genes_drug_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7bfcaddf-a5cf-4e82-9020-5b059770b5be",
   "metadata": {},
   "source": [
    "# Drug Response Prediction Using Gene Expression Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d307056-9fe2-4561-b868-c033ea305879",
   "metadata": {},
   "source": [
    "### Reading the Gene Response Dataset"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Loading and Preparing the Dataset\n",
    "\n",
    "In this cell, we are performing the following steps to load and prepare our dataset for analysis:\n",
    "\n",
    "1. **Suppress Warnings**:\n",
    "   - We start by importing the `warnings` module to filter out any non-critical warnings, such as `FutureWarning`, which may arise from the underlying libraries (e.g., `pandas`). These warnings do not affect the functionality of the code but can clutter the output, so we choose to ignore them for a cleaner output.\n",
    "\n",
    "2. **Import Required Libraries**:\n",
    "   - We import `parse` from the `cmapPy.pandasGEXpress` library to read `.gctx` files, which is the file format of our dataset. \n",
    "\n",
    "3. **Define Dataset Path**:\n",
    "   - We specify the file path to the `.gctx` file containing our dataset. In this case, the file is located at `'/home/batman/Downloads/Datasets/level5_beta_trt_misc_n8283x12328.gctx'`.\n",
    "\n",
    "4. **Parse the .gctx File**:\n",
    "   - Using the `parse` function from the `cmapPy.pandasGEXpress` library, we read the `.gctx` file. This function loads the `.gctx` file and stores it as an object containing various components, including the main data matrix.\n",
    "\n",
    "5. **Extract Main Data as a DataFrame**:\n",
    "   - We extract the main data matrix (the primary data content) from the parsed object and convert it into a `pandas` DataFrame. This allows us to perform various data manipulation and analysis tasks using `pandas` functionality.\n",
    "\n",
    "6. **Reset Index**:\n",
    "   - We reset the index of the DataFrame so that any hierarchical indices or other metadata are transformed into regular columns, making it easier to work with the dataset.\n",
    "\n",
    "7. **Display the Data**:\n",
    "   - Finally, we display the first few rows of the DataFrame using `df.head()` to inspect the data structure and verify that it was loaded correctly.\n",
    "\n",
    "By the end of this cell, we have successfully loaded the `.gctx` dataset into a `pandas` DataFrame, ready for further exploration and analysis.\n"
   ],
   "id": "cb1245e8dd944e92"
  },
  {
   "cell_type": "code",
   "id": "9e64544c-d8ae-4e6d-9aa8-8de720b5042c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T18:53:05.350249Z",
     "start_time": "2024-11-03T18:53:04.269397Z"
    }
   },
   "source": [
    "import warnings\n",
    "\n",
    "# Suppress the specific FutureWarning\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "from cmapPy.pandasGEXpress.parse import parse\n",
    "\n",
    "# Dataset path\n",
    "gctx_file_path = '/home/batman/Downloads/Datasets/level5_beta_trt_misc_n8283x12328.gctx'\n",
    "\n",
    "# Read the .gctx file\n",
    "data = parse(gctx_file_path)\n",
    "\n",
    "# Extract the main data as a pandas DataFrame\n",
    "df = data.data_df\n",
    "df = df.reset_index()\n",
    "\n",
    "# Display the first few rows of the data\n",
    "print(df.head())"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cid    rid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:0.3125:24  \\\n",
      "0       10                                           0.071321    \n",
      "1      100                                           1.174841    \n",
      "2     1000                                           0.396398    \n",
      "3    10000                                          -0.301154    \n",
      "4    10001                                           0.040449    \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:0.3125:3  \\\n",
      "0                                            -0.135682   \n",
      "1                                             0.447374   \n",
      "2                                             0.241946   \n",
      "3                                             0.012122   \n",
      "4                                             1.011675   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:0.625:24  \\\n",
      "0                                            -0.244771   \n",
      "1                                             1.119571   \n",
      "2                                            -0.008839   \n",
      "3                                             0.760897   \n",
      "4                                            -1.769143   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:0.625:3  \\\n",
      "0                                            0.211430   \n",
      "1                                           -0.686314   \n",
      "2                                            0.594513   \n",
      "3                                            1.045681   \n",
      "4                                           -0.340021   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:1.25:24  \\\n",
      "0                                            0.608704   \n",
      "1                                           -1.130964   \n",
      "2                                            0.815789   \n",
      "3                                           -0.166172   \n",
      "4                                           -0.647292   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:1.25:3  \\\n",
      "0                                           -0.64815   \n",
      "1                                           -0.10980   \n",
      "2                                            0.50880   \n",
      "3                                            0.86105   \n",
      "4                                            0.81695   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:10:24  \\\n",
      "0                                          0.234743   \n",
      "1                                          0.108335   \n",
      "2                                         -0.030346   \n",
      "3                                          0.111529   \n",
      "4                                          0.140018   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:10:3  \\\n",
      "0                                        -0.349367   \n",
      "1                                        -0.501767   \n",
      "2                                         0.034433   \n",
      "3                                         0.239800   \n",
      "4                                         0.082800   \n",
      "\n",
      "cid  ABY001_A375_XH:ADO-TRASTUZUMAB_EMTANSINE:2.5:24  ...  \\\n",
      "0                                          -0.083109  ...   \n",
      "1                                          -0.711716  ...   \n",
      "2                                           0.260089  ...   \n",
      "3                                           0.542667  ...   \n",
      "4                                           0.787081  ...   \n",
      "\n",
      "cid  PAL002_PC3_XH:SILOC401397_SCRAMBLE:-666:72  \\\n",
      "0                                     -0.183729   \n",
      "1                                      1.514741   \n",
      "2                                      1.161394   \n",
      "3                                      1.307680   \n",
      "4                                     -0.023515   \n",
      "\n",
      "cid  PAL002_PC3_XH:SIMARCKS_NO.1:-666:72  PAL002_PC3_XH:SIMEG3_NO.1:-666:72  \\\n",
      "0                               1.617597                          -0.252270   \n",
      "1                               0.644751                           0.179930   \n",
      "2                              -1.537711                          -0.435996   \n",
      "3                               0.428915                          -0.958781   \n",
      "4                              -1.883208                           0.258609   \n",
      "\n",
      "cid  PAL002_PC3_XH:SIMEG3_NO.2:-666:72  PAL002_PC3_XH:SISNHG15_NO.1:-666:72  \\\n",
      "0                            -0.508461                              0.36540   \n",
      "1                             1.211608                             -1.04550   \n",
      "2                            -1.253765                             -0.25765   \n",
      "3                            -0.747577                             -0.14945   \n",
      "4                            -0.633736                             -0.49875   \n",
      "\n",
      "cid  PAL002_PC3_XH:SISNHG15_NO.2:-666:72  PAL002_PC3_XH:SISNHG15_NO.3:-666:72  \\\n",
      "0                              -0.918341                            -0.423680   \n",
      "1                              -1.164688                            -0.542607   \n",
      "2                               0.492278                             0.716338   \n",
      "3                               0.524831                             0.353201   \n",
      "4                              -0.556474                             0.863898   \n",
      "\n",
      "cid  PAL002_PC3_XH:SISNHG15_SCRAMBLE:-666:72  \\\n",
      "0                                  -0.669038   \n",
      "1                                  -0.361089   \n",
      "2                                  -0.215844   \n",
      "3                                  -0.166087   \n",
      "4                                  -1.180899   \n",
      "\n",
      "cid  PAL002_PC3_XH:SIZNFX1-AS1_NO.1:-666:72  \\\n",
      "0                                  0.058717   \n",
      "1                                 -0.745974   \n",
      "2                                  0.578918   \n",
      "3                                  0.161758   \n",
      "4                                  0.483186   \n",
      "\n",
      "cid  PAL002_PC3_XH:SIZNFX1-AS1_NO.2:-666:72  \n",
      "0                                 -0.531332  \n",
      "1                                 -0.249617  \n",
      "2                                 -0.153059  \n",
      "3                                 -0.625520  \n",
      "4                                 -0.249417  \n",
      "\n",
      "[5 rows x 8284 columns]\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Extracting Metadata from Column Names\n",
    "\n",
    "In this cell, we extract metadata from the column names of our dataset to understand the different **cell lines**, **drugs**, **concentration values**, and **time points** represented in the data. Each column name in the dataset is structured with these attributes, separated by colons (e.g., `CellLine:Drug:Concentration:Time`). By extracting these values, we can get an overview of the diversity and scope of experimental conditions in the dataset.\n",
    "\n",
    "### Steps in the Code\n",
    "\n",
    "1. **Define a Regular Expression Pattern**:\n",
    "   - We use a regular expression (regex) to identify and extract different parts of each column name. The regex pattern used is:\n",
    "     ```regex\n",
    "     (?P<cell_line>[^:]+):(?P<drug>[^:]+)(:(?P<concentration>\\d*\\.?\\d*))?(:(?P<time>\\d+))?\n",
    "     ```\n",
    "   - The pattern captures four possible parts:\n",
    "     - **`cell_line`**: The first component before the colon, representing the cell line name.\n",
    "     - **`drug`**: The second component, representing the drug name.\n",
    "     - **`concentration`**: The optional third component, representing the drug concentration.\n",
    "     - **`time`**: The optional fourth component, representing the time point of measurement.\n",
    "\n",
    "2. **Initialize Lists**:\n",
    "   - We initialize empty lists (`cell_line`, `drugs`, `conc_values`, and `time_points`) to store the extracted values for each respective category.\n",
    "\n",
    "3. **Iterate Through Columns and Extract Values**:\n",
    "   - For each column name in the dataset, we apply the regex pattern to extract the cell line, drug, concentration, and time point.\n",
    "   - If a match is found:\n",
    "     - We append the extracted values to their respective lists if they exist.\n",
    "   - If a column name doesn’t match the expected pattern, it prints \"No Match found!!\" to flag any unexpected column format.\n",
    "\n",
    "4. **Summarize and Print Unique Values**:\n",
    "   - After extracting values from all columns, we convert each list to a set to identify unique values and print:\n",
    "     - **Number of unique cell lines** and the list of cell line names.\n",
    "     - **Number of unique drugs** and the list of drug names.\n",
    "     - **Number of unique concentration values** and the list of concentrations.\n",
    "     - **Number of unique time points** and the list of time points.\n",
    "\n",
    "### Results\n",
    "\n",
    "By the end of this cell, we get a detailed breakdown of the experimental setup, which is essential for understanding the dataset’s structure. Specifically, the output shows:\n",
    "- **69 unique cell lines**\n",
    "- **1018 unique drugs**\n",
    "- **63 unique concentration values**\n",
    "- **2 unique time points**\n",
    "\n",
    "This information provides a foundation for further analysis, such as grouping or filtering the data based on these categories.\n"
   ],
   "id": "8180df95b55a4964"
  },
  {
   "cell_type": "code",
   "id": "62d48f35-89a2-48e8-a93e-581d7d171c52",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T18:53:14.801108Z",
     "start_time": "2024-11-03T18:53:14.777189Z"
    }
   },
   "source": [
    "# Example cell line and drug for testing\n",
    "# cell_line = 'A375'\n",
    "# drug = 'ADO-TRASTUZUMAB_EMTANSINE'\n",
    "\n",
    "# 8283 columns in the dataset\n",
    "\n",
    "# ---------------------------------#\n",
    "# 69 cell lines type               #\n",
    "# 1018 Drug types                  #\n",
    "# 63 conc values                   #\n",
    "# 2 values for time                #\n",
    "# ---------------------------------#\n",
    "\n",
    "import re\n",
    "\n",
    "cols = df.columns\n",
    "cell_line = []\n",
    "drugs = []\n",
    "conc_values = []\n",
    "time_points = []\n",
    "\n",
    "for col in cols:\n",
    "    # tokens = col.split(\":\")\n",
    "    regex = r'(?P<cell_line>[^:]+):(?P<drug>[^:]+)(:(?P<concentration>\\d*\\.?\\d*))?(:(?P<time>\\d+))?'\n",
    "    # Apply regex to extract the values\n",
    "    match = re.match(regex, col)\n",
    "    if match:\n",
    "        cell_line_extracted = match.group(\"cell_line\")\n",
    "        if cell_line_extracted:\n",
    "            cell_line.append(cell_line_extracted)\n",
    "        \n",
    "        drug_extracted = match.group(\"drug\")\n",
    "        if drug_extracted:\n",
    "            drugs.append(drug_extracted)\n",
    "\n",
    "        concentration_extracted = match.group(\"concentration\")\n",
    "        if concentration_extracted:\n",
    "            conc_values.append(concentration_extracted)\n",
    "        \n",
    "        time_extracted = match.group(\"time\")\n",
    "        if time_extracted:\n",
    "            time_points.append(time_extracted)\n",
    "\n",
    "    else:\n",
    "        print(\"No Match found!!=====================================\")\n",
    "\n",
    "print(len(set(cell_line)), \"Cell Lines: \\n--------------------------\\n\", set(cell_line))\n",
    "print(\"\\n\")\n",
    "print(len(set(drugs)), \"Drugs: \\n--------------------------\\n\", set(drugs))\n",
    "print(\"\\n\")\n",
    "print(len(set(conc_values)), \"Conc Values: \\n--------------------------\\n\", set(conc_values))\n",
    "print(\"\\n\")\n",
    "print(len(set(time_points)), \"Time Points: \\n--------------------------\\n\", set(time_points))\n",
    "print(\"\\n\")\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Match found!!=====================================\n",
      "69 Cell Lines: \n",
      "--------------------------\n",
      " {'ABY001_PC3_XH', 'CYT001_PC3_2H', 'ABY001_NCIH596_XH', 'GMB001_HT29_2H', 'LJP003_MDAMB231_24H', 'LJP004_SKBR3_1H', 'LJP004_MDAMB231_1H', 'LJP003_BT20_24H', 'CYT001_MCF7_2H', 'ABY001_SUDHL4_XH', 'CYT001_PC3_4H', 'ABY001_HT29_XH', 'CYT001_HCC515_2H', 'LJP003_HS578T_24H', 'LJP003_SKBR3_6H', 'LJP003_MDAMB231_1H', 'ABY001_NCIH2073_XH', 'LJP004_MCF10A_6H', 'CYT001_HEPG2_4H', 'LJP004_SKBR3_6H', 'CYT001_HA1E_2H', 'ABY001_NCIH508_XH', 'CYT001_A375_2H', 'CYT001_A375_4H', 'ABY001_NCIH1975_XH', 'LJP003_HS578T_1H', 'LJP003_MCF7_1H', 'LJP003_MCF7_6H', 'LJP003_MDAMB231_3H', 'LJP004_HS578T_1H', 'LJP004_MCF10A_1H', 'LJP004_MDAMB231_6H', 'CYT001_HT29_4H', 'PAL002_PC3_XH', 'LJP003_MCF10A_1H', 'LJP003_MCF7_24H', 'ABY001_A375_XH', 'GMB001_HT29_24H', 'ABY001_A549_XH', 'LJP003_SKBR3_24H', 'CYT001_HA1E_4H', 'LJP003_BT20_1H', 'LJP003_MCF10A_6H', 'LJP004_MCF7_1H', 'ABY001_SKBR3_XH', 'GMB001_HL60_2H', 'CYT001_VCAP_2H', 'LJP003_BT20_3H', 'GMB001_HL60_24H', 'LJP004_MCF7_6H', 'LJP003_SKBR3_1H', 'CYT001_HCC515_4H', 'PAL001_PC3_XH', 'CYT001_A549_2H', 'CYT001_HT29_2H', 'LJP003_MCF7_3H', 'ABY001_HEPG2_XH', 'CYT001_MCF7_4H', 'LJP004_HS578T_6H', 'ABY001_H1975_XH', 'LJP003_HS578T_3H', 'LJP004_BT20_6H', 'CYT001_VCAP_4H', 'LJP003_MCF10A_24H', 'LJP004_MCF7_24H', 'LJP003_BT20_6H', 'LJP004_BT20_1H', 'CYT001_HEPG2_2H', 'CYT001_A549_4H'}\n",
      "\n",
      "\n",
      "1018 Drugs: \n",
      "--------------------------\n",
      " {'SILINC-BRAP-2_NO.2', 'J22', 'CMAP-CYT-SRP4754', 'BRD-U69226250', 'CMAP-CYT-U7257', 'CSF2', 'E22', 'SILINC-PTCHD2-2_NO.2', 'D20', 'CMAP-CYT-SRP4887', 'CMAP-CYT-F4021', 'D08', 'WISP2', 'CMAP-CYT-N1408', 'IFNW1', 'BMP6', 'CMAP-CYT-M252', 'CMAP-CYT-SRP3130', 'L11', 'CMAP-CYT-I2644', 'SILOC401397_NO.3', 'N06', 'CCL4', 'C04', 'CMAP-CYT-I5528', 'F10', 'H15', 'H11', 'O08', 'CMAP-L174', 'SILINC-ZNF726-1_NO.5', 'CXCL16', 'CMAP-CYT-SRP4545', 'B21', 'MIA', 'CMAP-CYT-A6955', 'M21', 'CMAP-L101', 'CMAP-CYT-SRP3161', 'CXCL10', 'C19', 'EPO', 'VTN', 'L02', 'BRD-U28350452', 'IFNA2', 'O07', 'SILINC00493_NO.2', 'E02', 'D15', 'SILINC-AJAP1-4_NO.2', 'CMAP-CYT-M6667', 'B12', 'CMAP-CYT-C5983', 'M22', 'CMAP-CYT-SRP4969', 'H02', 'CMAP-CYT-SRP3063', 'WISP3', 'N10', 'B09', 'J17', 'CMAP-CYT-SRP4580', 'K08', 'O14', 'C05', 'CMAP-CYT-SRP3163', 'CMAP-CYT-SRP3081', 'PSPN', 'SILINC-CTDSP2-1_SCRAMBLE', 'CMAP-CYT-SRP3064', 'CMAP-CYT-SRP3029', 'FGF18', 'BDNF', 'L18', 'B19', 'IL16', 'BMP2', 'COL18A1', 'O03', 'CMAP-CYT-C5813', 'PRSS1', 'CMAP-CYT-P1588', 'O06', 'CETUXIMAB', 'L14', 'SILINC-ZNF681-4_NO.1', 'CMAP-CYT-SRP4596', 'J16', 'SILINC00493_NO.3', 'GAL', 'J23', 'TNFRSF11A', 'IL19', 'BRD-U74249166', 'K23', 'CMAP-CYT-E5627', 'CMAP-CYT-SRP3076', 'CMAP-CYT-F1300', 'C18', 'VIP', 'L17', 'CXCL9', 'SILINC-SPANXB1-2_SCRAMBLE', 'CSF1', 'RLN2', 'F18', 'CMAP-CYT-C0663', 'CMAP-CYT-SRP4675', 'G06', 'CMAP-CYT-T3573', 'H09', 'SIZNFX1-AS1_NO.2', 'CMAP-CYT-G0278', 'CMAP-L244', 'CMAP-CYT-C5977', 'SILINC-CTDSP2-1_NO.1', 'CMAP-CYT-G1667', 'D11', 'UCN3', 'CMAP-CYT-SRP4757', 'O11', 'SILINC-BRAP-2_NO.3', 'CCL15', 'TNFRSF17', 'CMAP-CYT-N5017', 'CMAP-CYT-SRP3052', 'CMAP-CYT-K2638', 'CMAP-CYT-SRP3059', 'CMAP-L111', 'CMAP-CYT-SRP3044', 'SILINC-WNT8B_NO.2', 'SILINC-ZNF726-1_NO.1', 'CMAP-CYT-I3400', 'EFNA5', 'ELN', 'IL28A', 'CMAP-CYT-B6681', 'SILINC-PGS1-1_SCRAMBLE', 'CMAP-CYT-P1306', 'FGF19', 'IGFBP7', 'CFI', 'CMAP-CYT-D2441', 'EDN2', 'CMAP-CYT-SRP2091', 'CMAP-CYT-SRP4491', 'SILINC-PGS1-1_NO.2', 'SERPINC1', 'N03', 'FLT3', 'CMAP-CYT-SRP4572', 'CMAP-CYT-SRP3131', 'SIMEG3_NO.1', 'SILINC-C7ORF23_NO.3', 'FSHB', 'E14', 'H20', 'SILINC-BRAP-2_NO.1', 'D06', 'SIGAS5_NO.2', 'CMAP-CYT-I4151', 'CMAP-CYT-T1327', 'ANGPT2', 'AHSG', 'EFNA3', 'CMAP-CYT-SRP3058', 'CMAP-CYT-C5533', 'CTSS', 'CMAP-CYT-L4021', 'N21', 'M09', 'O16', 'CCL7', 'C02', 'SILINC-PRDM2_NO.3', 'CMAP-CYT-V3628', 'CMAP-CYT-SRP3103', 'CMAP-CYT-E8399', 'CMAP-CYT-SRP4651', 'CMAP-CYT-C8946', 'D17', 'F19', 'N09', 'CCL21', 'CMAP-CYT-S7901', 'FLT3LG', 'CMAP-CYT-SRP4463', 'J06', 'L13', 'ACHE', 'J08', 'CMAP-CYT-B3259', 'FGF1', 'CMAP-CYT-SRP3146', 'CMAP-CYT-SRP4137', 'CMAP-CYT-S1763', 'CMAP-CYT-SRP3183', 'M05', 'CMAP-L030', 'SILINC-ZNF726-1_NO.6', 'KITLG', 'CTRB1', 'M17', 'LCN2', 'TGFB3', 'SILINC-ZNF-516-4_NO.1', 'CMAP-CYT-A6150', 'CMAP-CYT-SRP3049', 'E23', 'CMAP-CYT-E3641', 'E15', 'NOG', 'F09', 'I02', 'CMAP-CYT-F8424', 'CMAP-CYT-SRP3112', 'CMAP-CYT-SRP3152', 'BRD-U82759277', 'LIPC', 'CMAP-CYT-SRP3050', 'IL12B', 'BRD-U16461923', 'IFNG', 'CMAP-CYT-G1777', 'CMAP-CYT-S1437', 'FGB', 'CMAP-CYT-T6424', 'CMAP-CYT-SRP3172', 'C09', 'IFNA13', 'ELANE', 'HBEGF', 'SILINC-ZNF681-4_NO.2', 'CMAP-CYT-R2156', 'CMAP-CYT-SRP4260', 'B17', 'CMAP-CYT-SRP3008', 'CMAP-CYT-SRP3065', 'SILINC-ZNF-516-4_SCRAMBLE', 'CMAP-CYT-F1175', 'PPY', 'CMAP-CYT-SRP3072', 'N19', 'CMAP-CYT-SRP3060', 'SILINC-LAMA1-5_NO.3', 'CCL17', 'IL21', 'CMAP-CYT-C5993', 'CMAP-CYT-SRP4647', 'G03', 'CMAP-CYT-SRP4011', 'IFNA1', 'M19', 'RLN3', 'F06', 'CMAP-L224', 'K15', 'J14', 'CMAP-CYT-SRP3106', 'FGF5', 'CMAP-CYT-M6417', 'G08', 'CMAP-CYT-SRP4760', 'CMAP-CYT-G0516', 'E04', 'SILINC-LRFN5-1_NO.1', 'H07', 'I17', 'E12', 'K10', 'GDF5', 'EPHA3', 'I23', 'FGF20', 'NPPC', 'MSMB', 'UMOD', 'CMAP-CYT-M4890', 'CMAP-L165', 'FGF16', 'IGF1', 'NPY', 'K16', 'FGF23', 'L06', 'CMAP-L285', 'CMAP-CYT-T7924', 'L19', 'SILINC-PRDM2_NO.2', 'L15', 'SILARP1_NO.1', 'C20', 'F13', 'NGF', 'LAMA1', 'SILINC-AJAP1-4_NO.1', 'CMAP-CYT-M3552', 'PTHLH', 'CMAP-CYT-SRP3083', 'SILINC-MAP3K9-1_NO.2', 'CCL18', 'CMAP-CYT-F0166', 'SILINC-ZNF681-4_NO.3', 'ORM2', 'CMAP-CYT-SRP4063', 'H05', 'CMAP-CYT-B3670', 'BRD-A68593933', 'IL2', 'H04', 'CMAP-CYT-228-10702-2', 'CMAP-CYT-H1404', 'CMAP-CYT-SRP3061', 'D07', 'CMAP-CYT-SRP3067', 'E17', 'TGFA', 'SILINC-LAMA1-5_SCRAMBLE', 'F04', 'L07', 'CMAP-CYT-SRP4028', 'IL9', 'D22', 'CMAP-CYT-C5938', 'CMAP-CYT-SRP3062', 'IL24', 'CMAP-CYT-F3879', 'CMAP-CYT-F7176', 'C23', 'CNTF', 'TNFSF13B', 'F2', 'H14', 'SILINC-SPANXB1-2_NO.1', 'CMAP-CYT-A9024', 'SILINC-ZNF726-1_NO.4', 'CMAP-CYT-SRP4497', 'B14', 'CXCL3', 'F23', 'IL20', 'GREM2', 'HGF', 'CMAP-CYT-SRP3069', 'D16', 'CMAP-CYT-SRP3132', 'F22', 'SILINC-MAP3K9-1_NO.3', 'G04', 'CMAP-CYT-E0403', 'CMAP-CYT-P7489', 'H06', 'BMP11', 'TNFRSF18', 'CX3CL1', 'WNT1', 'IGFBP1', 'CMAP-CYT-E7409', 'J13', 'SIZNFX1-AS1_NO.1', 'L05', 'CSH1', 'CMAP-CYT-F4537', 'J21', 'ANGPT1', 'CMAP-CYT-N1780', 'J12', 'CMAP-CYT-B3555', 'FGF8', 'I04', 'CMAP-CYT-SRP3077', 'K18', 'K14', 'CCL19', 'D14', 'D18', 'IGF2', 'CMAP-CYT-SRP4928', 'CMAP-L262', 'SST', 'CMAP-CYT-SRP3141', 'M08', 'CMAP-CYT-SRP3055', 'CCL3', 'CMAP-CYT-G0657', 'CMAP-CYT-A4941', 'RETNLB', 'CCL13', 'L22', 'TIMP3', 'M14', 'L23', 'CMAP-CYT-E0903', 'FN1', 'COL5A1', 'CMAP-CYT-G0407', 'CMAP-CYT-I5896', 'K03', 'CMAP-CYT-C6745', 'TNFSF12', 'CMAP-CYT-SRP3031', 'CMAP-CYT-SRP3087', 'E21', 'O22', 'CMAP-L274', 'CMAP-CYT-SRP4581', 'SILARP1_NO.2', 'SISNHG15_SCRAMBLE', 'CMAP-CYT-C3710', 'SILINC-MAP3K9-1_NO.1', 'CMAP-CYT-SRP3040', 'CMAP-CYT-SRP4237', 'IL17D', 'CMAP-CYT-SRP4542', 'O18', 'CCL11', 'CMAP-CYT-SRP3105', 'SIMARCKS_NO.1', 'I13', 'CMAP-CYT-M251', 'EREG', 'J19', 'L03', 'CMAP-L206', 'E16', 'IL13', 'BRD-U00016877', 'UTS2', 'CMAP-CYT-SRP4975', 'I10', 'KNG', 'F13A1', 'B07', 'G21', 'CMAP-CYT-U1008', 'TNFSF11', 'IGFBP2', 'CMAP-CYT-E7764', 'E07', 'N04', 'D21', 'O12', 'IL8', 'CMAP-CYT-SRP3139', 'FGF6', 'B15', 'CMAP-CYT-S9636', 'J04', 'I11', 'O05', 'CMAP-CYT-L9788', 'SILOC401397_NO.1', 'CMAP-CYT-SRP4988', 'G16', 'O19', 'B03', 'CMAP-CYT-C6249', 'CMAP-CYT-T2702', 'D02', 'IL17', 'CMAP-CYT-SRP4576', 'K17', 'VEGFA', 'I21', 'O04', 'GAS6', 'CMAP-CYT-SRP4257', 'F15', 'I15', 'SILINC-LAMA1-5_NO.2', 'APOE', 'I03', 'CMAP-CYT-I1398', 'BMP13', 'DKK1', 'CMAP-L020', 'CMAP-CYT-SRP3068', 'CCL5', 'B18', 'CMAP-CYT-E0528', 'H13', 'J10', 'COL9A1', 'CMAP-CYT-SRP5030', 'N14', 'SISNHG15_NO.3', 'SERPINA1', 'CMAP-CYT-A9285', 'D03', 'K02', 'M23', 'CMAP-CYT-F4662', 'SILINC-MAP3K9-1_SCRAMBLE', 'CMAP-CYT-SRP3170', 'ADM', 'E11', 'IL22', 'I12', 'K06', 'SILINC-SPANXB1-2_NO.2', 'NTF4', 'SISNHG15_NO.1', 'E20', 'SILINC-PRDM2_NO.1', 'C21', 'CMAP-L207', 'CMAP-CYT-SRP4654', 'NRG1.ALPHA', 'C17', 'SILINC-LAMA1-5_NO.1', 'BRD-U66535478', 'BRD-U64017662', 'TNFSF13', 'SILINC-C7ORF23_NO.1', 'IL17F', 'CMAP-CYT-C3042', 'G02', 'CCL14', 'O13', 'SIGAS5_NO.1', 'CMAP-CYT-A2562', 'CMAP-CYT-T3309', 'M04', 'SILINC-LRFN5-1_NO.4', 'CMAP-CYT-SRP4623P', 'GDNF', 'CMAP-CYT-L4146', 'CMAP-CYT-SRP4594', 'EFNB3', 'NRTN', 'IGFBP3', 'BEVACIZUMAB', 'LYZ', 'CMAP-CYT-SRP3098', 'B13', 'CMAP-CYT-M246', 'SILINC-AJAP1-4_NO.3', 'EGFR', 'CMAP-L163', 'BMP10', 'CMAP-CYT-5898-NR-050', 'G18', 'N05', 'IL17E', 'CCL22', 'CMAP-CYT-N8768', 'GHRH', 'CCL20', 'CMAP-CYT-SRP3147', 'L08', 'CMAP-CYT-F0931', 'SFN', 'G19', 'BRD-U54861151', 'CMAP-L153', 'CCL25', 'I18', 'K19', 'SILINC-C7ORF23_NO.2', 'H19', 'CMAP-CYT-N1905', 'CMAP-CYT-SRP4548', 'CMAP-L138', 'SILINC-ZNF726-1_NO.2', 'CPB1', 'CCL26', 'CMAP-CYT-I4401', 'CMAP-CYT-SRP3178', 'CMAP-CYT-V6012', 'K21', 'BRD-U81753521', 'AREG', 'N02', 'CMAP-CYT-SRP3073', 'CMAP-L219', 'E18', 'C11', 'J15', 'INS', 'VEGFC', 'SPP1', 'TNF', 'IL15', 'E19', 'M11', 'OXT', 'K20', 'F05', 'CMAP-L155', 'CMAP-CYT-I2276', 'J05', 'O20', 'N20', 'F16', 'I19', 'CMAP-CYT-SRP3010', 'KLK1', 'CMAP-CYT-L5283', 'I16', 'FGFR2', 'TF', 'L12', 'CMAP-CYT-SRP3100', 'CMAP-CYT-SRP4639', 'M20', 'O09', 'SILINC-WNT8B_NO.1', 'EGF', 'CCL4L1', 'PROK2', 'TNFRSF1A', 'BMP4', 'H18', 'XCL1', 'PYY', 'NRG1.BETA', 'CMAP-L157', 'CMAP-CYT-S5171', 'F08', 'CMAP-CYT-SRP3091', 'I05', 'N23', 'G09', 'NTS', 'N12', 'CMAP-CYT-S190', 'CCL16', 'GDF2', 'CMAP-CYT-P0059', 'E06', 'E10', 'F21', 'N18', 'H08', 'CRP', 'CXCL6', 'C16', 'B04', 'F17', 'CMAP-CYT-H0656', 'I20', 'CMAP-CYT-SRP3079', 'CMAP-CYT-S4776', 'CMAP-CYT-O5761', 'CMAP-CYT-SRP4066', 'K13', 'CXCL11', 'B08', 'CCL1', 'NRP1', 'SILOC401397_SCRAMBLE', 'SILINC-PGS1-1_NO.1', 'CMAP-CYT-T6674', 'CMAP-CYT-377-HB-050', 'I08', 'CMAP-CYT-G8022', 'CMAP-CYT-L1667', 'CMAP-CYT-SRP4515', 'SILINC-ZNF-516-4_NO.3', 'CMAP-CYT-G150', 'CMAP-CYT-L9780', 'F20', 'GDF7', 'OSM', 'BTC', 'CMAP-L097', 'CMAP-CYT-E9137', 'CMA1', 'FOLR2', 'MSTN', 'I06', 'D19', 'LTA', 'MMP7', 'FGF22', 'ANG', 'CMAP-CYT-A9847', 'IL1A', 'SILOC401397_NO.2', 'CMAP-CYT-L7134', 'J03', 'CMAP-CYT-E9644', 'CMAP-CYT-F7426', 'CMAP-CYT-G3644', 'H21', 'VEGFB', 'CMAP-CYT-SRP3070', 'CXCL13', 'G11', 'C08', 'COL4A6', 'GNRH1', 'G17', 'CMAP-CYT-SRP3116', 'SILINC-ZNF726-1_SCRAMBLE', 'TNFSF14', 'CMAP-CYT-G1260', 'CCL23', 'CMAP-CYT-SRP3033', 'G13', 'CMAP-CYT-U4127', 'H12', 'CMAP-CYT-C1617', 'CMAP-CYT-SRP3104', 'ANGPT4', 'CMAP-CYT-G8764', 'CMAP-CYT-SRP3171', 'I09', 'CMAP-L014', 'RITUXIMAB', 'CMAP-CYT-SRP3188', 'BMP7', 'PNOC', 'CMAP-CYT-A6469', 'G15', 'MMP12', 'IL29', 'E08', 'M13', 'CMAP-CYT-E7277', 'B11', 'CMAP-CYT-SRP3162', 'SILINC-LRFN5-1_SCRAMBLE', 'F07', 'IL5', 'CMAP-CYT-A7080', 'FGF2', 'SILINC-ZNF681-4_SCRAMBLE', 'D10', 'C12', 'CXCL5', 'EPGN', 'CMAP-L199', 'CXCL1', 'CMAP-CYT-SRP3258', 'C14', 'SILARP1_NO.3', 'SILINC-WNT8B_NO.3', 'CMAP-CYT-SRP3124', 'CMAP-L146', 'PDGFB', 'F14', 'CMAP-CYT-B1434', 'CMAP-CYT-R2031', 'INHBA', 'B16', 'CMAP-CYT-SRP4025', 'C06', 'CMAP-CYT-T7039', 'IL4', 'CMAP-CYT-I152', 'GDF9', 'RLN1', 'G10', 'N16', 'SHBG', 'SILINC-CTDSP2-1_NO.2', 'CMAP-CYT-SRP3089', 'CMAP-CYT-E0278', 'CMAP-CYT-SRP4589', 'GH1', 'CMAP-CYT-SRP3173', 'M07', 'SERPINA3', 'CMAP-CYT-SRP4176F', 'O21', 'CMAP-CYT-SRP3039', 'IFNB1', 'SERPINI1', 'B05', 'B20', 'CMAP-CYT-N6383', 'CMAP-CYT-SRP3126', 'H23', 'SILINC-PTCHD2-2_NO.1', 'CMAP-L158', 'SILINC00493_SCRAMBLE', 'J07', 'M16', 'SILINC-PGS1-1_NO.3', 'UCN', 'BRD-U13773494', 'B06', 'J02', 'CMAP-CYT-T1568', 'CMAP-CYT-SRP4176E', 'CMAP-CYT-S5269', 'PPBP', 'M12', 'IL1B', 'C10', 'TGFBR3', 'CHGA', 'CMAP-CYT-M9695', 'H22', 'E09', 'L20', 'C15', 'IL3', 'C07', 'NPPA', 'B10', 'TGFB2', 'IGFBP5', 'CMAP-CYT-G9885', 'CMAP-L105', 'F11', 'CMAP-CYT-SRP3096', 'CMAP-CYT-O6379', 'IL17B', 'CMAP-CYT-A2221', 'CMAP-CYT-SRP3066', 'IGFBP6', 'THBD', 'CMAP-CYT-L6274', 'CMAP-CYT-F8924', 'CMAP-CYT-SRP4333', 'CMAP-CYT-SRP4657', 'CCL3L1', 'N07', 'SILINC00493_NO.1', 'CMAP-CYT-B2805', 'SILINC-C7ORF23_SCRAMBLE', 'F12', 'APLN', 'B22', 'CMAP-CYT-A1663', 'EFNA4', 'C22', 'SILINC-LRFN5-1_NO.3', 'EDN3', 'CMAP-CYT-P9903', 'CMAP-CYT-SRP4869', 'TGFB1', 'CMAP-CYT-SRP3071', 'GDF3', 'F03', 'G12', 'CMAP-CYT-SRP4082', 'GC', 'CMAP-CYT-M8543', 'TIMP1', 'I14', 'H10', 'CMAP-CYT-N0524', 'G07', 'I22', 'CMAP-CYT-A2327', 'K12', 'M18', 'CRH', 'F5', 'CMAP-CYT-SRP3035', 'SILINC-PTCHD2-2_NO.3', 'J11', 'CMAP-CYT-SRP3007', 'CMAP-CYT-SRP4134', 'CMAP-CYT-E9012', 'CMAP-CYT-B2929', 'CMAP-CYT-T4567', 'M03', 'J09', 'SILINC-WNT8B_SCRAMBLE', 'COL4A1', 'NRG1', 'F02', 'EDN1', 'HF1', 'CMAP-CYT-I2778', 'SERPINF1', 'COL1A2', 'TNFRSF11B', 'K07', 'IL7', 'CMAP-CYT-SRP3121', 'APOA4', 'LIF', 'AGT', 'AVP', 'CCL2', 'CMAP-CYT-I3394', 'PF4', 'CMAP-CYT-M6292', 'C03', 'N08', 'D12', 'TNFRSF1B', 'ARTN', 'N15', 'CMAP-L029', 'L21', 'BRD-U40889895', 'IL12', 'ADO-TRASTUZUMAB_EMTANSINE', 'IL31', 'APCS', 'J20', 'CMAP-CYT-885-GS-050', 'CMAP-L182', 'L09', 'CMAP-CYT-SRP3038', 'CMAP-CYT-M4565', 'G22', 'CMAP-CYT-SRP4360', 'G14', 'CMAP-CYT-C8118', 'SIMEG3_NO.2', 'CXCL14', 'CMAP-CYT-SRP3142', 'SILINC-ZNF-516-4_NO.2', 'CMAP-CYT-SRP4872', 'NRP2', 'CMAP-CYT-SRP4037', 'O10', 'BRD-U13016227', 'CMAP-CYT-SRP3022', 'N22', 'B2M', 'CMAP-L166', 'FGF4', 'CMAP-CYT-C3657', 'N13', 'H17', 'NTF3', 'N17', 'FGF10', 'LEP', 'CMAP-CYT-B3795', 'CMAP-CYT-L1567', 'IL11', 'CMAP-CYT-SRP3186', 'SILINC-BRAP-2_SCRAMBLE', 'TNFRSF6B', 'CTF1', 'H03', 'M15', 'THPO', 'CMAP-CYT-F9174', 'L16', 'SILINC-CTDSP2-1_NO.3', 'IL10', 'GIP', 'E13', 'CMAP-CYT-SRP3019', 'CMAP-CYT-SRP3080', 'SILINC-ZNF726-1_NO.3', 'CMAP-CYT-SRP3082', 'FST', 'AIMP1', 'L04', 'E03', 'FIGF', 'CMAP-CYT-C5483', 'CMAP-CYT-SRP3184', 'PGF', 'CMAP-CYT-SRP4053', 'PROZ', 'CCL24', 'CMAP-CYT-F2006', 'CMAP-CYT-SRP4107', 'CMAP-CYT-T1063', 'G05', 'N11', 'CLEC11A', 'SOD3', 'O15', 'CMAP-CYT-SRP3123', 'G23', 'FGF9', 'CMAP-CYT-A1479', 'CMAP-CYT-T7799', 'K11', 'M06', 'CMAP-CYT-R6267', 'BRD-U10666775', 'PRL', 'GAST', 'PDGFC', 'CMAP-CYT-V9879', 'CMAP-CYT-F7057', 'SILINC-SPANXB1-2_NO.3', 'CCL28', 'CMAP-CYT-B2680', 'D23', 'CMAP-CYT-I5273', 'FGFR1', 'CMAP-CYT-SRP3174', 'FGF21', 'M02', 'M10', 'CMAP-CYT-SRP3023', 'SISNHG15_NO.2', 'CMAP-L201', 'D09', 'CSF3', 'TPO', 'CXCL12', 'CMAP-CYT-P3201', 'BGLAP', 'K04', 'IGFBP4', 'CMAP-CYT-I9278', 'IL6', 'CARTPT', 'CMAP-CYT-G2269', 'FGF17', 'K05', 'K22', 'TIMP2', 'D04', 'I07', 'CMAP-CYT-SRP3168', 'CMAP-CYT-SRP4363', 'FGF7', 'LGALS7', 'J18', 'GRP'}\n",
      "\n",
      "\n",
      "63 Conc Values: \n",
      "--------------------------\n",
      " {'0.8', '0.03125', '10000', '0.3', '0.25', '1', '250', '16', '0.15', '300', '0.2', '0.032', '500', '150', '7.5', '5', '0.3125', '20', '0.008', '2', '2500', '0.125', '0.04', '400', '200', '0.625', '0.1', '0.004', '3', '1.875', '50', '0.5', '0.9375', '80', '3.75', '30', '1.25', '0.03', '100000', '0.16', '0.02', '10', '0.0008', '1.65', '0.015625', '2.5', '2000', '1000', '5000', '200000', '8300', '0.0045', '0.01', '25', '45', '4', '100', '15', '50000', '800', '0.0625', '3000', '40'}\n",
      "\n",
      "\n",
      "2 Time Points: \n",
      "--------------------------\n",
      " {'3', '24'}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Plotting Dose-Response Curve for a Specific Cell Line and Drug\n",
    "\n",
    "In this cell, we create a **dose-response curve** for a selected cell line and drug. The dose-response curve illustrates how varying drug concentrations affect cell viability or response. This type of analysis is crucial for understanding drug efficacy at different doses.\n",
    "\n",
    "### Steps in the Code\n",
    "\n",
    "1. **User Input for Cell Line and Drug**:\n",
    "   - We prompt the user to enter a **cell line** and a **drug** name. These inputs are used to filter the dataset for columns specific to that cell line and drug.\n",
    "\n",
    "2. **Define the `extract_info` Function**:\n",
    "   - We define a function, `extract_info`, that uses a **regular expression** to extract the **concentration** and **time point** from each column name.\n",
    "   - The regular expression pattern (`rf'{cell_line}.*{drug}:(\\d+\\.?\\d*):(\\d+)'`) looks for columns that match the specified cell line and drug, followed by concentration and time values. \n",
    "   - If a match is found, the function returns the extracted concentration and time point values.\n",
    "\n",
    "3. **Filter Columns for the Selected Cell Line and Drug**:\n",
    "   - We use list comprehension to apply `extract_info` to each column in the dataset. This creates a list of tuples containing columns that match the specified cell line and drug, along with their extracted concentration and time point values.\n",
    "   - If no columns match the specified cell line and drug, the code prints a message saying no matching columns were found.\n",
    "\n",
    "4. **Create a Filtered DataFrame**:\n",
    "   - If matching columns are found, we extract the columns and organize the data into a new DataFrame (`df_filtered`), containing only the relevant data for the selected cell line and drug.\n",
    "   - We then **stack** this data into a single column format for easy plotting, storing it in `stacked_data`.\n",
    "\n",
    "5. **Generate Arrays for Concentration and Time Point**:\n",
    "   - We create two lists, `concentrations` and `time_points`, that replicate the concentration and time point values for each data point in `stacked_data`. This ensures that each response value has a corresponding concentration and time point.\n",
    "\n",
    "6. **Construct the Final DataFrame**:\n",
    "   - Using `stacked_data`, `concentrations`, and `time_points`, we create a new DataFrame (`df_final`) that contains:\n",
    "     - `response`: The cell response data (e.g., viability).\n",
    "     - `concentration`: The drug concentration for each response value.\n",
    "     - `time_point`: The time point at which each response was measured.\n",
    "\n",
    "7. **Plot the Dose-Response Curve**:\n",
    "   - We use Seaborn's `lineplot` to plot **concentration vs. response**.\n",
    "   - The x-axis represents **concentration** (in µM), while the y-axis represents **response** (e.g., cell viability).\n",
    "   - The resulting plot shows how cell response changes with different concentrations of the specified drug at the selected time points.\n",
    "\n",
    "### Purpose of the Plot\n",
    "\n",
    "The dose-response curve helps us understand:\n",
    "- **Drug Potency**: By analyzing the response at varying concentrations, we can see at what concentration the drug starts to have a significant effect.\n",
    "- **Efficacy**: The plot highlights the maximum effect the drug can produce at high concentrations.\n",
    "- **Toxicity**: A sharp drop in viability or response at high concentrations may indicate potential toxicity.\n",
    "\n",
    "This visualization is essential in pharmacology to determine the optimal concentration range for therapeutic efficacy while minimizing adverse effects.\n"
   ],
   "id": "1ef4208684099c1d"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T18:53:55.804772Z",
     "start_time": "2024-11-03T18:53:45.396573Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "\"\"\"\n",
    "Example cell line and drug for testing\n",
    "cell_line = 'A375'\n",
    "drug = 'ADO-TRASTUZUMAB_EMTANSINE'\n",
    "\"\"\"\n",
    "\n",
    "cell_line = input(\"Cell Line: \")\n",
    "drug = input(\"Drug: \")\n",
    "\n",
    "# Extract columns corresponding to the specific cell line and drug\n",
    "def extract_info(col_name):\n",
    "    # Using regex to extract cell line, drug, concentration, and time\n",
    "    match = re.search(rf'{cell_line}.*{drug}:(\\d+\\.?\\d*):(\\d+)', col_name)\n",
    "    if match:\n",
    "        concentration = float(match.group(1))  # concentration\n",
    "        time_point = int(match.group(2))       # time point (Eg: 24, 72)\n",
    "        return concentration, time_point\n",
    "    return None\n",
    "\n",
    "# Get the columns that match the cell line and drug\n",
    "filtered_columns = [(col, extract_info(col)) for col in df.columns if extract_info(col)]\n",
    "\n",
    "\"\"\"\n",
    "New DataFrame for the filtered columns\n",
    "\"\"\"\n",
    "if filtered_columns:\n",
    "    selected_columns, extracted_info = zip(*filtered_columns)\n",
    "    df_filtered = df[list(selected_columns)]\n",
    "\n",
    "    # Stack the data and replicate concentration and time point for each value in the column\n",
    "    stacked_data = df_filtered.stack().reset_index(drop=True)\n",
    "\n",
    "    # Create arrays for concentration and time point to match the stacked data length\n",
    "    concentrations = []\n",
    "    time_points = []\n",
    "    for _, (concentration, time_point) in enumerate(extracted_info):\n",
    "        concentrations.extend([concentration] * len(df_filtered))\n",
    "        time_points.extend([time_point] * len(df_filtered))\n",
    "\n",
    "    # Create a new DataFrame with the stacked data, concentration, and time point\n",
    "    df_final = pd.DataFrame({\n",
    "        'response': stacked_data,\n",
    "        'concentration': concentrations[:len(stacked_data)],  # Match the lengths\n",
    "        'time_point': time_points[:len(stacked_data)]  # Match the lengths\n",
    "    })\n",
    "\n",
    "    # Plot the dose-response curve (concentration vs response)\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.lineplot(data=df_final, x='concentration', y='response', marker='o')\n",
    "    plt.title(f'Dose-Response Curve for {drug} on {cell_line}')\n",
    "    plt.xlabel('Concentration (µM)')\n",
    "    plt.ylabel('Response (e.g., cell viability)')\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(f\"No matching columns found for {cell_line} and {drug}\")"
   ],
   "id": "618ee458-1c52-43c8-bc47-60c361d54e34"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Dose-Response Curve for ADO-TRASTUZUMAB_EMTANSINE on A375 Cell Line\n",
    "\n",
    "This plot visualizes the **dose-response relationship** for the drug **ADO-TRASTUZUMAB_EMTANSINE** on the cell line **A375**. The x-axis represents the **drug concentration (in µM)**, while the y-axis represents the **cell response** (e.g., cell viability). Here’s a breakdown of the key components in the plot:\n",
    "\n",
    "1. **Data Points and Line**:\n",
    "   - Each data point on the plot represents the measured response of the A375 cell line at a specific concentration of the drug.\n",
    "   - The line connecting these points shows how the response changes as the drug concentration increases. This provides a visual representation of the relationship between drug concentration and cell viability.\n",
    "\n",
    "2. **Shaded Region (Confidence Interval)**:\n",
    "   - The shaded region around the line represents the **confidence interval** for the response measurements. This interval indicates the level of uncertainty in the response values at each concentration.\n",
    "   - A wider shaded area suggests higher variability in the response data, whereas a narrower shaded area suggests more consistent responses.\n",
    "\n",
    "3. **Interpretation of the Response Trend**:\n",
    "   - At low concentrations, the response shows some variability, indicating mixed levels of viability.\n",
    "   - As the concentration increases, there is a noticeable change in the response, though the pattern is not strictly linear or monotonic. This could indicate that ADO-TRASTUZUMAB_EMTANSINE has a complex interaction with the A375 cell line, where intermediate concentrations may have varying levels of effectiveness.\n",
    "   - The trend at higher concentrations might suggest a stabilization in the response, potentially indicating a plateau effect where additional increases in concentration do not produce significant changes in cell viability.\n",
    "\n",
    "4. **Implications**:\n",
    "   - This dose-response relationship helps us understand the drug’s **potency and efficacy**. It highlights the concentrations at which the drug starts to show significant effects and whether there is a point beyond which increasing the dose does not improve cell response significantly.\n",
    "   - The observed variability can provide insights into the optimal concentration range for therapeutic use while minimizing potential adverse effects.\n",
    "\n",
    "In summary, this plot provides a visual summary of how the A375 cell line responds to increasing concentrations of ADO-TRASTUZUMAB_EMTANSINE, with implications for determining effective dosing in experimental or clinical settings.\n"
   ],
   "id": "44ebdd1e4ae4a6db"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Visualizing Dose-Response Over Time for a Specific Cell Line and Drug\n",
    "\n",
    "This code generates a dose-response plot for a specific cell line and drug across different time points, allowing us to visualize how the cell response varies not only with drug concentration but also over time. This type of analysis is essential for understanding the dynamics of drug action over time.\n",
    "\n",
    "### Explanation of the Code\n",
    "\n",
    "1. **Input Cell Line and Drug**:\n",
    "   - We prompt the user to input a **cell line** and a **drug** name. These inputs are used to filter the dataset, focusing only on columns that match this specific cell line and drug combination.\n",
    "\n",
    "2. **Filter for Selected Cell Line and Drug**:\n",
    "   - Using `df.filter(like=f\"{cell_line}:{drug}\", axis=1)`, we select only the columns in the DataFrame (`df`) that contain the specified cell line and drug combination in their names.\n",
    "   - This subset (`df_filtered`) now includes data for multiple concentrations and time points for the chosen cell line and drug.\n",
    "\n",
    "3. **Reshape Data for Plotting**:\n",
    "   - We use the `.melt()` function to transform `df_filtered` into a long format, creating a DataFrame (`df_melted`) with two new columns:\n",
    "     - **`Condition`**: Holds the column names.\n",
    "     - **`Response`**: Holds the response values.\n",
    "   - We then create two additional columns:\n",
    "     - **`Concentration`**: Extracted from the column names (third component).\n",
    "     - **`Time`**: Extracted from the column names (fourth component).\n",
    "\n",
    "4. **Plotting the Dose-Response Over Time**:\n",
    "   - We use Seaborn's `lineplot` to create a line plot with:\n",
    "     - **x-axis**: Concentration\n",
    "     - **y-axis**: Response\n",
    "     - **Hue**: Time, to differentiate between different time points.\n",
    "   - This plot shows how cell response varies with drug concentration and distinguishes the data based on time points, allowing us to observe changes in response over time for each concentration.\n",
    "\n",
    "5. **Final Plot Customization**:\n",
    "   - We set the plot title as `Dose-Response Over Time for {drug} on {cell_line}`, indicating the selected cell line and drug.\n",
    "   - The x-axis is labeled **Concentration**, and the y-axis is labeled **Response**.\n",
    "   - We also add a grid for readability.\n",
    "\n",
    "### Purpose of the Plot\n",
    "\n",
    "The plot generated by this code provides insights into:\n",
    "- **Time-Dependent Drug Action**: It shows how the cell response to the drug changes over time at different concentrations, which is crucial for understanding both immediate and prolonged drug effects.\n",
    "- **Optimal Dosing and Timing**: By comparing response patterns across concentrations and time points, we can identify the doses that yield the most beneficial responses and observe any delayed or cumulative effects over time.\n",
    "\n",
    "This type of visualization helps in exploring the complex dynamics of drug-cell interactions and can assist in optimizing treatment schedules based on both dose and duration.\n"
   ],
   "id": "5c08b9c211ed3bb9"
  },
  {
   "cell_type": "code",
   "id": "9d61c14c-b702-4d1f-b87e-8c9e4a52cf7e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T18:54:28.318427Z",
     "start_time": "2024-11-03T18:54:03.659778Z"
    }
   },
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\"\"\"\n",
    "Example cell line and drug for testing\n",
    "cell_line = 'ABY001_A375_XH'\n",
    "drug = 'ADO-TRASTUZUMAB_EMTANSINE'\n",
    "\"\"\"\n",
    "\n",
    "cell_line = input(\"Cell Line: \")\n",
    "drug = input(\"Drug: \")\n",
    "\n",
    "# Filter for the specific cell line and drug combination\n",
    "df_filtered = df.filter(like=f\"{cell_line}:{drug}\", axis=1)\n",
    "\n",
    "# Reshaping the data for easier plotting\n",
    "df_melted = df_filtered.melt(var_name='Condition', value_name='Response')\n",
    "df_melted['Concentration'] = df_melted['Condition'].apply(lambda x: x.split(':')[2])\n",
    "df_melted['Time'] = df_melted['Condition'].apply(lambda x: x.split(':')[3])\n",
    "\n",
    "# Plotting the dose-response over time\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(data=df_melted, x='Concentration', y='Response', hue='Time', marker='o')\n",
    "plt.title(f'Dose-Response Over Time for {drug} on {cell_line}')\n",
    "plt.xlabel('Concentration')\n",
    "plt.ylabel('Response')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Dose-Response Over Time for ADO-TRASTUZUMAB_EMTANSINE on ABY001_A375_XH\n",
    "\n",
    "This plot displays the **dose-response relationship** for the drug **ADO-TRASTUZUMAB_EMTANSINE** on the cell line **ABY001_A375_XH** across two time points, labeled as **3** and **24**. The x-axis represents the **drug concentration (in µM)**, while the y-axis represents the **response** (e.g., cell viability), with higher values indicating a positive response and lower values indicating a reduction in cell viability.\n",
    "\n",
    "### Key Observations\n",
    "\n",
    "1. **Effect of Concentration**:\n",
    "   - The plot shows the cell response at various drug concentrations, ranging from approximately **0.3125 µM to 10 µM**.\n",
    "   - At lower concentrations, there is noticeable variability in response, with a distinct dip at around **0.625 µM** for both time points, indicating reduced viability or higher toxicity.\n",
    "   - As the concentration increases, the response values tend to stabilize, showing an upward trend at higher concentrations (2.5 µM and 5 µM).\n",
    "\n",
    "2. **Time-Dependent Response**:\n",
    "   - The lines represent the response at two different time points (3 and 24 hours), as indicated by the legend on the right.\n",
    "   - **Time Point 3** (orange line) generally shows a slightly higher response than **Time Point 24** (blue line) at most concentration levels, suggesting an initial higher response that decreases slightly over time.\n",
    "   - This could imply that the drug has a stronger effect initially, which may reduce over time as the cell line potentially adapts to the drug or other factors come into play.\n",
    "\n",
    "3. **Confidence Intervals**:\n",
    "   - The shaded regions around each line represent the **confidence intervals**, indicating the variability or uncertainty in the response measurements at each concentration level.\n",
    "   - A wider shaded area suggests more variability in the response at that concentration, while a narrower area indicates more consistent responses across measurements.\n",
    "\n",
    "4. **Interpretation of Results**:\n",
    "   - This plot allows us to assess the impact of both **dose** and **exposure time** on the drug's efficacy. For example, the drop in response at 0.625 µM for both time points suggests a potential toxicity threshold, while the upward trend at higher concentrations suggests increased efficacy or cell viability recovery.\n",
    "   - The fact that the response stabilizes and rises at higher concentrations could be indicative of either a reduced toxic effect or an optimal therapeutic range.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "This dose-response plot over time provides valuable insights into the behavior of **ADO-TRASTUZUMAB_EMTANSINE** on the **ABY001_A375_XH** cell line. It helps in determining the optimal concentration and time for achieving desired therapeutic effects, as well as identifying concentration levels that may pose toxicity risks. Such time-dose relationships are critical in drug development for optimizing treatment regimens and improving efficacy while minimizing adverse effects.\n"
   ],
   "id": "235617123d845ed0"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Analysis of Drug Concentration Effects Using PCA and Random Forest Regression\n",
    "\n",
    "In this code, we explore how to predict the cell response to a drug concentration using Principal Component Analysis (PCA) for dimensionality reduction and a Random Forest Regressor for prediction. This approach allows us to reduce the complexity of high-dimensional data and train a model on essential features for predicting the impact of drug concentration on cell viability.\n",
    "\n",
    "### Problem Context and Approach\n",
    "\n",
    "Our goal is to predict the response (e.g., cell viability or drug efficacy) of a specific cell line (**A375**) when treated with a particular drug (**ADO-TRASTUZUMAB_EMTANSINE**) at various concentrations and time points. The dataset has a high-dimensional structure, with each column name encoding multiple experimental conditions such as **cell line**, **drug**, **concentration**, and **time point**. \n",
    "\n",
    "Given the large number of columns and the complexity of the experimental conditions, we chose the following approach:\n",
    "1. **Extract Relevant Information**: Use regular expressions to extract data only for the specific cell line and drug of interest.\n",
    "2. **Dimensionality Reduction with PCA**: Reduce the dataset’s dimensionality, simplifying the feature space while preserving important variance and relationships.\n",
    "3. **Regression Modeling**: Use Random Forest Regression to predict the cell response based on concentration and time point.\n",
    "\n",
    "This approach allows us to focus on the essential features, improving model interpretability and reducing overfitting risks by removing irrelevant or redundant information.\n",
    "\n",
    "### Explanation of the Code\n",
    "\n",
    "1. **Define Cell Line and Drug of Interest**:\n",
    "   - We specify the **cell line** and **drug** we want to analyze: `cell_line = 'A375'` and `drug = 'ADO-TRASTUZUMAB_EMTANSINE'`.\n",
    "\n",
    "2. **Extract Concentration and Time Information**:\n",
    "   - We define the `extract_info` function to use a **regular expression** to match columns that correspond to our chosen cell line and drug.\n",
    "   - For each matched column, we extract the **concentration** and **time point** values. This ensures that we only analyze data relevant to our chosen conditions.\n",
    "\n",
    "3. **Filter Columns for the Selected Cell Line and Drug**:\n",
    "   - We apply the `extract_info` function to the dataset columns, creating a list of matching columns with their associated concentration and time point values.\n",
    "   - If no columns match the specified cell line and drug, a message is printed indicating no matches were found.\n",
    "\n",
    "4. **Create Filtered DataFrame**:\n",
    "   - Using the filtered columns, we create a new DataFrame (`df_filtered`) containing only the response data for the selected cell line and drug.\n",
    "   - We **stack** this DataFrame to convert the response data into a single column, simplifying further processing.\n",
    "\n",
    "5. **Replicate Concentration and Time Data**:\n",
    "   - We create arrays for **concentration** and **time point** values, which are repeated to match the length of the response data. This ensures that each response value has a corresponding concentration and time point.\n",
    "\n",
    "6. **Construct Final DataFrame**:\n",
    "   - We combine the response, concentration, and time point values into a new DataFrame (`df_final`) with columns:\n",
    "     - `response`: The cell response value.\n",
    "     - `concentration`: The drug concentration.\n",
    "     - `time_point`: The time point of the measurement.\n",
    "   - This DataFrame serves as the input for dimensionality reduction and regression analysis.\n",
    "\n",
    "7. **Apply PCA for Dimensionality Reduction**:\n",
    "   - We use **PCA** to reduce the dimensionality of the data, aiming to capture the main sources of variance in just two principal components. This step simplifies the feature space and retains only essential patterns.\n",
    "   - The PCA-transformed features are stored in a new DataFrame, `df_pca`, which we use as our feature matrix for regression.\n",
    "\n",
    "8. **Prepare Feature Matrix and Target Variable**:\n",
    "   - We set `X` to be the PCA-transformed features (`df_pca`) and `y` to be the original `response` values from `df_final`.\n",
    "   - This setup allows us to use a simplified, lower-dimensional feature space for prediction while retaining the response as the target variable.\n",
    "\n",
    "9. **Train-Test Split**:\n",
    "   - We split the data into training and test sets with an 80-20 ratio. This ensures that we have separate data for model training and evaluation.\n",
    "\n",
    "10. **Train the Random Forest Regressor**:\n",
    "    - We initialize a **Random Forest Regressor** with 100 estimators and fit it to the training data.\n",
    "    - Random Forest is chosen for its ability to handle complex, nonlinear relationships and provide robust predictions even with high-dimensional data.\n",
    "\n",
    "11. **Predict and Evaluate**:\n",
    "    - We use the trained model to predict the response on the test data.\n",
    "    - We calculate two evaluation metrics:\n",
    "      - **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n",
    "      - **R-squared (R²)**: Indicates how well the model explains the variance in the response data.\n",
    "\n",
    "### Why This Approach?\n",
    "\n",
    "This method is well-suited to our dataset and problem statement for the following reasons:\n",
    "\n",
    "- **Dimensionality Reduction**: The dataset has numerous features due to multiple drug concentrations and time points. PCA simplifies this complexity by transforming the data into a smaller feature space, preserving only the most significant sources of variance.\n",
    "- **Regression Modeling**: By applying Random Forest Regression, we can capture the relationship between drug concentration, time point, and cell response, which helps predict the cell's behavior under various conditions.\n",
    "- **Interpretability and Efficiency**: Reducing dimensions before training a regression model makes the approach more computationally efficient and interpretable, especially with complex biological data.\n",
    "\n",
    "### Summary\n",
    "\n",
    "In summary, this code demonstrates a streamlined approach for predicting cell responses to drug treatments. By combining **PCA** for dimensionality reduction with **Random Forest Regression** for prediction, we create an effective model for understanding and predicting how cell lines respond to varying drug concentrations over time. This approach is applicable to a range of drug response analyses, facilitating insights that support drug development and optimization.\n"
   ],
   "id": "51dd0bcb90cf040b"
  },
  {
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2024-11-03T18:55:14.196723Z",
     "start_time": "2024-11-03T18:54:59.950901Z"
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   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Matrix Shape: (147936, 2)\n",
      "Target Variable Shape: (147936,)\n",
      "Mean Squared Error: 2.7964433096902083e-05\n",
      "R-squared: 0.999928171125235\n"
     ]
    }
   ],
   "execution_count": 8,
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "\"\"\"\n",
    "Example cell line and drug for testing\n",
    "cell_line = 'ABY001_A375_XH'\n",
    "drug = 'ADO-TRASTUZUMAB_EMTANSINE'\n",
    "\"\"\"\n",
    "cell_line = 'A375'\n",
    "drug = 'ADO-TRASTUZUMAB_EMTANSINE'\n",
    "\n",
    "# Function to extract concentration and time point using regex\n",
    "def extract_info(col_name):\n",
    "    # Use regex to extract cell line, drug, concentration, and time\n",
    "    match = re.search(rf'{cell_line}.*{drug}:(\\d+\\.?\\d*):(\\d+)', col_name)\n",
    "    if match:\n",
    "        concentration = float(match.group(1))  # Extract concentration\n",
    "        time_point = int(match.group(2))       # Extract time point (e.g., 24, 72)\n",
    "        return concentration, time_point\n",
    "    return None\n",
    "\n",
    "# Get the columns that match the cell line and drug\n",
    "filtered_columns = [(col, extract_info(col)) for col in df.columns if extract_info(col)]\n",
    "\n",
    "# Create a new DataFrame for the filtered columns\n",
    "if filtered_columns:\n",
    "    selected_columns, extracted_info = zip(*filtered_columns)\n",
    "    df_filtered = df[list(selected_columns)]\n",
    "\n",
    "    # Stack the data and replicate concentration and time point for each value in the column\n",
    "    stacked_data = df_filtered.stack().reset_index(drop=True)\n",
    "\n",
    "    # Create arrays for concentration and time point to match the stacked data length\n",
    "    concentrations = []\n",
    "    time_points = []\n",
    "    for _, (concentration, time_point) in enumerate(extracted_info):\n",
    "        concentrations.extend([concentration] * df_filtered.shape[0])\n",
    "        time_points.extend([time_point] * df_filtered.shape[0])\n",
    "\n",
    "    # Create a new DataFrame with the stacked data, concentration, and time point\n",
    "    df_final = pd.DataFrame({\n",
    "        'response': stacked_data,\n",
    "        'concentration': concentrations[:len(stacked_data)],  # Match the lengths\n",
    "        'time_point': time_points[:len(stacked_data)]  # Match the lengths\n",
    "    })\n",
    "\n",
    "    features = df_final[['response', 'concentration', 'time_point']]\n",
    "\n",
    "    pca = PCA(n_components=2)  # Choose the number of components based on explained variance or total number of features\n",
    "    pca_features = pca.fit_transform(features)\n",
    "\n",
    "    # Add PCA features to the DataFrame\n",
    "    df_pca = pd.DataFrame(pca_features, columns=[f'PCA_{i+1}' for i in range(pca.n_components_)])\n",
    "\n",
    "    # Prepare feature matrix and target variable\n",
    "    X = df_pca  # Use PCA-transformed features\n",
    "    y = df_final['response']  # Assuming 'response' is still the target variable\n",
    "    \n",
    "    # Print shapes to verify\n",
    "    print(\"Feature Matrix Shape:\", X.shape)\n",
    "    print(\"Target Variable Shape:\", y.shape)\n",
    "   \n",
    "\n",
    "    # Split into training and test sets\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "    # Train a Random Forest Regressor\n",
    "    model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # Predict and evaluate\n",
    "    y_pred = model.predict(X_test)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    print(f'Mean Squared Error: {mse}')\n",
    "    print(f'R-squared: {r2}')\n",
    "else:\n",
    "    print(f\"No matching columns found for {cell_line} and {drug}\")\n"
   ],
   "id": "70cadee2-c1e0-4797-a989-e7e9e96df638"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Model Performance Evaluation\n",
    "\n",
    "After applying Principal Component Analysis (PCA) and training a Random Forest Regressor, we evaluated the model's performance on our test set. Below is an explanation of the results:\n",
    "\n",
    "### Output Summary\n",
    "\n",
    "- **Mean Squared Error (MSE)**: `2.7964433096902083e-05`\n",
    "  - MSE measures the average squared difference between the predicted values and the actual response values in the test set. A very low MSE indicates that the model's predictions are very close to the actual responses, which suggests a high degree of accuracy in our model's predictions.\n",
    "\n",
    "- **R-squared (R²)**: `0.999928171125235`\n",
    "  - The R² score indicates how well our model explains the variance in the response data. An R² close to 1 (0.9999) shows that the model explains nearly all of the variance in the response variable, meaning the model fits the data exceptionally well.\n",
    "\n",
    "### Interpretation in Context of the Approach\n",
    "\n",
    "Our approach involved:\n",
    "1. **Dimensionality Reduction using PCA**: By transforming our high-dimensional data into two principal components, we retained the most relevant information while reducing noise and redundancy. This step allowed us to work with a manageable feature space that captures the essential patterns in drug response without overfitting.\n",
    "  \n",
    "2. **Random Forest Regression**: Using a Random Forest Regressor provided flexibility in modeling nonlinear relationships between drug concentration, time, and cell response. Random Forests also handle feature interactions effectively, making them well-suited for complex biological datasets.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "The high R² score and low MSE indicate that the approach successfully captured the relationship between concentration, time point, and cell response. By reducing dimensionality with PCA, we retained essential features without losing predictive power, allowing the Random Forest model to make accurate predictions. This result suggests that our model can reliably predict cell response to varying drug concentrations and time points, making this approach effective for understanding drug efficacy and informing experimental or clinical decisions.\n"
   ],
   "id": "6992d299e9c3dae8"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Cross-Validation\n",
    "\n",
    "In this code, we are performing **5-fold cross-validation** on the trained model. Cross-validation is a statistical method used to assess the generalization ability of a model, ensuring it performs well on unseen data. Here’s why we use it:\n",
    "\n",
    "1. **Validation on Multiple Data Splits**:\n",
    "   - Cross-validation divides the dataset into multiple subsets (or \"folds\") and trains the model on each fold while using the remaining data as the test set. This way, we evaluate the model on multiple subsets of the data, rather than relying on a single train-test split. This helps prevent overfitting and provides a more reliable measure of the model’s performance.\n",
    "\n",
    "2. **Improved Generalization**:\n",
    "   - By averaging the performance across different folds, we obtain an estimate of the model’s ability to generalize to new data. This is especially useful when the dataset has patterns that could vary across different subsets, as it allows us to understand if the model consistently performs well.\n",
    "\n",
    "3. **Model Stability**:\n",
    "   - Cross-validation provides insights into the model's stability by showing how performance varies across different folds. If scores are consistent, it indicates a stable and reliable model.\n",
    "\n",
    "4. **Evaluation Metric**:\n",
    "   - We use the **R-squared (R²)** metric as our evaluation score. A high R² score across folds would confirm that the model explains a large proportion of the variance in the response variable, and thus, can be trusted for making predictions.\n",
    "\n",
    "Overall, cross-validation helps us gain a comprehensive understanding of the model's performance and minimizes the risk of selecting a model that only performs well on a specific subset of data.\n"
   ],
   "id": "245e40f7b6b76d47"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T18:59:16.219719Z",
     "start_time": "2024-11-03T18:58:13.442527Z"
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   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-Validation R-squared Scores: [0.99957799 0.99894469 0.99940484 0.99985777 0.99973222]\n",
      "Mean Cross-Validation R-squared Score: 0.9995035012484788\n"
     ]
    }
   ],
   "execution_count": 9,
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Perform 5-fold cross-validation\n",
    "scores = cross_val_score(model, X, y, cv=5, scoring='r2')\n",
    "print(\"Cross-Validation R-squared Scores:\", scores)\n",
    "print(\"Mean Cross-Validation R-squared Score:\", scores.mean())\n"
   ],
   "id": "8b8ea48a-53ac-4cd0-a282-cca98e848f07"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Cross-Validation Results Explanation\n",
    "\n",
    "**Cross-Validation R-squared Scores**: `[0.99957799, 0.99894469, 0.99940484, 0.99985777, 0.99973222]`\n",
    "\n",
    "- These scores represent the R² values obtained from each of the 5 folds during cross-validation. Each value shows the proportion of variance explained by the model for that fold, with values close to 1 indicating a good fit.\n",
    "- The consistency of the scores across folds (all around 0.999) indicates that the model performs reliably and has minimal variance across different subsets of the data.\n",
    "\n",
    "**Mean Cross-Validation R-squared Score**: `0.9995035012484788`\n",
    "\n",
    "- The mean R² score is the average of the R² scores across all folds. A mean score of approximately 0.9995 suggests that the model explains nearly all of the variance in the response variable on average across folds, confirming excellent predictive performance.\n",
    "- This high score indicates that the model is highly accurate and generalizes well to unseen data, making it robust for predicting cell responses to drug concentrations and time points in this context.\n",
    "\n",
    "### Conclusion\n",
    "\n",
    "The cross-validation results show that the model not only performs well on the initial train-test split but also maintains its accuracy across multiple splits of the data. This further validates the model's reliability and robustness, reinforcing confidence in its predictions and its suitability for drug response analysis.\n"
   ],
   "id": "5586706f6175d981"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Predicting Response for User-Defined Concentration and Time Point\n",
    "\n",
    "In this code, we allow the user to input a specific drug concentration and time point for which they want to predict the cell response. This process involves:\n",
    "\n",
    "1. **Collecting User Input**:\n",
    "   - The user is prompted to enter values for `example_concentration` and `example_time_point`.\n",
    "   - These values represent the concentration of the drug and the time point at which the response is measured.\n",
    "\n",
    "2. **Preparing the Input Data for PCA Transformation**:\n",
    "   - We create a DataFrame (`example_df`) with columns `response`, `concentration`, and `time_point`. The `response` value is set to a placeholder of `0` since we only need the `concentration` and `time_point` for the PCA transformation.\n",
    "\n",
    "3. **Applying PCA Transformation**:\n",
    "   - The `example_df` DataFrame is transformed using the previously fitted PCA model. This projects the input data into the same lower-dimensional space that was used to train the model.\n",
    "   - This transformation generates the PCA features needed for prediction, which we store in `example_pca_features`.\n",
    "\n",
    "4. **Converting to a DataFrame for Model Compatibility**:\n",
    "   - We convert `example_pca_features` to a DataFrame (`example_pca_df`) with the same column names as used during training (`PCA_1`, `PCA_2`, etc.). This ensures compatibility with the trained model, which was fitted on a DataFrame with these column names.\n",
    "\n",
    "5. **Making the Prediction**:\n",
    "   - Finally, we use the trained Random Forest model to predict the cell response based on the PCA-transformed input values.\n",
    "   - The predicted response value is printed, indicating the model's estimation of cell viability or reaction for the given concentration and time point.\n",
    "\n",
    "### Purpose\n",
    "\n",
    "This code enables flexible prediction by allowing the user to input custom concentration and time values, making it useful for exploratory analyses. By transforming the input through PCA, we ensure that the input format aligns with the data structure used during training.\n"
   ],
   "id": "232aaf2710b8837c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T19:37:22.175540Z",
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   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Response: 9.51025130909806e-05\n"
     ]
    }
   ],
   "execution_count": 18,
   "source": [
    "import numpy as np\n",
    "\n",
    "# Example input values for prediction\n",
    "example_concentration = float(input(\"Enter the concentration: \"))\n",
    "example_time_point = float(input(\"Enter the time point: \"))\n",
    "\n",
    "\n",
    "# Create a DataFrame for the example input\n",
    "example_df = pd.DataFrame({\n",
    "    'response': [0],  # Placeholder value for response, as we only need concentration and time_point for PCA\n",
    "    'concentration': [example_concentration],\n",
    "    'time_point': [example_time_point]\n",
    "})\n",
    "\n",
    "# Apply PCA transformation on the example input\n",
    "example_pca_features = pca.transform(example_df[['response', 'concentration', 'time_point']])\n",
    "\n",
    "# Convert example_pca_features to a DataFrame with the same feature names used for training\n",
    "example_pca_df = pd.DataFrame(example_pca_features, columns=[f'PCA_{i+1}' for i in range(pca.n_components_)])\n",
    "\n",
    "# Make the prediction\n",
    "predicted_response = model.predict(example_pca_df)\n",
    "print(f\"Predicted Response: {predicted_response[0]}\")\n"
   ],
   "id": "649bde8e1c4f7d88"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Interpretation of the Predicted Value\n",
    "\n",
    "**Predicted Response**: `9.51025130909806e-05`\n",
    "\n",
    "For the input values:\n",
    "- **Concentration** = 3.77 µM\n",
    "- **Time Point** = 3 hours\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "The predicted response value is approximately **9.51e-05**. Given that this is a small number, it suggests a relatively low cell viability or a weak response at this specific concentration and time point.\n",
    "\n",
    "### Context\n",
    "\n",
    "The low response could indicate:\n",
    "- **Limited Efficacy**: At 3.77 µM concentration, the drug may not yet be at a dosage that significantly affects the cell line within 3 hours.\n",
    "- **Initial Low Impact**: The short time duration of 3 hours may not provide enough exposure time for the drug to take effect at this concentration.\n",
    "\n",
    "This result helps in understanding that either the concentration needs to be adjusted, or the time point extended, to observe a more substantial response. Such predictions can assist in determining optimal conditions for drug efficacy.\n"
   ],
   "id": "e965528495bcd32d"
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    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Assuming df_final['response'] contains all response values for a particular cell line and drug\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df_final['response'], kde=True, bins=50)\n",
    "plt.title(\"Distribution of Response Values\")\n",
    "plt.xlabel(\"Response\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ],
   "id": "15ebcc7f6b28ecd7"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Analysis of the Distribution of Response Values\n",
    "\n",
    "This histogram represents the distribution of response values in the dataset, indicating how the cell response varies under different experimental conditions.\n",
    "\n",
    "1. **Centered Distribution**:\n",
    "   - The response values appear to be centered around **0**, with a significant number of values close to this point.\n",
    "   - This indicates that, on average, the drug treatments have a relatively **neutral effect** on cell viability in many cases, with slight variations around zero.\n",
    "\n",
    "2. **Symmetry and Shape**:\n",
    "   - The distribution is **approximately symmetric**, resembling a normal (Gaussian) distribution.\n",
    "   - Most response values are concentrated between approximately **-2.5 and 2.5**, with a sharp peak around zero, suggesting that extreme responses (either very positive or very negative) are rare.\n",
    "\n",
    "3. **Interpretation of Response Range**:\n",
    "   - Since the majority of responses are close to zero, this could imply that many of the drug treatments have a minimal effect on cell viability under the conditions tested.\n",
    "   - Negative response values might indicate reduced cell viability or a cytotoxic effect of the drug, while positive values might suggest a slightly increased cell viability or resilience.\n",
    "\n",
    "4. **Tail Analysis**:\n",
    "   - There are some values extending to around **-10** on the negative side, which could indicate a strong cytotoxic response for certain drug concentrations and time points.\n",
    "   - The presence of positive response values above 5 is rare, showing that very few conditions result in highly positive effects on cell viability.\n"
   ],
   "id": "4879fdb4e744c36a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-03T19:42:08.701944Z",
     "start_time": "2024-11-03T19:42:08.692315Z"
    }
   },
   "cell_type": "code",
   "source": [
    "low_threshold = df_final['response'].quantile(0.33)  # 33rd percentile\n",
    "high_threshold = df_final['response'].quantile(0.66)  # 66th percentile\n",
    "\n",
    "print(f\"Low Viability Threshold: {low_threshold}\")\n",
    "print(f\"High Viability Threshold: {high_threshold}\")\n"
   ],
   "id": "73a268ccec1cc45d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low Viability Threshold: -0.3113468930125236\n",
      "High Viability Threshold: 0.18926739990711222\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Viability Thresholds Based on Dataset Distribution\n",
    "\n",
    "The thresholds for categorizing cell viability based on the distribution of response values in the dataset are as follows:\n",
    "\n",
    "- **Low Viability Threshold**: **-0.3113**\n",
    "  - Any response value below this threshold is considered to indicate **low cell viability**.\n",
    "  - This means that the drug is likely having a stronger inhibitory or cytotoxic effect, significantly reducing cell viability under the given conditions.\n",
    "\n",
    "- **High Viability Threshold**: **0.1893**\n",
    "  - Any response value above this threshold is categorized as **high cell viability**.\n",
    "  - In these cases, the drug has a minimal inhibitory effect, or cells might be showing resilience, suggesting that the drug concentration and exposure time are not sufficient to reduce viability substantially.\n",
    "\n",
    "### Interpretation\n",
    "\n",
    "With these thresholds, we can categorize response values into three levels:\n",
    "- **Low Viability** (response < -0.3113): Indicates a strong drug effect, leading to substantial cell death or reduced cell viability.\n",
    "- **Medium Viability** (-0.3113 ≤ response ≤ 0.1893): Represents a moderate drug effect where the response is closer to neutral, with partial or limited impact on viability.\n",
    "- **High Viability** (response > 0.1893): Suggests that the drug concentration and exposure conditions are not significantly affecting cell survival.\n",
    "\n",
    "These thresholds allow us to interpret predicted response values more effectively, helping to assess drug efficacy in terms of its impact on cell viability.\n"
   ],
   "id": "5f226e0cf0a2ca7e"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ab064f4-54e5-4d64-a7a3-f3203b367186",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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