{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb2564ae",
   "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 = \"Head_and_Neck_Cancer\"\n",
    "cohort = \"GSE104006\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Head_and_Neck_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Head_and_Neck_Cancer/GSE104006\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Head_and_Neck_Cancer/GSE104006.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Head_and_Neck_Cancer/gene_data/GSE104006.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Head_and_Neck_Cancer/clinical_data/GSE104006.csv\"\n",
    "json_path = \"../../output/preprocess/Head_and_Neck_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed4d88b",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d3d0929",
   "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": "04323746",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0a0605b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the series title which mentions \"miRNA and gene expression profiling\", \n",
    "# we can infer that gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait: Looking at key 0, we see disease status which matches our Head_and_Neck_Cancer trait\n",
    "trait_row = 0\n",
    "\n",
    "# For age: The age information is available in key 2\n",
    "age_row = 2\n",
    "\n",
    "# For gender: Gender information is available in key 3 as \"Sex\"\n",
    "gender_row = 3\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary format (0 for control, 1 for case)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon and strip spaces\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # For Head and Neck Cancer, map thyroid carcinoma to case (1) and non-neoplastic to control (0)\n",
    "    if \"Thyroid_carcinoma\" in value or \"carcinoma\" in value.lower():\n",
    "        return 1\n",
    "    elif \"Non-neoplastic\" in value:\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous format\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon and strip spaces\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except (ValueError, TypeError):\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary format (0 for female, 1 for male)\"\"\"\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon and strip spaces\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.upper() == 'F':\n",
    "        return 0\n",
    "    elif value.upper() == 'M':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if 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",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the library function\n",
    "    clinical_features_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(clinical_features_df)\n",
    "    print(\"Preview of clinical features dataframe:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    clinical_features_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd69a911",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30e48cc6",
   "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": "57c9db6e",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16c094a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyzing the gene identifiers in the dataset\n",
    "\n",
    "# Looking at the first 20 identifiers, they include:\n",
    "# - 'Blank' (likely control spots)\n",
    "# - 'dmr_X' prefixed identifiers (likely custom probes)\n",
    "# - 'hsa-let-X' prefixed identifiers (these are human microRNA identifiers)\n",
    "\n",
    "# These identifiers appear to be microRNA (miRNA) names/IDs rather than standard human gene symbols\n",
    "# The 'hsa-' prefix indicates human (Homo sapiens) miRNAs\n",
    "# This is a miRNA profiling dataset, not a standard gene expression dataset\n",
    "\n",
    "# Since these are already standard miRNA identifiers, no mapping to gene symbols is needed\n",
    "# We would use these miRNA identifiers directly in analysis\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0e7ce3",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8a798fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Save the gene data (miRNA data in this case) since we determined no mapping is needed\n",
    "print(f\"Gene data shape: {gene_data.shape}\")\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",
    "\n",
    "# 2. Use the clinical data we extracted in step 2\n",
    "if 'clinical_features_df' not in locals():\n",
    "    # Get clinical data from matrix file again if needed\n",
    "    background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "    \n",
    "    # Define proper conversion functions for the trait\n",
    "    def convert_trait(value):\n",
    "        \"\"\"Convert histology information to binary trait values (0: control, 1: cancer)\"\"\"\n",
    "        if not isinstance(value, str):\n",
    "            return None\n",
    "        value = value.lower().split(': ')[-1]  # Extract value after colon\n",
    "        \n",
    "        # Map to binary: 1 for any tumor tissue (PDTC/PTC variants), 0 for non-neoplastic\n",
    "        if 'non-neoplastic_thyroid' in value:\n",
    "            return 0  # Control\n",
    "        elif 'ptc' in value or 'pdtc' in value:\n",
    "            return 1  # Cancer\n",
    "        else:\n",
    "            return None  # Unclear\n",
    "    \n",
    "    # Use the trait_row, age_row, and gender_row from step 2\n",
    "    clinical_features_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",
    "# Print clinical data shape and preview\n",
    "print(\"Clinical data shape:\", clinical_features_df.shape)\n",
    "print(\"Clinical data preview:\", preview_df(clinical_features_df))\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features_df, gene_data)\n",
    "print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle 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",
    "# 5. Evaluate bias in trait and demographic features\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 6. Conduct final quality validation\n",
    "note = \"Dataset contains thyroid carcinoma expression data (PDTC/PTC), which is relevant for head and neck cancer. This is a miRNA dataset.\"\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=note\n",
    ")\n",
    "\n",
    "# 7. Save 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 deemed not usable due to quality issues - linked data not saved\")"
   ]
  }
 ],
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}