{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f60932d",
   "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 = \"Breast_Cancer\"\n",
    "cohort = \"GSE283522\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Breast_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Breast_Cancer/GSE283522\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Breast_Cancer/GSE283522.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Breast_Cancer/gene_data/GSE283522.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Breast_Cancer/clinical_data/GSE283522.csv\"\n",
    "json_path = \"../../output/preprocess/Breast_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9505ea4",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4a5273b",
   "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": "2b13b1c5",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f5284ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the information in the background metadata, this dataset contains RNA-seq data\n",
    "# from FFPE breast tumors, indicating it contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Trait (Breast Cancer) data is available\n",
    "# Row 6 contains 'sample category' which indicates whether the sample is invasive breast cancer \n",
    "# or other types like healthy, DCIS, etc.\n",
    "trait_row = 6\n",
    "\n",
    "# Age data is available in row 2\n",
    "age_row = 2\n",
    "\n",
    "# Gender/Sex data is available in row 5\n",
    "gender_row = 5\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait data to binary (0: healthy/no cancer, 1: cancer).\"\"\"\n",
    "    if pd.isna(value):\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",
    "    # Map sample categories to binary values\n",
    "    if \"invasive breast cancer\" in value:\n",
    "        return 1\n",
    "    elif \"true healthy\" in value or \"no tumor\" in value:\n",
    "        return 0\n",
    "    elif \"DCIS\" in value or \"LCIS\" in value or \"extra ROI\" in value:\n",
    "        # These are precursor lesions or special cases, not considered invasive cancer\n",
    "        return 0\n",
    "    elif \"positive control\" in value:\n",
    "        # Controls shouldn't be counted as cases\n",
    "        return None\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to continuous values.\"\"\"\n",
    "    if pd.isna(value):\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",
    "    if \"not applicable\" in value or \"missing\" in value:\n",
    "        return None\n",
    "    \n",
    "    # Age is given in ranges like \"55 - 59\"\n",
    "    if \"-\" in value:\n",
    "        # Extract the range and use the midpoint\n",
    "        try:\n",
    "            parts = value.replace(' ', '').split('-')\n",
    "            lower = int(parts[0])\n",
    "            upper = int(parts[1])\n",
    "            return (lower + upper) / 2\n",
    "        except:\n",
    "            return None\n",
    "    \n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender/sex data to binary (0: female, 1: male).\"\"\"\n",
    "    if pd.isna(value):\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",
    "    if \"not applicable\" in value or \"missing\" in value:\n",
    "        return None\n",
    "    \n",
    "    if \"female\" in value.lower():\n",
    "        return 0\n",
    "    elif \"male\" in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Conduct initial filtering on the usability of the dataset\n",
    "# trait_row is not None, so 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",
    "# We need to create a DataFrame from the sample characteristics dictionary\n",
    "# The sample characteristics are the values provided in the previous output\n",
    "\n",
    "# Create a dictionary to store the sample characteristics for each row\n",
    "sample_chars = {}\n",
    "for row_idx, values in Sample_Characteristics_Dictionary.items():\n",
    "    sample_chars[row_idx] = values\n",
    "\n",
    "# Convert the dictionary to a DataFrame\n",
    "clinical_data = pd.DataFrame(sample_chars)\n",
    "\n",
    "# Now extract the clinical features\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 selected clinical features\n",
    "preview = preview_df(selected_clinical_df)\n",
    "print(\"Preview of clinical features:\")\n",
    "print(preview)\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, index=False)\n",
    "print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2aa5260",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab227b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import json\n",
    "import gzip\n",
    "import re\n",
    "from typing import Callable, Optional\n",
    "\n",
    "# Check what files are available in the cohort directory\n",
    "cohort_files = os.listdir(in_cohort_dir)\n",
    "print(f\"Files in cohort directory: {cohort_files}\")\n",
    "\n",
    "# Function to parse GEO series matrix file\n",
    "def parse_geo_series_matrix(file_path):\n",
    "    with gzip.open(file_path, 'rt') as f:\n",
    "        lines = f.readlines()\n",
    "    \n",
    "    # Extract sample characteristics\n",
    "    characteristics_rows = {}\n",
    "    sample_ids = []\n",
    "    data_start = False\n",
    "    \n",
    "    for i, line in enumerate(lines):\n",
    "        if line.startswith('!Sample_geo_accession'):\n",
    "            sample_ids = line.strip().split('\\t')[1:]\n",
    "        elif line.startswith('!Sample_characteristics_ch1'):\n",
    "            parts = line.strip().split('\\t')\n",
    "            header = parts[0]\n",
    "            values = parts[1:]\n",
    "            if len(values) > 0:\n",
    "                row_idx = len(characteristics_rows)\n",
    "                characteristics_rows[row_idx] = values\n",
    "        elif line.startswith('!series_matrix_table_begin'):\n",
    "            data_start = True\n",
    "            data_start_line = i\n",
    "            break\n",
    "    \n",
    "    # Create clinical dataframe\n",
    "    clinical_df = pd.DataFrame(index=sample_ids)\n",
    "    for row_idx, values in characteristics_rows.items():\n",
    "        clinical_df[f'characteristic_{row_idx}'] = values\n",
    "    \n",
    "    # Check if there's gene expression data\n",
    "    has_gene_data = data_start\n",
    "    \n",
    "    return clinical_df, has_gene_data\n",
    "\n",
    "# Parse the GEO series matrix file\n",
    "series_matrix_path = os.path.join(in_cohort_dir, \"GSE283522_series_matrix.txt.gz\")\n",
    "if os.path.exists(series_matrix_path):\n",
    "    clinical_data, is_gene_available = parse_geo_series_matrix(series_matrix_path)\n",
    "    print(f\"Clinical data shape: {clinical_data.shape}\")\n",
    "    \n",
    "    # Display unique values for each sample characteristic\n",
    "    for i, col in enumerate(clinical_data.columns):\n",
    "        unique_values = clinical_data[col].unique()\n",
    "        print(f\"Row {i}, Column '{col}': {unique_values}\")\n",
    "else:\n",
    "    print(f\"Series matrix file not found: {series_matrix_path}\")\n",
    "    clinical_data = pd.DataFrame()\n",
    "    is_gene_available = False\n",
    "\n",
    "# Identify and process clinical variables\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: 1 for breast cancer, 0 for control/normal\n",
    "    if any(term in value for term in ['cancer', 'tumor', 'malignant', 'carcinoma']):\n",
    "        return 1\n",
    "    elif any(term in value for term in ['normal', 'control', 'benign', 'healthy']):\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value)\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract age as a number\n",
    "    age_match = re.search(r'(\\d+)', value)\n",
    "    if age_match:\n",
    "        return float(age_match.group(1))\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value in ['female', 'f', 'woman']:\n",
    "        return 0\n",
    "    elif value in ['male', 'm', 'man']:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Initialize row indices as None\n",
    "trait_row = None\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# Look through the sample characteristics to find the appropriate rows\n",
    "if not clinical_data.empty:\n",
    "    for i, col in enumerate(clinical_data.columns):\n",
    "        col_values = clinical_data[col].astype(str).str.lower()\n",
    "        \n",
    "        # Check for trait-related information\n",
    "        trait_terms = ['tissue', 'diagnosis', 'sample type', 'status', 'source', 'histology', 'disease']\n",
    "        if any(term in col.lower() for term in trait_terms):\n",
    "            # Check if values indicate cancer/normal distinction\n",
    "            has_trait_terms = any(('cancer' in val or 'tumor' in val or 'normal' in val or \n",
    "                                 'control' in val or 'benign' in val or 'malignant' in val) \n",
    "                                for val in col_values)\n",
    "            # Check if there's more than one unique value\n",
    "            has_multiple_values = len(set([convert_trait(val) for val in col_values if convert_trait(val) is not None])) > 1\n",
    "            \n",
    "            if has_trait_terms and has_multiple_values:\n",
    "                trait_row = i\n",
    "        \n",
    "        # Check for age information\n",
    "        if 'age' in col.lower():\n",
    "            # Check if there's more than one unique value after conversion\n",
    "            ages = [convert_age(val) for val in col_values if convert_age(val) is not None]\n",
    "            if len(ages) > 1 and len(set(ages)) > 1:\n",
    "                age_row = i\n",
    "        \n",
    "        # Check for gender information\n",
    "        if 'gender' in col.lower() or 'sex' in col.lower():\n",
    "            # Check if there's more than one unique value after conversion\n",
    "            genders = [convert_gender(val) for val in col_values if convert_gender(val) is not None]\n",
    "            if len(genders) > 1 and len(set(genders)) > 1:\n",
    "                gender_row = i\n",
    "\n",
    "# Save metadata - initial filtering\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path, \n",
    "                             is_gene_available=is_gene_available, \n",
    "                             is_trait_available=is_trait_available)\n",
    "\n",
    "# Extract clinical features if trait data is available\n",
    "if is_trait_available:\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 if age_row is not None else None,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender if gender_row is not None else None\n",
    "    )\n",
    "    \n",
    "    # Preview the selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(f\"Preview of selected clinical features: {preview}\")\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04bf58c2",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36f952e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# Set gene availability flag\n",
    "is_gene_available = True  # Initially assume gene data is available\n",
    "\n",
    "# First check if the matrix file contains the expected marker\n",
    "found_marker = False\n",
    "try:\n",
    "    with gzip.open(matrix_file, 'rt') as file:\n",
    "        for line in file:\n",
    "            if \"!series_matrix_table_begin\" in line:\n",
    "                found_marker = True\n",
    "                break\n",
    "    \n",
    "    if found_marker:\n",
    "        print(\"Found the matrix table marker in the file.\")\n",
    "    else:\n",
    "        print(\"Warning: Could not find '!series_matrix_table_begin' marker in the file.\")\n",
    "        \n",
    "    # Try to extract gene data from the matrix file\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    \n",
    "    if gene_data.shape[0] == 0:\n",
    "        print(\"Warning: Extracted gene data has 0 rows.\")\n",
    "        is_gene_available = False\n",
    "    else:\n",
    "        print(f\"Gene data shape: {gene_data.shape}\")\n",
    "        # Print the first 20 gene/probe identifiers\n",
    "        print(\"First 20 gene/probe identifiers:\")\n",
    "        print(gene_data.index[:20].tolist())\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    is_gene_available = False\n",
    "    \n",
    "    # Try to diagnose the file format\n",
    "    print(\"Examining file content to diagnose the issue:\")\n",
    "    try:\n",
    "        with gzip.open(matrix_file, 'rt') as file:\n",
    "            for i, line in enumerate(file):\n",
    "                if i < 10:  # Print first 10 lines to diagnose\n",
    "                    print(f\"Line {i}: {line.strip()[:100]}...\")  # Print first 100 chars of each line\n",
    "                else:\n",
    "                    break\n",
    "    except Exception as e2:\n",
    "        print(f\"Error examining file: {e2}\")\n",
    "\n",
    "if not is_gene_available:\n",
    "    print(\"Gene expression data could not be successfully extracted from this dataset.\")"
   ]
  }
 ],
 "metadata": {},
 "nbformat": 4,
 "nbformat_minor": 5
}