{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7491392e",
   "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 = \"Alzheimers_Disease\"\n",
    "cohort = \"GSE109887\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Alzheimers_Disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Alzheimers_Disease/GSE109887\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Alzheimers_Disease/GSE109887.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Alzheimers_Disease/gene_data/GSE109887.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Alzheimers_Disease/clinical_data/GSE109887.csv\"\n",
    "json_path = \"../../output/preprocess/Alzheimers_Disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30656eb1",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e614c493",
   "metadata": {},
   "outputs": [],
   "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": "2809aba3",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7c52aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine if gene expression data is available\n",
    "# Based on the background information, this dataset contains gene expression data from Illumina HumanHT-12 V4.0\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Data Availability and Type Conversion Functions\n",
    "# 2.1 Identify rows in sample characteristics where data is recorded\n",
    "trait_row = 3  # The trait (AD vs Control) is in row 3 as 'disease state'\n",
    "age_row = 1    # Age is in row 1\n",
    "gender_row = 0  # Gender is in row 0\n",
    "\n",
    "# 2.2 Data type conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to binary (0 for Control, 1 for AD)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == \"ad\" or value.lower() == \"alzheimer's disease\":\n",
    "        return 1\n",
    "    elif value.lower() == \"control\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous numeric values\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to float if possible\n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (0 for Female, 1 for Male)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == \"male\":\n",
    "        return 1\n",
    "    elif value.lower() == \"female\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\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 need to extract clinical features\n",
    "if trait_row is not None:\n",
    "    # Define the sample characteristics dictionary from the previous output\n",
    "    sample_char_dict = {\n",
    "        0: ['gender: Male', 'gender: Female'], \n",
    "        1: ['age: 91', 'age: 87', 'age: 82', 'age: 73', 'age: 94', 'age: 72', 'age: 90', 'age: 86', \n",
    "            'age: 92', 'age: 81', 'age: 95', 'age: 75', 'age: 77', 'age: 84', 'age: 85', 'age: 89', \n",
    "            'age: 78', 'age: 70', 'age: 88', 'age: 79'], \n",
    "        2: ['tissue: brain, middle temporal gyrus'], \n",
    "        3: ['disease state: AD', 'disease state: Control']\n",
    "    }\n",
    "    \n",
    "    # Create a compatible DataFrame for geo_select_clinical_features\n",
    "    # The function expects a DataFrame where rows are features and columns are samples\n",
    "    # For this test case, we'll create a minimal DataFrame with the expected structure\n",
    "    # Create a dummy DataFrame with the right structure\n",
    "    data = {}\n",
    "    for i in range(2):  # Create 2 sample columns for testing\n",
    "        col_name = f\"GSM{i+1}\"\n",
    "        data[col_name] = [\n",
    "            sample_char_dict[0][i % len(sample_char_dict[0])],  # Gender\n",
    "            sample_char_dict[1][i % len(sample_char_dict[1])],  # Age\n",
    "            sample_char_dict[2][0],                             # Tissue (constant)\n",
    "            sample_char_dict[3][i % len(sample_char_dict[3])]   # Disease state\n",
    "        ]\n",
    "    \n",
    "    clinical_data = pd.DataFrame(data)\n",
    "    \n",
    "    # Extract clinical features using the geo_select_clinical_features function\n",
    "    selected_clinical_data = geo_select_clinical_features(\n",
    "        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",
    "    print(\"Preview of extracted clinical data:\")\n",
    "    print(preview_df(selected_clinical_data))\n",
    "    \n",
    "    # Save the clinical data to CSV\n",
    "    selected_clinical_data.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28ab1ae",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6746b4a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths again to access the matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_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) for future observation\n",
    "print(\"First 20 gene/probe identifiers:\")\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53882535",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bc25cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Review the gene identifiers\n",
    "# Looking at the first 20 identifiers shows a mix of conventional gene symbols like A1BG, A2M, AAAS, AACS\n",
    "# These appear to be standard human gene symbols (HGNC symbols)\n",
    "# Names like A1CF, A2BP1, etc. are older gene symbols that have been updated in newer nomenclature\n",
    "# There are also some less common identifiers like 7A5 and AAA1, but overall these appear to be gene symbols\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9b7325",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62d8734b",
   "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. Extract gene expression data from the matrix file\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Since we determined that gene mapping is not required (requires_gene_mapping = False),\n",
    "# we can directly normalize the gene symbols in the index\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Original gene data shape: {gene_data.shape}\")\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the processed gene data to file\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\"Processed gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 4. Re-extract clinical data properly\n",
    "background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']\n",
    "clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']\n",
    "_, clinical_raw = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "\n",
    "# Define conversion functions again to ensure they're available\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to binary (0 for Control, 1 for AD)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == \"ad\" or value.lower() == \"alzheimer's disease\":\n",
    "        return 1\n",
    "    elif value.lower() == \"control\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous numeric values\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to float if possible\n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (0 for Female, 1 for Male)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Split by colon and get the value part\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if value.lower() == \"male\":\n",
    "        return 1\n",
    "    elif value.lower() == \"female\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Extract clinical features properly\n",
    "clinical_data = geo_select_clinical_features(\n",
    "    clinical_raw, \n",
    "    trait=trait, \n",
    "    trait_row=3,  # From previous step\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=1,    # From previous step\n",
    "    convert_age=convert_age,\n",
    "    gender_row=0, # From previous step\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_data.T))\n",
    "\n",
    "# Save the extracted clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_data.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 5. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 6. Handle missing values in the linked data\n",
    "# The trait column name should be 'Alzheimers_Disease' based on the variable we passed to geo_select_clinical_features\n",
    "linked_data = handle_missing_values(linked_data, trait_col=trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 7. Evaluate whether the trait and demographic features are biased\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 8. Conduct final quality validation and save cohort info\n",
    "note = \"Gene expression data from Illumina HumanHT-12 V4.0 in middle temporal gyrus brain tissue.\"\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,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 9. Save the linked data if it is 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, index=True)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Dataset {cohort} was determined to be unusable due to bias or other issues. Data not saved.\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}