{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed3d3307",
   "metadata": {},
   "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 = \"Allergies\"\n",
    "cohort = \"GSE230164\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Allergies\"\n",
    "in_cohort_dir = \"../../input/GEO/Allergies/GSE230164\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Allergies/GSE230164.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Allergies/gene_data/GSE230164.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Allergies/clinical_data/GSE230164.csv\"\n",
    "json_path = \"../../output/preprocess/Allergies/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f25c6e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dbe3593",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f61931c3",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48e922d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e07dac68",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6cd71d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d8656551",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c57135c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, let's load the gene expression data to examine the gene identifiers\n",
    "try:\n",
    "    # Load gene expression data from a previous step\n",
    "    gene_file = os.path.join(in_cohort_dir, 'gene_expression.txt')\n",
    "    gene_data = pd.read_csv(gene_file, sep='\\t', index_col=0)\n",
    "    \n",
    "    # Look at the first few gene identifiers\n",
    "    gene_identifiers = gene_data.index.tolist()[:10]  # Sample of gene identifiers\n",
    "    print(\"Sample gene identifiers:\", gene_identifiers)\n",
    "    \n",
    "    # Check if identifiers are likely human gene symbols\n",
    "    # Human gene symbols typically have format like \"BRCA1\", \"TP53\", etc.\n",
    "    # Other formats might be Ensembl IDs (ENSG...), Affymetrix IDs, or probe IDs\n",
    "    \n",
    "    # Simple heuristic: If most identifiers match pattern of human gene symbols\n",
    "    # (typically uppercase letters with some numbers, not starting with numbers)\n",
    "    import re\n",
    "    \n",
    "    gene_symbol_pattern = re.compile(r'^[A-Z][A-Z0-9]*$')\n",
    "    ensembl_pattern = re.compile(r'^ENS[A-Z]*[0-9]+')\n",
    "    probe_pattern = re.compile(r'^[0-9]+_')\n",
    "    \n",
    "    gene_symbol_count = sum(1 for gene_id in gene_identifiers if gene_symbol_pattern.match(gene_id))\n",
    "    ensembl_count = sum(1 for gene_id in gene_identifiers if ensembl_pattern.match(gene_id))\n",
    "    probe_count = sum(1 for gene_id in gene_identifiers if probe_pattern.match(gene_id))\n",
    "    \n",
    "    print(f\"Gene symbol pattern matches: {gene_symbol_count}/{len(gene_identifiers)}\")\n",
    "    print(f\"Ensembl pattern matches: {ensembl_count}/{len(gene_identifiers)}\")\n",
    "    print(f\"Probe pattern matches: {probe_count}/{len(gene_identifiers)}\")\n",
    "    \n",
    "    # Determine if mapping is needed\n",
    "    requires_gene_mapping = not (gene_symbol_count / len(gene_identifiers) > 0.8)\n",
    "    print(f\"requires_gene_mapping = {requires_gene_mapping}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error examining gene identifiers: {e}\")\n",
    "    # If we can't determine, default to requiring mapping\n",
    "    requires_gene_mapping = True\n",
    "    print(f\"requires_gene_mapping = {requires_gene_mapping}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be820d6a",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b40bbd7",
   "metadata": {},
   "outputs": [],
   "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. 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",
    "# 3. 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": "4eb875c7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adb23b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's identify the relevant columns for mapping\n",
    "# Based on the preview, 'ID' seems to be the probe identifier, and 'Symbol' contains gene symbols\n",
    "# These appear to match what we need for mapping\n",
    "\n",
    "# 2. Get gene expression data\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "print(f\"Gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few probe IDs from gene expression data:\")\n",
    "print(gene_data.index[:5])\n",
    "\n",
    "# 3. Get gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "print(f\"Gene mapping dataframe shape: {gene_mapping.shape}\")\n",
    "print(\"First few rows of gene mapping:\")\n",
    "print(gene_mapping.head())\n",
    "\n",
    "# 4. Apply gene mapping to convert probe-level measurements to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(f\"After mapping: Gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 5. Normalize gene symbols using synonym information\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"After normalization: Gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# 6. Save gene data to file\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": "a810ba3d",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9d33a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. The gene data is already normalized in the previous step\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "print(\"Extracting clinical data directly from the source...\")\n",
    "# Get the matrix file again to ensure we have the proper data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Get unique values dictionary to identify feature rows\n",
    "unique_values_dict = get_unique_values_by_row(clinical_data)\n",
    "print(\"Sample characteristics (first few entries):\")\n",
    "for i, (key, values) in enumerate(unique_values_dict.items()):\n",
    "    print(f\"{key}: {values}\")\n",
    "    if i > 5:  # Limit output to first few entries\n",
    "        print(\"...\")\n",
    "        break\n",
    "\n",
    "# Define conversion functions based on the data inspection\n",
    "# These would normally be defined in Step 2\n",
    "def convert_trait(cell):\n",
    "    \"\"\"Convert allergies information to binary (1: has allergies, 0: healthy control)\"\"\"\n",
    "    if isinstance(cell, str):\n",
    "        if 'allergy' in cell.lower() or 'allergic' in cell.lower():\n",
    "            return 1\n",
    "        elif 'healthy' in cell.lower() or 'control' in cell.lower():\n",
    "            return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(cell):\n",
    "    \"\"\"Extract age value from cell\"\"\"\n",
    "    if isinstance(cell, str) and 'age:' in cell.lower():\n",
    "        # Extract numbers after \"age:\"\n",
    "        import re\n",
    "        match = re.search(r'age:\\s*(\\d+)', cell.lower())\n",
    "        if match:\n",
    "            return float(match.group(1))\n",
    "    return None\n",
    "\n",
    "def convert_gender(cell):\n",
    "    \"\"\"Convert gender to binary (0: female, 1: male)\"\"\"\n",
    "    if isinstance(cell, str):\n",
    "        cell = cell.lower()\n",
    "        if 'female' in cell or 'f' in cell:\n",
    "            return 0\n",
    "        elif 'male' in cell or 'm' in cell:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# Find appropriate rows for trait, age, and gender\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Scan through the unique values to identify feature rows\n",
    "for idx, values in unique_values_dict.items():\n",
    "    values_str = str(values).lower()\n",
    "    if 'allergy' in values_str or 'allergic' in values_str or 'healthy' in values_str:\n",
    "        trait_row = idx\n",
    "    elif 'age' in values_str:\n",
    "        age_row = idx\n",
    "    elif 'gender' in values_str or 'sex' in values_str or ('male' in values_str and 'female' in values_str):\n",
    "        gender_row = idx\n",
    "\n",
    "print(f\"Identified trait_row: {trait_row}, age_row: {age_row}, gender_row: {gender_row}\")\n",
    "\n",
    "# Extract clinical features\n",
    "if trait_row is not None:\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(\"Clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Save the clinical data to a CSV file\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 data 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)\n",
    "    print(f\"Linked data shape: {linked_data.shape}\")\n",
    "    \n",
    "    # 3. Handle missing values in the linked data\n",
    "    print(\"Handling missing values...\")\n",
    "    linked_data = handle_missing_values(linked_data, trait)\n",
    "    print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "    \n",
    "    # 4. Check if trait is biased\n",
    "    print(\"Checking for bias in trait distribution...\")\n",
    "    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "    \n",
    "    # 5. Final validation\n",
    "    note = \"Dataset contains gene expression data from peripheral blood related to food allergies.\"\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,  # We have gene data\n",
    "        is_trait_available=True,  # We've identified the trait row\n",
    "        is_biased=is_biased,\n",
    "        df=linked_data,\n",
    "        note=note\n",
    "    )\n",
    "    \n",
    "    print(f\"Dataset usability: {is_usable}\")\n",
    "    \n",
    "    # 6. Save linked data if usable\n",
    "    if is_usable:\n",
    "        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "        linked_data.to_csv(out_data_file)\n",
    "        print(f\"Linked data saved to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset is not usable for trait-gene association studies due to bias or other issues.\")\n",
    "else:\n",
    "    print(\"No trait information found in the clinical data. Cannot proceed with linking.\")\n",
    "    # Save validation 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=False,\n",
    "        is_biased=None,\n",
    "        df=None,\n",
    "        note=\"No trait information found in the clinical data.\"\n",
    "    )\n",
    "    print(f\"Dataset usability: {is_usable}\")"
   ]
  }
 ],
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