{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8bc88bd",
   "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 = \"Height\"\n",
    "cohort = \"GSE97475\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Height\"\n",
    "in_cohort_dir = \"../../input/GEO/Height/GSE97475\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Height/GSE97475.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Height/gene_data/GSE97475.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Height/clinical_data/GSE97475.csv\"\n",
    "json_path = \"../../output/preprocess/Height/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3d32da",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74cb49cd",
   "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": "5a025d18",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d167cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series title and summary, this appears to be a gene expression study of healthy hepatitis B vaccine recipients.\n",
    "# The summary mentions \"transcriptomic\" data collection, which suggests gene expression data is available.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# Height (trait)\n",
    "# Looking at 'subjects.demographics.height: NA' - all values are NA, so height data is not available\n",
    "trait_row = None  # Setting to None since all values appear to be NA\n",
    "\n",
    "# Age Data\n",
    "# Key 81 contains age information with multiple unique values\n",
    "age_row = 81\n",
    "\n",
    "# Gender Data\n",
    "# Key 118 contains gender information with two values: Male and Female\n",
    "gender_row = 118\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert height data to a float value in cm.\"\"\"\n",
    "    if not value or value == 'NA':\n",
    "        return None\n",
    "    # Extract the value after the colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    # Try to convert to float, assuming height is in cm\n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to integer.\"\"\"\n",
    "    if not value or value == 'NA':\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    try:\n",
    "        return int(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender data to binary (0 for Female, 1 for Male).\"\"\"\n",
    "    if not value or value == 'NA':\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if value.lower() == 'female':\n",
    "        return 0\n",
    "    elif value.lower() == 'male':\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait availability based on whether trait_row is None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Initial filtering on usability and save metadata\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",
    "# Only proceed if trait_row is not None and the clinical data file exists\n",
    "import os\n",
    "\n",
    "if trait_row is not None:\n",
    "    clinical_data_path = f\"{in_cohort_dir}/clinical_data.csv\"\n",
    "    if os.path.exists(clinical_data_path):\n",
    "        # Load the clinical data\n",
    "        clinical_data = pd.read_csv(clinical_data_path)\n",
    "        \n",
    "        # Extract clinical features using the library function\n",
    "        selected_clinical = 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 output\n",
    "        preview = preview_df(selected_clinical)\n",
    "        print(\"Preview of selected clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save the selected clinical features\n",
    "        selected_clinical.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "    else:\n",
    "        print(f\"Clinical data file not found at {clinical_data_path}\")\n",
    "        print(\"Skipping clinical feature extraction.\")\n",
    "else:\n",
    "    print(f\"Trait data ({trait}) is not available in this dataset.\")\n",
    "    print(\"Skipping clinical feature extraction.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d60c63c",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "110de7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get the file paths for the SOFT file and matrix file\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. First, let's examine the structure of the matrix file to understand its format\n",
    "import gzip\n",
    "\n",
    "# Peek at the first few lines of the file to understand its structure\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Read first 100 lines to find the header structure\n",
    "    for i, line in enumerate(file):\n",
    "        if '!series_matrix_table_begin' in line:\n",
    "            print(f\"Found data marker at line {i}\")\n",
    "            # Read the next line which should be the header\n",
    "            header_line = next(file)\n",
    "            print(f\"Header line: {header_line.strip()}\")\n",
    "            # And the first data line\n",
    "            first_data_line = next(file)\n",
    "            print(f\"First data line: {first_data_line.strip()}\")\n",
    "            break\n",
    "        if i > 100:  # Limit search to first 100 lines\n",
    "            print(\"Matrix table marker not found in first 100 lines\")\n",
    "            break\n",
    "\n",
    "# 3. Now try to get the genetic data with better error handling\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(gene_data.index[:20])\n",
    "except KeyError as e:\n",
    "    print(f\"KeyError: {e}\")\n",
    "    \n",
    "    # Alternative approach: manually extract the data\n",
    "    print(\"\\nTrying alternative approach to read the gene data:\")\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        # Find the start of the data\n",
    "        for line in file:\n",
    "            if '!series_matrix_table_begin' in line:\n",
    "                break\n",
    "                \n",
    "        # Read the headers and data\n",
    "        import pandas as pd\n",
    "        df = pd.read_csv(file, sep='\\t', index_col=0)\n",
    "        print(f\"Column names: {df.columns[:5]}\")\n",
    "        print(f\"First 20 row IDs: {df.index[:20]}\")\n",
    "        gene_data = df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cce52d5",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada39933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's analyze the gene identifiers\n",
    "from typing import List\n",
    "\n",
    "def is_human_gene_symbol(gene_ids: List[str]) -> bool:\n",
    "    \"\"\"\n",
    "    Determine if a list of identifiers are human gene symbols.\n",
    "    \n",
    "    Human gene symbols are typically:\n",
    "    - Short (usually 1-8 characters)\n",
    "    - Uppercase letters\n",
    "    - May contain numbers, usually at the end\n",
    "    - Sometimes include hyphens\n",
    "    \n",
    "    Returns True if identifiers appear to be human gene symbols.\n",
    "    \"\"\"\n",
    "    # Check a sample of genes that are clearly human gene symbols\n",
    "    known_human_genes = {'A1BG', 'AAAS', 'AACS', 'AAMP', 'AARS'}\n",
    "    \n",
    "    # Count how many match our known set\n",
    "    matches = sum(1 for gene in gene_ids if gene in known_human_genes)\n",
    "    \n",
    "    # If we have multiple matches to known gene symbols, these are likely human gene symbols\n",
    "    if matches >= 3:\n",
    "        return True\n",
    "        \n",
    "    return False\n",
    "\n",
    "# Check the sample gene IDs shown in the output\n",
    "sample_genes = ['A1BG', 'A26C3', 'A2LD1', 'A4GNT', 'AAAS', 'AACS', 'AACSL', 'AADACL1',\n",
    "                'AADAT', 'AAGAB', 'AAK1', 'AAMP', 'AARS', 'AARS2', 'AARSD1', 'AASDH',\n",
    "                'AASDHPPT', 'AASS', 'AATF', 'AATK']\n",
    "\n",
    "requires_gene_mapping = not is_human_gene_symbol(sample_genes)\n",
    "print(f\"requires_gene_mapping = {requires_gene_mapping}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3c3202",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf942a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's load the gene expression data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Normalize gene symbols in the gene expression data\n",
    "# The gene_data object from Step 3 already contains our gene expression data\n",
    "# Let's first load it again to make sure we have the correct data\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Normalize gene symbols using the NCBI Gene database information\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to the output 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Attempt to link clinical and genetic data\n",
    "# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n",
    "# Therefore, we need to indicate that trait data is not available\n",
    "\n",
    "# Create a minimal clinical dataframe with the trait column (containing NaNs)\n",
    "sample_ids = gene_data.columns\n",
    "# Create a DataFrame with sample IDs and a placeholder Height column\n",
    "minimal_clinical_df = pd.DataFrame(index=sample_ids)\n",
    "minimal_clinical_df[trait] = np.nan  # Add the trait column with NaN values\n",
    "minimal_clinical_df.index.name = 'Sample'\n",
    "\n",
    "# Save this minimal clinical data for reference\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "minimal_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Minimal clinical data with placeholder {trait} column saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Create a linked dataset \n",
    "linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n",
    "linked_data.index.name = 'Sample'\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 5. Validate and save cohort information\n",
    "# trait_row was None in Step 2, so we know Height data is not available\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_trait_available = False  # Height data is not available\n",
    "\n",
    "note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n",
    "\n",
    "# For datasets without trait data, set is_biased to False\n",
    "# This indicates the dataset is not usable due to missing trait data, not due to bias\n",
    "is_biased = False\n",
    "\n",
    "# Final 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=is_gene_available, \n",
    "    is_trait_available=is_trait_available, \n",
    "    is_biased=is_biased,  # Explicitly set to False instead of None\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# Since there is no trait data, the dataset is not usable for our association study\n",
    "print(\"Dataset does not contain Height data and cannot be used for association studies.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b286368",
   "metadata": {},
   "source": [
    "### Step 6: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97bcc2f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 1. Let's load the gene expression data\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Extract gene expression data\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Normalize gene symbols using the NCBI Gene database information\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to the output 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\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link clinical and genetic data\n",
    "# In Step 2, we determined that Height data is not available in this dataset (trait_row = None)\n",
    "# Create a minimal clinical dataframe with the trait column (containing NaNs)\n",
    "sample_ids = normalized_gene_data.columns\n",
    "minimal_clinical_df = pd.DataFrame(index=sample_ids)\n",
    "minimal_clinical_df[trait] = np.nan  # Add the trait column with NaN values\n",
    "\n",
    "# If we have age and gender data from Step 2, add those columns\n",
    "if age_row is not None:\n",
    "    minimal_clinical_df['Age'] = get_feature_data(clinical_data, age_row, 'Age', convert_age).iloc[0]\n",
    "\n",
    "if gender_row is not None:\n",
    "    minimal_clinical_df['Gender'] = get_feature_data(clinical_data, gender_row, 'Gender', convert_gender).iloc[0]\n",
    "\n",
    "minimal_clinical_df.index.name = 'Sample'\n",
    "\n",
    "# Save this minimal clinical data for reference\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "minimal_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Create a linked dataset \n",
    "linked_data = pd.concat([minimal_clinical_df, normalized_gene_data.T], axis=1)\n",
    "linked_data.index.name = 'Sample'\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# We would normally handle missing values here, but since all trait values are missing,\n",
    "# the dataset will be empty after removing samples with missing trait values\n",
    "# Therefore, we'll skip that step\n",
    "\n",
    "# 4 & 5. Validate and save cohort information\n",
    "# Since trait_row was None in Step 2, we know Height data is not available\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_trait_available = False  # Height data is not available\n",
    "\n",
    "note = \"Dataset contains gene expression data but no Height measurements. This dataset is not usable for studying Height associations.\"\n",
    "\n",
    "# For datasets without trait data, we set is_biased to False\n",
    "# This indicates the dataset is not usable due to missing trait data, not due to bias\n",
    "is_biased = False\n",
    "\n",
    "# Final 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=is_gene_available, \n",
    "    is_trait_available=is_trait_available, \n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Since there is no trait data, the dataset is not usable for our association study\n",
    "# So we should not save it to out_data_file\n",
    "print(f\"Dataset usability: {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)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset does not contain Height data and cannot be used for association studies.\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}