{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e74b8683",
   "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 = \"Rectal_Cancer\"\n",
    "cohort = \"GSE170999\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Rectal_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Rectal_Cancer/GSE170999\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Rectal_Cancer/GSE170999.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Rectal_Cancer/gene_data/GSE170999.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Rectal_Cancer/clinical_data/GSE170999.csv\"\n",
    "json_path = \"../../output/preprocess/Rectal_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55782a8",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86fa40bb",
   "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": "55710cf0",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21c47658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the Series_summary information, this dataset contains gene expression data from Affymetrix U133 platform\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Identifying rows containing trait, age, and gender information\n",
    "trait_row = 0  # KRAS mutation status is in row 0\n",
    "age_row = None  # Age information is not available\n",
    "gender_row = None  # Gender information is not available\n",
    "\n",
    "# 2.2 Data Type Conversion functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert KRAS mutation status to binary (0: wild-type, 1: mutant)\"\"\"\n",
    "    if value is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract value after the colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if \"wild-type\" in value.lower():\n",
    "        return 0  # KRAS wild-type\n",
    "    elif \"mutant\" in value.lower():\n",
    "        return 1  # KRAS mutant\n",
    "    else:\n",
    "        return None  # Unknown or other values\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to numeric (continuous) value\"\"\"\n",
    "    # Not used since age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0: female, 1: male)\"\"\"\n",
    "    # Not used since gender data is not available\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available (trait_row is not None)\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",
    "# Since trait_row is not None, clinical data is available\n",
    "if trait_row is not None:\n",
    "    # Create clinical data DataFrame from the sample characteristics dictionary\n",
    "    sample_chars = {0: ['kras_mutant or wild_type: KRAS-wild-type', 'kras_mutant or wild_type: KRAS-mutant']}\n",
    "    clinical_data = pd.DataFrame()\n",
    "    \n",
    "    for key, values in sample_chars.items():\n",
    "        clinical_data[key] = values\n",
    "    \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 extracted features\n",
    "    print(\"Clinical Features Preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Save the clinical data\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7fb41a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d07a12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. First get the file paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Use the get_genetic_data function from the library to get the gene_data\n",
    "gene_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# 3. Print the first 20 row IDs (gene or probe identifiers) for future observation\n",
    "print(gene_data.index[:20])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7610db1",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47af28d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These identifiers are not standard human gene symbols, which typically follow conventions like \n",
    "# HGNC symbols (e.g., BRCA1, TP53) or Ensembl IDs (starting with ENSG).\n",
    "# \n",
    "# The identifiers shown (like '1007_s_at', '1053_at') appear to be Affymetrix probe IDs,\n",
    "# which are specific to the microarray platform used for gene expression profiling.\n",
    "# These need to be mapped to standard gene symbols for meaningful analysis.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0129196c",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bd0c5c0",
   "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": "0b33c6d6",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ba66294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Based on the previews, we need to map:\n",
    "# - 'ID' in the gene_annotation dataframe (contains probe IDs like '1007_s_at')\n",
    "# - 'Gene Symbol' in the gene_annotation dataframe (contains gene symbols like 'DDR1 /// MIR4640')\n",
    "\n",
    "# 2. Get gene mapping dataframe by extracting the identifier and symbol columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "# This function handles dividing expression values among multiple genes and summing by gene\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Print preview of gene expression data after mapping\n",
    "print(\"Gene expression data after mapping (first 5 genes):\")\n",
    "print(gene_data.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff1e46af",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44e0c5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Extract clinical features from the original clinical_data\n",
    "clinical_features = 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 features data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_features.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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. Link the clinical and genetic data\n",
    "# Ensure clinical data has the proper format\n",
    "clinical_df = clinical_features.copy()\n",
    "clinical_df = clinical_df.T\n",
    "clinical_df.columns = [trait]\n",
    "\n",
    "linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n",
    "print(f\"Linked data shape before handling missing values: {linked_data.shape}\")\n",
    "print(linked_data.head())\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape after handling missing values: {linked_data_cleaned.shape}\")\n",
    "\n",
    "# Check if we have sufficient data after handling missing values\n",
    "if linked_data_cleaned.shape[0] == 0 or linked_data_cleaned.shape[1] <= 1:\n",
    "    print(f\"Insufficient data after handling missing values. All samples were filtered out.\")\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=False,  # Set to False since we have no usable trait data\n",
    "        is_biased=None, \n",
    "        df=linked_data_cleaned,\n",
    "        note=f\"No usable samples after handling missing values. All samples had missing trait values.\"\n",
    "    )\n",
    "    print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n",
    "else:\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_cleaned, 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 from rectal cancer patients with KRAS mutation status.\"\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": "15f885df",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1571787a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Normalized gene data shape: {normalized_gene_data.shape}\")\n",
    "print(f\"First few normalized gene symbols: {list(normalized_gene_data.index[:10])}\")\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. Extract KRAS mutation information from the SOFT file\n",
    "# Read the SOFT file to look for sample characteristics that indicate KRAS status for each sample\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    soft_content = f.read()\n",
    "\n",
    "# Extract sample blocks from the SOFT file\n",
    "sample_blocks = re.findall(r'^\\^SAMPLE = (GSM\\d+).*?!Sample_title = \"(.*?)\".*?!Sample_characteristics_ch1 = (.*?)(?=\\n\\n|\\n\\^|\\Z)', \n",
    "                          soft_content, re.DOTALL | re.MULTILINE)\n",
    "\n",
    "# Create a dictionary to map sample IDs to KRAS status\n",
    "kras_status = {}\n",
    "for sample_id, title, characteristics in sample_blocks:\n",
    "    # Look for KRAS status in the characteristics\n",
    "    if 'KRAS-mutant' in characteristics or 'KRAS-mutant' in title:\n",
    "        kras_status[sample_id] = 1  # Mutant\n",
    "    elif 'KRAS-wild-type' in characteristics or 'KRAS-wild-type' in title:\n",
    "        kras_status[sample_id] = 0  # Wild-type\n",
    "    else:\n",
    "        # If not found in characteristics, try to extract from sample title\n",
    "        if 'KRAS-mutant' in title.lower():\n",
    "            kras_status[sample_id] = 1\n",
    "        elif 'KRAS-wild-type' in title.lower() or 'KRAS-wt' in title.lower():\n",
    "            kras_status[sample_id] = 0\n",
    "\n",
    "# Create a clinical DataFrame with sample IDs as index\n",
    "sample_ids = normalized_gene_data.columns\n",
    "clinical_df = pd.DataFrame(index=sample_ids)\n",
    "\n",
    "# Fill in the KRAS status for each sample\n",
    "clinical_df[trait] = clinical_df.index.map(lambda x: kras_status.get(x))\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "print(f\"Sample KRAS status: {clinical_df[trait].value_counts().to_dict()}\")\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "linked_data = pd.concat([clinical_df, normalized_gene_data.T], axis=1)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(linked_data.head())\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape after handling missing values: {linked_data_cleaned.shape}\")\n",
    "\n",
    "# Check if we still have data after handling missing values\n",
    "if linked_data_cleaned.shape[0] == 0 or linked_data_cleaned.shape[1] <= 1:\n",
    "    print(\"All samples were filtered out during missing value handling.\")\n",
    "    # Create a minimal DataFrame for validation purposes\n",
    "    dummy_df = pd.DataFrame({trait: [0, 1]})\n",
    "    # Validate and save information indicating the dataset is not usable\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=True,\n",
    "        df=dummy_df,\n",
    "        note=\"Dataset contains gene expression data from rectal cancer patients with KRAS mutation status, but sample IDs couldn't be properly linked between clinical and genetic data.\"\n",
    "    )\n",
    "    print(f\"Data quality check failed. The dataset is not suitable for association studies.\")\n",
    "else:\n",
    "    # 5. Determine whether the trait and demographic features are severely biased\n",
    "    is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, 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=f\"Dataset contains gene expression data from rectal cancer patients with KRAS mutation status.\"\n",
    "    )\n",
    "\n",
    "    # 7. 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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 "nbformat": 4,
 "nbformat_minor": 5
}