{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8485cb4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:06.499146Z",
     "iopub.status.busy": "2025-03-25T08:32:06.498785Z",
     "iopub.status.idle": "2025-03-25T08:32:06.666657Z",
     "shell.execute_reply": "2025-03-25T08:32:06.666312Z"
    }
   },
   "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 = \"Crohns_Disease\"\n",
    "cohort = \"GSE123088\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE123088\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE123088.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE123088.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE123088.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97dd5573",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea92ea77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:06.668092Z",
     "iopub.status.busy": "2025-03-25T08:32:06.667946Z",
     "iopub.status.idle": "2025-03-25T08:32:06.944372Z",
     "shell.execute_reply": "2025-03-25T08:32:06.944010Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell type: CD4+ T cells'], 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], 4: [nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']}\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": "348fa83c",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c306f14c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:06.945685Z",
     "iopub.status.busy": "2025-03-25T08:32:06.945566Z",
     "iopub.status.idle": "2025-03-25T08:32:06.972002Z",
     "shell.execute_reply": "2025-03-25T08:32:06.971702Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical data:\n",
      "{'GSM3494884': [nan, 56.0, 1.0], 'GSM3494885': [nan, nan, nan], 'GSM3494886': [nan, 20.0, 0.0], 'GSM3494887': [nan, 51.0, 0.0], 'GSM3494888': [nan, 37.0, 1.0], 'GSM3494889': [nan, 61.0, 1.0], 'GSM3494890': [nan, nan, nan], 'GSM3494891': [nan, 31.0, 1.0], 'GSM3494892': [nan, 56.0, 0.0], 'GSM3494893': [nan, 41.0, 0.0], 'GSM3494894': [nan, 61.0, 0.0], 'GSM3494895': [nan, nan, nan], 'GSM3494896': [nan, 80.0, 1.0], 'GSM3494897': [nan, 53.0, 1.0], 'GSM3494898': [nan, 61.0, 1.0], 'GSM3494899': [nan, 73.0, 1.0], 'GSM3494900': [nan, 60.0, 1.0], 'GSM3494901': [nan, 76.0, 1.0], 'GSM3494902': [nan, 77.0, 0.0], 'GSM3494903': [nan, 74.0, 0.0], 'GSM3494904': [nan, 69.0, 1.0], 'GSM3494905': [nan, 77.0, 0.0], 'GSM3494906': [nan, 81.0, 0.0], 'GSM3494907': [nan, 70.0, 0.0], 'GSM3494908': [nan, 82.0, 0.0], 'GSM3494909': [nan, 69.0, 0.0], 'GSM3494910': [nan, 82.0, 0.0], 'GSM3494911': [nan, 67.0, 0.0], 'GSM3494912': [nan, 67.0, 0.0], 'GSM3494913': [nan, 78.0, 0.0], 'GSM3494914': [nan, 67.0, 0.0], 'GSM3494915': [nan, 74.0, 1.0], 'GSM3494916': [nan, nan, nan], 'GSM3494917': [nan, 51.0, 1.0], 'GSM3494918': [nan, 72.0, 1.0], 'GSM3494919': [nan, 66.0, 1.0], 'GSM3494920': [nan, 80.0, 0.0], 'GSM3494921': [1.0, 36.0, 1.0], 'GSM3494922': [1.0, 67.0, 0.0], 'GSM3494923': [1.0, 31.0, 0.0], 'GSM3494924': [1.0, 31.0, 0.0], 'GSM3494925': [1.0, 45.0, 0.0], 'GSM3494926': [1.0, 56.0, 0.0], 'GSM3494927': [1.0, 65.0, 0.0], 'GSM3494928': [1.0, 53.0, 0.0], 'GSM3494929': [1.0, 48.0, 0.0], 'GSM3494930': [1.0, 50.0, 0.0], 'GSM3494931': [1.0, 76.0, 1.0], 'GSM3494932': [nan, nan, nan], 'GSM3494933': [nan, 24.0, 0.0], 'GSM3494934': [nan, 42.0, 0.0], 'GSM3494935': [nan, 76.0, 1.0], 'GSM3494936': [nan, 22.0, 1.0], 'GSM3494937': [nan, nan, nan], 'GSM3494938': [nan, 23.0, 0.0], 'GSM3494939': [0.0, 34.0, 1.0], 'GSM3494940': [0.0, 43.0, 1.0], 'GSM3494941': [0.0, 47.0, 1.0], 'GSM3494942': [0.0, 24.0, 0.0], 'GSM3494943': [0.0, 55.0, 1.0], 'GSM3494944': [0.0, 48.0, 1.0], 'GSM3494945': [0.0, 58.0, 1.0], 'GSM3494946': [0.0, 30.0, 0.0], 'GSM3494947': [0.0, 28.0, 1.0], 'GSM3494948': [0.0, 41.0, 0.0], 'GSM3494949': [0.0, 63.0, 1.0], 'GSM3494950': [0.0, 55.0, 0.0], 'GSM3494951': [0.0, 55.0, 0.0], 'GSM3494952': [0.0, 67.0, 1.0], 'GSM3494953': [0.0, 47.0, 0.0], 'GSM3494954': [0.0, 46.0, 0.0], 'GSM3494955': [0.0, 49.0, 1.0], 'GSM3494956': [0.0, 23.0, 1.0], 'GSM3494957': [0.0, 68.0, 1.0], 'GSM3494958': [0.0, 39.0, 1.0], 'GSM3494959': [0.0, 24.0, 1.0], 'GSM3494960': [0.0, 36.0, 0.0], 'GSM3494961': [0.0, 58.0, 0.0], 'GSM3494962': [0.0, 38.0, 0.0], 'GSM3494963': [0.0, 27.0, 0.0], 'GSM3494964': [0.0, 67.0, 0.0], 'GSM3494965': [0.0, 61.0, 1.0], 'GSM3494966': [0.0, 69.0, 1.0], 'GSM3494967': [0.0, 63.0, 1.0], 'GSM3494968': [0.0, 60.0, 0.0], 'GSM3494969': [0.0, 17.0, 1.0], 'GSM3494970': [0.0, 10.0, 0.0], 'GSM3494971': [0.0, 9.0, 1.0], 'GSM3494972': [0.0, 13.0, 0.0], 'GSM3494973': [0.0, 10.0, 1.0], 'GSM3494974': [0.0, 13.0, 0.0], 'GSM3494975': [0.0, 15.0, 1.0], 'GSM3494976': [0.0, 12.0, 1.0], 'GSM3494977': [0.0, 13.0, 1.0], 'GSM3494978': [nan, 81.0, 0.0], 'GSM3494979': [nan, 94.0, 0.0], 'GSM3494980': [nan, 51.0, 1.0], 'GSM3494981': [nan, 40.0, 1.0], 'GSM3494982': [nan, nan, nan], 'GSM3494983': [nan, 97.0, 1.0], 'GSM3494984': [nan, 23.0, 1.0], 'GSM3494985': [nan, 93.0, 0.0], 'GSM3494986': [nan, 58.0, 1.0], 'GSM3494987': [nan, 28.0, 0.0], 'GSM3494988': [nan, 54.0, 1.0], 'GSM3494989': [nan, 15.0, 1.0], 'GSM3494990': [nan, 8.0, 1.0], 'GSM3494991': [nan, 11.0, 1.0], 'GSM3494992': [nan, 12.0, 1.0], 'GSM3494993': [nan, 8.0, 0.0], 'GSM3494994': [nan, 14.0, 1.0], 'GSM3494995': [nan, 8.0, 0.0], 'GSM3494996': [nan, 10.0, 1.0], 'GSM3494997': [nan, 14.0, 1.0], 'GSM3494998': [nan, 13.0, 1.0], 'GSM3494999': [nan, 40.0, 0.0], 'GSM3495000': [nan, 52.0, 0.0], 'GSM3495001': [nan, 42.0, 0.0], 'GSM3495002': [nan, 29.0, 0.0], 'GSM3495003': [nan, 43.0, 0.0], 'GSM3495004': [nan, 41.0, 0.0], 'GSM3495005': [nan, 54.0, 1.0], 'GSM3495006': [nan, 42.0, 1.0], 'GSM3495007': [nan, 49.0, 1.0], 'GSM3495008': [nan, 45.0, 0.0], 'GSM3495009': [nan, 56.0, 1.0], 'GSM3495010': [nan, 64.0, 0.0], 'GSM3495011': [nan, 71.0, 0.0], 'GSM3495012': [nan, 48.0, 0.0], 'GSM3495013': [nan, 20.0, 1.0], 'GSM3495014': [nan, 53.0, 0.0], 'GSM3495015': [nan, 32.0, 0.0], 'GSM3495016': [nan, 26.0, 0.0], 'GSM3495017': [nan, 28.0, 0.0], 'GSM3495018': [nan, 47.0, 0.0], 'GSM3495019': [nan, 24.0, 0.0], 'GSM3495020': [nan, 48.0, 0.0], 'GSM3495021': [nan, nan, nan], 'GSM3495022': [nan, 19.0, 0.0], 'GSM3495023': [nan, 41.0, 0.0], 'GSM3495024': [nan, 38.0, 0.0], 'GSM3495025': [nan, nan, nan], 'GSM3495026': [nan, 15.0, 0.0], 'GSM3495027': [nan, 12.0, 1.0], 'GSM3495028': [nan, 13.0, 0.0], 'GSM3495029': [nan, nan, nan], 'GSM3495030': [nan, 11.0, 1.0], 'GSM3495031': [nan, nan, nan], 'GSM3495032': [nan, 16.0, 1.0], 'GSM3495033': [nan, 11.0, 1.0], 'GSM3495034': [nan, nan, nan], 'GSM3495035': [nan, 35.0, 0.0], 'GSM3495036': [nan, 26.0, 0.0], 'GSM3495037': [nan, 39.0, 0.0], 'GSM3495038': [nan, 46.0, 0.0], 'GSM3495039': [nan, 42.0, 0.0], 'GSM3495040': [nan, 20.0, 1.0], 'GSM3495041': [nan, 69.0, 1.0], 'GSM3495042': [nan, 69.0, 0.0], 'GSM3495043': [nan, 47.0, 1.0], 'GSM3495044': [nan, 47.0, 1.0], 'GSM3495045': [nan, 56.0, 0.0], 'GSM3495046': [nan, 54.0, 0.0], 'GSM3495047': [nan, 53.0, 0.0], 'GSM3495048': [nan, 50.0, 0.0], 'GSM3495049': [nan, 22.0, 1.0], 'GSM3495050': [nan, 62.0, 0.0], 'GSM3495051': [nan, 74.0, 0.0], 'GSM3495052': [0.0, 57.0, 0.0], 'GSM3495053': [0.0, 47.0, 0.0], 'GSM3495054': [nan, 70.0, 0.0], 'GSM3495055': [nan, 50.0, 0.0], 'GSM3495056': [0.0, 52.0, 0.0], 'GSM3495057': [nan, 43.0, 0.0], 'GSM3495058': [0.0, 57.0, 0.0], 'GSM3495059': [nan, 53.0, 0.0], 'GSM3495060': [nan, 70.0, 0.0], 'GSM3495061': [0.0, 41.0, 0.0], 'GSM3495062': [nan, 61.0, 0.0], 'GSM3495063': [0.0, 39.0, 0.0], 'GSM3495064': [0.0, 58.0, 0.0], 'GSM3495065': [nan, 55.0, 0.0], 'GSM3495066': [nan, 63.0, 0.0], 'GSM3495067': [0.0, 60.0, 0.0], 'GSM3495068': [nan, 43.0, 0.0], 'GSM3495069': [nan, 68.0, 0.0], 'GSM3495070': [nan, 67.0, 0.0], 'GSM3495071': [nan, 50.0, 0.0], 'GSM3495072': [nan, 67.0, 0.0], 'GSM3495073': [0.0, 51.0, 0.0], 'GSM3495074': [0.0, 59.0, 0.0], 'GSM3495075': [0.0, 44.0, 0.0], 'GSM3495076': [nan, 35.0, 0.0], 'GSM3495077': [nan, 83.0, 0.0], 'GSM3495078': [nan, 78.0, 0.0], 'GSM3495079': [nan, 88.0, 0.0], 'GSM3495080': [nan, 41.0, 0.0], 'GSM3495081': [0.0, 60.0, 0.0], 'GSM3495082': [nan, 72.0, 0.0], 'GSM3495083': [nan, 53.0, 0.0]}\n",
      "Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE123088.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset likely contains gene expression data, so:\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Trait: Locate Crohn's Disease data in sample characteristics dictionary\n",
    "trait_row = 1  # Primary diagnosis is in row 1, which includes \"CROHN_DISEASE\"\n",
    "\n",
    "# Age: Age information is available in row 3 and 4\n",
    "age_row = 3  # Using row 3 as the main source for age data\n",
    "\n",
    "# Gender: Gender information is in row 2 and 3 (labeled as \"Sex\")\n",
    "gender_row = 2  # Using row 2 as the main source for gender data\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to binary (1 for Crohn's Disease, 0 for Control)\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        value = value.strip().lower()\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        if 'crohn' in value or 'crohn_disease' in value:\n",
    "            return 1\n",
    "        elif 'control' in value or 'healthy_control' in value:\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous numeric values\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        value = value.strip()\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        try:\n",
    "            return float(value)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (0 for Female, 1 for Male)\"\"\"\n",
    "    if isinstance(value, str):\n",
    "        value = value.strip().lower()\n",
    "        if ':' in value:\n",
    "            value = value.split(':', 1)[1].strip()\n",
    "        \n",
    "        if 'female' in value:\n",
    "            return 0\n",
    "        elif 'male' in value:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Initial filtering on usability\n",
    "is_trait_available = trait_row is not None\n",
    "initial_validation = 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. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features\n",
    "    clinical_selected = geo_select_clinical_features(\n",
    "        clinical_df=clinical_data,\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 extracted clinical data\n",
    "    preview = preview_df(clinical_selected)\n",
    "    print(\"Preview of clinical data:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save clinical data to CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_selected.to_csv(out_clinical_data_file, index=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "560141d1",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "82351f47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:06.973196Z",
     "iopub.status.busy": "2025-03-25T08:32:06.973090Z",
     "iopub.status.idle": "2025-03-25T08:32:07.491225Z",
     "shell.execute_reply": "2025-03-25T08:32:07.490840Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19',\n",
      "       '20', '21', '22', '23', '24', '25', '26', '27'],\n",
      "      dtype='object', name='ID')\n",
      "\n",
      "Gene data dimensions: 24166 genes × 204 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract the gene expression data from the matrix file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers)\n",
    "print(\"\\nFirst 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n",
    "\n",
    "# 4. Print the dimensions of the gene expression data\n",
    "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n",
    "\n",
    "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b66390f1",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3630afeb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:07.492620Z",
     "iopub.status.busy": "2025-03-25T08:32:07.492499Z",
     "iopub.status.idle": "2025-03-25T08:32:07.494449Z",
     "shell.execute_reply": "2025-03-25T08:32:07.494154Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers\n",
    "# These appear to be integer/numeric identifiers rather than standard HGNC gene symbols\n",
    "# Human gene symbols are typically alphanumeric (like \"TP53\", \"BRCA1\", \"IL6\")\n",
    "# The identifiers shown are simple numbers which likely need mapping to gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc89361",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "40247968",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:07.495710Z",
     "iopub.status.busy": "2025-03-25T08:32:07.495601Z",
     "iopub.status.idle": "2025-03-25T08:32:11.926403Z",
     "shell.execute_reply": "2025-03-25T08:32:11.925945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation dataframe column names:\n",
      "Index(['ID', 'ENTREZ_GENE_ID', 'SPOT_ID'], dtype='object')\n",
      "\n",
      "Preview of gene annotation data:\n",
      "{'ID': ['1', '2', '3'], 'ENTREZ_GENE_ID': ['1', '2', '3'], 'SPOT_ID': [1.0, 2.0, 3.0]}\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the file paths using geo_get_relevant_filepaths function\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Extract gene annotation data from the SOFT file\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 3. Preview the gene annotation dataframe\n",
    "print(\"Gene annotation dataframe column names:\")\n",
    "print(gene_annotation.columns)\n",
    "\n",
    "# Preview the first few rows to understand the data structure\n",
    "print(\"\\nPreview of gene annotation data:\")\n",
    "annotation_preview = preview_df(gene_annotation, n=3)\n",
    "print(annotation_preview)\n",
    "\n",
    "# Maintain gene availability status as True based on previous steps\n",
    "is_gene_available = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13a5c0e2",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "301c359b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:11.927890Z",
     "iopub.status.busy": "2025-03-25T08:32:11.927767Z",
     "iopub.status.idle": "2025-03-25T08:32:20.401799Z",
     "shell.execute_reply": "2025-03-25T08:32:20.401238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of probes with mappings: 4740924\n",
      "Sample of mapping data:\n",
      "   ID Gene\n",
      "0   1    1\n",
      "1   2    2\n",
      "2   3    3\n",
      "3   9    9\n",
      "4  10   10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "After mapping, gene data dimensions: (0, 204)\n",
      "First 10 mapped gene symbols (if any):\n",
      "No genes were successfully mapped. This might indicate an issue with the gene annotation format.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using original probe data: 24166 probes × 204 samples\n"
     ]
    }
   ],
   "source": [
    "# 1. Observe the gene identifiers in both datasets\n",
    "# From previous outputs:\n",
    "# - Gene expression data: identifiers are numeric strings like '1', '2', '3'\n",
    "# - Gene annotation data: has columns 'ID', 'ENTREZ_GENE_ID', 'SPOT_ID'\n",
    "\n",
    "# The 'ID' column in gene annotation matches the identifiers in gene expression data\n",
    "# The 'ENTREZ_GENE_ID' column appears to contain Entrez Gene IDs which we need to map to gene symbols\n",
    "\n",
    "# 2. Create gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='ENTREZ_GENE_ID')\n",
    "\n",
    "# Print information about the mapping\n",
    "print(f\"Number of probes with mappings: {len(mapping_df)}\")\n",
    "print(f\"Sample of mapping data:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the mapped gene data\n",
    "print(\"\\nAfter mapping, gene data dimensions:\", gene_data.shape)\n",
    "print(\"First 10 mapped gene symbols (if any):\")\n",
    "if len(gene_data) > 0:\n",
    "    print(gene_data.index[:10])\n",
    "else:\n",
    "    print(\"No genes were successfully mapped. This might indicate an issue with the gene annotation format.\")\n",
    "    \n",
    "    # Save original probe data as gene data for downstream processes\n",
    "    # This is a fallback to allow the pipeline to continue\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    gene_data.index.name = 'Gene'\n",
    "    print(f\"\\nUsing original probe data: {gene_data.shape[0]} probes × {gene_data.shape[1]} samples\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b205378",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc757bc5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:20.403429Z",
     "iopub.status.busy": "2025-03-25T08:32:20.403303Z",
     "iopub.status.idle": "2025-03-25T08:32:21.008844Z",
     "shell.execute_reply": "2025-03-25T08:32:21.008320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original probe data dimensions: (24166, 204)\n",
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: 0 genes × 204 samples\n",
      "Gene expression data saved to ../../output/preprocess/Crohns_Disease/gene_data/GSE123088.csv\n",
      "Loading clinical features...\n",
      "Loading clinical features from ../../output/preprocess/Crohns_Disease/clinical_data/GSE123088.csv\n",
      "Clinical data preview:\n",
      "{'GSM3494884': [nan, 56.0, 1.0], 'GSM3494885': [nan, nan, nan], 'GSM3494886': [nan, 20.0, 0.0], 'GSM3494887': [nan, 51.0, 0.0], 'GSM3494888': [nan, 37.0, 1.0], 'GSM3494889': [nan, 61.0, 1.0], 'GSM3494890': [nan, nan, nan], 'GSM3494891': [nan, 31.0, 1.0], 'GSM3494892': [nan, 56.0, 0.0], 'GSM3494893': [nan, 41.0, 0.0], 'GSM3494894': [nan, 61.0, 0.0], 'GSM3494895': [nan, nan, nan], 'GSM3494896': [nan, 80.0, 1.0], 'GSM3494897': [nan, 53.0, 1.0], 'GSM3494898': [nan, 61.0, 1.0], 'GSM3494899': [nan, 73.0, 1.0], 'GSM3494900': [nan, 60.0, 1.0], 'GSM3494901': [nan, 76.0, 1.0], 'GSM3494902': [nan, 77.0, 0.0], 'GSM3494903': [nan, 74.0, 0.0], 'GSM3494904': [nan, 69.0, 1.0], 'GSM3494905': [nan, 77.0, 0.0], 'GSM3494906': [nan, 81.0, 0.0], 'GSM3494907': [nan, 70.0, 0.0], 'GSM3494908': [nan, 82.0, 0.0], 'GSM3494909': [nan, 69.0, 0.0], 'GSM3494910': [nan, 82.0, 0.0], 'GSM3494911': [nan, 67.0, 0.0], 'GSM3494912': [nan, 67.0, 0.0], 'GSM3494913': [nan, 78.0, 0.0], 'GSM3494914': [nan, 67.0, 0.0], 'GSM3494915': [nan, 74.0, 1.0], 'GSM3494916': [nan, nan, nan], 'GSM3494917': [nan, 51.0, 1.0], 'GSM3494918': [nan, 72.0, 1.0], 'GSM3494919': [nan, 66.0, 1.0], 'GSM3494920': [nan, 80.0, 0.0], 'GSM3494921': [1.0, 36.0, 1.0], 'GSM3494922': [1.0, 67.0, 0.0], 'GSM3494923': [1.0, 31.0, 0.0], 'GSM3494924': [1.0, 31.0, 0.0], 'GSM3494925': [1.0, 45.0, 0.0], 'GSM3494926': [1.0, 56.0, 0.0], 'GSM3494927': [1.0, 65.0, 0.0], 'GSM3494928': [1.0, 53.0, 0.0], 'GSM3494929': [1.0, 48.0, 0.0], 'GSM3494930': [1.0, 50.0, 0.0], 'GSM3494931': [1.0, 76.0, 1.0], 'GSM3494932': [nan, nan, nan], 'GSM3494933': [nan, 24.0, 0.0], 'GSM3494934': [nan, 42.0, 0.0], 'GSM3494935': [nan, 76.0, 1.0], 'GSM3494936': [nan, 22.0, 1.0], 'GSM3494937': [nan, nan, nan], 'GSM3494938': [nan, 23.0, 0.0], 'GSM3494939': [0.0, 34.0, 1.0], 'GSM3494940': [0.0, 43.0, 1.0], 'GSM3494941': [0.0, 47.0, 1.0], 'GSM3494942': [0.0, 24.0, 0.0], 'GSM3494943': [0.0, 55.0, 1.0], 'GSM3494944': [0.0, 48.0, 1.0], 'GSM3494945': [0.0, 58.0, 1.0], 'GSM3494946': [0.0, 30.0, 0.0], 'GSM3494947': [0.0, 28.0, 1.0], 'GSM3494948': [0.0, 41.0, 0.0], 'GSM3494949': [0.0, 63.0, 1.0], 'GSM3494950': [0.0, 55.0, 0.0], 'GSM3494951': [0.0, 55.0, 0.0], 'GSM3494952': [0.0, 67.0, 1.0], 'GSM3494953': [0.0, 47.0, 0.0], 'GSM3494954': [0.0, 46.0, 0.0], 'GSM3494955': [0.0, 49.0, 1.0], 'GSM3494956': [0.0, 23.0, 1.0], 'GSM3494957': [0.0, 68.0, 1.0], 'GSM3494958': [0.0, 39.0, 1.0], 'GSM3494959': [0.0, 24.0, 1.0], 'GSM3494960': [0.0, 36.0, 0.0], 'GSM3494961': [0.0, 58.0, 0.0], 'GSM3494962': [0.0, 38.0, 0.0], 'GSM3494963': [0.0, 27.0, 0.0], 'GSM3494964': [0.0, 67.0, 0.0], 'GSM3494965': [0.0, 61.0, 1.0], 'GSM3494966': [0.0, 69.0, 1.0], 'GSM3494967': [0.0, 63.0, 1.0], 'GSM3494968': [0.0, 60.0, 0.0], 'GSM3494969': [0.0, 17.0, 1.0], 'GSM3494970': [0.0, 10.0, 0.0], 'GSM3494971': [0.0, 9.0, 1.0], 'GSM3494972': [0.0, 13.0, 0.0], 'GSM3494973': [0.0, 10.0, 1.0], 'GSM3494974': [0.0, 13.0, 0.0], 'GSM3494975': [0.0, 15.0, 1.0], 'GSM3494976': [0.0, 12.0, 1.0], 'GSM3494977': [0.0, 13.0, 1.0], 'GSM3494978': [nan, 81.0, 0.0], 'GSM3494979': [nan, 94.0, 0.0], 'GSM3494980': [nan, 51.0, 1.0], 'GSM3494981': [nan, 40.0, 1.0], 'GSM3494982': [nan, nan, nan], 'GSM3494983': [nan, 97.0, 1.0], 'GSM3494984': [nan, 23.0, 1.0], 'GSM3494985': [nan, 93.0, 0.0], 'GSM3494986': [nan, 58.0, 1.0], 'GSM3494987': [nan, 28.0, 0.0], 'GSM3494988': [nan, 54.0, 1.0], 'GSM3494989': [nan, 15.0, 1.0], 'GSM3494990': [nan, 8.0, 1.0], 'GSM3494991': [nan, 11.0, 1.0], 'GSM3494992': [nan, 12.0, 1.0], 'GSM3494993': [nan, 8.0, 0.0], 'GSM3494994': [nan, 14.0, 1.0], 'GSM3494995': [nan, 8.0, 0.0], 'GSM3494996': [nan, 10.0, 1.0], 'GSM3494997': [nan, 14.0, 1.0], 'GSM3494998': [nan, 13.0, 1.0], 'GSM3494999': [nan, 40.0, 0.0], 'GSM3495000': [nan, 52.0, 0.0], 'GSM3495001': [nan, 42.0, 0.0], 'GSM3495002': [nan, 29.0, 0.0], 'GSM3495003': [nan, 43.0, 0.0], 'GSM3495004': [nan, 41.0, 0.0], 'GSM3495005': [nan, 54.0, 1.0], 'GSM3495006': [nan, 42.0, 1.0], 'GSM3495007': [nan, 49.0, 1.0], 'GSM3495008': [nan, 45.0, 0.0], 'GSM3495009': [nan, 56.0, 1.0], 'GSM3495010': [nan, 64.0, 0.0], 'GSM3495011': [nan, 71.0, 0.0], 'GSM3495012': [nan, 48.0, 0.0], 'GSM3495013': [nan, 20.0, 1.0], 'GSM3495014': [nan, 53.0, 0.0], 'GSM3495015': [nan, 32.0, 0.0], 'GSM3495016': [nan, 26.0, 0.0], 'GSM3495017': [nan, 28.0, 0.0], 'GSM3495018': [nan, 47.0, 0.0], 'GSM3495019': [nan, 24.0, 0.0], 'GSM3495020': [nan, 48.0, 0.0], 'GSM3495021': [nan, nan, nan], 'GSM3495022': [nan, 19.0, 0.0], 'GSM3495023': [nan, 41.0, 0.0], 'GSM3495024': [nan, 38.0, 0.0], 'GSM3495025': [nan, nan, nan], 'GSM3495026': [nan, 15.0, 0.0], 'GSM3495027': [nan, 12.0, 1.0], 'GSM3495028': [nan, 13.0, 0.0], 'GSM3495029': [nan, nan, nan], 'GSM3495030': [nan, 11.0, 1.0], 'GSM3495031': [nan, nan, nan], 'GSM3495032': [nan, 16.0, 1.0], 'GSM3495033': [nan, 11.0, 1.0], 'GSM3495034': [nan, nan, nan], 'GSM3495035': [nan, 35.0, 0.0], 'GSM3495036': [nan, 26.0, 0.0], 'GSM3495037': [nan, 39.0, 0.0], 'GSM3495038': [nan, 46.0, 0.0], 'GSM3495039': [nan, 42.0, 0.0], 'GSM3495040': [nan, 20.0, 1.0], 'GSM3495041': [nan, 69.0, 1.0], 'GSM3495042': [nan, 69.0, 0.0], 'GSM3495043': [nan, 47.0, 1.0], 'GSM3495044': [nan, 47.0, 1.0], 'GSM3495045': [nan, 56.0, 0.0], 'GSM3495046': [nan, 54.0, 0.0], 'GSM3495047': [nan, 53.0, 0.0], 'GSM3495048': [nan, 50.0, 0.0], 'GSM3495049': [nan, 22.0, 1.0], 'GSM3495050': [nan, 62.0, 0.0], 'GSM3495051': [nan, 74.0, 0.0], 'GSM3495052': [0.0, 57.0, 0.0], 'GSM3495053': [0.0, 47.0, 0.0], 'GSM3495054': [nan, 70.0, 0.0], 'GSM3495055': [nan, 50.0, 0.0], 'GSM3495056': [0.0, 52.0, 0.0], 'GSM3495057': [nan, 43.0, 0.0], 'GSM3495058': [0.0, 57.0, 0.0], 'GSM3495059': [nan, 53.0, 0.0], 'GSM3495060': [nan, 70.0, 0.0], 'GSM3495061': [0.0, 41.0, 0.0], 'GSM3495062': [nan, 61.0, 0.0], 'GSM3495063': [0.0, 39.0, 0.0], 'GSM3495064': [0.0, 58.0, 0.0], 'GSM3495065': [nan, 55.0, 0.0], 'GSM3495066': [nan, 63.0, 0.0], 'GSM3495067': [0.0, 60.0, 0.0], 'GSM3495068': [nan, 43.0, 0.0], 'GSM3495069': [nan, 68.0, 0.0], 'GSM3495070': [nan, 67.0, 0.0], 'GSM3495071': [nan, 50.0, 0.0], 'GSM3495072': [nan, 67.0, 0.0], 'GSM3495073': [0.0, 51.0, 0.0], 'GSM3495074': [0.0, 59.0, 0.0], 'GSM3495075': [0.0, 44.0, 0.0], 'GSM3495076': [nan, 35.0, 0.0], 'GSM3495077': [nan, 83.0, 0.0], 'GSM3495078': [nan, 78.0, 0.0], 'GSM3495079': [nan, 88.0, 0.0], 'GSM3495080': [nan, 41.0, 0.0], 'GSM3495081': [0.0, 60.0, 0.0], 'GSM3495082': [nan, 72.0, 0.0], 'GSM3495083': [nan, 53.0, 0.0]}\n",
      "Clinical data shape: (3, 204)\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (204, 3)\n",
      "Error: Linked data is missing trait data or gene expression data.\n",
      "Abnormality detected in the cohort: GSE123088. Preprocessing failed.\n",
      "Dataset deemed not usable due to incomplete data.\n"
     ]
    }
   ],
   "source": [
    "# 1. Make sure we have the original gene_data before any mapping attempts\n",
    "original_gene_data = get_genetic_data(matrix_file)\n",
    "print(f\"Original probe data dimensions: {original_gene_data.shape}\")\n",
    "\n",
    "# Handle the case where gene data is empty after mapping\n",
    "if gene_data.shape[0] == 0:\n",
    "    print(\"Warning: No genes were successfully mapped to symbols. Using probe IDs as features instead.\")\n",
    "    # Use the original probe-level data instead of the empty mapped data\n",
    "    gene_data_normalized = original_gene_data\n",
    "    print(f\"Using original probe data as features: {gene_data_normalized.shape[0]} probes × {gene_data_normalized.shape[1]} samples\")\n",
    "else:\n",
    "    # If we have mapped gene data, proceed with normalization\n",
    "    print(\"Normalizing gene symbols...\")\n",
    "    gene_data_normalized = normalize_gene_symbols_in_index(gene_data)\n",
    "    print(f\"Gene data shape after normalization: {gene_data_normalized.shape[0]} genes × {gene_data_normalized.shape[1]} samples\")\n",
    "\n",
    "# Save the gene data (normalized or probe-based)\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "gene_data_normalized.to_csv(out_gene_data_file)\n",
    "print(f\"Gene expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Load clinical data\n",
    "print(\"Loading clinical features...\")\n",
    "# Check if the clinical data file exists\n",
    "if os.path.exists(out_clinical_data_file):\n",
    "    print(f\"Loading clinical features from {out_clinical_data_file}\")\n",
    "    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "else:\n",
    "    # Extract clinical features from the original source\n",
    "    print(\"Extracting clinical features from the original source...\")\n",
    "    # Get background information and clinical data\n",
    "    background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "    clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "    background_info, clinical_raw = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "    \n",
    "    # Extract clinical features\n",
    "    clinical_data = geo_select_clinical_features(\n",
    "        clinical_df=clinical_raw,\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",
    "    # Save the extracted clinical features\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_data.to_csv(out_clinical_data_file)\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_data))\n",
    "print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "\n",
    "# Link clinical and genetic data\n",
    "print(\"Linking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data_normalized)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# Verify we have both trait and gene data in the linked dataset\n",
    "has_trait_data = trait in linked_data.columns\n",
    "has_genetic_data = len(linked_data.columns) > 3  # More than just trait, age, and gender columns\n",
    "\n",
    "if not has_trait_data or not has_genetic_data:\n",
    "    print(\"Error: Linked data is missing trait data or gene expression data.\")\n",
    "    is_usable = validate_and_save_cohort_info(\n",
    "        is_final=True,\n",
    "        cohort=cohort,\n",
    "        info_path=json_path,\n",
    "        is_gene_available=has_genetic_data,\n",
    "        is_trait_available=has_trait_data,\n",
    "        is_biased=True,\n",
    "        df=linked_data,\n",
    "        note=\"Failed to properly link gene expression data with clinical features.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to incomplete data.\")\n",
    "else:\n",
    "    # Handle missing values\n",
    "    print(\"Handling missing values...\")\n",
    "    linked_data_clean = handle_missing_values(linked_data, trait_col=trait)\n",
    "    print(f\"Data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "    \n",
    "    # If we have samples after missing value handling\n",
    "    if linked_data_clean.shape[0] > 0:\n",
    "        # Check for bias in features\n",
    "        print(\"\\nChecking for bias in feature variables:\")\n",
    "        is_biased, linked_data_final = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "        \n",
    "        # Final quality validation\n",
    "        note = \"Dataset contains gene expression data (using probe IDs) for Crohn's Disease patients.\"\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_biased,\n",
    "            df=linked_data_final,\n",
    "            note=note\n",
    "        )\n",
    "        \n",
    "        # Save linked data if usable\n",
    "        if is_usable:\n",
    "            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "            linked_data_final.to_csv(out_data_file)\n",
    "            print(f\"Linked data saved to {out_data_file}\")\n",
    "            print(f\"Final dataset shape: {linked_data_final.shape}\")\n",
    "        else:\n",
    "            print(\"Dataset deemed not usable for trait association studies, linked data not saved.\")\n",
    "    else:\n",
    "        print(\"Error: No samples remain after handling missing values.\")\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=True,\n",
    "            df=pd.DataFrame(),\n",
    "            note=\"All samples were removed during missing value handling.\"\n",
    "        )\n",
    "        print(\"Dataset deemed not usable as all samples were filtered out.\")"
   ]
  }
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