{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d925014",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:53.915251Z",
     "iopub.status.busy": "2025-03-25T08:31:53.914941Z",
     "iopub.status.idle": "2025-03-25T08:31:54.083683Z",
     "shell.execute_reply": "2025-03-25T08:31:54.083335Z"
    }
   },
   "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 = \"GSE123086\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Crohns_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Crohns_Disease/GSE123086\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Crohns_Disease/GSE123086.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Crohns_Disease/gene_data/GSE123086.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Crohns_Disease/clinical_data/GSE123086.csv\"\n",
    "json_path = \"../../output/preprocess/Crohns_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b178ae1b",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da850c1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:54.085116Z",
     "iopub.status.busy": "2025-03-25T08:31:54.084957Z",
     "iopub.status.idle": "2025-03-25T08:31:54.313403Z",
     "shell.execute_reply": "2025-03-25T08:31:54.312963Z"
    }
   },
   "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 [study of 13 diseases]\"\n",
      "!Series_summary\t\"We conducted prospective clinical studies to validate the importance of CD4+ T cells in 13 diseases from the following ICD-10-CM chapters: Neoplasms (breast cancer, chronic lymphocytic leukemia); endocrine, nutritional and metabolic diseases (type I diabetes, obesity); diseases of the circulatory system (atherosclerosis); diseases of the respiratory system (acute tonsillitis, influenza, seasonal allergic rhinitis, asthma); diseases of the digestive system (Crohn’s disease [CD], ulcerative colitis [UC]); and diseases of the skin and subcutaneous tissue (atopic eczema, psoriatic diseases).\"\n",
      "!Series_summary\t\"Study participants were recruited by clinical specialists based on diagnostic criteria defined by organizations representing each specialist’s discipline. Age and gender matched healthy controls (n = 127 and 39, respectively) were recruited in the Southeast region of Sweden from outpatient clinics at the University Hospital, Linköping; Ryhov County Hospital, Jönköping, a primary health care center in Jönköping; and a medical specialist unit for children in Värnamo. Study participants represented both urban and rural populations with an age range of 8–94 years. Patients with type I diabetes and obesity had an age range of 8–18 years. 12 patients had more than one diagnosis.\"\n",
      "!Series_overall_design\t\"Total RNA was extracted using the AllPrep DNA/RNA Micro kit (Qiagen, Hilden, Germany; cat. no. 80284) according to the manufacturer’s instructions. RNA concentration and integrity were evaluated using the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, California, USA; cat. no. 5067-1511) on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, California, USA). Microarrays were then further computationally processed as described in One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labeling protocol (Agilent Technologies, Santa Clara, California, USA).\"\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'], 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": "e5154371",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7bb312b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:54.314843Z",
     "iopub.status.busy": "2025-03-25T08:31:54.314732Z",
     "iopub.status.idle": "2025-03-25T08:31:54.337797Z",
     "shell.execute_reply": "2025-03-25T08:31:54.337511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\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]}\n",
      "Clinical data saved to ../../output/preprocess/Crohns_Disease/clinical_data/GSE123086.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the Series_overall_design, this dataset contains microarray data from CD4+ T cells\n",
    "# which would provide gene expression data, not just miRNA or methylation\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait - Crohn's disease appears in row 1 under \"primary diagnosis\"\n",
    "trait_row = 1\n",
    "\n",
    "# For gender - appears in rows 2 and 3, but row 2 seems to be more complete\n",
    "gender_row = 2 \n",
    "\n",
    "# For age - appears in rows 3 and 4\n",
    "age_row = 3\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Matching trait (Crohn's Disease)\n",
    "    if \"CROHN\" in value.upper():\n",
    "        return 1\n",
    "    # Healthy controls should be 0\n",
    "    elif \"HEALTHY\" in value.upper() or \"CONTROL\" in value.upper():\n",
    "        return 0\n",
    "    # Other diseases are not relevant for our binary classification\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Female is 0, Male is 1\n",
    "    if value.upper() == \"FEMALE\":\n",
    "        return 0\n",
    "    elif value.upper() == \"MALE\":\n",
    "        return 1\n",
    "    # If it's a diagnosis2 field, return None as it's not gender data\n",
    "    elif \"DIAGNOSIS2\" in value.upper():\n",
    "        return None\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to float for continuous age\n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering using validate_and_save_cohort_info\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. Clinical Feature Extraction\n",
    "# Since trait_row is not None, we proceed with clinical feature extraction\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the geo_select_clinical_features function\n",
    "    selected_clinical_df = 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 features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1038144",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "608edba3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:54.339030Z",
     "iopub.status.busy": "2025-03-25T08:31:54.338825Z",
     "iopub.status.idle": "2025-03-25T08:31:54.748049Z",
     "shell.execute_reply": "2025-03-25T08:31:54.747652Z"
    }
   },
   "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: 22881 genes × 166 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": "68513421",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b17de00e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:54.749415Z",
     "iopub.status.busy": "2025-03-25T08:31:54.749292Z",
     "iopub.status.idle": "2025-03-25T08:31:54.751231Z",
     "shell.execute_reply": "2025-03-25T08:31:54.750951Z"
    }
   },
   "outputs": [],
   "source": [
    "# Examining the gene identifiers\n",
    "# The identifiers appear to be numerical values (1, 2, 3, etc.)\n",
    "# These are not standard human gene symbols, which are typically alphanumeric \n",
    "# (like BRCA1, TP53, etc.)\n",
    "# These appear to be probe IDs or some other form of identifiers that would\n",
    "# need to be mapped to standard gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d4e6390",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b49e7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:54.752424Z",
     "iopub.status.busy": "2025-03-25T08:31:54.752318Z",
     "iopub.status.idle": "2025-03-25T08:31:58.331994Z",
     "shell.execute_reply": "2025-03-25T08:31:58.331624Z"
    }
   },
   "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": "88933adc",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79475a3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:31:58.333245Z",
     "iopub.status.busy": "2025-03-25T08:31:58.333119Z",
     "iopub.status.idle": "2025-03-25T08:32:05.569628Z",
     "shell.execute_reply": "2025-03-25T08:32:05.569287Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation first few rows:\n",
      "   ID ENTREZ_GENE_ID  SPOT_ID\n",
      "0   1              1      1.0\n",
      "1   2              2      2.0\n",
      "2   3              3      3.0\n",
      "3   9              9      9.0\n",
      "4  10             10     10.0\n",
      "\n",
      "Sample values in ENTREZ_GENE_ID column:\n",
      "0     1\n",
      "1     2\n",
      "2     3\n",
      "3     9\n",
      "4    10\n",
      "5    12\n",
      "6    13\n",
      "7    14\n",
      "8    15\n",
      "9    16\n",
      "Name: ENTREZ_GENE_ID, dtype: object\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Check if gene symbols are available in the SOFT file:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cleaned gene mapping:\n",
      "   ID Gene\n",
      "0   1    1\n",
      "1   2    2\n",
      "2   3    3\n",
      "3   9    9\n",
      "4  10   10\n",
      "Mapping shape after cleaning: (3822578, 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene expression data after mapping:\n",
      "Number of genes: 0\n",
      "Number of samples: 166\n",
      "No genes were mapped successfully.\n"
     ]
    }
   ],
   "source": [
    "# Let's examine the gene_annotation data more carefully to understand the structure\n",
    "print(\"Gene annotation first few rows:\")\n",
    "print(gene_annotation.head())\n",
    "\n",
    "# Check what's in the ENTREZ_GENE_ID column - we need actual gene identifiers\n",
    "print(\"\\nSample values in ENTREZ_GENE_ID column:\")\n",
    "print(gene_annotation['ENTREZ_GENE_ID'].head(10))\n",
    "\n",
    "# The issue is that we need proper gene symbols, not just Entrez IDs\n",
    "# Let's check if we have access to proper gene symbols by fetching the platform annotation\n",
    "# from the SOFT file\n",
    "\n",
    "# Parse the SOFT file to get platform information including gene symbols\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    soft_content = f.read()\n",
    "\n",
    "# Look for sections containing gene symbol information\n",
    "print(\"\\nCheck if gene symbols are available in the SOFT file:\")\n",
    "gene_symbol_lines = [line for line in soft_content.split('\\n') if 'gene_symbol' in line.lower()][:5]\n",
    "print(gene_symbol_lines)\n",
    "\n",
    "# If we don't find gene symbols directly, we'll use the Entrez Gene IDs as identifiers\n",
    "# since they can be mapped to gene symbols later\n",
    "\n",
    "# Create a mapping dataframe with ID and Entrez Gene ID\n",
    "gene_mapping = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n",
    "gene_mapping.rename(columns={'ENTREZ_GENE_ID': 'Gene'}, inplace=True)\n",
    "\n",
    "# Filter out any rows with missing values\n",
    "gene_mapping = gene_mapping.dropna()\n",
    "\n",
    "# Print a preview of the cleaned mapping\n",
    "print(\"\\nCleaned gene mapping:\")\n",
    "print(gene_mapping.head())\n",
    "print(f\"Mapping shape after cleaning: {gene_mapping.shape}\")\n",
    "\n",
    "# Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# We'll use the standard function but make sure our Gene column has proper values\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print statistics about the resulting gene data\n",
    "print(f\"\\nGene expression data after mapping:\")\n",
    "print(f\"Number of genes: {gene_data.shape[0]}\")\n",
    "print(f\"Number of samples: {gene_data.shape[1]}\")\n",
    "\n",
    "# Check the first few gene identifiers - they should be Entrez IDs\n",
    "if gene_data.shape[0] > 0:\n",
    "    print(\"First 5 gene identifiers:\")\n",
    "    print(gene_data.index[:5])\n",
    "else:\n",
    "    print(\"No genes were mapped successfully.\")\n",
    "\n",
    "# For this dataset, since we don't have access to proper gene symbols, \n",
    "# we'll treat the Entrez Gene IDs as our gene identifiers\n",
    "# Later normalization can map these to standard gene symbols if needed\n",
    "\n",
    "# Verify we have non-empty gene expression data\n",
    "is_gene_available = gene_data.shape[0] > 0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4c1efa",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2e150f28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:32:05.571074Z",
     "iopub.status.busy": "2025-03-25T08:32:05.570955Z",
     "iopub.status.idle": "2025-03-25T08:32:05.577791Z",
     "shell.execute_reply": "2025-03-25T08:32:05.577484Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: Gene expression matrix is empty after mapping.\n",
      "Abnormality detected in the cohort: GSE123086. Preprocessing failed.\n",
      "A new JSON file was created at: ../../output/preprocess/Crohns_Disease/cohort_info.json\n",
      "Dataset deemed not usable due to lack of gene expression data.\n"
     ]
    }
   ],
   "source": [
    "# 1. Check if gene data is available after mapping\n",
    "if gene_data.shape[0] == 0:\n",
    "    print(\"Error: Gene expression matrix is empty after mapping.\")\n",
    "    # Mark the dataset as not usable due to lack of 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=False,  # No usable gene data\n",
    "        is_trait_available=True,\n",
    "        is_biased=True,\n",
    "        df=pd.DataFrame(),\n",
    "        note=\"Failed to map probe IDs to gene symbols. The annotation format may not be compatible with the extraction methods.\"\n",
    "    )\n",
    "    print(\"Dataset deemed not usable due to lack of gene expression data.\")\n",
    "else:\n",
    "    # Only proceed with normalization if we have gene data\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 normalized gene data\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\"Normalized gene expression data saved to {out_gene_data_file}\")\n",
    "    \n",
    "    # Extract clinical features from the original data source\n",
    "    print(\"Extracting clinical features from the original source...\")\n",
    "    # Get background information and clinical data again\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",
    "    # Extract clinical features\n",
    "    selected_clinical_df = 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",
    "    print(\"Extracted clinical features preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    \n",
    "    # Save the extracted clinical features\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    # Link clinical and genetic data\n",
    "    print(\"Linking clinical and genetic data...\")\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data_normalized)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # Check if the linked data has adequate data\n",
    "    if linked_data.shape[0] == 0 or linked_data.shape[1] <= 4:  # 4 is an arbitrary small number\n",
    "        print(\"Error: Linked data has insufficient samples or features.\")\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=linked_data,\n",
    "            note=\"Failed to properly link gene expression data with clinical features.\"\n",
    "        )\n",
    "        print(\"Dataset deemed not usable due to linking failure.\")\n",
    "    else:\n",
    "        # Handle missing values systematically\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",
    "        # Check if there are still samples after missing value handling\n",
    "        if linked_data_clean.shape[0] == 0:\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.\")\n",
    "        else:\n",
    "            # Check if the dataset is biased\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",
    "            # Conduct final quality validation\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=\"Dataset contains gene expression data for Crohn's Disease patients, examining response to Infliximab treatment.\"\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.\")"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}