{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "756aada9",
   "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 = \"Chronic_kidney_disease\"\n",
    "cohort = \"GSE60861\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Chronic_kidney_disease\"\n",
    "in_cohort_dir = \"../../input/GEO/Chronic_kidney_disease/GSE60861\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Chronic_kidney_disease/GSE60861.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Chronic_kidney_disease/gene_data/GSE60861.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Chronic_kidney_disease/clinical_data/GSE60861.csv\"\n",
    "json_path = \"../../output/preprocess/Chronic_kidney_disease/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a63a3beb",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9edd49f1",
   "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": "36557a74",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c3e3f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "I'll correct the code to properly handle the sample characteristics and extract clinical features.\n",
    "\n",
    "```python\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from typing import Dict, List, Any, Optional, Callable\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset appears to have both miRNA and mRNA data\n",
    "# Since it mentions \"renal miRNA- and mRNA-expression signatures\", we can set is_gene_available to True\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (CKD progression status)\n",
    "# Looking at the sample characteristics dictionary, we can see that key 4 contains 'clinical course: stable/progressive'\n",
    "trait_row = 4\n",
    "\n",
    "# For age\n",
    "# Looking at the sample characteristics dictionary, keys 1 and 2 contain age data\n",
    "# Key 1 has more consistent age data format, so we'll use it\n",
    "age_row = 1\n",
    "\n",
    "# For gender\n",
    "# Looking at the sample characteristics dictionary, key 0 contains gender data\n",
    "gender_row = 0\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert trait data to binary format:\n",
    "    - 1 for progressive CKD\n",
    "    - 0 for stable CKD\n",
    "    - None for unknown/missing values\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = value.lower().strip() if isinstance(value, str) else str(value).lower().strip()\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value == \"progressive\":\n",
    "        return 1\n",
    "    elif value == \"stable\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age data to continuous format.\n",
    "    Extract numeric age value after the colon.\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).strip()\n",
    "    \n",
    "    if \":\" in value:\n",
    "        age_str = value.split(\":\", 1)[1].strip()\n",
    "        try:\n",
    "            return float(age_str)\n",
    "        except ValueError:\n",
    "            return None\n",
    "    else:\n",
    "        try:\n",
    "            return float(value)\n",
    "        except ValueError:\n",
    "            return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"\n",
    "    Convert gender data to binary format:\n",
    "    - 0 for female\n",
    "    - 1 for male\n",
    "    - None for unknown/missing values\n",
    "    \"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = value.lower().strip() if isinstance(value, str) else str(value).lower().strip()\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value == \"male\":\n",
    "        return 1\n",
    "    elif value == \"female\":\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort information\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 (if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Create clinical_data DataFrame\n",
    "    # The expected format for geo_select_clinical_features is a DataFrame where\n",
    "    # each row corresponds to a feature category (gender, age, etc.)\n",
    "    sample_chars_dict = {\n",
    "        0: ['gender: male', 'gender: female', 'tissue: kidney biopsy'],\n",
    "        1: ['age (yrs): 72', 'age (yrs): 20', 'age (yrs): 64', 'age (yrs): 17', 'age (yrs): 46', 'age (yrs): 55', 'age (yrs): 74', 'age (yrs): 49', 'age (yrs): 42', 'age (yrs): 73', 'age (yrs): 63', 'age (yrs): 33', 'age (yrs): 24', 'age (yrs): 45', 'age (yrs): 70', 'age (yrs): 60', 'age (yrs): 67', 'age (yrs): 31', 'age (yrs): 53', 'age (yrs): 22', 'age (yrs): 54', 'age (yrs): 40', 'age (yrs): 38', 'age (yrs): 19', 'age (yrs): 28', 'age (yrs): 65', 'age (yrs): 58', 'age (yrs): 56', 'age (yrs): 34', 'age (yrs): 59'],\n",
    "        2: ['diagnosis: Diabetic Nephropathy', 'diagnosis: Focal-Segmental Glomerulosclerosis', 'diagnosis: Hypertensive Nephropathy', 'diagnosis: IgA-Nephropathy', 'diagnosis: Membranous Nephropathy', 'diagnosis: Minimal-Change Disease', 'diagnosis: Other/Unknown', 'age (yrs): 41.6', 'age (yrs): 59.0', 'age (yrs): 21.0', 'age (yrs): 33.0', 'age (yrs): 35.0', 'age (yrs): 24.0', 'age (yrs): 70.0', 'age (yrs): 43.0', 'age (yrs): 45.0', 'age (yrs): 44.0', 'age (yrs): 54.0', 'age (yrs): 74.0', 'age (yrs): 31.0', 'age (yrs): 49.0', 'age (yrs): 28.0', 'age (yrs): 26.0', 'age (yrs): 47.0', 'age (yrs): 20.0', 'age (yrs): 71.0', 'age (yrs): 58.0', 'age (yrs): 18.0', 'age (yrs): 32.0', 'age (yrs): 55.0'],\n",
    "        3: ['clinical course: stable', 'clinical course: progressive', 'diagnosis: IgA nephropathy', 'diagnosis: Glomerulonephritis, not specified', 'diagnosis: Lupus nephritis class 4', 'diagnosis: Lupus nephritis class 5', 'diagnosis: Membranoproliferative glomerulonephritis', 'diagnosis: Focal-segmental glomerulosclerosis', 'diagnosis: Vasculitis', 'diagnosis: Membranous nephropathy', 'diagnosis: Lupus nephritis class 3', 'diagnosis: Minimal change disease', 'diagnosis: Diabetic nephropathy'],\n",
    "        4: [np.nan, 'clinical course: progressive', 'clinical course: stable']\n",
    "    }\n",
    "    \n",
    "    # Create a DataFrame with the correct structure for geo_select_clinical_features\n",
    "    # Each row is a feature category, with the row index corresponding to the feature row numbers\n",
    "    max_row = max(sample_chars_dict.keys()) + 1\n",
    "    clinical_data = pd.DataFrame(index=range(max_row))\n",
    "    \n",
    "    # Add each feature's unique values as columns in the DataFrame\n",
    "    for row_idx, values in sample_chars_dict.items():\n",
    "        for col_idx, value in enumerate(values):\n",
    "            clinical_data.loc[row_idx, col_idx] = value\n",
    "    \n",
    "    # Extract 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 extracted clinical features\n",
    "    print(\"Preview of extracted clinical features:\")\n",
    "    preview_result = preview_df(selected_clinical_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8bf64d",
   "metadata": {},
   "source": [
    "### Step 3: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c72cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "import gzip\n",
    "\n",
    "# Get background information and clinical data from the dataset\n",
    "background_info, clinical_data = get_background_and_clinical_data(in_cohort_dir)\n",
    "\n",
    "# Get unique values for each row to understand the data structure\n",
    "row_values = get_unique_values_by_row(clinical_data)\n",
    "\n",
    "print(\"Background Information:\")\n",
    "for key, value in background_info.items():\n",
    "    print(f\"{key}: {value}\")\n",
    "\n",
    "print(\"\\nSample Characteristics by Row:\")\n",
    "for row_idx, values in row_values.items():\n",
    "    print(f\"Row {row_idx}: {values}\")\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Check if gene expression data is available based on background info\n",
    "is_gene_available = True\n",
    "if \"platform_technology\" in background_info:\n",
    "    tech = background_info[\"platform_technology\"].lower()\n",
    "    if \"mirna\" in tech or \"methylation\" in tech:\n",
    "        is_gene_available = False\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# Analyze the rows to identify trait, age, and gender information\n",
    "\n",
    "# For trait (Chronic kidney disease)\n",
    "trait_row = None\n",
    "# Look for rows that might contain disease status information\n",
    "for row_idx, values in row_values.items():\n",
    "    values_str = ' '.join([str(v).lower() for v in values])\n",
    "    if ('ckd' in values_str or \n",
    "        'chronic kidney disease' in values_str or \n",
    "        'kidney' in values_str or \n",
    "        'control' in values_str or \n",
    "        'case' in values_str or \n",
    "        'disease' in values_str or\n",
    "        'patient' in values_str):\n",
    "        trait_row = row_idx\n",
    "        print(f\"Found trait information in row {row_idx}: {values}\")\n",
    "        break\n",
    "\n",
    "def convert_trait(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if ('ckd' in value or \n",
    "        'chronic kidney disease' in value or \n",
    "        'renal disease' in value or \n",
    "        'kidney disease' in value or \n",
    "        'patient' in value or \n",
    "        'case' in value):\n",
    "        return 1\n",
    "    elif ('control' in value or \n",
    "          'healthy' in value or \n",
    "          'normal' in value):\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "# For age\n",
    "age_row = None\n",
    "# Look for rows that might contain age information\n",
    "for row_idx, values in row_values.items():\n",
    "    values_str = ' '.join([str(v).lower() for v in values])\n",
    "    if 'age' in values_str:\n",
    "        age_row = row_idx\n",
    "        print(f\"Found age information in row {row_idx}: {values}\")\n",
    "        break\n",
    "\n",
    "def convert_age(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    value = str(value)\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to extract numeric age\n",
    "    import re\n",
    "    matches = re.findall(r'\\d+\\.?\\d*', value)\n",
    "    if matches:\n",
    "        try:\n",
    "            return float(matches[0])\n",
    "        except:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "# For gender\n",
    "gender_row = None\n",
    "# Look for rows that might contain gender information\n",
    "for row_idx, values in row_values.items():\n",
    "    values_str = ' '.join([str(v).lower() for v in values])\n",
    "    if 'gender' in values_str or 'sex' in values_str or 'male' in values_str or 'female' in values_str:\n",
    "        gender_row = row_idx\n",
    "        print(f\"Found gender information in row {row_idx}: {values}\")\n",
    "        break\n",
    "\n",
    "def convert_gender(value):\n",
    "    if value is None:\n",
    "        return None\n",
    "    value = str(value).lower()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if 'female' in value or 'f' == value.strip():\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value.strip():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Check if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Validate and save cohort info\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 is_trait_available:\n",
    "    # Extract 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 dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"\\nPreview of selected clinical features:\")\n",
    "    for key, values in preview.items():\n",
    "        print(f\"{key}: {values}\")\n",
    "    \n",
    "    # Save the clinical data\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",
    "else:\n",
    "    print(\"Cannot process clinical data: trait information not found in the dataset.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c36012e4",
   "metadata": {},
   "source": [
    "### Step 4: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a237c321",
   "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  # Assume gene data is available\n",
    "\n",
    "# Extract gene data\n",
    "try:\n",
    "    # Extract gene data from the matrix file\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # Print the first 20 gene/probe identifiers\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20].tolist())\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    print(f\"File path: {matrix_file}\")\n",
    "    print(\"Please check if the file exists and contains the expected markers.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ac7f0f",
   "metadata": {},
   "source": [
    "### Step 5: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e52bb4e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the gene identifiers shown in the previous output\n",
    "# These identifiers (like 'A_23_P100001') appear to be Agilent microarray probe IDs\n",
    "# They are not standard human gene symbols and will need to be mapped\n",
    "\n",
    "# Agilent probe IDs typically start with 'A_' followed by numbers and letters\n",
    "# This format indicates these are probe identifiers, not gene symbols\n",
    "# We will need to map these to standard gene symbols for analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b46831",
   "metadata": {},
   "source": [
    "### Step 6: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf609b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Look more closely at columns that might contain gene information\n",
    "print(\"\\nExamining potential gene mapping columns:\")\n",
    "potential_gene_columns = ['gene_assignment', 'mrna_assignment', 'swissprot', 'unigene']\n",
    "for col in potential_gene_columns:\n",
    "    if col in gene_annotation.columns:\n",
    "        print(f\"\\nSample values from '{col}' column:\")\n",
    "        print(gene_annotation[col].head(3).tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bd22cf7",
   "metadata": {},
   "source": [
    "### Step 7: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e43a7c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Analyze the gene annotation dataframe to identify the correct columns for mapping\n",
    "# Based on the previous output, we need:\n",
    "# - 'ID' column which contains the probe identifiers matching those in gene_data\n",
    "# - 'GENE_SYMBOL' column which contains the gene symbols we want to map to\n",
    "\n",
    "# First get the gene expression data and annotation data again\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "gene_expression_data = get_genetic_data(matrix_file)\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# Display the first few rows of gene annotation to confirm column selection\n",
    "print(\"First few rows of gene annotation:\")\n",
    "print(gene_annotation[['ID', 'GENE_SYMBOL']].head())\n",
    "\n",
    "# 2. Get a gene mapping dataframe\n",
    "# Extract the relevant columns for mapping: probe IDs and gene symbols\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='GENE_SYMBOL')\n",
    "print(f\"\\nMapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First few rows of mapping data:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_expression_data, mapping_df)\n",
    "print(f\"\\nConverted gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10].tolist())\n",
    "\n",
    "# Normalize gene symbols to ensure consistency\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"\\nAfter normalization, gene data shape: {gene_data.shape}\")\n",
    "print(\"First few normalized gene symbols:\")\n",
    "print(gene_data.index[:10].tolist())\n",
    "\n",
    "# Save the gene data to a file\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\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": "af08ca6f",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a29771d5",
   "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",
    "\n",
    "# Define the functions for extracting clinical features\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert clinical course to binary format: 1 for progressive, 0 for stable\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower().strip()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value == 'progressive':\n",
    "        return 1\n",
    "    elif value == 'stable':\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to float\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).strip()\n",
    "    if ':' in value:\n",
    "        age_str = value.split(':', 1)[1].strip()\n",
    "        try:\n",
    "            return float(age_str)\n",
    "        except ValueError:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary: 0 for female, 1 for male\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    value = str(value).lower().strip()\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value == 'female':\n",
    "        return 0\n",
    "    elif value == 'male':\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# Re-extract clinical data if the saved file doesn't exist\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "trait_row = 4  # Based on the analysis in step 2\n",
    "age_row = 1    # Based on the analysis in step 2\n",
    "gender_row = 0 # Based on the analysis in step 2\n",
    "\n",
    "# Extract 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",
    "print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 1. Load the normalized gene data (already done in step 7)\n",
    "if 'gene_data' not in locals():\n",
    "    gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "    print(f\"Loaded gene data from {out_gene_data_file}\")\n",
    "\n",
    "# 2. Link the clinical and genetic data\n",
    "print(\"\\nLinking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, 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",
    "# 3. Handle missing values in the linked data\n",
    "print(\"\\nHandling missing values...\")\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine if the trait and demographic features are biased\n",
    "print(\"\\nChecking for bias in trait and demographic features...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct final quality validation and save relevant information\n",
    "print(\"\\nConducting final quality validation...\")\n",
    "is_gene_available = True  # We've confirmed gene data is available in previous steps\n",
    "is_trait_available = True  # We've confirmed trait data is available in previous steps\n",
    "\n",
    "note = \"This dataset contains gene expression data from kidney biopsies. It classifies samples based on clinical course (stable or progressive chronic kidney disease).\"\n",
    "\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. Save the linked data if it's 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(\"Linked data not saved as dataset is not usable for the current trait study.\")"
   ]
  }
 ],
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