{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb8a37fb",
   "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 = \"Hypertension\"\n",
    "cohort = \"GSE256539\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Hypertension\"\n",
    "in_cohort_dir = \"../../input/GEO/Hypertension/GSE256539\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Hypertension/GSE256539.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Hypertension/gene_data/GSE256539.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Hypertension/clinical_data/GSE256539.csv\"\n",
    "json_path = \"../../output/preprocess/Hypertension/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6745206",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d1e7f20",
   "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": "28327849",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f38482a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from typing import Optional, Callable, Dict, Any\n",
    "\n",
    "# Step 1: Gene Expression Data Availability\n",
    "# Based on the background information, this is a digital spatial transcriptomics dataset\n",
    "# It mentions \"whole genome sequencing\" and \"genome-wide differential transcriptomic signature\"\n",
    "# So it's likely to contain gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# Step 2: Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary, we don't see explicit trait (hypertension), age, or gender information\n",
    "# The dataset seems to be comparing IPAH vs control samples, but individual characteristics are not provided\n",
    "# Based on the background information, the dataset contains IPAH (Idiopathic Pulmonary Arterial Hypertension) patients vs. controls\n",
    "\n",
    "# Let's look for trait (Hypertension) data\n",
    "# Since the dataset is about IPAH, we could potentially infer IPAH vs control from the \"individuial\" field\n",
    "# However, there's no clear indication which individuals are cases vs controls\n",
    "trait_row = None  # No explicit trait information available for individuals\n",
    "\n",
    "# Age data\n",
    "age_row = None  # No age information available\n",
    "\n",
    "# Gender data\n",
    "gender_row = None  # No gender information available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "# Even though we don't have this information, we'll define placeholder conversion functions\n",
    "\n",
    "def convert_trait(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert trait value to binary format (0: control, 1: case)\"\"\"\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",
    "    # Since we don't have explicit trait information, return None\n",
    "    return None\n",
    "\n",
    "def convert_age(value: str) -> Optional[float]:\n",
    "    \"\"\"Convert age value 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",
    "    # Since we don't have age information, return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(value: str) -> Optional[int]:\n",
    "    \"\"\"Convert gender value to binary format (0: female, 1: 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",
    "    # Since we don't have gender information, return None\n",
    "    return None\n",
    "\n",
    "# Step 3: Save Metadata\n",
    "# Initial filtering based on trait and gene availability\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",
    "# Step 4: Clinical Feature Extraction\n",
    "# Since trait_row is None, we skip this step\n",
    "# If trait_row were not None, we would execute:\n",
    "# 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",
    "# preview = preview_df(clinical_df)\n",
    "# print(\"Clinical data preview:\", preview)\n",
    "# os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "# clinical_df.to_csv(out_clinical_data_file, index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9207fb1",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f29a9ae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the get_genetic_data function from the library to get the gene_data from the matrix_file previously defined.\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 2. Print the first 20 row IDs (gene or probe identifiers) for future observation.\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0b1a14",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db5e7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These identifiers appear to be human gene symbols.\n",
    "# A2M is Alpha-2-Macroglobulin\n",
    "# A4GALT is Alpha 1,4-Galactosyltransferase\n",
    "# AAAS is Aladin WD Repeat Nucleoporin\n",
    "# AACS is Acetoacetyl-CoA Synthetase\n",
    "# etc.\n",
    "\n",
    "# These are standard HGNC gene symbols for human genes, not probe IDs or other types of identifiers\n",
    "# that would require mapping to gene symbols.\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46942177",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c3848fc",
   "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. Since trait_row is None, as determined in Step 2, we cannot extract clinical features.\n",
    "# Therefore, we cannot link clinical and genetic data.\n",
    "# We'll proceed with the gene data only and properly report the absence of trait data.\n",
    "\n",
    "# Check if trait data is available (from Step 2)\n",
    "is_trait_available = trait_row is not None\n",
    "print(f\"Trait data availability: {is_trait_available}\")\n",
    "\n",
    "# 3. Since there is no trait data, we'll create a minimal dataframe with only gene data\n",
    "# to satisfy the requirements of validate_and_save_cohort_info\n",
    "minimal_df = pd.DataFrame(index=normalized_gene_data.columns[:5], \n",
    "                          data={gene: normalized_gene_data.loc[gene, :5] \n",
    "                                for gene in normalized_gene_data.index[:5]})\n",
    "\n",
    "# We'll set is_biased to False since there's no trait to evaluate bias\n",
    "is_biased = False\n",
    "\n",
    "# 4. Conduct quality check and save the cohort information\n",
    "note = \"Dataset contains gene expression data from IPAH and control samples, but lacks individual trait, age, and gender annotations needed for associational studies.\"\n",
    "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_biased,\n",
    "    df=minimal_df,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(\"Dataset is not usable for trait-gene association studies due to missing trait information.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1e025b",
   "metadata": {},
   "source": [
    "### Step 6: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56e6b2e2",
   "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. Since trait_row is None, as determined in Step 2, we cannot extract clinical features.\n",
    "# Therefore, we cannot link clinical and genetic data.\n",
    "# We'll proceed with the gene data only and properly report the absence of trait data.\n",
    "\n",
    "# Check if trait data is available (from Step 2)\n",
    "is_trait_available = trait_row is not None\n",
    "print(f\"Trait data availability: {is_trait_available}\")\n",
    "\n",
    "# 3. Create a minimal dataframe with only gene data to satisfy the requirements for validation\n",
    "# We'll use a minimal representation of the gene data\n",
    "minimal_df = pd.DataFrame(index=normalized_gene_data.columns[:5], \n",
    "                          columns=normalized_gene_data.index[:5])\n",
    "\n",
    "# 4. Conduct quality check and save the cohort information\n",
    "note = \"Dataset contains gene expression data from IPAH and control samples, but lacks individual trait, age, and gender annotations needed for associational studies.\"\n",
    "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=False,  # Set to False since there's no trait to evaluate bias\n",
    "    df=minimal_df,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "print(\"Dataset is not usable for trait-gene association studies due to missing trait information.\")"
   ]
  }
 ],
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}