{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b78dc44",
   "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 = \"Liver_cirrhosis\"\n",
    "cohort = \"GSE185529\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Liver_cirrhosis\"\n",
    "in_cohort_dir = \"../../input/GEO/Liver_cirrhosis/GSE185529\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Liver_cirrhosis/GSE185529.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Liver_cirrhosis/gene_data/GSE185529.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Liver_cirrhosis/clinical_data/GSE185529.csv\"\n",
    "json_path = \"../../output/preprocess/Liver_cirrhosis/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656fdf80",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c39a895e",
   "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": "66786c37",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3ec5fc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this appears to focus on genes (BNC2) regulated in myofibroblasts\n",
    "# and liver fibrosis, but we need to be cautious as it's a SuperSeries composed of SubSeries.\n",
    "# Without seeing the actual gene data, we'll assume it likely contains gene expression data.\n",
    "is_gene_available = True  # Set to True assuming gene expression data is available\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary, we only see 'treatment' information\n",
    "# There is no explicit trait (liver cirrhosis), age, or gender information\n",
    "trait_row = None  # No explicit liver cirrhosis trait information\n",
    "age_row = None    # No age information\n",
    "gender_row = None # No gender information\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "# Since we don't have trait, age, or gender data, these functions are placeholders\n",
    "def convert_trait(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value_str = str(value)\n",
    "    if ':' in value_str:\n",
    "        value_str = value_str.split(':', 1)[1].strip()\n",
    "    \n",
    "    # For liver cirrhosis, typically would convert to binary (0: no cirrhosis, 1: cirrhosis)\n",
    "    # But since we don't have the data, this is just a placeholder\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value_str = str(value)\n",
    "    if ':' in value_str:\n",
    "        value_str = value_str.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Would convert to continuous numeric value\n",
    "    # But since we don't have the data, this is just a placeholder\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    if pd.isna(value) or value is None:\n",
    "        return None\n",
    "    \n",
    "    value_str = str(value)\n",
    "    if ':' in value_str:\n",
    "        value_str = value_str.split(':', 1)[1].strip().lower()\n",
    "    \n",
    "    # Would convert to binary (0: female, 1: male)\n",
    "    # But since we don't have the data, this is just a placeholder\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine trait data availability based on whether trait_row is None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Save the initial filtering results\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",
    "# Skip this step since trait_row is None (clinical data not available for our needed traits)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1265d380",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10bea7ea",
   "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": "c24eac0c",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5dfe2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examining the gene identifiers in the data\n",
    "# These identifiers (like '2824546_st') are not standard human gene symbols\n",
    "# They appear to be probe IDs from a microarray platform (GPL17586)\n",
    "# These would need to be mapped to standard gene symbols for biological interpretation\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44aa7d8a",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b90179e0",
   "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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Check for gene information in the SPOT_ID.1 column which appears to contain gene names\n",
    "print(\"\\nAnalyzing SPOT_ID.1 column for gene symbols:\")\n",
    "if 'SPOT_ID.1' in gene_annotation.columns:\n",
    "    # Extract a few sample values\n",
    "    sample_values = gene_annotation['SPOT_ID.1'].head(3).tolist()\n",
    "    for i, value in enumerate(sample_values):\n",
    "        print(f\"Sample {i+1} excerpt: {value[:200]}...\")  # Print first 200 chars\n",
    "        # Test the extract_human_gene_symbols function on these values\n",
    "        symbols = extract_human_gene_symbols(value)\n",
    "        print(f\"  Extracted gene symbols: {symbols}\")\n",
    "\n",
    "# Try to find the probe IDs in the gene annotation\n",
    "gene_data_id_prefix = gene_data.index[0].split('_')[0]  # Get prefix of first gene ID\n",
    "print(f\"\\nGene data ID prefix: {gene_data_id_prefix}\")\n",
    "\n",
    "# Look for columns that might match the gene data IDs\n",
    "for col in gene_annotation.columns:\n",
    "    if gene_annotation[col].astype(str).str.contains(gene_data_id_prefix).any():\n",
    "        print(f\"Column '{col}' contains values matching gene data ID pattern\")\n",
    "\n",
    "# Check if there's any column that might contain transcript or gene IDs\n",
    "print(\"\\nChecking for columns containing transcript or gene related terms:\")\n",
    "for col in gene_annotation.columns:\n",
    "    if any(term in col.upper() for term in ['GENE', 'TRANSCRIPT', 'SYMBOL', 'NAME', 'DESCRIPTION']):\n",
    "        print(f\"Column '{col}' may contain gene-related information\")\n",
    "        # Show sample values\n",
    "        print(f\"Sample values: {gene_annotation[col].head(3).tolist()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed5cb45",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0081992f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the probe_id and gene_symbols from gene_annotation\n",
    "# Looking at the gene_data IDs (2824546_st) and the gene_annotation data\n",
    "# Need to map between probe IDs and gene symbols\n",
    "\n",
    "# Create a mapping between the probe IDs in gene_data and gene symbols\n",
    "# The ID column in gene_annotation needs to be linked to the probe IDs in gene_data\n",
    "# We need to explore how the IDs match up\n",
    "\n",
    "# Extract the ID suffix pattern from gene_data\n",
    "id_pattern = gene_data.index[0].split('_')[1]  # Should be \"st\"\n",
    "print(f\"ID suffix pattern in gene_data: {id_pattern}\")\n",
    "\n",
    "# Check how the IDs in gene_annotation can be matched to gene_data\n",
    "sample_gene_data_ids = gene_data.index[:5].tolist()\n",
    "print(f\"Sample gene data IDs: {sample_gene_data_ids}\")\n",
    "\n",
    "# Looking at \"gene_assignment\" column which contains gene symbols\n",
    "# First check if gene_assignment actually contains mappable data (not all \"---\")\n",
    "gene_mappings_available = gene_annotation['gene_assignment'].apply(lambda x: x != '---').sum()\n",
    "print(f\"Number of rows with gene mappings: {gene_mappings_available} out of {len(gene_annotation)}\")\n",
    "\n",
    "# 1. Create the gene mapping dataframe\n",
    "# We'll map from the ID in gene_annotation to the gene symbols in gene_assignment\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'gene_assignment')\n",
    "print(f\"Gene mapping shape: {mapping_df.shape}\")\n",
    "print(\"First few mappings:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 2. Apply the gene mapping to convert probe-level measurements to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Mapped gene data shape: {gene_data.shape}\")\n",
    "print(\"First few genes:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the gene data to CSV\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 data saved to {out_gene_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1dd3de8",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f92245e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "# Use normalize_gene_symbols_in_index to standardize gene symbols\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape before normalization: {gene_data.shape}\")\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "\n",
    "# Save the normalized gene data to file\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 expression data saved to {out_gene_data_file}\")\n",
    "\n",
    "# Check if trait_row is None (indicating no clinical data is available)\n",
    "if trait_row is None:\n",
    "    print(\"No clinical data available for this dataset, skipping clinical data processing.\")\n",
    "    \n",
    "    # Validate and save cohort information with trait_available=False\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,\n",
    "        is_biased=True,  # Set to True since we can't use this data without clinical features\n",
    "        df=pd.DataFrame(),  # Empty DataFrame since we have no linked data\n",
    "        note=\"Dataset contains gene expression data from cell lines with HCV infection, which is not appropriate for liver cirrhosis trait analysis.\"\n",
    "    )\n",
    "    \n",
    "    print(\"Dataset is not usable for liver cirrhosis analysis due to lack of clinical data. No linked data file saved.\")\n",
    "else:\n",
    "    # If clinical data is available, proceed with the linking and processing\n",
    "    # 2. Link the clinical and genetic data\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",
    "    print(f\"Selected clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(selected_clinical_df.head())\n",
    "\n",
    "    # Link the clinical and genetic data\n",
    "    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)\n",
    "    print(f\"Linked data shape before processing: {linked_data.shape}\")\n",
    "    print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "    print(linked_data.iloc[:5, :5] if not linked_data.empty else \"Empty dataframe\")\n",
    "\n",
    "    # 3. Handle missing values\n",
    "    try:\n",
    "        linked_data = handle_missing_values(linked_data, trait)\n",
    "        print(f\"Data shape after handling missing values: {linked_data.shape}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error handling missing values: {e}\")\n",
    "        linked_data = pd.DataFrame()  # Create empty dataframe if error occurs\n",
    "\n",
    "    # 4. Check for bias in features\n",
    "    if not linked_data.empty:\n",
    "        is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "        print(f\"Data shape after removing biased features: {linked_data.shape}\")\n",
    "    else:\n",
    "        is_biased = True\n",
    "        print(\"Cannot check for bias as dataframe is empty after missing value handling\")\n",
    "\n",
    "    # 5. Validate and save 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_biased,\n",
    "        df=linked_data,\n",
    "        note=\"Dataset contains gene expression data for liver fibrosis progression, which is relevant to liver cirrhosis research.\"\n",
    "    )\n",
    "\n",
    "    # 6. Save the 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 is not usable for analysis. No linked data file saved.\")"
   ]
  }
 ],
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}