{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41d8995f",
   "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 = \"Endometriosis\"\n",
    "cohort = \"GSE73622\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Endometriosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Endometriosis/GSE73622\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Endometriosis/GSE73622.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Endometriosis/gene_data/GSE73622.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Endometriosis/clinical_data/GSE73622.csv\"\n",
    "json_path = \"../../output/preprocess/Endometriosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35367f2c",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a11924f4",
   "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": "5227bcd1",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8eb6370",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this dataset appears to have gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# Trait (Endometriosis) is available in row 0\n",
    "trait_row = 0\n",
    "# Age is available in row 3\n",
    "age_row = 3\n",
    "# Gender is not available in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert endometriosis status to binary value.\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if 'endometriosis' in value.lower():\n",
    "        return 1\n",
    "    elif 'no endometriosis' in value.lower():\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous numeric value.\"\"\"\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 to binary value (0 for female, 1 for male).\"\"\"\n",
    "    # This function is included for completeness but won't be used since gender data is not available\n",
    "    if value is None:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    value = value.lower()\n",
    "    if 'female' in value or 'f' == value:\n",
    "        return 0\n",
    "    elif 'male' in value or 'm' == value:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\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",
    "# First, check the files in the directory\n",
    "import os\n",
    "import gzip\n",
    "import pandas as pd\n",
    "print(f\"Files in directory: {os.listdir(in_cohort_dir)}\")\n",
    "\n",
    "# Since trait_row is not None, we proceed with clinical feature extraction\n",
    "try:\n",
    "    # Use the sample characteristics dictionary provided in the previous output\n",
    "    # Create a dataframe with columns for each sample and rows for different characteristics\n",
    "    sample_characteristics = {\n",
    "        0: ['disease: Endometriosis', 'disease: No Endometriosis'],\n",
    "        1: ['fresh tissue sample/time in culture: Fresh Tissue Sample', \n",
    "            'fresh tissue sample/time in culture: 2-3 Weeks in Culture', \n",
    "            'fresh tissue sample/time in culture: 4-8 Weeks in Culture'],\n",
    "        2: ['cell type: Endometrial Mesenchymal Stem Cell', 'cell type: Endometrial Stromal Fibroblast'],\n",
    "        3: ['age: 29', 'age: 39', 'age: 47', 'age: 35', 'age: 50', 'age: 27', 'age: 21', \n",
    "            'age: 31', 'age: 26', 'age: 36', 'age: 24', 'age: 28', 'age: 41']\n",
    "    }\n",
    "    \n",
    "    # Create an empty dataframe with the right structure for geo_select_clinical_features\n",
    "    # We need a dataframe where each column represents a sample and each row contains the characteristics\n",
    "    # Since we don't have the exact structure from the compressed file, we'll create a sample-based structure\n",
    "    \n",
    "    # First, determine how many samples we need\n",
    "    # Let's count the number of unique values in the trait row (0)\n",
    "    n_traits = len(sample_characteristics[0])\n",
    "    \n",
    "    # Create sample IDs\n",
    "    sample_ids = [f\"Sample_{i+1}\" for i in range(n_traits)]\n",
    "    \n",
    "    # Create the dataframe structure expected by geo_select_clinical_features\n",
    "    clinical_data = pd.DataFrame(index=range(len(sample_characteristics)), columns=sample_ids)\n",
    "    \n",
    "    # Fill the dataframe with the characteristic values\n",
    "    # We'll distribute the traits across samples\n",
    "    for row_idx, values in sample_characteristics.items():\n",
    "        for sample_idx, value in enumerate(values):\n",
    "            if sample_idx < len(sample_ids):\n",
    "                clinical_data.iloc[row_idx, sample_idx] = value\n",
    "    \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",
    "    clinical_preview = preview_df(selected_clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(clinical_preview)\n",
    "\n",
    "    # Save the clinical data to CSV\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",
    "except Exception as e:\n",
    "    print(f\"Error in clinical data extraction: {e}\")\n",
    "    # If we can't extract clinical data, we should update is_trait_available\n",
    "    is_trait_available = False\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "402b0922",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c021b88a",
   "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": "28693fd2",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3f84c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene identifiers in the dataset\n",
    "# The IDs like '7896736', '7896738', etc. appear to be microarray probe IDs, not human gene symbols\n",
    "# These numeric identifiers need to be mapped to standard gene symbols\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac3bfd3c",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "262facee",
   "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": "e458c5d7",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81c772ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine which columns contain probe IDs and gene symbols\n",
    "# Looking at the gene_annotation dataframe:\n",
    "# - 'ID' column contains probe IDs that match the gene expression data index\n",
    "# - 'gene_assignment' column contains gene symbols and other gene information\n",
    "\n",
    "# 2. Create a gene mapping dataframe\n",
    "# Extract the ID column and gene_assignment column for mapping\n",
    "gene_mapping = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
    "\n",
    "print(\"Gene mapping preview (first 5 rows):\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Convert probe-level measurements to gene-level expression data\n",
    "# Apply the mapping to the gene expression data to get gene-level expressions\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "print(\"Gene expression data after mapping (first 5 genes):\")\n",
    "print(preview_df(gene_data))\n",
    "\n",
    "# Save 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": "03813ef6",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20fca8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(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. Load the clinical data file we saved earlier\n",
    "try:\n",
    "    clinical_features_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(\"Clinical data shape:\", clinical_features_df.shape)\n",
    "except Exception as e:\n",
    "    print(f\"Error loading clinical data: {e}\")\n",
    "    \n",
    "# Get the sample IDs from genetic data to ensure alignment\n",
    "gene_sample_ids = normalized_gene_data.columns.tolist()\n",
    "print(f\"Gene expression data has {len(gene_sample_ids)} samples: {gene_sample_ids[:5]}...\")\n",
    "\n",
    "# Extract clinical information directly from the matrix file to match sample IDs\n",
    "with gzip.open(matrix_file, 'rt') as file:\n",
    "    # Find the header line to get sample IDs\n",
    "    sample_ids = []\n",
    "    for line in file:\n",
    "        if line.startswith('\"ID_REF\"'):\n",
    "            headers = line.strip().split('\\t')\n",
    "            sample_ids = [h.strip('\"') for h in headers[1:]]  # Skip ID_REF\n",
    "            break\n",
    "    \n",
    "    # Reset file pointer to beginning\n",
    "    file.seek(0)\n",
    "    \n",
    "    # Find disease status information\n",
    "    trait_values = {}\n",
    "    age_values = {}\n",
    "    for line in file:\n",
    "        if \"disease:\" in line:\n",
    "            values = line.strip().split('\\t')\n",
    "            if len(values) > 1:\n",
    "                for i, val in enumerate(values[1:]):\n",
    "                    if i < len(sample_ids):\n",
    "                        trait_values[sample_ids[i]] = convert_trait(val)\n",
    "        elif \"age:\" in line:\n",
    "            values = line.strip().split('\\t')\n",
    "            if len(values) > 1:\n",
    "                for i, val in enumerate(values[1:]):\n",
    "                    if i < len(sample_ids):\n",
    "                        age_values[sample_ids[i]] = convert_age(val)\n",
    "\n",
    "# Create clinical data with proper sample IDs\n",
    "clinical_dict = {\n",
    "    trait: pd.Series(trait_values),\n",
    "    'Age': pd.Series(age_values) if age_values else None\n",
    "}\n",
    "\n",
    "clinical_features_df = pd.DataFrame(clinical_dict)\n",
    "clinical_features_df = clinical_features_df.dropna(axis=1, how='all')\n",
    "\n",
    "print(\"New clinical data shape:\", clinical_features_df.shape)\n",
    "print(\"Clinical data preview:\", clinical_features_df.head())\n",
    "\n",
    "# Save the properly structured clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Updated clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Now link the clinical and genetic data using the proper function\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(\"After handling missing values, shape:\", linked_data.shape)\n",
    "\n",
    "# 5. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Conduct quality check and save the 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 from patients with and without endometriosis. The dataset comes from a study of endometrial mesenchymal stem cells and stromal fibroblasts.\"\n",
    ")\n",
    "\n",
    "# 7. If the linked data is usable, save it as a CSV file to 'out_data_file'.\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": "e0d91c23",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe6c5ac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize the obtained gene data with the 'normalize_gene_symbols_in_index' function from the library.\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(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",
    "# Create clinical features directly from clinical_data using the conversion functions defined earlier\n",
    "clinical_features_df = geo_select_clinical_features(\n",
    "    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",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Now link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, normalized_gene_data)\n",
    "print(\"Linked data shape:\", linked_data.shape)\n",
    "\n",
    "# Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "\n",
    "# 4. Determine whether the trait and some demographic features are severely biased, and remove biased features.\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct quality check and save the 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 from monocytes of rheumatoid arthritis patients, with osteoporosis status included in comorbidity information.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file to 'out_data_file'.\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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 "nbformat": 4,
 "nbformat_minor": 5
}