{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b3b5a7d",
   "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 = \"Pheochromocytoma_and_Paraganglioma\"\n",
    "cohort = \"GSE67066\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Pheochromocytoma_and_Paraganglioma\"\n",
    "in_cohort_dir = \"../../input/GEO/Pheochromocytoma_and_Paraganglioma/GSE67066\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/GSE67066.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/gene_data/GSE67066.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/clinical_data/GSE67066.csv\"\n",
    "json_path = \"../../output/preprocess/Pheochromocytoma_and_Paraganglioma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8179c186",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359267ea",
   "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": "0d9e7cbe",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be582542",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Assess gene expression data availability\n",
    "is_gene_available = True  # From the background information, we can see that mRNA expression arrays were performed\n",
    "\n",
    "# 2. Assess variable availability and create conversion functions\n",
    "\n",
    "# 2.1 Trait (Malignancy)\n",
    "trait_row = 1  # \"tissue type: benign\" or \"tissue type: malignant\"\n",
    "\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.lower()\n",
    "    if 'malignant' in value:\n",
    "        return 1  # Malignant\n",
    "    elif 'benign' in value:\n",
    "        return 0  # Benign\n",
    "    return None\n",
    "\n",
    "# 2.2 Age\n",
    "age_row = None  # Age information is not available in the sample characteristics\n",
    "\n",
    "def convert_age(value):\n",
    "    # Placeholder function since age data is not available\n",
    "    return None\n",
    "\n",
    "# 2.3 Gender\n",
    "gender_row = None  # Gender information is not available in the sample characteristics\n",
    "\n",
    "def convert_gender(value):\n",
    "    # Placeholder function 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. Extract clinical features if trait data is available\n",
    "if trait_row is not None:\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",
    "    \n",
    "    # Preview the clinical dataframe\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    \n",
    "    # Save the clinical data\n",
    "    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": "7592e1c4",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff7f96e",
   "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": "cbefd162",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7040577b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers\n",
    "# These look like Affymetrix probe IDs (for example \"1007_s_at\", \"1053_at\") rather than standard human gene symbols\n",
    "# Human gene symbols would typically be like BRCA1, TP53, etc.\n",
    "# Affymetrix IDs need to be mapped to gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45c9ad20",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1ec1164",
   "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": "d55361c2",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6eaf93d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looking at the gene annotation data, we can see that:\n",
    "# - \"ID\" contains Affymetrix probe IDs like '1007_s_at', '1053_at', etc. (same format as in gene_data)\n",
    "# - \"Gene Symbol\" contains human gene symbols like 'DDR1 /// MIR4640', 'RFC2', etc.\n",
    "\n",
    "# 1. Identify the columns in the annotation data for mapping\n",
    "probe_col = 'ID'  # Column with probe identifiers\n",
    "gene_col = 'Gene Symbol'  # Column with gene symbols\n",
    "\n",
    "# 2. Create the gene mapping dataframe\n",
    "gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)\n",
    "\n",
    "# Preview the mapping dataframe\n",
    "print(\"Gene mapping preview:\")\n",
    "print(preview_df(gene_mapping))\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the gene expression data after mapping\n",
    "print(\"\\nGene expression data after mapping (first 5 genes, 5 samples):\")\n",
    "preview_gene_data = gene_data.iloc[:5, :5]\n",
    "print(preview_gene_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b99eeff0",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf4bc5b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Save the normalized 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 from step 2\n",
    "clinical_data = pd.read_csv(out_clinical_data_file)\n",
    "# Convert to transposed format for linking (samples as columns)\n",
    "clinical_df = pd.DataFrame(clinical_data.values.T, columns=[trait], index=clinical_data.columns)\n",
    "print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n",
    "\n",
    "# Link the clinical and genetic data on sample IDs\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are severely biased\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=f\"Dataset contains gene expression data for pheochromocytoma/paraganglioma samples with benign/malignant classification.\"\n",
    ")\n",
    "\n",
    "# 6. Save the data if it's usable\n",
    "if is_usable:\n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    # Save the data\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d460b567",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb384ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Save the normalized 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 actual clinical data that was created in step 2\n",
    "clinical_data = pd.read_csv(out_clinical_data_file)\n",
    "# Since the clinical data's columns are sample IDs and the only row is the trait data\n",
    "# We need to transpose it to have samples as rows and traits as columns\n",
    "clinical_df = pd.DataFrame(clinical_data.T.values, \n",
    "                           index=clinical_data.columns,\n",
    "                           columns=[trait])\n",
    "print(f\"Loaded clinical data with shape: {clinical_df.shape}\")\n",
    "\n",
    "# Link the clinical and genetic data on sample IDs\n",
    "linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape after handling missing values: {linked_data.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait and demographic features are severely biased\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=f\"Dataset contains gene expression data for pheochromocytoma/paraganglioma samples with benign/malignant classification.\"\n",
    ")\n",
    "\n",
    "# 6. Save the data if it's usable\n",
    "if is_usable:\n",
    "    # Create directory if it doesn't exist\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    # Save the data\n",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Data quality check failed. The dataset is not suitable for association studies.\")"
   ]
  }
 ],
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}