{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08f3d535",
   "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 = \"Obsessive-Compulsive_Disorder\"\n",
    "cohort = \"GSE78104\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Obsessive-Compulsive_Disorder\"\n",
    "in_cohort_dir = \"../../input/GEO/Obsessive-Compulsive_Disorder/GSE78104\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Obsessive-Compulsive_Disorder/GSE78104.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Obsessive-Compulsive_Disorder/gene_data/GSE78104.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Obsessive-Compulsive_Disorder/clinical_data/GSE78104.csv\"\n",
    "json_path = \"../../output/preprocess/Obsessive-Compulsive_Disorder/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6f65344",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36c11cd",
   "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": "7eed811b",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9fe7312",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# This dataset mentions mRNA expression data in the title and design,\n",
    "# so it likely contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Identifying rows\n",
    "trait_row = 1  # disease state\n",
    "age_row = 3    # age\n",
    "gender_row = 2 # gender\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait data to binary format (0 for control, 1 for OCD)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if 'obsessive' in value.lower() or 'ocd' in value.lower():\n",
    "        return 1\n",
    "    elif 'normal' in value.lower() or 'control' in value.lower():\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age data to continuous format\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Extract numeric age from format like \"25y\"\n",
    "    if 'y' in value:\n",
    "        try:\n",
    "            return float(value.replace('y', '').strip())\n",
    "        except ValueError:\n",
    "            return None\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender data to binary format (0 for female, 1 for male)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon if present\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    if value.lower() == 'male':\n",
    "        return 1\n",
    "    elif value.lower() == 'female':\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\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",
    "    # 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 data\n",
    "    preview_data = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of clinical data:\")\n",
    "    print(preview_data)\n",
    "    \n",
    "    # Save as CSV\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c7452b",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1bf40e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Get the SOFT and matrix file paths again \n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "print(f\"Matrix file found: {matrix_file}\")\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(f\"Gene data shape: {gene_data.shape}\")\n",
    "    \n",
    "    # 3. Print the first 20 row IDs (gene or probe identifiers)\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e43958",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d15d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining gene identifiers in data\n",
    "# The identifiers look like microarray probe IDs rather than gene symbols\n",
    "# The format is consistent with Agilent microarray designations (e.g., \"A_19_P00315459\")\n",
    "# These are not standard human gene symbols and will need to be mapped\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735df700",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e6da8fa",
   "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",
    "# Check if there are any platforms defined in the SOFT file that might contain annotation data\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    soft_content = f.read()\n",
    "\n",
    "# Look for platform sections in the SOFT file\n",
    "platform_sections = re.findall(r'^!Platform_title\\s*=\\s*(.+)$', soft_content, re.MULTILINE)\n",
    "if platform_sections:\n",
    "    print(f\"Platform title found: {platform_sections[0]}\")\n",
    "\n",
    "# Try to extract more annotation data by reading directly from the SOFT file\n",
    "# Look for lines that might contain gene symbol mappings\n",
    "symbol_pattern = re.compile(r'ID_REF\\s+Symbol|ID\\s+Gene Symbol', re.IGNORECASE)\n",
    "annotation_lines = []\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for line in f:\n",
    "        if symbol_pattern.search(line):\n",
    "            annotation_lines.append(line)\n",
    "            # Collect the next few lines to see the annotation structure\n",
    "            for _ in range(10):\n",
    "                annotation_lines.append(next(f, ''))\n",
    "\n",
    "if annotation_lines:\n",
    "    print(\"Found potential gene symbol mappings:\")\n",
    "    for line in annotation_lines:\n",
    "        print(line.strip())\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(preview_df(gene_annotation, n=10))\n",
    "\n",
    "# If we need an alternative source of mapping, check if there are any other annotation files in the cohort directory\n",
    "cohort_files = os.listdir(in_cohort_dir)\n",
    "annotation_files = [f for f in cohort_files if 'annotation' in f.lower() or 'platform' in f.lower()]\n",
    "if annotation_files:\n",
    "    print(\"\\nAdditional annotation files found in the cohort directory:\")\n",
    "    for file in annotation_files:\n",
    "        print(file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45e09dff",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e4e071e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify which columns in the gene annotation contain probe IDs and gene symbols\n",
    "# From the preview, 'ID' contains probe identifiers that match expression data\n",
    "# 'GeneSymbol' appears to contain gene symbols, but we should check for nulls\n",
    "probe_col = 'ID'\n",
    "gene_col = 'GeneSymbol'\n",
    "\n",
    "print(f\"Mapping probes using columns: {probe_col} → {gene_col}\")\n",
    "print(f\"Number of probes in annotation: {len(gene_annotation)}\")\n",
    "print(f\"Number of non-null gene symbols: {gene_annotation[gene_col].notna().sum()}\")\n",
    "\n",
    "# 2. Get the gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col=probe_col, gene_col=gene_col)\n",
    "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First few rows of mapping:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene-level expression\n",
    "# This will handle the many-to-many relationship properly\n",
    "gene_data = apply_gene_mapping(expression_df=gene_data, mapping_df=mapping_df)\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save gene expression data to 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": "7ff76b3b",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5df04557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Load clinical data that was already saved in Step 2\n",
    "clinical_data_df = pd.read_csv(out_clinical_data_file)\n",
    "# Set proper index for clinical data \n",
    "clinical_data_df.set_index(clinical_data_df.columns[0], inplace=True)\n",
    "print(f\"Loaded clinical data shape: {clinical_data_df.shape}\")\n",
    "print(clinical_data_df.head())\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
    "\n",
    "# 3. Handle missing values\n",
    "# Identify trait column - it's the first column in the linked data\n",
    "trait_col = linked_data.columns[0]  # This gets the actual column name where trait values are stored\n",
    "linked_data = handle_missing_values(linked_data, trait_col)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait_col)\n",
    "\n",
    "# 5. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from peripheral blood of patients with Obsessive-Compulsive Disorder.\"\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d409082f",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33b827d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data - we already did this in step 6\n",
    "# No need to do it again\n",
    "print(f\"Gene data already normalized and saved to {out_gene_data_file}\")\n",
    "\n",
    "# 2. Load clinical data that was already saved in Step 2\n",
    "clinical_data_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "print(f\"Loaded clinical data shape: {clinical_data_df.shape}\")\n",
    "print(clinical_data_df.head())\n",
    "\n",
    "# 2. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_data_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Linked data is empty\")\n",
    "\n",
    "# 3. Handle missing values\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Check for bias in features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Validate 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_biased,\n",
    "    df=linked_data,\n",
    "    note=\"Dataset contains gene expression data from dorsolateral prefrontal cortex of postmortem tissue with Eating Disorders.\"\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if 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(\"Dataset is not usable for analysis. No linked data file saved.\")"
   ]
  }
 ],
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 "nbformat": 4,
 "nbformat_minor": 5
}