{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da9ed20",
   "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 = \"Substance_Use_Disorder\"\n",
    "cohort = \"GSE103580\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Substance_Use_Disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Substance_Use_Disorder/GSE103580\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Substance_Use_Disorder/GSE103580.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Substance_Use_Disorder/gene_data/GSE103580.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Substance_Use_Disorder/clinical_data/GSE103580.csv\"\n",
    "json_path = \"../../output/preprocess/Substance_Use_Disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35931135",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "788ffdd4",
   "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": "715ec1c8",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b13cc94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "is_gene_available = True  # The background information mentions \"gene expression profiling\", so gene data is available\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "trait_row = 0  # Disease status information is available in the sample characteristics dictionary at key 0\n",
    "age_row = None  # Age information is not available in the sample characteristics\n",
    "gender_row = None  # Gender information is not available in the sample characteristics\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert alcoholic liver disease status to binary representation.\n",
    "    0: Control or less severe (Alcoholic steatosis or Alcoholic cirrhosis)\n",
    "    1: Alcoholic hepatitis (more severe condition)\n",
    "    \"\"\"\n",
    "    if value is None or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if it exists\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on presence of alcoholic hepatitis\n",
    "    if \"alcoholic hepatitis\" in value.lower():\n",
    "        return 1\n",
    "    elif \"alcoholic steatosis\" in value.lower() or \"alcoholic cirrhosis\" in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value (not used as age data is unavailable).\"\"\"\n",
    "    if value is None or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (not used as gender data is unavailable).\"\"\"\n",
    "    if value is None or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    value = value.lower()\n",
    "    if \"female\" in value or \"f\" == value:\n",
    "        return 0\n",
    "    elif \"male\" in value or \"m\" == value:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\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",
    "if trait_row is not None:\n",
    "    # Extract clinical features\n",
    "    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 clinical features\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to CSV\n",
    "    clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be47179f",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b1b4491",
   "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": "1ad8b25c",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c28e24ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the gene identifiers shown, these appear to be Affymetrix probeset IDs (like \"11715100_at\"),\n",
    "# not standard human gene symbols. The format with \"_at\", \"_s_at\", and \"_x_at\" suffixes is typical for\n",
    "# Affymetrix microarray probesets. These identifiers will need to be mapped to standard gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088857f6",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b89faef9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's first examine the structure of the SOFT file before trying to parse it\n",
    "import gzip\n",
    "\n",
    "# Look at the first few lines of the SOFT file to understand its structure\n",
    "print(\"Examining SOFT file structure:\")\n",
    "try:\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        # Read first 20 lines to understand the file structure\n",
    "        for i, line in enumerate(file):\n",
    "            if i < 20:\n",
    "                print(f\"Line {i}: {line.strip()}\")\n",
    "            else:\n",
    "                break\n",
    "except Exception as e:\n",
    "    print(f\"Error reading SOFT file: {e}\")\n",
    "\n",
    "# 2. Now let's try a more robust approach to extract the gene annotation\n",
    "# Instead of using the library function which failed, we'll implement a custom approach\n",
    "try:\n",
    "    # First, look for the platform section which contains gene annotation\n",
    "    platform_data = []\n",
    "    with gzip.open(soft_file, 'rt') as file:\n",
    "        in_platform_section = False\n",
    "        for line in file:\n",
    "            if line.startswith('^PLATFORM'):\n",
    "                in_platform_section = True\n",
    "                continue\n",
    "            if in_platform_section and line.startswith('!platform_table_begin'):\n",
    "                # Next line should be the header\n",
    "                header = next(file).strip()\n",
    "                platform_data.append(header)\n",
    "                # Read until the end of the platform table\n",
    "                for table_line in file:\n",
    "                    if table_line.startswith('!platform_table_end'):\n",
    "                        break\n",
    "                    platform_data.append(table_line.strip())\n",
    "                break\n",
    "    \n",
    "    # If we found platform data, convert it to a DataFrame\n",
    "    if platform_data:\n",
    "        import pandas as pd\n",
    "        import io\n",
    "        platform_text = '\\n'.join(platform_data)\n",
    "        gene_annotation = pd.read_csv(io.StringIO(platform_text), delimiter='\\t', \n",
    "                                      low_memory=False, on_bad_lines='skip')\n",
    "        print(\"\\nGene annotation preview:\")\n",
    "        print(preview_df(gene_annotation))\n",
    "    else:\n",
    "        print(\"Could not find platform table in SOFT file\")\n",
    "        \n",
    "        # Try an alternative approach - extract mapping from other sections\n",
    "        with gzip.open(soft_file, 'rt') as file:\n",
    "            for line in file:\n",
    "                if 'ANNOTATION information' in line or 'annotation information' in line:\n",
    "                    print(f\"Found annotation information: {line.strip()}\")\n",
    "                if line.startswith('!Platform_title') or line.startswith('!platform_title'):\n",
    "                    print(f\"Platform title: {line.strip()}\")\n",
    "            \n",
    "except Exception as e:\n",
    "    print(f\"Error processing gene annotation: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b190ecab",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c976d4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the gene identifier and gene symbol columns from the gene annotation DataFrame\n",
    "prob_col = 'ID'  # The column in gene_annotation that contains probe IDs (matching gene_data index)\n",
    "gene_col = 'Gene Symbol'  # The column in gene_annotation that contains gene symbols\n",
    "\n",
    "# 2. Extract the mapping from probe IDs to gene symbols\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col, gene_col)\n",
    "print(f\"Gene mapping preview: {mapping_df.head()}\")\n",
    "print(f\"Number of probes with gene mapping: {len(mapping_df)}\")\n",
    "\n",
    "# 3. Convert the probe-level measurements to gene-level expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(f\"First few gene symbols: {list(gene_data.index[:5])}\")\n",
    "\n",
    "# Save the gene expression data\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": "c31e19f4",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4644fd45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the obtained gene expression data\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "\n",
    "# Normalize gene symbols using NCBI Gene database\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\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. Check if trait data is available before proceeding with clinical data extraction\n",
    "if trait_row is None:\n",
    "    print(\"Trait row is None. Cannot extract trait information from clinical data.\")\n",
    "    # Create an empty dataframe for clinical features\n",
    "    clinical_features = pd.DataFrame()\n",
    "    \n",
    "    # Create an empty dataframe for linked data\n",
    "    linked_data = pd.DataFrame()\n",
    "    \n",
    "    # Validate and save cohort info\n",
    "    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,  # Trait data is not available\n",
    "        is_biased=True,  # Not applicable but required\n",
    "        df=pd.DataFrame(),  # Empty dataframe\n",
    "        note=f\"Dataset contains gene expression data but lacks clear trait indicators for {trait} status.\"\n",
    "    )\n",
    "    print(\"Data was determined to be unusable due to missing trait indicators and was not saved\")\n",
    "else:\n",
    "    try:\n",
    "        # Get the file paths for the matrix file to extract clinical data\n",
    "        _, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "        \n",
    "        # Get raw clinical data from the matrix file\n",
    "        _, clinical_raw = get_background_and_clinical_data(matrix_file)\n",
    "        \n",
    "        # Verify clinical data structure\n",
    "        print(\"Raw clinical data shape:\", clinical_raw.shape)\n",
    "        \n",
    "        # Extract clinical features using the defined conversion functions\n",
    "        clinical_features = 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",
    "        print(\"Clinical features:\")\n",
    "        print(clinical_features)\n",
    "        \n",
    "        # Save clinical features to file\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        clinical_features.to_csv(out_clinical_data_file)\n",
    "        print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
    "        \n",
    "        # 3. Link clinical and genetic data\n",
    "        linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "        print(f\"Linked data shape: {linked_data.shape}\")\n",
    "        print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "        print(linked_data.iloc[:5, :5])\n",
    "        \n",
    "        # 4. Handle missing values\n",
    "        print(\"Missing values before handling:\")\n",
    "        print(f\"  Trait ({trait}) missing: {linked_data[trait].isna().sum()} out of {len(linked_data)}\")\n",
    "        if 'Age' in linked_data.columns:\n",
    "            print(f\"  Age missing: {linked_data['Age'].isna().sum()} out of {len(linked_data)}\")\n",
    "        if 'Gender' in linked_data.columns:\n",
    "            print(f\"  Gender missing: {linked_data['Gender'].isna().sum()} out of {len(linked_data)}\")\n",
    "        \n",
    "        gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
    "        print(f\"  Genes with >20% missing: {sum(linked_data[gene_cols].isna().mean() > 0.2)}\")\n",
    "        print(f\"  Samples with >5% missing genes: {sum(linked_data[gene_cols].isna().mean(axis=1) > 0.05)}\")\n",
    "        \n",
    "        cleaned_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Data shape after handling missing values: {cleaned_data.shape}\")\n",
    "        \n",
    "        # 5. Evaluate bias in trait and demographic features\n",
    "        is_trait_biased = False\n",
    "        if len(cleaned_data) > 0:\n",
    "            trait_biased, cleaned_data = judge_and_remove_biased_features(cleaned_data, trait)\n",
    "            is_trait_biased = trait_biased\n",
    "        else:\n",
    "            print(\"No data remains after handling missing values.\")\n",
    "            is_trait_biased = True\n",
    "        \n",
    "        # 6. Final validation and save\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_trait_biased, \n",
    "            df=cleaned_data,\n",
    "            note=f\"Dataset contains gene expression data for {trait} analysis.\"\n",
    "        )\n",
    "        \n",
    "        # 7. Save if usable\n",
    "        if is_usable and len(cleaned_data) > 0:\n",
    "            os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "            cleaned_data.to_csv(out_data_file)\n",
    "            print(f\"Linked data saved to {out_data_file}\")\n",
    "        else:\n",
    "            print(\"Data was determined to be unusable or empty and was not saved\")\n",
    "            \n",
    "    except Exception as e:\n",
    "        print(f\"Error processing data: {e}\")\n",
    "        # Handle the error case by still recording cohort info\n",
    "        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,  # Mark as not available due to processing issues\n",
    "            is_biased=True, \n",
    "            df=pd.DataFrame(),  # Empty dataframe\n",
    "            note=f\"Error processing data for {trait}: {str(e)}\"\n",
    "        )\n",
    "        print(\"Data was determined to be unusable and was not saved\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}