2023_01_03_proteome_drugs.ipynb

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      "authorship_tag": "ABX9TyPREzf5TBPeOnIvqo39npYy",
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chupvl/gcolab/blob/main/2023_01_03_proteome_drugs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2023-01-03: Comments on paper \"A proteome-wide atlas of drug mechanism of action\""
      ],
      "metadata": {
        "id": "iOSBKt7it-Et"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Libs"
      ],
      "metadata": {
        "id": "AQuSXrMYuEoB"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sqhQgy-Otv2P"
      },
      "outputs": [],
      "source": [
        "!pip install rdkit-pypi -qqq"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# \n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import PandasTools\n",
        "\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import rdDepictor\n",
        "rdDepictor.SetPreferCoordGen(True)\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from IPython.display import SVG\n",
        "\n",
        "from rdkit.Chem.Fingerprints import FingerprintMols\n",
        "from rdkit import DataStructs\n",
        "\n",
        "def show_df(df):\n",
        "    return HTML(df.to_html(notebook=True))\n",
        "\n",
        "def getMol(smiles):\n",
        "  try:\n",
        "    return Chem.MolFromSmiles(smiles)\n",
        "  except:\n",
        "    return None\n",
        "\n",
        "def getSim(m1, m2):\n",
        "  fp1 = FingerprintMols.FingerprintMol(m1)\n",
        "  fp2 = FingerprintMols.FingerprintMol(m2)\n",
        "  return DataStructs.TanimotoSimilarity(fp1, fp2)\n",
        "\n",
        "def get_mscaffold(mol):\n",
        "  '''\n",
        "  for a given rdkit.Mol return Murcko scaffold and its inchikey\n",
        "  '''\n",
        "  try:\n",
        "    Chem.RemoveStereochemistry(mol)\n",
        "    sk = Chem.Scaffolds.MurckoScaffold.GetScaffoldForMol(mol)\n",
        "    return sk, Chem.inchi.MolToInchiKey(sk)\n",
        "  except:\n",
        "    return None, None\n",
        "#\n"
      ],
      "metadata": {
        "id": "2YBHxl08uQxj"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getDataForComps(lst_names):\n",
        "  smiles = [dfs2[dfs2['Compound Name'].str.contains(i)]['SMILES'].values[0] for i in lst_names]\n",
        "  mols = [getMol(s) for s in smiles]\n",
        "  sims = [getSim(i, mols[0]) for i in mols]\n",
        "  D = {}\n",
        "  D['names'] = lst_names\n",
        "  D['mols'] = mols\n",
        "  D['sims'] = [round(i, 4) for i in sims]\n",
        "  D['prots'] = len(dfs1_mlt[(dfs1_mlt['value_abs'] > 1) & (dfs1_mlt['variable'].str.contains(lst_names[0]))]['Gene Name'].to_list())\n",
        "  return D"
      ],
      "metadata": {
        "id": "dy9rkOv_taZH"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getImage(DataForComps):\n",
        "  legend = [str(i) for i in DataForComps['sims']]\n",
        "  legend = [' '.join(i) for i in list(zip(DataForComps['names'], legend))]\n",
        "  legend[0] = DataForComps['names'][0]\n",
        "  return Draw.MolsToGridImage(DataForComps['mols'], molsPerRow=5, subImgSize=(200,200), legends=legend)"
      ],
      "metadata": {
        "id": "vi1lSwM1yypQ"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def getProteins(CompoundName, cutoff=1):\n",
        "  propt_lst = dfs1_mlt[(dfs1_mlt['variable'].str.contains(CompoundName)) & (dfs1_mlt['value_abs'] > cutoff)]['Gene Name'].to_list()\n",
        "  return propt_lst"
      ],
      "metadata": {
        "id": "TE9_DnwRYVjg"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_ez_stats(df_mlt, cutoff=-0.5):\n",
        "  cmps_vc = df_mlt[df_mlt['value'] <= cutoff]['variable'].value_counts()\n",
        "  cmps_vc = cmps_vc.reset_index()\n",
        "  cmps_vc.columns = ['entity', 'nTProteins']\n",
        "  cmps_vc['Compound Name'] = [i.split('_')[0] for i in cmps_vc['entity']]\n",
        "  cmps_ezstats = cmps_vc.merge(dfs2, on='Compound Name')\n",
        "  return cmps_ezstats"
      ],
      "metadata": {
        "id": "nvBBABBuKJXk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "IFQ35gkm1tTi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://static-content.springer.com/esm/art%3A10.1038%2Fs41587-022-01539-0/MediaObjects/41587_2022_1539_MOESM3_ESM.csv"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqSMEbdszX_d",
        "outputId": "759a40f0-517d-4c52-8d28-3f5ab969d832"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-14 03:39:06--  https://static-content.springer.com/esm/art%3A10.1038%2Fs41587-022-01539-0/MediaObjects/41587_2022_1539_MOESM3_ESM.csv\n",
            "Resolving static-content.springer.com (static-content.springer.com)... 151.101.0.95, 151.101.64.95, 151.101.128.95, ...\n",
            "Connecting to static-content.springer.com (static-content.springer.com)|151.101.0.95|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 43215683 (41M) [text/csv]\n",
            "Saving to: ‘41587_2022_1539_MOESM3_ESM.csv.2’\n",
            "\n",
            "\r          41587_202   0%[                    ]       0  --.-KB/s               \r41587_2022_1539_MOE 100%[===================>]  41.21M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-01-14 03:39:06 (289 MB/s) - ‘41587_2022_1539_MOESM3_ESM.csv.2’ saved [43215683/43215683]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# profiles\n",
        "dfs1 = pd.read_csv('41587_2022_1539_MOESM3_ESM.csv', encoding='unicode_escape')\n",
        "# ok, those are compounds with smiles\n",
        "dfs2 = pd.read_excel('https://static-content.springer.com/esm/art%3A10.1038%2Fs41587-022-01539-0/MediaObjects/41587_2022_1539_MOESM4_ESM.xlsx', dtype=str)\n",
        "Chem.PandasTools.AddMoleculeColumnToFrame(dfs2, 'SMILES', 'mol', includeFingerprints=True)"
      ],
      "metadata": {
        "id": "-86YvrJ4uYl_"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking compound names\n",
        "dfs1_cn = set([ i.split('_')[0] for i in dfs1.columns[2:] ])\n",
        "dfs2_cn = set(dfs2['Compound Name'])\n",
        "dfs1_cn.difference(dfs2_cn)\n",
        "# skipping compound-compound mapping issues for now\n",
        "cn_ovlp = list(dfs1_cn & dfs2_cn)\n",
        "print(f'Number of compounds for analysis: {len(cn_ovlp)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4nhSSQDziM5",
        "outputId": "ff74cf55-b03b-4c1c-ff3e-0b23ae731e28"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of compounds for analysis: 872\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# most of the compounds were screened in 10uM concentration\n",
        "dfs2['Screening Concentration'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovGkCA5pvHvl",
        "outputId": "92199fe4-d9b8-4f44-97d4-0896442a0d5f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10uM    847\n",
              "1uM      28\n",
              "Name: Screening Concentration, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Proteome fingerprint"
      ],
      "metadata": {
        "id": "ipUFTqX0AVfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# checking if the same gene name is mapped to several UniprotIDs\n",
        "# UniprotID is a unique protein identifier, gene symbol is not\n",
        "nUUID = len(set(dfs1['UniprotID']))\n",
        "nUGN = len(set(dfs1['Gene Name']))\n",
        "print(f'Numer of unique gene names: {nUUID}')\n",
        "print(f'Numer of unique uniprot IDs: {nUGN}')\n",
        "print(f'Total number of entries: {len(dfs1)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NdGTzRIBAZTx",
        "outputId": "d31db5f5-79de-44a5-bd25-e0d19c8c81c0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numer of unique gene names: 9960\n",
            "Numer of unique uniprot IDs: 9715\n",
            "Total number of entries: 9960\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# that would be easier to handle\n",
        "dfs1_mlt = pd.melt(dfs1, id_vars=['Gene Name', 'UniprotID'])\n",
        "dfs1_mlt['value_abs'] = dfs1_mlt['value'].abs()\n",
        "dfs1_mlt.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fi2E2NvSC6FC",
        "outputId": "17dc39bf-fc42-46b7-f77c-4df11a6b948e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gene Name UniprotID         variable  value  value_abs\n",
              "0      AAAS    Q9NRG9  SR 142948_NTSR1   0.00       0.00\n",
              "1      AACS    Q86V21  SR 142948_NTSR1  -0.03       0.03\n",
              "2     AADAT  Q8N5Z0-2  SR 142948_NTSR1   0.02       0.02\n",
              "3     AAGAB    Q6PD74  SR 142948_NTSR1   0.16       0.16\n",
              "4      AAK1    Q2M2I8  SR 142948_NTSR1  -0.02       0.02"
            ],
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              "\n",
              "  <div id=\"df-1ffafd34-1fa5-42cc-8200-982d96e4a5c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gene Name</th>\n",
              "      <th>UniprotID</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "      <th>value_abs</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AAAS</td>\n",
              "      <td>Q9NRG9</td>\n",
              "      <td>SR 142948_NTSR1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AACS</td>\n",
              "      <td>Q86V21</td>\n",
              "      <td>SR 142948_NTSR1</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AADAT</td>\n",
              "      <td>Q8N5Z0-2</td>\n",
              "      <td>SR 142948_NTSR1</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AAGAB</td>\n",
              "      <td>Q6PD74</td>\n",
              "      <td>SR 142948_NTSR1</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AAK1</td>\n",
              "      <td>Q2M2I8</td>\n",
              "      <td>SR 142948_NTSR1</td>\n",
              "      <td>-0.02</td>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmps_m05_ezstats = get_ez_stats(dfs1_mlt)\n",
        "cmps_m05_ezstats[cmps_m05_ezstats['nTProteins'] > 50]['Primary Target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLz8TRCLBOUP",
        "outputId": "451f325d-cef3-4eb1-ae95-e460468abc62"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PTK2        3\n",
              "PIK3CA      3\n",
              "BRD4        3\n",
              "CDK4        3\n",
              "MDM2        3\n",
              "           ..\n",
              "EWSR1       1\n",
              "CHEK1       1\n",
              "ABL1;SRC    1\n",
              "TYK2        1\n",
              "PSEN1       1\n",
              "Name: Primary Target, Length: 166, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmps_m1_ezstats = get_ez_stats(dfs1_mlt, -1)\n",
        "cmps_m1_ezstats = cmps_m1_ezstats[['nTProteins', 'Compound Name', 'Primary Target']]\n",
        "cmps_m1_ezstats['Primary Target'] = cmps_m1_ezstats['Primary Target'].str.split(';')\n",
        "cmps_m1_ezstats = cmps_m1_ezstats.explode('Primary Target').sort_values('Primary Target').reset_index(drop=True)\n",
        "cmps_m1_ezstats[cmps_m1_ezstats['nTProteins'] > 50]['Primary Target'].value_counts().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bVUdDnZLXEv",
        "outputId": "6cdd8d8b-00d4-4e64-b072-8cb97e8bbbc1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['HDAC1', 'PIK3CA', 'BRD4', 'MDM2', 'ABL1', 'PTK2', 'KIF11', 'PLK1',\n",
              "       'HDAC3', 'HDAC6', 'PSMB5', 'HSP90AA1', 'FLT3', 'RAF1', 'IGF1R', 'SYK',\n",
              "       'JAK2', 'NAE1', 'MELK', 'MAPK1', 'MAP2K1', 'LCK', 'MTOR', 'PLD2',\n",
              "       'OXTR', 'PAK4', 'PRKD1', 'PRKDC', 'PSMB8', 'RXRB', 'SLC6A3', 'SRC',\n",
              "       'SSRP1', 'STAT6', 'TOP1', 'TTK', 'VCP', 'KIT', 'JAK1', 'KDR', 'CXCR2',\n",
              "       'AKT2', 'ATP6V1H', 'AURKB', 'BCL2L1', 'BRAF', 'CDC7', 'CDK2', 'CDK4',\n",
              "       'CDK9', 'CENPE', 'CHEK1', 'CTNNB1', 'DHODH', 'KDM1A', 'EHMT1', 'EHMT2',\n",
              "       'EPHB4', 'EWSR1', 'FGFR1', 'GHSR', 'GPER1', 'HSP90B1', 'IKBKB', 'IL1B',\n",
              "       'IMPDH2', 'ADRA2C', 'XPO1'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmps_m1_ezstats[cmps_m1_ezstats['nTProteins'] > 100]['Primary Target'].value_counts().head(20)"
      ],
      "metadata": {
        "id": "JC6Vvq2FM6pg",
        "outputId": "b08fec49-257d-4523-9509-4eb10b7c19cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HDAC1     3\n",
              "MDM2      3\n",
              "BRD4      3\n",
              "PLK1      2\n",
              "RAF1      2\n",
              "PIK3CA    2\n",
              "HDAC6     2\n",
              "PTK2      2\n",
              "IGF1R     2\n",
              "KIF11     2\n",
              "FLT3      2\n",
              "OXTR      1\n",
              "PLD2      1\n",
              "NAE1      1\n",
              "PAK4      1\n",
              "MELK      1\n",
              "MAPK1     1\n",
              "ABL1      1\n",
              "LCK       1\n",
              "PRKD1     1\n",
              "Name: Primary Target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmps_m1_ezstats[cmps_m1_ezstats['Primary Target'] == 'OXTR']"
      ],
      "metadata": {
        "id": "cvCvjcZErhek",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "749e30be-9384-46a2-ec3c-8ff4e1c9025b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     nTProteins Compound Name Primary Target\n",
              "541         123  CHEMBL200403           OXTR"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>541</th>\n",
              "      <td>123</td>\n",
              "      <td>CHEMBL200403</td>\n",
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              "          document.querySelector('#df-e2cb9001-0261-487c-8082-2087f74e7777 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2cb9001-0261-487c-8082-2087f74e7777');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CHEMBL200403 = dfs2[dfs2['Compound Name'] == 'CHEMBL200403']['SMILES'].values[0]\n",
        "Chem.MolFromSmiles(CHEMBL200403)\n",
        "# S is for suspicious"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "id": "BQBxgyLgUacL",
        "outputId": "527bae8a-5591-43a3-82b3-7691ed17b8e8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<rdkit.Chem.rdchem.Mol at 0x7fb8921a6e40>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Case 1: RN486"
      ],
      "metadata": {
        "id": "AgDXGSn2rh41"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "case1_d = getDataForComps(['RN486', 'SB-224289', 'UNC 0638', 'Cediranib', 'STK410283'])\n",
        "case1_d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAlXzXfsugLN",
        "outputId": "8a918d8f-96be-4d34-de4e-d4ba38485b42"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'names': ['RN486', 'SB-224289', 'UNC 0638', 'Cediranib', 'STK410283'],\n",
              " 'mols': [<rdkit.Chem.rdchem.Mol at 0x7fb8920cf040>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf0b0>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf120>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf190>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf200>],\n",
              " 'sims': [1.0, 0.5721, 0.4398, 0.442, 0.4042],\n",
              " 'prots': 88}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "getImage(case1_d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "CPf6VyvVxdgR",
        "outputId": "3837719f-8b4d-427c-f822-f1e84540f5e6"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Case 2: JP1302"
      ],
      "metadata": {
        "id": "-Eq5e9NZtQJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "case2_d = getDataForComps(['JP1302', 'Flavopiridol', 'AZD-5438', 'PHA 767491', 'CBL0137'])\n",
        "case2_d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxvIOnFLtSw1",
        "outputId": "9b0ccbbb-3ee8-4bd1-f41f-125b88ac899e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'names': ['JP1302', 'Flavopiridol', 'AZD-5438', 'PHA 767491', 'CBL0137'],\n",
              " 'mols': [<rdkit.Chem.rdchem.Mol at 0x7fb8920cf5f0>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf660>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf6d0>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf740>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cf7b0>],\n",
              " 'sims': [1.0, 0.4152, 0.4179, 0.3753, 0.4088],\n",
              " 'prots': 672}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "getImage(case2_d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "1DX_KAsByt_h",
        "outputId": "eaee1b4b-e30f-4d1a-8454-9586ab2534ee"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Case 3: nutlin3a"
      ],
      "metadata": {
        "id": "zvDukNqchcPu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "case3_d = getDataForComps(['Nutlin3a', 'Idasanutlin', 'MI-773', 'UNC2250'])\n",
        "case3_d"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6q3Rfr_hhV9",
        "outputId": "369468dd-1a68-4a0a-a27d-1279fede24e0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'names': ['Nutlin3a', 'Idasanutlin', 'MI-773', 'UNC2250'],\n",
              " 'mols': [<rdkit.Chem.rdchem.Mol at 0x7fb8920cfac0>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cfb30>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cfba0>,\n",
              "  <rdkit.Chem.rdchem.Mol at 0x7fb8920cfc10>],\n",
              " 'sims': [1.0, 0.5459, 0.5679, 0.3899],\n",
              " 'prots': 365}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "getImage(case3_d)"
      ],
      "metadata": {
        "id": "H02V4MPykvHd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "40bd08ac-d5a8-404d-b0b1-f4ad1e9e8aef"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Case 4: Identical Murcko scaffolds"
      ],
      "metadata": {
        "id": "CYnapkm8RLQg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# adding scaffold information\n",
        "dfs2[['scaffold_mol', 'scaffold_inchikey']] = [get_mscaffold(i) for i in dfs2['mol'].values ]"
      ],
      "metadata": {
        "id": "VuO0jh6E15jG"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# taking compounds with identical scaffolds\n",
        "dfs2_ik_dupl = dfs2.dropna(subset=['scaffold_inchikey'], how='any')\n",
        "dfs2_ik_dupl = dfs2_ik_dupl[dfs2_ik_dupl['scaffold_inchikey'] != '']\n",
        "dfs2_ik_dupl = dfs2_ik_dupl[dfs2_ik_dupl['scaffold_inchikey'].duplicated()].reset_index(drop=True)"
      ],
      "metadata": {
        "id": "rHFdyWem7gRb"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# let's check how big scaffold are in duplicated scaffolds\n",
        "# we are definitely interested in >6 atoms per scaffold\n",
        "dfs2_ik_dupl['scaffold_mol_nHAtoms'] = [m.GetNumHeavyAtoms() for m in dfs2_ik_dupl['scaffold_mol']]\n",
        "dfs2_ik_dupl['scaffold_mol_nHAtoms'].value_counts()"
      ],
      "metadata": {
        "id": "B2XZX738_sA4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20930d4e-65ae-41f0-b1b7-9b78026faa35"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6     46\n",
              "17     9\n",
              "16     7\n",
              "13     6\n",
              "9      5\n",
              "18     5\n",
              "11     4\n",
              "24     3\n",
              "14     3\n",
              "26     3\n",
              "5      3\n",
              "10     2\n",
              "12     2\n",
              "25     2\n",
              "19     2\n",
              "29     2\n",
              "28     2\n",
              "30     1\n",
              "21     1\n",
              "35     1\n",
              "15     1\n",
              "40     1\n",
              "23     1\n",
              "20     1\n",
              "Name: scaffold_mol_nHAtoms, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "skik_g6 = dfs2_ik_dupl[dfs2_ik_dupl['scaffold_mol_nHAtoms'] > 6]['scaffold_inchikey'].value_counts()\n",
        "top_scaffolds_ik = skik_g6[skik_g6 >=2 ].index.to_list()\n",
        "print(f'Number of compounds with duplicated Murcko scaffold: {len(top_scaffolds_ik)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yz3Lvh3rR5vn",
        "outputId": "30669b4c-c072-4212-c7fd-d0f253b7f2ce"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of compounds with duplicated Murcko scaffold: 11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# let's see them\n",
        "top_scaffolds_exmpls = dfs2_ik_dupl[dfs2_ik_dupl['scaffold_inchikey'].isin(top_scaffolds_ik)].drop_duplicates(subset=['scaffold_inchikey'])\n",
        "top_scaffolds_exmpls_ik = top_scaffolds_exmpls['scaffold_inchikey'].to_list()\n",
        "Draw.MolsToGridImage(top_scaffolds_exmpls['scaffold_mol'])\n",
        "# most of them are not very interesting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "-KxWY9s9U3N-",
        "outputId": "128fff22-c3cf-4f94-d70f-79dcf5bbd4bb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Draw.MolsToGridImage(dfs2[dfs2['scaffold_inchikey'] == top_scaffolds_exmpls_ik[3]]['mol'])\n",
        "# nope, pharmacophore is drastically different"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "wizpDt3vSe-_",
        "outputId": "404c0665-2739-4928-dc59-9c5f0b896096"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Draw.MolsToGridImage(dfs2[dfs2['scaffold_inchikey'] == top_scaffolds_exmpls_ik[-2]]['mol'])\n",
        "# that might be interesting, but steroids are always tricky"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "vNUii9iQTUSC",
        "outputId": "24f4529a-6d8b-4842-d945-d469339d3ec3"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#case4_example0 = dfs2[dfs2['scaffold_inchikey'] == top_scaffolds_exmpls_ik[-2]]['Compound Name'].to_list()\n",
        "#case4_example0_prots_g10 = {k: set(getProteins(k, 1)) for k in case4_example0}"
      ],
      "metadata": {
        "id": "YPLZiNaxVA1l"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Draw.MolsToGridImage(dfs2[dfs2['scaffold_inchikey'] == top_scaffolds_exmpls_ik[-1]]['mol'])\n",
        "# yep, that's interesting\n",
        "# not sure why they have Tazarotene and Tazarotene1\t and Tazarotene2 names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "OciARiPpZtxL",
        "outputId": "fc2501f5-4efe-4801-eb91-443aa48e49cb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Case 4: identical Murcko scaffolds - example 1"
      ],
      "metadata": {
        "id": "rL_40hMPdcGv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1 = ['AGN 190299', 'Tazarotene1', 'Tazarotene2']"
      ],
      "metadata": {
        "id": "bPkI_OaaXhaQ"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfs2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rPSeuGFapl9",
        "outputId": "e75bc9a8-aed2-4dfa-b6cd-e345dc37ec0b"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Compound Name', 'Primary Target', 'Secondary Target', 'SMILES', 'MW',\n",
              "       'Formula', 'Screening Concentration', 'mol', 'scaffold_mol',\n",
              "       'scaffold_inchikey'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfs2[dfs2['Compound Name'].isin(case4_example1)][['Compound Name', 'Primary Target', 'Secondary Target', 'MW', 'Formula', 'Screening Concentration', ]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "7gQnFa0labz2",
        "outputId": "891b407f-f67e-4b46-f3c0-bd8578c31a4f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Compound Name Primary Target Secondary Target      MW     Formula  \\\n",
              "59     AGN 190299           RARB              NaN   323.1  C19H17NO2S   \n",
              "559   Tazarotene1           RARB        RARG;RARA  351.13  C21H21NO2S   \n",
              "569   Tazarotene2           RARB        RARG;RARA  351.13  C21H21NO2S   \n",
              "\n",
              "    Screening Concentration  \n",
              "59                     10uM  \n",
              "559                    10uM  \n",
              "569                    10uM  "
            ],
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              "\n",
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              "      <th>Compound Name</th>\n",
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              "      <td>10uM</td>\n",
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              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>Tazarotene1</td>\n",
              "      <td>RARB</td>\n",
              "      <td>RARG;RARA</td>\n",
              "      <td>351.13</td>\n",
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              "      <td>RARB</td>\n",
              "      <td>RARG;RARA</td>\n",
              "      <td>351.13</td>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb309ef1-6de4-4af6-8e2e-3bc33685f03e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb309ef1-6de4-4af6-8e2e-3bc33685f03e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1_prots_g05 = {k: set(getProteins(k, 0.5)) for k in case4_example1}\n",
        "case4_example1_prots_g10 = {k: set(getProteins(k, 1)) for k in case4_example1}\n",
        "for k,v in case4_example1_prots_g05.items():\n",
        "  print(f'Number of proteins for {k} at 0.5 cutoff: {len(v)}')\n",
        "  print(f'Number of proteins for {k} at 1.0 cutoff: {len(case4_example1_prots_g10[k])}')\n",
        "  print('\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeP0GrDDWNwy",
        "outputId": "825fab3a-c83a-4c31-a86a-738e3f1c17e7"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of proteins for AGN 190299 at 0.5 cutoff: 18\n",
            "Number of proteins for AGN 190299 at 1.0 cutoff: 1\n",
            "\n",
            "\n",
            "Number of proteins for Tazarotene1 at 0.5 cutoff: 23\n",
            "Number of proteins for Tazarotene1 at 1.0 cutoff: 4\n",
            "\n",
            "\n",
            "Number of proteins for Tazarotene2 at 0.5 cutoff: 30\n",
            "Number of proteins for Tazarotene2 at 1.0 cutoff: 6\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1_prots_g10['Tazarotene1'] & case4_example1_prots_g10['Tazarotene2']\n",
        "# that's really weird"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ae-NozZfZSOd",
        "outputId": "7d018b9b-161a-421b-979d-256aeb28e6f3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1_prots_g05['Tazarotene1'] & case4_example1_prots_g05['Tazarotene2']\n",
        "# that's a bit better"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1C5bEMWaIek",
        "outputId": "b2207e62-3e23-4102-ee18-bfad86c93ca8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'CYP1A1', 'NRIP1', 'PLIN2'}"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1_prots_g05['AGN 190299'] & case4_example1_prots_g05['Tazarotene1']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i11PJBSYV6Cs",
        "outputId": "bca6573b-3af0-4ecc-cfdb-c9410f91bd79"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "set()"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "case4_example1_prots_g05['AGN 190299'] & case4_example1_prots_g05['Tazarotene2']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aiSZ4T_0aQUS",
        "outputId": "9d1a9ae9-22ed-4dde-b86b-f716b87fa2f7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DHRS3', 'ELF3', 'KLK6', 'LTBP3'}"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, for three compounds of which two are duplicates we have following:\n",
        "- duplicates does not have overlapping proteins at 1.0 log units cutoffs (absolute values)\n",
        "- duplicates does have some overlapping proteins at 0.5 log units cutoffs (absolute values)\n",
        "- \"AGN 190299\" that is a \"parent\" compound for Tazarotene does not have overlapping proteins with Tazarotene1, but does have overlapping proteins with Tazarotene2\n",
        "- A very strange issue... "
      ],
      "metadata": {
        "id": "bWiTskVYcSYl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HCT116 proteome"
      ],
      "metadata": {
        "id": "X8HIx1HbRWP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compound is quit a hurricane\n",
        "dfs1_mlt[(dfs1_mlt['value_abs'] > 2) & (dfs1_mlt['variable'].str.contains('Nutlin3a'))]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ftRqn7TgqArf",
        "outputId": "ce8ad2ff-d35e-45fa-c61b-fefe177bc630"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Gene Name UniprotID       variable  value  value_abs\n",
              "7679319    ADAMTS7    Q9UKP4  Nutlin3a_MDM2   2.04       2.04\n",
              "7679563       ANLN    Q9NQW6  Nutlin3a_MDM2  -3.68       3.68\n",
              "7679668  ARHGAP11A    Q6P4F7  Nutlin3a_MDM2  -2.46       2.46\n",
              "7679672   ARHGAP19  Q14CB8-2  Nutlin3a_MDM2  -2.80       2.80\n",
              "7679708   ARHGEF39    Q8N4T4  Nutlin3a_MDM2  -2.30       2.30\n",
              "...            ...       ...            ...    ...        ...\n",
              "7688260      UBE2T    Q9NPD8  Nutlin3a_MDM2  -2.26       2.26\n",
              "7688318      UHRF1  Q96T88-2  Nutlin3a_MDM2  -2.19       2.19\n",
              "7688394      USP37    Q86T82  Nutlin3a_MDM2  -2.69       2.69\n",
              "7688581      WDR76    Q9H967  Nutlin3a_MDM2  -3.90       3.90\n",
              "7689103      ZWINT    O95229  Nutlin3a_MDM2  -3.13       3.13\n",
              "\n",
              "[125 rows x 5 columns]"
            ],
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              "\n",
              "  <div id=\"df-f8233609-cca5-4486-9f16-8749546a0c4d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gene Name</th>\n",
              "      <th>UniprotID</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "      <th>value_abs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7679319</th>\n",
              "      <td>ADAMTS7</td>\n",
              "      <td>Q9UKP4</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>2.04</td>\n",
              "      <td>2.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7679563</th>\n",
              "      <td>ANLN</td>\n",
              "      <td>Q9NQW6</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-3.68</td>\n",
              "      <td>3.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7679668</th>\n",
              "      <td>ARHGAP11A</td>\n",
              "      <td>Q6P4F7</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.46</td>\n",
              "      <td>2.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7679672</th>\n",
              "      <td>ARHGAP19</td>\n",
              "      <td>Q14CB8-2</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.80</td>\n",
              "      <td>2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7679708</th>\n",
              "      <td>ARHGEF39</td>\n",
              "      <td>Q8N4T4</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.30</td>\n",
              "      <td>2.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7688260</th>\n",
              "      <td>UBE2T</td>\n",
              "      <td>Q9NPD8</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.26</td>\n",
              "      <td>2.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7688318</th>\n",
              "      <td>UHRF1</td>\n",
              "      <td>Q96T88-2</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.19</td>\n",
              "      <td>2.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7688394</th>\n",
              "      <td>USP37</td>\n",
              "      <td>Q86T82</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-2.69</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7688581</th>\n",
              "      <td>WDR76</td>\n",
              "      <td>Q9H967</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-3.90</td>\n",
              "      <td>3.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7689103</th>\n",
              "      <td>ZWINT</td>\n",
              "      <td>O95229</td>\n",
              "      <td>Nutlin3a_MDM2</td>\n",
              "      <td>-3.13</td>\n",
              "      <td>3.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8233609-cca5-4486-9f16-8749546a0c4d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8233609-cca5-4486-9f16-8749546a0c4d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8233609-cca5-4486-9f16-8749546a0c4d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -O cl_proteome.txt https://www.ebi.ac.uk/gxa/experiments-content/E-PROT-19/download/PROTEOMICS_BASELINE?geneQuery=%5B%5D&unit=parts+per+billion&cutoff=0.0&selectedColumnIds=g1%2Cg2%2Cg3%2Cg4%2Cg5&type=PROTEOMICS_BASELINE "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrGhaCsppkEy",
        "outputId": "2c5682ae-ffbb-44cb-9e65-c2a3febd4872"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-01-14 03:39:54--  https://www.ebi.ac.uk/gxa/experiments-content/E-PROT-19/download/PROTEOMICS_BASELINE?geneQuery=%5B%5D\n",
            "Resolving www.ebi.ac.uk (www.ebi.ac.uk)... 193.62.193.80\n",
            "Connecting to www.ebi.ac.uk (www.ebi.ac.uk)|193.62.193.80|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 \n",
            "Length: unspecified [text/plain]\n",
            "Saving to: ‘cl_proteome.txt’\n",
            "\n",
            "cl_proteome.txt         [     <=>            ] 639.32K   566KB/s    in 1.1s    \n",
            "\n",
            "2023-01-14 03:39:56 (566 KB/s) - ‘cl_proteome.txt’ saved [654664]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HCT116 = pd.read_csv('cl_proteome.txt', sep='\\t', skiprows=4)\n",
        "print(df_HCT116.shape)\n",
        "df_HCT116.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "XO4RQWIxpyYP",
        "outputId": "d2f3de56-ace0-4a7c-a291-bbac2271f6d5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11536, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Gene ID Gene Name     A549    HCT116      HeLa     MCF-7   SH-SY5Y\n",
              "0  ENSG00000000003    TSPAN6   3287.0    4202.0    5610.0     978.0    1105.0\n",
              "1  ENSG00000000419      DPM1  85916.0  116570.0  135471.0  173859.0  237237.0\n",
              "2  ENSG00000000457     SCYL3   9725.0    4903.0    1246.0    6427.0    9118.0\n",
              "3  ENSG00000000460  C1orf112   3912.0    5324.0    4790.0    2402.0   11578.0\n",
              "4  ENSG00000000971       CFH   3751.0      64.0    5107.0     558.0      55.0"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gene ID</th>\n",
              "      <th>Gene Name</th>\n",
              "      <th>A549</th>\n",
              "      <th>HCT116</th>\n",
              "      <th>HeLa</th>\n",
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              "      <th>0</th>\n",
              "      <td>ENSG00000000003</td>\n",
              "      <td>TSPAN6</td>\n",
              "      <td>3287.0</td>\n",
              "      <td>4202.0</td>\n",
              "      <td>5610.0</td>\n",
              "      <td>978.0</td>\n",
              "      <td>1105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ENSG00000000419</td>\n",
              "      <td>DPM1</td>\n",
              "      <td>85916.0</td>\n",
              "      <td>116570.0</td>\n",
              "      <td>135471.0</td>\n",
              "      <td>173859.0</td>\n",
              "      <td>237237.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ENSG00000000457</td>\n",
              "      <td>SCYL3</td>\n",
              "      <td>9725.0</td>\n",
              "      <td>4903.0</td>\n",
              "      <td>1246.0</td>\n",
              "      <td>6427.0</td>\n",
              "      <td>9118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ENSG00000000460</td>\n",
              "      <td>C1orf112</td>\n",
              "      <td>3912.0</td>\n",
              "      <td>5324.0</td>\n",
              "      <td>4790.0</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>11578.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ENSG00000000971</td>\n",
              "      <td>CFH</td>\n",
              "      <td>3751.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>5107.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>55.0</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_HCT116[df_HCT116['Gene Name'].str.contains('ADR')]"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 143
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        "id": "LUfihPdxq2nb",
        "outputId": "b21914d1-191f-4c96-8d8a-61a9ba7ae011"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Gene ID Gene Name      A549   HCT116      HeLa     MCF-7  \\\n",
              "4588  ENSG00000130706     ADRM1  144803.0  99441.0  171824.0  159340.0   \n",
              "6625  ENSG00000150594    ADRA2A       NaN      NaN    2415.0       NaN   \n",
              "6922  ENSG00000154639     CXADR   39120.0  33064.0   34247.0     267.0   \n",
              "\n",
              "       SH-SY5Y  \n",
              "4588  114771.0  \n",
              "6625       NaN  \n",
              "6922   28731.0  "
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              "      <th>Gene ID</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "df_HCT116[df_HCT116['Gene Name'].str.contains('RAR')]"
      ],
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        "id": "102l0hxrlWPD",
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        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Gene ID Gene Name      A549    HCT116      HeLa     MCF-7  \\\n",
              "3134  ENSG00000113643     RARS1  375832.0  412980.0  303648.0  406507.0   \n",
              "4710  ENSG00000131759      RARA     965.0    2202.0    1114.0    6409.0   \n",
              "6318  ENSG00000146282     RARS2   12699.0   16485.0   15949.0   13287.0   \n",
              "9017  ENSG00000172819      RARG    1324.0    5268.0     979.0   10221.0   \n",
              "\n",
              "       SH-SY5Y  \n",
              "3134  473539.0  \n",
              "4710    3164.0  \n",
              "6318   21583.0  \n",
              "9017    2870.0  "
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    }
  ]
}