurement)\n",
    "# BAK1 (reliable measurement)\n",
    "# TRAF6 (reliable measurement)\n",
    "# DDX3X (reliable measurement)\n",
    "# C4B (unreliable measurement, but values are available)\n",
    "# PPIA (unreliable measurement, but values are available)\n",
    "# RHOA (measurement quality unknown, but values are available)\n",
    "# FGFR1 (reliable measurement)\n",
    "# EIF4E (reliable measurement)\n",
    "#\n",
    "# STRING ID and Viruses.STRING ID do not match for the following\n",
    "# proteins:\n",
    "# BAK1 has a STRING ID of ENSP00000363591 and a Viruses.STRING ID of\n",
    "# ENSP00000353878\n",
    "# TRAF6 has a STRING ID of ENSP00000433623 and a Viruses.STRING ID of\n",
    "# ENSP00000337853\n",
    "# DDX3X has a STRING ID of ENSP00000494040 and a Viruses.STRING ID of\n",
    "# ENSP00000382840\n",
    "#\n",
    "# The discrepancy between the 13 interspecies PPI recorded by\n",
    "# Viruses.STRING and the 7 human proteins comprised in the VACV screen\n",
    "# can be explained as follows:\n",
    "# For three proteins, namely BAK1, TRAF6 and DDX3X, the STRING ID and\n",
    "# the Viruses.STRING ID do not match for some reason (13 - 3 = 10)\n",
    "# Apart from that, three proteins, namely BCL2L11, TRAF6 and IRAK2, are\n",
    "# involved in two interspecies PPI each (10 - 3 = 7)\n",
    "#\n",
    "# In conclusion, the interspecies PPI recorded by Viruses.STRING involve\n",
    "# 10 different human interaction partners. Now, the following problem\n",
    "# emerged: For 3 of those 10 human interaction partners, the measurement\n",
    "# either is erroneous/of poor quality or the measurement quality is\n",
    "# unknown; nonetheless, measurement values are available for C4B, PPIA\n",
    "# and RHOA"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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