{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6769a68e",
   "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 = \"Stomach_Cancer\"\n",
    "cohort = \"GSE146361\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Stomach_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Stomach_Cancer/GSE146361\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Stomach_Cancer/GSE146361.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Stomach_Cancer/gene_data/GSE146361.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Stomach_Cancer/clinical_data/GSE146361.csv\"\n",
    "json_path = \"../../output/preprocess/Stomach_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fec3494",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95949586",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Let's first list the directory contents to understand what files are available\n",
    "import os\n",
    "\n",
    "print(\"Files in the cohort directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# Adapt file identification to handle different naming patterns\n",
    "soft_files = [f for f in files if 'soft' in f.lower() or '.soft' in f.lower() or '_soft' in f.lower()]\n",
    "matrix_files = [f for f in files if 'matrix' in f.lower() or '.matrix' in f.lower() or '_matrix' in f.lower()]\n",
    "\n",
    "# If no files with these patterns are found, look for alternative file types\n",
    "if not soft_files:\n",
    "    soft_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
    "if not matrix_files:\n",
    "    matrix_files = [f for f in files if f.endswith('.txt') or f.endswith('.gz')]\n",
    "\n",
    "print(\"Identified SOFT files:\", soft_files)\n",
    "print(\"Identified matrix files:\", matrix_files)\n",
    "\n",
    "# Use the first files found, if any\n",
    "if len(soft_files) > 0 and len(matrix_files) > 0:\n",
    "    soft_file = os.path.join(in_cohort_dir, soft_files[0])\n",
    "    matrix_file = os.path.join(in_cohort_dir, matrix_files[0])\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(\"\\nBackground Information:\")\n",
    "    print(background_info)\n",
    "    print(\"\\nSample Characteristics Dictionary:\")\n",
    "    print(sample_characteristics_dict)\n",
    "else:\n",
    "    print(\"No appropriate files found in the directory.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61199793",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d7037e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analyze the output to determine the dataset characteristics\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, we can see this dataset contains gene expression data\n",
    "# It mentions \"gene expression profile\" and \"HumanHT-12 v3.0 Expression BeadChip array (Illumina)\"\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# For the trait (Stomach Cancer), we can see all samples have \"disease: Gastric Cancer\" (key 0)\n",
    "trait_row = 0\n",
    "\n",
    "# For age, there's no information available\n",
    "age_row = None\n",
    "\n",
    "# For gender, there's no information available\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (1 for disease, 0 for control)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    # Extract value after colon if present\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # All samples have gastric cancer, so all will be 1\n",
    "    if \"gastric cancer\" in value.lower():\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous\"\"\"\n",
    "    # Not used as age data is unavailable\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender value to binary (0 for female, 1 for male)\"\"\"\n",
    "    # Not used as gender data is unavailable\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",
    "    # Create a DataFrame from the sample characteristics dictionary\n",
    "    # Based on the sample characteristics, we know the dataset contains 27 cell lines\n",
    "    # Each cell line has the same disease status (Gastric Cancer)\n",
    "    \n",
    "    # Create the clinical data DataFrame\n",
    "    sample_chars = {\n",
    "        0: ['disease: Gastric Cancer'], \n",
    "        1: ['organism part: Stomach'], \n",
    "        2: ['cell line: Gastric Cancer Cell line'], \n",
    "        3: ['cell line: Hs746T', 'cell line: YCC-16', 'cell line: YCC-2', 'cell line: SNU-16', \n",
    "            'cell line: SNU-719', 'cell line: YCC-9', 'cell line: SNU-668', 'cell line: MKN-74', \n",
    "            'cell line: SNU-1', 'cell line: SNU-5', 'cell line: MKN-45', 'cell line: SNU-638', \n",
    "            'cell line: SNU-216', 'cell line: YCC-6', 'cell line: YCC-7', 'cell line: MKN-1', \n",
    "            'cell line: MKN-28', 'cell line: NCI-N87', 'cell line: SNU-484', 'cell line: SNU-601', \n",
    "            'cell line: SNU-620', 'cell line: YCC-3', 'cell line: YCC-11', 'cell line: YCC-1', \n",
    "            'cell line: AGS', 'cell line: KATOIII', 'cell line: SNU-520']\n",
    "    }\n",
    "    \n",
    "    # Extract cell line names from the sample characteristics\n",
    "    cell_lines = [line.split(\": \")[1] for line in sample_chars[3]]\n",
    "    \n",
    "    # Create a DataFrame with all samples having the same trait value\n",
    "    clinical_data = pd.DataFrame(index=cell_lines)\n",
    "    \n",
    "    # Add sample characteristics as columns\n",
    "    for row_idx, values in sample_chars.items():\n",
    "        # Handle the case where row 3 has multiple values (one per cell line)\n",
    "        if row_idx == 3:\n",
    "            continue  # Skip as we've already used these to create the index\n",
    "        \n",
    "        # For other rows, all cell lines share the same value\n",
    "        for value in values:\n",
    "            # Use the part before colon as column name, and after colon as value\n",
    "            if \":\" in value:\n",
    "                col_name, val = value.split(\":\", 1)\n",
    "                clinical_data[col_name.strip()] = val.strip()\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 data\n",
    "    print(\"Preview of selected clinical data:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "    \n",
    "    # Save the data\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "    selected_clinical_df.to_csv(out_clinical_data_file)\n",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efadfcc7",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc2a6aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the helper function to get the proper file paths\n",
    "soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Extract gene expression data\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file_path)\n",
    "    \n",
    "    # Print the first 20 row IDs (gene or probe identifiers)\n",
    "    print(\"First 20 gene/probe identifiers:\")\n",
    "    print(gene_data.index[:20])\n",
    "    \n",
    "    # Print shape to understand the dataset dimensions\n",
    "    print(f\"\\nGene expression data shape: {gene_data.shape}\")\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene data: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786f9d52",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed1aacdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on biomedical knowledge, these are Illumina probe IDs (indicated by the \"ILMN_\" prefix)\n",
    "# and not human gene symbols. These probe IDs need to be mapped to gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a7c414",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "083ffd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "try:\n",
    "    # Use the correct variable name from previous steps\n",
    "    gene_annotation = get_gene_annotation(soft_file_path)\n",
    "    \n",
    "    # 2. Preview the gene annotation dataframe\n",
    "    print(\"Gene annotation preview:\")\n",
    "    print(preview_df(gene_annotation))\n",
    "    \n",
    "except UnicodeDecodeError as e:\n",
    "    print(f\"Unicode decoding error: {e}\")\n",
    "    print(\"Trying alternative approach...\")\n",
    "    \n",
    "    # Read the file with Latin-1 encoding which is more permissive\n",
    "    import gzip\n",
    "    import pandas as pd\n",
    "    \n",
    "    # Manually read the file line by line with error handling\n",
    "    data_lines = []\n",
    "    with gzip.open(soft_file_path, 'rb') as f:\n",
    "        for line in f:\n",
    "            # Skip lines starting with prefixes we want to filter out\n",
    "            line_str = line.decode('latin-1')\n",
    "            if not line_str.startswith('^') and not line_str.startswith('!') and not line_str.startswith('#'):\n",
    "                data_lines.append(line_str)\n",
    "    \n",
    "    # Create dataframe from collected lines\n",
    "    if data_lines:\n",
    "        gene_data_str = '\\n'.join(data_lines)\n",
    "        gene_annotation = pd.read_csv(pd.io.common.StringIO(gene_data_str), sep='\\t', low_memory=False)\n",
    "        print(\"Gene annotation preview (alternative method):\")\n",
    "        print(preview_df(gene_annotation))\n",
    "    else:\n",
    "        print(\"No valid gene annotation data found after filtering.\")\n",
    "        gene_annotation = pd.DataFrame()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error extracting gene annotation data: {e}\")\n",
    "    gene_annotation = pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60de818",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f9b46b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Determine the appropriate columns for mapping\n",
    "# Based on the preview of gene_annotation, we can see:\n",
    "# - 'ID' contains the Illumina probe IDs (same as in gene expression data)\n",
    "# - 'Symbol' contains the gene symbols we need\n",
    "\n",
    "# 2. Get the gene mapping dataframe using the specified columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Symbol')\n",
    "\n",
    "# Print a preview of the mapping\n",
    "print(\"Preview of gene mapping:\")\n",
    "print(gene_mapping.head())\n",
    "print(f\"Gene mapping shape: {gene_mapping.shape}\")\n",
    "\n",
    "# 3. Convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, gene_mapping)\n",
    "\n",
    "# Preview the resulting gene expression data\n",
    "print(\"\\nPreview of mapped gene expression data:\")\n",
    "print(gene_data.head())\n",
    "print(f\"Gene expression data shape after mapping: {gene_data.shape}\")\n",
    "\n",
    "# Normalize gene symbols to handle synonyms\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(\"\\nPreview after normalizing gene symbols:\")\n",
    "print(gene_data.head())\n",
    "print(f\"Gene expression data shape after normalization: {gene_data.shape}\")\n",
    "\n",
    "# Save the processed gene 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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2191b622",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeffbed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Load the normalized gene data that was saved in the previous step\n",
    "try:\n",
    "    gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "    print(f\"Loaded normalized gene data shape: {gene_data.shape}\")\n",
    "except Exception as e:\n",
    "    print(f\"Error loading gene data: {e}\")\n",
    "    raise ValueError(\"Cannot proceed without gene expression data\")\n",
    "\n",
    "# 2. Load the clinical data created in Step 2\n",
    "try:\n",
    "    clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)\n",
    "    print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(clinical_df.head())\n",
    "except Exception as e:\n",
    "    print(f\"Error loading clinical data: {e}\")\n",
    "    clinical_df = pd.DataFrame()\n",
    "\n",
    "# The sample IDs in the gene expression data are GSM IDs\n",
    "gene_sample_ids = gene_data.columns\n",
    "print(f\"Gene data has {len(gene_sample_ids)} samples\")\n",
    "print(f\"First few sample IDs: {list(gene_sample_ids[:5])}\")\n",
    "\n",
    "# Create a clinical DataFrame with the same sample IDs as the gene data\n",
    "# We know from previous steps that all samples are cancer cell lines (all have trait=1)\n",
    "clinical_features = pd.DataFrame(index=gene_sample_ids)\n",
    "clinical_features[trait] = 1  # All samples are gastric cancer cell lines\n",
    "\n",
    "print(f\"Prepared clinical features shape: {clinical_features.shape}\")\n",
    "print(clinical_features.head())\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. 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",
    "# 4. Determine whether the trait and demographic features are biased\n",
    "is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "print(f\"Is trait biased: {is_trait_biased}\")\n",
    "print(f\"Linked data shape after removing biased features: {linked_data.shape}\")\n",
    "\n",
    "# 5. Conduct quality check and save the cohort information\n",
    "note = \"Dataset contains gene expression data from 27 gastric cancer cell lines. All samples have the same trait value (all are cancer), making the dataset unsuitable for associative studies.\"\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=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if it's usable\n",
    "print(f\"Data quality check result: {'Usable' if is_usable else 'Not 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(f\"Data not saved due to quality issues.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf02978c",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b6795e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. The gene data was already normalized in Step 6\n",
    "gene_data = pd.read_csv(out_gene_data_file, index_col=0)\n",
    "print(f\"Loaded normalized gene data shape: {gene_data.shape}\")\n",
    "\n",
    "# 2. Create clinical features for the same samples that are in the gene data\n",
    "# From previous steps, we know all samples are gastric cancer cell lines\n",
    "clinical_features = pd.DataFrame(index=gene_data.columns)\n",
    "clinical_features[trait] = 1  # All samples are gastric cancer\n",
    "print(f\"Prepared clinical features shape: {clinical_features.shape}\")\n",
    "print(clinical_features.head())\n",
    "\n",
    "# Direct approach to link clinical and genetic data\n",
    "linked_data = clinical_features.copy()\n",
    "# Add gene expression data as additional columns\n",
    "for gene in gene_data.index:\n",
    "    linked_data[gene] = gene_data.loc[gene]\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data_processed = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data_processed.shape}\")\n",
    "\n",
    "# 4. Determine whether the trait is biased\n",
    "is_trait_biased, linked_data_processed = judge_and_remove_biased_features(linked_data_processed, trait)\n",
    "print(f\"Is trait biased: {is_trait_biased}\")\n",
    "print(f\"Linked data shape after removing biased features: {linked_data_processed.shape}\")\n",
    "\n",
    "# 5. Conduct quality check and save cohort information\n",
    "note = \"Dataset contains gene expression data from 27 gastric cancer cell lines. All samples have the same trait value (all are cancer), making the dataset unsuitable for case-control associative studies.\"\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=linked_data_processed,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if it's usable\n",
    "print(f\"Data quality check result: {'Usable' if is_usable else 'Not usable'}\")\n",
    "if is_usable:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data_processed.to_csv(out_data_file)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(f\"Data not saved due to quality issues.\")"
   ]
  }
 ],
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 "nbformat": 4,
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}