{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d593c7c0",
   "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 = \"Cystic_Fibrosis\"\n",
    "cohort = \"GSE76347\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Cystic_Fibrosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Cystic_Fibrosis/GSE76347\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Cystic_Fibrosis/GSE76347.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Cystic_Fibrosis/gene_data/GSE76347.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Cystic_Fibrosis/clinical_data/GSE76347.csv\"\n",
    "json_path = \"../../output/preprocess/Cystic_Fibrosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47df863c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4301e45",
   "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": "1c4345ba",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53d0032a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import os\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset contains nasal epithelial cell samples analyzed for microarray\n",
    "# analysis, which suggests gene expression data, not just miRNA or methylation data.\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (Cystic Fibrosis):\n",
    "# From sample characteristics, all patients have CF (row 0: 'disease state: CF')\n",
    "# Since everyone has CF, we can't do a case-control study within this dataset alone\n",
    "# But we can still use this dataset for gene expression analysis related to CF\n",
    "trait_row = 0  # Everyone has CF, so this is a constant feature but we still record it\n",
    "\n",
    "# For age:\n",
    "# No age information is available in the sample characteristics\n",
    "age_row = None  # Age data not available\n",
    "\n",
    "# For gender:\n",
    "# No gender information is available in the sample characteristics\n",
    "gender_row = None  # Gender data not available\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait data to binary format (1 for CF, 0 for non-CF).\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract the value after colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    # If the value indicates CF, return 1 (all patients in this study have CF)\n",
    "    if value.lower() == \"cf\":\n",
    "        return 1\n",
    "    return None  # For any other value, return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to continuous format.\"\"\"\n",
    "    # This function is included for completeness but won't be used since age_row is None\n",
    "    if value is None:\n",
    "        return None\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender data to binary format (0 for female, 1 for male).\"\"\"\n",
    "    # This function is included for completeness but won't be used since gender_row is None\n",
    "    if value is None:\n",
    "        return None\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    if value.lower() in [\"female\", \"f\"]:\n",
    "        return 0\n",
    "    elif value.lower() in [\"male\", \"m\"]:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability (all have CF, so trait data is available)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering on dataset usability\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",
    "# Create a properly structured clinical data DataFrame\n",
    "# We need to create a DataFrame where each row corresponds to a clinical feature\n",
    "# and each column corresponds to a sample\n",
    "\n",
    "# First, get the sample characteristics dictionary\n",
    "sample_characteristics_dict = {\n",
    "    0: ['disease state: CF'], \n",
    "    1: ['individual: patient # 001', 'individual: patient # 002', 'individual: patient # 004', 'individual: patient # 006', \n",
    "        'individual: patient # 009', 'individual: patient # 013', 'individual: patient # 015', 'individual: patient # 017', \n",
    "        'individual: patient # 019', 'individual: patient # 020', 'individual: patient # 021', 'individual: patient # 024', \n",
    "        'individual: patient # 025', 'individual: patient # 028', 'individual: patient # 030', 'individual: patient # 031', \n",
    "        'individual: patient # 003', 'individual: patient # 005', 'individual: patient # 010', 'individual: patient # 014', \n",
    "        'individual: patient # 018', 'individual: patient # 022', 'individual: patient # 027'],\n",
    "    2: ['treatment: digitoxin', 'treatment: placebo'],\n",
    "    3: ['dosage: 50 micro gram/daily', 'dosage: 100 micro gram/daily'],\n",
    "    4: ['time: post treatment', 'time: pre treatment'],\n",
    "    5: ['cell type: nasal epithelial cells']\n",
    "}\n",
    "\n",
    "# Create a DataFrame that represents the structure expected by geo_select_clinical_features\n",
    "# The function expects rows as features, not directly from the sample characteristics dict\n",
    "clinical_data = pd.DataFrame()\n",
    "for row_idx, values in sample_characteristics_dict.items():\n",
    "    clinical_data.loc[row_idx, 0] = values[0] if values else None\n",
    "\n",
    "# Since trait_row is not None, we extract the clinical features\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the library function\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 data\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(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, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c6479a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4c692cf",
   "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": "49e2380f",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36ffa6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the identifiers in the first few rows of the gene expression data\n",
    "# The IDs like '2315100' appear to be numeric identifiers that are not standard gene symbols\n",
    "# These are likely probe IDs from a microarray platform that need to be mapped to gene symbols\n",
    "\n",
    "# Standard human gene symbols follow patterns like BRCA1, TP53, etc.\n",
    "# The numeric identifiers seen in this dataset (2315100, 2315106, etc.) are not recognizable gene symbols\n",
    "\n",
    "# Since these are numeric identifiers rather than human gene symbols, \n",
    "# they will require mapping to standard gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe84f699",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "154aad2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "gene_annotation = get_gene_annotation(soft_file)\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"Gene annotation preview:\")\n",
    "print(preview_df(gene_annotation))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5e1f51",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "579c2004",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify columns for mapping\n",
    "# From the previous output, we can see:\n",
    "# - 'ID' column contains the probe identifiers (matching the format in gene_data index)\n",
    "# - 'gene_assignment' column contains information about gene symbols\n",
    "\n",
    "# Create a gene mapping dataframe directly using the get_gene_mapping function\n",
    "# The 'ID' column contains the probe identifiers and the 'gene_assignment' column contains gene symbol information\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
    "\n",
    "# Preview the mapping dataframe\n",
    "print(\"Gene mapping preview:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "# Use the apply_gene_mapping function from the library\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# Print information about the resulting gene expression data\n",
    "print(f\"\\nGene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"\\nFirst few gene symbols:\")\n",
    "print(gene_data.index[:5])\n",
    "\n",
    "# Normalize gene symbols to ensure consistency\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"\\nGene expression data shape after normalization: {gene_data.shape}\")\n",
    "print(\"\\nFirst few normalized gene symbols:\")\n",
    "print(gene_data.index[:5])\n",
    "\n",
    "# Create directory if it doesn't exist\n",
    "os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)\n",
    "\n",
    "# Save the gene expression data\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"\\nGene expression data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928d905e",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "031eb6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. For step 1, we'll skip normalizing gene symbols again since it was already done in the previous step\n",
    "# and the normalized gene data is already saved\n",
    "\n",
    "# 2. Load the saved clinical data instead of reprocessing it\n",
    "clinical_data_path = out_clinical_data_file\n",
    "if os.path.exists(clinical_data_path):\n",
    "    selected_clinical_df = pd.read_csv(clinical_data_path)\n",
    "    print(f\"Loaded clinical data from {clinical_data_path}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "else:\n",
    "    # If the file doesn't exist for some reason, extract clinical features as a fallback\n",
    "    soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "    selected_clinical_df = geo_select_clinical_features(\n",
    "        clinical_data, \n",
    "        trait=trait,  # Use the trait variable from setup\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",
    "    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",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "\n",
    "# Load the normalized gene data \n",
    "gene_data_path = out_gene_data_file\n",
    "if os.path.exists(gene_data_path):\n",
    "    normalized_gene_data = pd.read_csv(gene_data_path, index_col=0)\n",
    "    print(f\"Loaded normalized gene data from {gene_data_path}\")\n",
    "    print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "else:\n",
    "    print(\"Error: Normalized gene data file not found\")\n",
    "    \n",
    "# 3. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "# Determine the actual column name for the trait\n",
    "trait_column = '0'  # Based on the clinical data preview\n",
    "linked_data = handle_missing_values(linked_data, trait_column)\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine whether the trait and demographic features are biased\n",
    "# Check if trait is biased\n",
    "if len(linked_data[trait_column].unique()) == 2:  # binary trait\n",
    "    is_trait_biased = judge_binary_variable_biased(linked_data, trait_column)\n",
    "else:  # continuous trait\n",
    "    is_trait_biased = judge_continuous_variable_biased(linked_data, trait_column)\n",
    "\n",
    "# We don't need to do any further processing for demographic variables since none exist in this dataset\n",
    "unbiased_linked_data = linked_data  # No biased features to remove\n",
    "\n",
    "# 6. Conduct final quality validation and save cohort information\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=unbiased_linked_data,\n",
    "    note=f\"Dataset contains gene expression data from nasal epithelial cells of cystic fibrosis patients in a study examining the effects of digitoxin on airway inflammation.\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_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 and was not saved\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcdefea6",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66ffc27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(\"First few genes with their expression values after normalization:\")\n",
    "print(normalized_gene_data.head())\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. Extract clinical features using the functions defined in step 2\n",
    "# First, let's load the clinical data again to ensure we have the latest version\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Extract clinical features using melanoma vs normal tissue as the binary trait\n",
    "selected_clinical_df = geo_select_clinical_features(\n",
    "    clinical_data, \n",
    "    trait=\"Melanoma\", \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",
    "# 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",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "# Transpose normalized gene data for linking\n",
    "gene_data_t = normalized_gene_data.T\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape (before handling missing values): {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, \"Melanoma\")\n",
    "print(f\"Data after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 5. Determine whether the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, \"Melanoma\")\n",
    "\n",
    "# 6. Conduct final quality validation and save cohort information\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=unbiased_linked_data,\n",
    "    note=\"Dataset contains gene expression data comparing melanoma (primary and metastatic) with normal tissue/nevi.\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    unbiased_linked_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 and was not saved\")"
   ]
  }
 ],
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}