{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a4104299",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.101929Z",
     "iopub.status.busy": "2025-03-25T08:36:11.101699Z",
     "iopub.status.idle": "2025-03-25T08:36:11.267036Z",
     "shell.execute_reply": "2025-03-25T08:36:11.266645Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '../..')))\n",
    "\n",
    "# Path Configuration\n",
    "from tools.preprocess import *\n",
    "\n",
    "# Processing context\n",
    "trait = \"Cystic_Fibrosis\"\n",
    "cohort = \"GSE67698\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE67698\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE67698.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv\"\n",
    "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f70ad0",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8ad100c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.268275Z",
     "iopub.status.busy": "2025-03-25T08:36:11.268127Z",
     "iopub.status.idle": "2025-03-25T08:36:11.458766Z",
     "shell.execute_reply": "2025-03-25T08:36:11.458203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptional profiling of CFBE41o-ΔF508 cells and CFBE41o−CFTR cells.\"\n",
      "!Series_summary\t\"Cystic fibrosis bronchial epithelial (CFBE41o-ΔF508) cells subjected to 23 bio-active small molecules including vehicle controls, at low temperature and untreated cells. Untreated Cystic fibrosis bronchial epithelial cells (CFBE41o−CFTR) are also included.\"\n",
      "!Series_overall_design\t\"Two-colors Dye-swap, two or three biological replicates with two technical replicates each and DMSO (vehicle control) were included in each hybridization batch.\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell line: polarized CFBE41o-cell line'], 1: ['transduction: TranzVector lentivectors containing deltaF508 CFTR (CFBE41o-deltaF508CFTR)', 'transduction: TranzVector lentivectors containing wildtype CFTR (CFBE41o-CFTR)']}\n"
     ]
    }
   ],
   "source": [
    "from tools.preprocess import *\n",
    "# 1. Identify the paths to the SOFT file and the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Read the matrix file to obtain background information and sample characteristics data\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "# 3. Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "\n",
    "# 4. Explicitly print out all the background information and the sample characteristics dictionary\n",
    "print(\"Background Information:\")\n",
    "print(background_info)\n",
    "print(\"Sample Characteristics Dictionary:\")\n",
    "print(sample_characteristics_dict)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9daf3bea",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5e192f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.460517Z",
     "iopub.status.busy": "2025-03-25T08:36:11.460407Z",
     "iopub.status.idle": "2025-03-25T08:36:11.488249Z",
     "shell.execute_reply": "2025-03-25T08:36:11.487750Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{'GSM1946634': [1.0], 'GSM1946635': [1.0], 'GSM1946636': [1.0], 'GSM1946637': [1.0], 'GSM1946638': [1.0], 'GSM1946639': [1.0], 'GSM1946640': [1.0], 'GSM1946641': [1.0], 'GSM1946642': [1.0], 'GSM1946643': [1.0], 'GSM1946644': [1.0], 'GSM1946645': [1.0], 'GSM1946646': [1.0], 'GSM1946647': [1.0], 'GSM1946648': [1.0], 'GSM1946649': [1.0], 'GSM1946650': [1.0], 'GSM1946651': [1.0], 'GSM1946652': [1.0], 'GSM1946653': [1.0], 'GSM1946654': [1.0], 'GSM1946655': [1.0], 'GSM1946656': [1.0], 'GSM1946657': [1.0], 'GSM1946658': [1.0], 'GSM1946659': [1.0], 'GSM1946660': [1.0], 'GSM1946661': [1.0], 'GSM1946662': [1.0], 'GSM1946663': [1.0], 'GSM1946664': [1.0], 'GSM1946665': [1.0], 'GSM1946666': [1.0], 'GSM1946667': [1.0], 'GSM1946668': [1.0], 'GSM1946669': [1.0], 'GSM1946670': [1.0], 'GSM1946671': [1.0], 'GSM1946672': [1.0], 'GSM1946673': [1.0], 'GSM1946674': [1.0], 'GSM1946675': [1.0], 'GSM1946676': [1.0], 'GSM1946677': [1.0], 'GSM1946678': [1.0], 'GSM1946679': [1.0], 'GSM1946680': [1.0], 'GSM1946681': [1.0], 'GSM1946682': [1.0], 'GSM1946683': [1.0], 'GSM1946684': [1.0], 'GSM1946685': [1.0], 'GSM1946686': [0.0], 'GSM1946687': [0.0], 'GSM1946688': [0.0], 'GSM1946689': [0.0], 'GSM1946690': [1.0], 'GSM1946691': [1.0], 'GSM1946692': [1.0], 'GSM1946693': [1.0], 'GSM1946694': [1.0], 'GSM1946695': [1.0], 'GSM1946696': [1.0], 'GSM1946697': [1.0], 'GSM1946698': [1.0], 'GSM1946699': [1.0], 'GSM1946700': [1.0], 'GSM1946701': [1.0], 'GSM1946702': [1.0], 'GSM1946703': [1.0], 'GSM1946704': [1.0], 'GSM1946705': [1.0], 'GSM1946706': [1.0], 'GSM1946707': [1.0], 'GSM1946708': [1.0], 'GSM1946709': [1.0], 'GSM1946710': [1.0], 'GSM1946711': [1.0], 'GSM1946712': [1.0], 'GSM1946713': [1.0], 'GSM1946714': [1.0], 'GSM1946715': [1.0], 'GSM1946716': [1.0], 'GSM1946717': [1.0], 'GSM1946718': [1.0], 'GSM1946719': [1.0], 'GSM1946720': [1.0], 'GSM1946721': [1.0], 'GSM1946722': [1.0], 'GSM1946723': [1.0], 'GSM1946724': [1.0], 'GSM1946725': [1.0], 'GSM1946726': [1.0], 'GSM1946727': [1.0], 'GSM1946728': [1.0], 'GSM1946729': [1.0], 'GSM1946730': [1.0], 'GSM1946731': [1.0], 'GSM1946732': [1.0], 'GSM1946733': [1.0], 'GSM1946734': [1.0], 'GSM1946735': [1.0], 'GSM1946736': [1.0], 'GSM1946737': [1.0], 'GSM1946738': [1.0], 'GSM1946739': [1.0], 'GSM1946740': [1.0], 'GSM1946741': [1.0], 'GSM1946742': [1.0], 'GSM1946743': [1.0], 'GSM1946744': [1.0], 'GSM1946745': [1.0], 'GSM1946746': [1.0], 'GSM1946747': [1.0], 'GSM1946748': [1.0], 'GSM1946749': [1.0], 'GSM1946750': [1.0], 'GSM1946751': [1.0], 'GSM1946752': [1.0], 'GSM1946753': [1.0], 'GSM1946754': [1.0], 'GSM1946755': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE67698.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import json\n",
    "from typing import Callable, Optional, Dict, Any\n",
    "\n",
    "# 1. Assess gene expression data availability\n",
    "# Based on the background information, this dataset contains transcriptional profiling data\n",
    "# which indicates gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Identify rows for trait, age, and gender\n",
    "# From the sample characteristics, we can see information about cell line and transduction type\n",
    "# The transduction type (row 1) indicates whether cells have deltaF508 CFTR mutation or wildtype CFTR\n",
    "# This can be used as our trait (presence of cystic fibrosis mutation)\n",
    "trait_row = 1\n",
    "# Age and gender information are not available in this dataset\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Define conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert CF status based on transduction information\n",
    "    deltaF508 CFTR = 1 (CF mutation present)\n",
    "    wildtype CFTR = 0 (No CF mutation)\n",
    "    \"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "        \n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if isinstance(value, str):\n",
    "        if \"deltaF508\" in value:\n",
    "            return 1  # CF mutation\n",
    "        elif \"wildtype CFTR\" in value:\n",
    "            return 0  # No CF mutation\n",
    "    \n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    # Not applicable for this dataset\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Not applicable for this dataset\n",
    "    return None\n",
    "\n",
    "# 3. Save metadata - perform initial filtering\n",
    "# Check if trait data is available (trait_row is not None)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial validation to check if dataset passes basic requirements\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=is_gene_available, \n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Extract clinical features if trait data is available\n",
    "if trait_row is not None:\n",
    "    # Make sure the output directory exists\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Access the clinical data that was loaded in a previous step\n",
    "    # This is typically a DataFrame with samples as rows and characteristic indices as columns\n",
    "    # We'll assume clinical_data exists from previous steps\n",
    "    \n",
    "    # Extract clinical features\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,  # Use existing clinical_data from previous step\n",
    "        trait=trait,\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=age_row,\n",
    "        convert_age=convert_age,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender\n",
    "    )\n",
    "    \n",
    "    # Preview the selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to CSV\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cf1d95",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "077fc49c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.489917Z",
     "iopub.status.busy": "2025-03-25T08:36:11.489809Z",
     "iopub.status.idle": "2025-03-25T08:36:11.889760Z",
     "shell.execute_reply": "2025-03-25T08:36:11.889120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found data marker at line 69\n",
      "Header line: \"ID_REF\"\t\"GSM1946634\"\t\"GSM1946635\"\t\"GSM1946636\"\t\"GSM1946637\"\t\"GSM1946638\"\t\"GSM1946639\"\t\"GSM1946640\"\t\"GSM1946641\"\t\"GSM1946642\"\t\"GSM1946643\"\t\"GSM1946644\"\t\"GSM1946645\"\t\"GSM1946646\"\t\"GSM1946647\"\t\"GSM1946648\"\t\"GSM1946649\"\t\"GSM1946650\"\t\"GSM1946651\"\t\"GSM1946652\"\t\"GSM1946653\"\t\"GSM1946654\"\t\"GSM1946655\"\t\"GSM1946656\"\t\"GSM1946657\"\t\"GSM1946658\"\t\"GSM1946659\"\t\"GSM1946660\"\t\"GSM1946661\"\t\"GSM1946662\"\t\"GSM1946663\"\t\"GSM1946664\"\t\"GSM1946665\"\t\"GSM1946666\"\t\"GSM1946667\"\t\"GSM1946668\"\t\"GSM1946669\"\t\"GSM1946670\"\t\"GSM1946671\"\t\"GSM1946672\"\t\"GSM1946673\"\t\"GSM1946674\"\t\"GSM1946675\"\t\"GSM1946676\"\t\"GSM1946677\"\t\"GSM1946678\"\t\"GSM1946679\"\t\"GSM1946680\"\t\"GSM1946681\"\t\"GSM1946682\"\t\"GSM1946683\"\t\"GSM1946684\"\t\"GSM1946685\"\t\"GSM1946686\"\t\"GSM1946687\"\t\"GSM1946688\"\t\"GSM1946689\"\t\"GSM1946690\"\t\"GSM1946691\"\t\"GSM1946692\"\t\"GSM1946693\"\t\"GSM1946694\"\t\"GSM1946695\"\t\"GSM1946696\"\t\"GSM1946697\"\t\"GSM1946698\"\t\"GSM1946699\"\t\"GSM1946700\"\t\"GSM1946701\"\t\"GSM1946702\"\t\"GSM1946703\"\t\"GSM1946704\"\t\"GSM1946705\"\t\"GSM1946706\"\t\"GSM1946707\"\t\"GSM1946708\"\t\"GSM1946709\"\t\"GSM1946710\"\t\"GSM1946711\"\t\"GSM1946712\"\t\"GSM1946713\"\t\"GSM1946714\"\t\"GSM1946715\"\t\"GSM1946716\"\t\"GSM1946717\"\t\"GSM1946718\"\t\"GSM1946719\"\t\"GSM1946720\"\t\"GSM1946721\"\t\"GSM1946722\"\t\"GSM1946723\"\t\"GSM1946724\"\t\"GSM1946725\"\t\"GSM1946726\"\t\"GSM1946727\"\t\"GSM1946728\"\t\"GSM1946729\"\t\"GSM1946730\"\t\"GSM1946731\"\t\"GSM1946732\"\t\"GSM1946733\"\t\"GSM1946734\"\t\"GSM1946735\"\t\"GSM1946736\"\t\"GSM1946737\"\t\"GSM1946738\"\t\"GSM1946739\"\t\"GSM1946740\"\t\"GSM1946741\"\t\"GSM1946742\"\t\"GSM1946743\"\t\"GSM1946744\"\t\"GSM1946745\"\t\"GSM1946746\"\t\"GSM1946747\"\t\"GSM1946748\"\t\"GSM1946749\"\t\"GSM1946750\"\t\"GSM1946751\"\t\"GSM1946752\"\t\"GSM1946753\"\t\"GSM1946754\"\t\"GSM1946755\"\n",
      "First data line: \"A_23_P100001\"\t-2.8787\t-2.5598\t-2.5796\t-2.5329\t-0.2065\t-0.6002\t-2.8878\t-3.1946\t-0.0517\t0.0390\t-3.1205\t-2.7660\t-3.3119\t-2.3934\t-3.4219\t-2.5577\t-2.7781\t-2.8961\t-2.4142\t-2.5723\t-1.8035\t-1.9974\t-2.6340\t-2.1938\t-3.3447\t-3.7593\t-2.3820\t-3.2490\t-2.7328\t-2.7992\t-3.5729\t-4.0990\t-2.2118\t-2.2726\t-3.3225\t-3.5793\t-2.3955\t-3.0237\t-3.0172\t-3.3335\t-2.6537\t-2.7303\t-1.9556\t-2.0283\t-1.8409\t-1.7097\t-1.6155\t-1.7088\t-2.6423\t-3.2713\t-2.5211\t-2.0271\t1.1176\t0.4275\t0.2648\t0.2237\t-2.9698\t-2.6803\t-1.9177\t-2.3395\t-1.7564\t-1.4396\t-2.5793\t-2.2490\t-2.6141\t-2.8285\t-2.5020\t-2.3917\t-2.5550\t-2.2365\t-3.1108\t-3.0147\t-3.2419\t-3.3621\t-2.8760\t-3.0787\t-2.2880\t-2.3686\t-2.6394\t-2.7403\t-2.1537\t-2.5304\t-2.6259\t-2.2200\t-2.3386\t-2.8193\t-2.9143\t-3.0036\t-2.8826\t-3.8299\t-2.4721\t-2.7392\t-3.1828\t-3.2706\t-2.6757\t-2.6363\t-2.8124\t-3.0037\t-2.8452\t-3.2319\t-3.1110\t-2.8970\t-2.4607\t-2.5720\t-3.5185\t-4.4907\t-2.5415\t-2.7348\t-2.8109\t-2.9797\t-3.2431\t-4.0171\t-2.6663\t-2.2648\t-1.4216\t-2.3937\t-2.6968\t-2.3184\t-1.9126\t-2.0352\t-3.3332\t-4.1652\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056',\n",
      "       'A_23_P100074', 'A_23_P100092', 'A_23_P100103', 'A_23_P100111',\n",
      "       'A_23_P100127', 'A_23_P100133', 'A_23_P100141', 'A_23_P100156',\n",
      "       'A_23_P100177', 'A_23_P100189', 'A_23_P100196', 'A_23_P100203',\n",
      "       'A_23_P100220', 'A_23_P100240', 'A_23_P10025', 'A_23_P100263'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. First, let's examine the structure of the matrix file to understand its format\n",
    "import gzip\n",
    "\n",
    "# Peek at the first few lines of the file to understand its structure\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Read first 100 lines to find the header structure\n",
    "    for i, line in enumerate(file):\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            print(f\"Found data marker at line {i}\")\n",
    "            # Read the next line which should be the header\n",
    "            header_line = next(file)\n",
    "            print(f\"Header line: {header_line.strip()}\")\n",
    "            # And the first data line\n",
    "            first_data_line = next(file)\n",
    "            print(f\"First data line: {first_data_line.strip()}\")\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Matrix table marker not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# 3. Now try to get the genetic data with better error handling\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(gene_data.index[:20])\n",
    "except KeyError as e:\n",
    "    print(f\"KeyError: {e}\")\n",
    "    \n",
    "    # Alternative approach: manually extract the data\n",
    "    print(\"\\nTrying alternative approach to read the gene data:\")\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        # Find the start of the data\n",
    "        for line in file:\n",
    "            if '!series_matrix_table_begin' in line:\n",
    "                break\n",
    "                \n",
    "        # Read the headers and data\n",
    "        import pandas as pd\n",
    "        df = pd.read_csv(file, sep='\\t', index_col=0)\n",
    "        print(f\"Column names: {df.columns[:5]}\")\n",
    "        print(f\"First 20 row IDs: {df.index[:20]}\")\n",
    "        gene_data = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f57e69d0",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6063f53f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.891524Z",
     "iopub.status.busy": "2025-03-25T08:36:11.891404Z",
     "iopub.status.idle": "2025-03-25T08:36:11.893817Z",
     "shell.execute_reply": "2025-03-25T08:36:11.893373Z"
    }
   },
   "outputs": [],
   "source": [
    "# Based on the gene identifiers, these appear to be Agilent microarray probe IDs (starting with A_23_P),\n",
    "# not standard human gene symbols. These will need to be mapped to official gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad5198d7",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a382c4f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:11.895466Z",
     "iopub.status.busy": "2025-03-25T08:36:11.895361Z",
     "iopub.status.idle": "2025-03-25T08:36:20.832272Z",
     "shell.execute_reply": "2025-03-25T08:36:20.831540Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'SPOT_ID': ['A_23_P100001', 'A_23_P100011', 'A_23_P100022', 'A_23_P100056', 'A_23_P100074'], 'CONTROL_TYPE': ['FALSE', 'FALSE', 'FALSE', 'FALSE', 'FALSE'], 'REFSEQ': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GB_ACC': ['NM_207446', 'NM_005829', 'NM_014848', 'NM_194272', 'NM_020371'], 'GENE': [400451.0, 10239.0, 9899.0, 348093.0, 57099.0], 'GENE_SYMBOL': ['FAM174B', 'AP3S2', 'SV2B', 'RBPMS2', 'AVEN'], 'GENE_NAME': ['family with sequence similarity 174, member B', 'adaptor-related protein complex 3, sigma 2 subunit', 'synaptic vesicle glycoprotein 2B', 'RNA binding protein with multiple splicing 2', 'apoptosis, caspase activation inhibitor'], 'UNIGENE_ID': ['Hs.27373', 'Hs.632161', 'Hs.21754', 'Hs.436518', 'Hs.555966'], 'ENSEMBL_ID': ['ENST00000557398', nan, 'ENST00000557410', 'ENST00000300069', 'ENST00000306730'], 'TIGR_ID': [nan, nan, nan, nan, nan], 'ACCESSION_STRING': ['ref|NM_207446|ens|ENST00000557398|ens|ENST00000553393|ens|ENST00000327355', 'ref|NM_005829|ref|NM_001199058|ref|NR_023361|ref|NR_037582', 'ref|NM_014848|ref|NM_001167580|ens|ENST00000557410|ens|ENST00000330276', 'ref|NM_194272|ens|ENST00000300069|gb|AK127873|gb|AK124123', 'ref|NM_020371|ens|ENST00000306730|gb|AF283508|gb|BC010488'], 'CHROMOSOMAL_LOCATION': ['chr15:93160848-93160789', 'chr15:90378743-90378684', 'chr15:91838329-91838388', 'chr15:65032375-65032316', 'chr15:34158739-34158680'], 'CYTOBAND': ['hs|15q26.1', 'hs|15q26.1', 'hs|15q26.1', 'hs|15q22.31', 'hs|15q14'], 'DESCRIPTION': ['Homo sapiens family with sequence similarity 174, member B (FAM174B), mRNA [NM_207446]', 'Homo sapiens adaptor-related protein complex 3, sigma 2 subunit (AP3S2), transcript variant 1, mRNA [NM_005829]', 'Homo sapiens synaptic vesicle glycoprotein 2B (SV2B), transcript variant 1, mRNA [NM_014848]', 'Homo sapiens RNA binding protein with multiple splicing 2 (RBPMS2), mRNA [NM_194272]', 'Homo sapiens apoptosis, caspase activation inhibitor (AVEN), mRNA [NM_020371]'], 'GO_ID': ['GO:0016020(membrane)|GO:0016021(integral to membrane)', 'GO:0005794(Golgi apparatus)|GO:0006886(intracellular protein transport)|GO:0008565(protein transporter activity)|GO:0016020(membrane)|GO:0016192(vesicle-mediated transport)|GO:0030117(membrane coat)|GO:0030659(cytoplasmic vesicle membrane)|GO:0031410(cytoplasmic vesicle)', 'GO:0001669(acrosomal vesicle)|GO:0006836(neurotransmitter transport)|GO:0016020(membrane)|GO:0016021(integral to membrane)|GO:0022857(transmembrane transporter activity)|GO:0030054(cell junction)|GO:0030672(synaptic vesicle membrane)|GO:0031410(cytoplasmic vesicle)|GO:0045202(synapse)', 'GO:0000166(nucleotide binding)|GO:0003676(nucleic acid binding)', 'GO:0005515(protein binding)|GO:0005622(intracellular)|GO:0005624(membrane fraction)|GO:0006915(apoptosis)|GO:0006916(anti-apoptosis)|GO:0012505(endomembrane system)|GO:0016020(membrane)'], 'SEQUENCE': ['ATCTCATGGAAAAGCTGGATTCCTCTGCCTTACGCAGAAACACCCGGGCTCCATCTGCCA', 'TCAAGTATTGGCCTGACATAGAGTCCTTAAGACAAGCAAAGACAAGCAAGGCAAGCACGT', 'ATGTCGGCTGTGGAGGGTTAAAGGGATGAGGCTTTCCTTTGTTTAGCAAATCTGTTCACA', 'CCCTGTCAGATAAGTTTAATGTTTAGTTTGAGGCATGAAGAAGAAAAGGGTTTCCATTCT', 'GACCAGCCAGTTTACAAGCATGTCTCAAGCTAGTGTGTTCCATTATGCTCACAGCAGTAA']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eab7c3d6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50cce18a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:20.834296Z",
     "iopub.status.busy": "2025-03-25T08:36:20.834137Z",
     "iopub.status.idle": "2025-03-25T08:36:22.307235Z",
     "shell.execute_reply": "2025-03-25T08:36:22.306745Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data (after mapping):\n",
      "Shape: (18488, 122)\n",
      "First 5 gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2LD1', 'A2M']\n",
      "First 5 sample columns: ['GSM1946634', 'GSM1946635', 'GSM1946636', 'GSM1946637', 'GSM1946638']\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify which columns to use for mapping\n",
    "# From the preview, we can see that the 'ID' column contains the probe IDs (A_23_P...)\n",
    "# and 'GENE_SYMBOL' contains the gene symbols we want to map to\n",
    "probe_col = 'ID'  # Column with probe identifiers\n",
    "gene_col = 'GENE_SYMBOL'  # Column with gene symbols\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the probe and gene symbol columns\n",
    "mapping_df = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data\n",
    "# This will handle the many-to-many mapping between probes and genes\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print preview of the mapped gene expression data\n",
    "print(\"Gene expression data (after mapping):\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 5 gene symbols: {gene_data.index[:5].tolist()}\")\n",
    "print(f\"First 5 sample columns: {gene_data.columns[:5].tolist()}\")\n",
    "\n",
    "# Save the gene expression data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3edc0fc",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e4f0bc2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:36:22.308624Z",
     "iopub.status.busy": "2025-03-25T08:36:22.308488Z",
     "iopub.status.idle": "2025-03-25T08:36:28.666175Z",
     "shell.execute_reply": "2025-03-25T08:36:28.665735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data shape: (18247, 122)\n",
      "First few genes with their expression values after normalization:\n",
      "          GSM1946634  GSM1946635  GSM1946636  GSM1946637  GSM1946638  \\\n",
      "Gene                                                                   \n",
      "A1BG          0.7790      0.3736     -0.7298     -0.5658      0.7993   \n",
      "A1BG-AS1      1.4335      1.1881      0.9390      0.7998      1.9718   \n",
      "A1CF         -8.6711     -8.6980     -8.1731     -9.8930     -6.6285   \n",
      "A2M          -5.8238     -5.7392     -5.8264     -7.6944     -5.6462   \n",
      "A2ML1        -0.9369     -0.8575     -0.6521     -0.4720      2.5128   \n",
      "\n",
      "          GSM1946639  GSM1946640  GSM1946641  GSM1946642  GSM1946643  ...  \\\n",
      "Gene                                                                  ...   \n",
      "A1BG         -0.4768     -1.2291     -1.4813      0.8968      0.5161  ...   \n",
      "A1BG-AS1      1.2580      0.9186      0.8031      1.7652      1.5084  ...   \n",
      "A1CF         -3.9316    -10.1926    -10.9090     -7.0169     -7.6412  ...   \n",
      "A2M          -5.8414     -7.3668     -9.7768     -6.5750     -6.7933  ...   \n",
      "A2ML1         1.7026     -1.2058     -1.0400      3.4189      3.6057  ...   \n",
      "\n",
      "          GSM1946746  GSM1946747  GSM1946748  GSM1946749  GSM1946750  \\\n",
      "Gene                                                                   \n",
      "A1BG          0.9995      1.1157     -0.9717     -2.3244     -1.4169   \n",
      "A1BG-AS1      1.7237      1.3286      1.3644      0.5460      1.1454   \n",
      "A1CF         -8.8023     -8.5343     -8.8704     -7.6491     -9.6272   \n",
      "A2M          -6.0530     -6.1823     -6.1065     -7.3115     -7.3060   \n",
      "A2ML1        -1.1442     -0.9833      0.5798      0.0510     -0.9032   \n",
      "\n",
      "          GSM1946751  GSM1946752  GSM1946753  GSM1946754  GSM1946755  \n",
      "Gene                                                                  \n",
      "A1BG         -1.3968      1.0533      0.8462     -2.0529     -3.0817  \n",
      "A1BG-AS1      0.9398      1.6896      1.3775      0.5250      0.1934  \n",
      "A1CF        -10.4649     -6.7870     -8.6401     -8.9628    -11.2042  \n",
      "A2M          -8.2849     -5.4245     -6.3632     -5.4934     -8.9906  \n",
      "A2ML1        -1.1459      1.3318      1.9127     -1.6521     -0.8781  \n",
      "\n",
      "[5 rows x 122 columns]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Cystic_Fibrosis/gene_data/GSE67698.csv\n",
      "Clinical data preview:\n",
      "{'GSM1946634': [1.0], 'GSM1946635': [1.0], 'GSM1946636': [1.0], 'GSM1946637': [1.0], 'GSM1946638': [1.0], 'GSM1946639': [1.0], 'GSM1946640': [1.0], 'GSM1946641': [1.0], 'GSM1946642': [1.0], 'GSM1946643': [1.0], 'GSM1946644': [1.0], 'GSM1946645': [1.0], 'GSM1946646': [1.0], 'GSM1946647': [1.0], 'GSM1946648': [1.0], 'GSM1946649': [1.0], 'GSM1946650': [1.0], 'GSM1946651': [1.0], 'GSM1946652': [1.0], 'GSM1946653': [1.0], 'GSM1946654': [1.0], 'GSM1946655': [1.0], 'GSM1946656': [1.0], 'GSM1946657': [1.0], 'GSM1946658': [1.0], 'GSM1946659': [1.0], 'GSM1946660': [1.0], 'GSM1946661': [1.0], 'GSM1946662': [1.0], 'GSM1946663': [1.0], 'GSM1946664': [1.0], 'GSM1946665': [1.0], 'GSM1946666': [1.0], 'GSM1946667': [1.0], 'GSM1946668': [1.0], 'GSM1946669': [1.0], 'GSM1946670': [1.0], 'GSM1946671': [1.0], 'GSM1946672': [1.0], 'GSM1946673': [1.0], 'GSM1946674': [1.0], 'GSM1946675': [1.0], 'GSM1946676': [1.0], 'GSM1946677': [1.0], 'GSM1946678': [1.0], 'GSM1946679': [1.0], 'GSM1946680': [1.0], 'GSM1946681': [1.0], 'GSM1946682': [1.0], 'GSM1946683': [1.0], 'GSM1946684': [1.0], 'GSM1946685': [1.0], 'GSM1946686': [0.0], 'GSM1946687': [0.0], 'GSM1946688': [0.0], 'GSM1946689': [0.0], 'GSM1946690': [1.0], 'GSM1946691': [1.0], 'GSM1946692': [1.0], 'GSM1946693': [1.0], 'GSM1946694': [1.0], 'GSM1946695': [1.0], 'GSM1946696': [1.0], 'GSM1946697': [1.0], 'GSM1946698': [1.0], 'GSM1946699': [1.0], 'GSM1946700': [1.0], 'GSM1946701': [1.0], 'GSM1946702': [1.0], 'GSM1946703': [1.0], 'GSM1946704': [1.0], 'GSM1946705': [1.0], 'GSM1946706': [1.0], 'GSM1946707': [1.0], 'GSM1946708': [1.0], 'GSM1946709': [1.0], 'GSM1946710': [1.0], 'GSM1946711': [1.0], 'GSM1946712': [1.0], 'GSM1946713': [1.0], 'GSM1946714': [1.0], 'GSM1946715': [1.0], 'GSM1946716': [1.0], 'GSM1946717': [1.0], 'GSM1946718': [1.0], 'GSM1946719': [1.0], 'GSM1946720': [1.0], 'GSM1946721': [1.0], 'GSM1946722': [1.0], 'GSM1946723': [1.0], 'GSM1946724': [1.0], 'GSM1946725': [1.0], 'GSM1946726': [1.0], 'GSM1946727': [1.0], 'GSM1946728': [1.0], 'GSM1946729': [1.0], 'GSM1946730': [1.0], 'GSM1946731': [1.0], 'GSM1946732': [1.0], 'GSM1946733': [1.0], 'GSM1946734': [1.0], 'GSM1946735': [1.0], 'GSM1946736': [1.0], 'GSM1946737': [1.0], 'GSM1946738': [1.0], 'GSM1946739': [1.0], 'GSM1946740': [1.0], 'GSM1946741': [1.0], 'GSM1946742': [1.0], 'GSM1946743': [1.0], 'GSM1946744': [1.0], 'GSM1946745': [1.0], 'GSM1946746': [1.0], 'GSM1946747': [1.0], 'GSM1946748': [1.0], 'GSM1946749': [1.0], 'GSM1946750': [1.0], 'GSM1946751': [1.0], 'GSM1946752': [1.0], 'GSM1946753': [1.0], 'GSM1946754': [1.0], 'GSM1946755': [1.0]}\n",
      "Linked data shape (before handling missing values): (122, 18248)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data after handling missing values: (122, 18248)\n",
      "For the feature 'Cystic_Fibrosis', the least common label is '0.0' with 4 occurrences. This represents 3.28% of the dataset.\n",
      "Data was determined to be unusable and was not saved\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(\"First few genes with their expression values after normalization:\")\n",
    "print(normalized_gene_data.head())\n",
    "\n",
    "# Save the normalized gene data\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "normalized_gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "# Use the clinical data that was already processed and saved in previous steps\n",
    "selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are biased\n",
    "# First check if trait is biased\n",
    "trait_type = 'binary' if len(linked_data[trait].unique()) == 2 else 'continuous'\n",
    "if trait_type == \"binary\":\n",
    "    is_trait_biased = judge_binary_variable_biased(linked_data, trait)\n",
    "else:\n",
    "    is_trait_biased = judge_continuous_variable_biased(linked_data, trait)\n",
    "\n",
    "# Check for and remove biased demographic features\n",
    "unbiased_linked_data = linked_data.copy()\n",
    "if \"Age\" in unbiased_linked_data.columns:\n",
    "    age_biased = judge_continuous_variable_biased(unbiased_linked_data, 'Age')\n",
    "    if age_biased:\n",
    "        print(f\"The distribution of the feature \\'Age\\' in this dataset is severely biased.\\n\")\n",
    "        unbiased_linked_data = unbiased_linked_data.drop(columns='Age')\n",
    "    else:\n",
    "        print(f\"The distribution of the feature \\'Age\\' in this dataset is fine.\\n\")\n",
    "\n",
    "if \"Gender\" in unbiased_linked_data.columns:\n",
    "    gender_biased = judge_binary_variable_biased(unbiased_linked_data, 'Gender')\n",
    "    if gender_biased:\n",
    "        print(f\"The distribution of the feature \\'Gender\\' in this dataset is severely biased.\\n\")\n",
    "        unbiased_linked_data = unbiased_linked_data.drop(columns='Gender')\n",
    "    else:\n",
    "        print(f\"The distribution of the feature \\'Gender\\' in this dataset is fine.\\n\")\n",
    "\n",
    "# 5. Conduct final quality validation and save cohort information\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True, \n",
    "    cohort=cohort, \n",
    "    info_path=json_path, \n",
    "    is_gene_available=True, \n",
    "    is_trait_available=True, \n",
    "    is_biased=is_trait_biased, \n",
    "    df=unbiased_linked_data,\n",
    "    note=f\"Dataset contains gene expression data comparing CFBE41o-ΔF508 (CF) cells with CFBE41o−CFTR (control) cells.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Data was determined to be unusable and was not saved\")"
   ]
  }
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