{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a94e06c",
   "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 = \"Mitochondrial_Disorders\"\n",
    "cohort = \"GSE30933\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Mitochondrial_Disorders\"\n",
    "in_cohort_dir = \"../../input/GEO/Mitochondrial_Disorders/GSE30933\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Mitochondrial_Disorders/GSE30933.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Mitochondrial_Disorders/gene_data/GSE30933.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Mitochondrial_Disorders/clinical_data/GSE30933.csv\"\n",
    "json_path = \"../../output/preprocess/Mitochondrial_Disorders/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80c30409",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53abbc08",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac93bd90",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "921f5728",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene expression data availability assessment\n",
    "# Based on the background information, this study focuses on gene expression profiling in PBMCs from FRDA patients.\n",
    "# The series title mentions \"A Gene Expression Phenotype In Lymphocytes From Friedreich's Ataxia Patients\"\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# Reviewing sample characteristics dictionary:\n",
    "# Row 0: 'disease status' contains information about FRDA (a type of mitochondrial disorder), Carrier, or Normal status\n",
    "trait_row = 0  # This contains the disease status information which matches our trait (Mitochondrial_Disorders)\n",
    "\n",
    "# Age and gender information are not provided in the sample characteristics\n",
    "age_row = None\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert disease status to binary format (1 for disease, 0 for non-disease).\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary: FRDA (disease) = 1, Normal/Carrier = 0\n",
    "    if value.lower() == \"frda\":\n",
    "        return 1\n",
    "    elif value.lower() in [\"normal\", \"carrier\"]:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to numerical value.\"\"\"\n",
    "    # Function defined but not used since age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 for female, 1 for male).\"\"\"\n",
    "    # Function defined but not used since gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - Initial Filtering\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",
    "    # Load the matrix file\n",
    "    matrix_file = os.path.join(in_cohort_dir, \"GSE30933_series_matrix.txt.gz\")\n",
    "    \n",
    "    # Parse the clinical data from the matrix file\n",
    "    # Since the tools module is already imported, we can directly access its functions\n",
    "    clinical_data = get_geo_clinical_data(matrix_file)\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 data\n",
    "    print(\"Preview of selected clinical features:\")\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b30929",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa4a67ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the path to the soft and matrix files\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Looking more carefully at the background information\n",
    "# This is a SuperSeries which doesn't contain direct gene expression data\n",
    "# Need to investigate the soft file to find the subseries\n",
    "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
    "\n",
    "# Open the SOFT file to try to identify subseries\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    subseries_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'Series_relation' in line and 'SuperSeries of' in line:\n",
    "            subseries_lines.append(line.strip())\n",
    "        if i > 1000:  # Limit search to first 1000 lines\n",
    "            break\n",
    "\n",
    "# Display the subseries found\n",
    "if subseries_lines:\n",
    "    print(\"Found potential subseries references:\")\n",
    "    for line in subseries_lines:\n",
    "        print(line)\n",
    "else:\n",
    "    print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
    "\n",
    "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(\"\\nGene data extraction result:\")\n",
    "    print(\"Number of rows:\", len(gene_data))\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n",
    "    print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438811ea",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee60ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the identifiers, these are Illumina probes (ILMN_*) and not human gene symbols\n",
    "# These probe IDs need to be mapped to standard gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6512cb3c",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5cb92af",
   "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": "77e92ed6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c05bebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the relevant columns for mapping\n",
    "# Based on the preview, we can see:\n",
    "# - Gene identifiers in expression data are in the 'ID' column (ILMN_* format)\n",
    "# - Gene symbols are in the 'SYMBOL' column\n",
    "\n",
    "# 2. Get the gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='SYMBOL')\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "print(\"\\nGene data after mapping:\")\n",
    "print(f\"Number of genes: {len(gene_data)}\")\n",
    "print(\"First few genes:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the gene expression data\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": "828f1b29",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3d21613",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Re-load the gene expression data that was saved in a previous step\n",
    "try:\n",
    "    gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "    print(f\"Successfully loaded gene data from {out_gene_data_file}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error loading gene data: {e}\")\n",
    "    raise\n",
    "\n",
    "# 2. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data.shape}\")\n",
    "\n",
    "# 3. Extract clinical data from the matrix file again to be sure we have the correct data\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",
    "# Based on the sample characteristics dictionary, the trait information is in row 0 (disease status)\n",
    "# Define conversion functions for the clinical features based on the actual data\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert FRDA disease status to binary (1 = FRDA, 0 = Normal or Carrier)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.lower() == \"frda\":\n",
    "        return 1\n",
    "    elif value.lower() in [\"normal\", \"carrier\"]:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# Create the clinical dataframe using the correct trait row\n",
    "trait_row = 0  # Row for disease status (FRDA)\n",
    "is_trait_available = True\n",
    "\n",
    "try:\n",
    "    clinical_df = geo_select_clinical_features(\n",
    "        clinical_data,\n",
    "        trait=trait,  # Using the predefined trait variable\n",
    "        trait_row=trait_row,\n",
    "        convert_trait=convert_trait,\n",
    "        age_row=None,    # Age information not available\n",
    "        convert_age=None,\n",
    "        gender_row=None,  # Gender information not available\n",
    "        convert_gender=None\n",
    "    )\n",
    "    \n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview_df(clinical_df.T))  # Transpose for better viewing\n",
    "    \n",
    "    # Save the clinical data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Saved clinical data to {out_clinical_data_file}\")\n",
    "    \n",
    "    # 3. Link clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
    "    print(f\"Shape of linked data: {linked_data.shape}\")\n",
    "    \n",
    "    # 4. Handle missing values in the linked data\n",
    "    linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "    print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
    "    \n",
    "    # 5. Check if the trait is biased\n",
    "    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
    "    \n",
    "    # 6. Validate the dataset and save cohort information\n",
    "    note = \"Dataset contains gene expression data from human samples with Friedreich's Ataxia (FRDA). The trait variable indicates FRDA status (1=FRDA, 0=Normal/Carrier).\"\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=is_trait_available,\n",
    "        is_biased=is_trait_biased,\n",
    "        df=unbiased_linked_data,\n",
    "        note=note\n",
    "    )\n",
    "    \n",
    "    # 7. 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",
    "        unbiased_linked_data.to_csv(out_data_file)\n",
    "        print(f\"Saved processed linked data to {out_data_file}\")\n",
    "    else:\n",
    "        print(\"Dataset validation failed. Final linked data not saved.\")\n",
    "        \n",
    "except Exception as e:\n",
    "    print(f\"Error in processing clinical data: {e}\")\n",
    "    # Make sure to properly handle validation in the exception case\n",
    "    df_empty = pd.DataFrame()\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=False,\n",
    "        is_biased=None,\n",
    "        df=df_empty,  # Empty DataFrame\n",
    "        note=\"Failed to extract or process clinical data, but gene expression data is available.\"\n",
    "    )\n",
    "    print(\"Dataset validation failed due to clinical data processing errors. Gene data was saved.\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}