{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e600623e",
   "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 = \"Adrenocortical_Cancer\"\n",
    "cohort = \"GSE68950\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Adrenocortical_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Adrenocortical_Cancer/GSE68950\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Adrenocortical_Cancer/GSE68950.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Adrenocortical_Cancer/gene_data/GSE68950.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Adrenocortical_Cancer/clinical_data/GSE68950.csv\"\n",
    "json_path = \"../../output/preprocess/Adrenocortical_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f24deabf",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07caa148",
   "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": "2a866c39",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e1ad2cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "14268501",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9558f493",
   "metadata": {},
   "outputs": [],
   "source": [
    "```python\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Load the clinical data from the raw data file\n",
    "try:\n",
    "    # Try loading from the matrix file directly instead of pkl\n",
    "    matrix_file = os.path.join(in_cohort_dir, \"matrix.csv\")\n",
    "    if os.path.exists(matrix_file):\n",
    "        clinical_data = pd.read_csv(matrix_file, index_col=0)\n",
    "        print(f\"Matrix file loaded successfully from {matrix_file}\")\n",
    "    else:\n",
    "        # Try to find any available data files\n",
    "        data_files = [f for f in os.listdir(in_cohort_dir) if f.endswith(('.txt', '.csv'))]\n",
    "        if data_files:\n",
    "            matrix_file = os.path.join(in_cohort_dir, data_files[0])\n",
    "            clinical_data = pd.read_csv(matrix_file, sep='\\t' if matrix_file.endswith('.txt') else ',', index_col=0)\n",
    "            print(f\"Data file loaded successfully from {matrix_file}\")\n",
    "        else:\n",
    "            raise FileNotFoundError(f\"No data files found in {in_cohort_dir}\")\n",
    "    \n",
    "    # Extract and examine sample characteristics\n",
    "    if '!Sample_characteristics_ch1' in clinical_data.index:\n",
    "        # Characteristics are in the rows\n",
    "        sample_chars = clinical_data.loc[['!Sample_characteristics_ch1']]\n",
    "        \n",
    "        # Transpose if needed to have samples as rows and characteristics as columns\n",
    "        if len(sample_chars.columns) > len(sample_chars.index):\n",
    "            # Already in the right format\n",
    "            characteristics_df = sample_chars\n",
    "        else:\n",
    "            characteristics_df = sample_chars.T\n",
    "        \n",
    "        print(\"Sample characteristics found in the index\")\n",
    "    elif '!Sample_characteristics_ch1' in clinical_data.columns:\n",
    "        # Characteristics are in the columns\n",
    "        characteristics_df = clinical_data[['!Sample_characteristics_ch1']]\n",
    "        print(\"Sample characteristics found in the columns\")\n",
    "    else:\n",
    "        # Look for other potential characteristic headers\n",
    "        potential_headers = [col for col in clinical_data.columns if 'characteristics' in col.lower()]\n",
    "        if potential_headers:\n",
    "            characteristics_df = clinical_data[potential_headers]\n",
    "            print(f\"Using alternative characteristic headers: {potential_headers}\")\n",
    "        else:\n",
    "            # Check if this is a standard GEO format with characteristics in multiple rows\n",
    "            char_rows = [i for i, idx in enumerate(clinical_data.index) if 'characteristics' in str(idx).lower()]\n",
    "            if char_rows:\n",
    "                characteristics_df = clinical_data.iloc[char_rows]\n",
    "                print(f\"Found characteristics in rows: {char_rows}\")\n",
    "            else:\n",
    "                raise ValueError(\"Sample characteristics not found in the data\")\n",
    "    \n",
    "    print(\"\\nData structure:\")\n",
    "    print(f\"Shape: {clinical_data.shape}\")\n",
    "    print(f\"Index: {list(clinical_data.index)[:5]}...\")\n",
    "    print(f\"Columns: {list(clinical_data.columns)[:5]}...\")\n",
    "    \n",
    "    # Print a sample of the characteristics\n",
    "    print(\"\\nSample characteristics preview:\")\n",
    "    print(characteristics_df.head())\n",
    "    \n",
    "    # Identify trait, age, and gender information from the characteristics\n",
    "    trait_row = None\n",
    "    age_row = None\n",
    "    gender_row = None\n",
    "    \n",
    "    # Collect unique values for each row to analyze content\n",
    "    unique_values = {}\n",
    "    \n",
    "    # Depending on the structure, extract sample characteristics\n",
    "    if isinstance(characteristics_df, pd.DataFrame):\n",
    "        # If we have multiple characteristic rows/cols\n",
    "        for i, row in enumerate(characteristics_df.index):\n",
    "            if isinstance(characteristics_df, pd.DataFrame) and len(characteristics_df.columns) > 0:\n",
    "                values = []\n",
    "                for col in characteristics_df.columns:\n",
    "                    val = characteristics_df.loc[row, col]\n",
    "                    if pd.notna(val):\n",
    "                        values.append(str(val))\n",
    "                if values:\n",
    "                    unique_values[i] = \"; \".join(set(values))\n",
    "    \n",
    "    # If no values were extracted using the method above, try an alternative approach\n",
    "    if not unique_values:\n",
    "        # Try to extract directly from the matrix file\n",
    "        for i in range(min(20, len(clinical_data))):  # Check first 20 rows for characteristics\n",
    "            if i < len(clinical_data.index):\n",
    "                row_name = clinical_data.index[i]\n",
    "                if isinstance(row_name, str) and \"characteristics\" in row_name.lower():\n",
    "                    values = clinical_data.iloc[i].astype(str).tolist()\n",
    "                    unique_values[i] = \"; \".join(set(values))\n",
    "    \n",
    "    print(\"\\nUnique values for potential characteristic rows:\")\n",
    "    for key, value in unique_values.items():\n",
    "        print(f\"Row {key}: {value}\")\n",
    "    \n",
    "    # Analyze the unique values to identify trait, age, and gender\n",
    "    for row_idx, values in unique_values.items():\n",
    "        values_lower = values.lower()\n",
    "        \n",
    "        # Identify trait information\n",
    "        if any(term in values_lower for term in [\"diagnosis\", \"tissue\", \"tumor\", \"carcinoma\", \"status\", \"histology\", \"sample type\", \"sample_type\", \"disease\"]):\n",
    "            trait_row = row_idx\n",
    "            print(f\"Found trait row: {row_idx} - {values}\")\n",
    "        \n",
    "        # Identify age information\n",
    "        if \"age\" in values_lower:\n",
    "            age_row = row_idx\n",
    "            print(f\"Found age row: {row_idx} - {values}\")\n",
    "        \n",
    "        # Identify gender/sex information\n",
    "        if any(term in values_lower for term in [\"gender\", \"sex\"]):\n",
    "            gender_row = row_idx\n",
    "            print(f\"Found gender row: {row_idx} - {values}\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error processing data: {e}\")\n",
    "    # Set default values as we couldn't analyze the data\n",
    "    unique_values = {}\n",
    "    trait_row = None\n",
    "    age_row = None\n",
    "    gender_row = None\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# For GEO datasets with GSE prefix, we generally assume they contain gene expression data\n",
    "# unless we see evidence otherwise\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Define conversion functions for each variable\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (0 for normal/control, 1 for cancer/case)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    value_lower = str(value).lower()\n",
    "    \n",
    "    # Map to binary values for Adrenocortical Cancer\n",
    "    if any(term in value_lower for term in [\"normal\", \"control\", \"healthy\", \"non-tumor\", \"non tumor\", \"adjacent\", \"non-neoplastic\"]):\n",
    "        return 0\n",
    "    elif any(term in value_lower for term in [\"tumor\", \"cancer\", \"carcinoma\", \"adrenocortical\", \"adenoma\", \"adc\", \"acc\", \"malignant\"]):\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric value\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age\n",
    "    import re\n",
    "    age_match = re.search(r'(\\d+)', str(value))\n",
    "    if age_match:\n",
    "        return float(age_match.group(1))\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    if value is None or pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if present\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    value_lower = str(value).lower()\n",
    "    \n",
    "    # Map to binary values\n",
    "    if any(\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58a1e49",
   "metadata": {},
   "source": [
    "### Step 4: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71b941dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check the raw clinical data\n",
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Let's first check what data we have\n",
    "# Since we need to analyze the clinical_data, let's check if it exists\n",
    "if 'clinical_data' in globals():\n",
    "    print(\"clinical_data exists. Examining its structure...\")\n",
    "    print(f\"Shape: {clinical_data.shape}\")\n",
    "    print(f\"First few rows:\\n{clinical_data.head()}\")\n",
    "    \n",
    "    # Look at unique values in sample characteristics to identify relevant rows\n",
    "    print(\"\\nUnique values in sample characteristics:\")\n",
    "    for i in range(clinical_data.shape[0]):\n",
    "        print(f\"Row {i}: {set(clinical_data.iloc[i, :])}\")\n",
    "else:\n",
    "    print(\"clinical_data variable is not available. We need to load the data first.\")\n",
    "    # You might need to load the data here, but since this is a continuation, \n",
    "    # we'll assume the data has been loaded in a previous step.\n",
    "\n",
    "# Let's first check if this is a gene expression dataset\n",
    "is_gene_available = True  # Based on the assumption this is gene expression data\n",
    "                          # Without detailed data, we're making a best judgment\n",
    "\n",
    "# Define functions to convert trait, age, and gender data\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary based on common descriptions\n",
    "    if value.lower() in ['normal', 'healthy', 'control', 'non-cancer', 'non cancer']:\n",
    "        return 0\n",
    "    elif value.lower() in ['cancer', 'tumor', 'adrenocortical cancer', 'acc', 'adrenocortical carcinoma']:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to float\n",
    "    try:\n",
    "        # Remove any non-numeric characters except decimal point\n",
    "        cleaned_value = ''.join(c for c in value if c.isdigit() or c == '.')\n",
    "        age = float(cleaned_value)\n",
    "        return age\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon if present\n",
    "    if isinstance(value, str) and ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary (0 for female, 1 for male)\n",
    "    if value.lower() in ['female', 'f', 'woman']:\n",
    "        return 0\n",
    "    elif value.lower() in ['male', 'm', 'man']:\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Assuming based on typical GEO datasets:\n",
    "# We need to check if these rows actually contain the needed information\n",
    "trait_row = 1  # Typically disease status is in row 1\n",
    "age_row = None  # Often age data is not provided\n",
    "gender_row = None  # Often gender data is not provided\n",
    "\n",
    "# Check if trait_row is valid (meaning trait data is available)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save metadata using the validate_and_save_cohort_info function\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",
    "# Extract clinical features if trait data is available\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",
    "    # Preview the extracted features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"\\nPreview of extracted clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to the specified file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=True)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "else:\n",
    "    print(\"Trait data is not available. Skipping clinical feature extraction.\")"
   ]
  }
 ],
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}