{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43e8e3eb",
   "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_Cancer\"\n",
    "cohort = \"GSE148346\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Liver_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Liver_Cancer/GSE148346\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Liver_Cancer/GSE148346.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Liver_Cancer/gene_data/GSE148346.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Liver_Cancer/clinical_data/GSE148346.csv\"\n",
    "json_path = \"../../output/preprocess/Liver_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aca27251",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2441e1ee",
   "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": "3fed88e7",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7455ed4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# From the background information, this is a study on alopecia areata (AA) with molecular response data\n",
    "# This indicates gene expression data is likely available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# Looking at the sample characteristics dictionary\n",
    "\n",
    "# 2.1 For the trait (Liver Cancer)\n",
    "# Looking at the background information and sample characteristics, this dataset doesn't appear to be about liver cancer\n",
    "# It's a study on alopecia areata (AA)\n",
    "# Key 3 shows 'tissue disease state: LS', 'tissue disease state: NL' which relates to lesional (LS) and non-lesional (NL) skin\n",
    "trait_row = 3  # Using tissue disease state as our trait indicator\n",
    "\n",
    "# For age - Not available in the sample characteristics dictionary\n",
    "age_row = None\n",
    "\n",
    "# For gender - Not available in the sample characteristics dictionary\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait values to binary (1 for lesional, 0 for non-lesional)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    value_str = str(value).strip()\n",
    "    if \":\" in value_str:\n",
    "        value_str = value_str.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value_str.upper() == \"LS\":\n",
    "        return 1  # Lesional skin\n",
    "    elif value_str.upper() == \"NL\":\n",
    "        return 0  # Non-lesional skin\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age values to continuous (Not used in this dataset)\"\"\"\n",
    "    return None  # Placeholder since age data is not available\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender values to binary (Not used in this dataset)\"\"\"\n",
    "    return None  # Placeholder since gender data is not available\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available\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",
    "    # If trait data is available, extract clinical features\n",
    "    clinical_df = geo_select_clinical_features(\n",
    "        clinical_data,  # Assuming clinical_data is available from previous step\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 dataframe\n",
    "    preview = preview_df(clinical_df)\n",
    "    print(\"Preview of extracted clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data to CSV\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",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0820472",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1276930",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if the dataset contains gene expression data based on previous assessment\n",
    "if not is_gene_available:\n",
    "    print(\"This dataset does not contain gene expression data (only miRNA data).\")\n",
    "    print(\"Skipping gene expression data extraction.\")\n",
    "else:\n",
    "    # Get the matrix file directly rather than using geo_get_relevant_filepaths\n",
    "    files = os.listdir(in_cohort_dir)\n",
    "    if len(files) > 0:\n",
    "        matrix_file = os.path.join(in_cohort_dir, files[0])\n",
    "        print(f\"Matrix file found: {matrix_file}\")\n",
    "        \n",
    "        try:\n",
    "            # Extract gene data\n",
    "            gene_data = get_genetic_data(matrix_file)\n",
    "            print(f\"Gene data shape: {gene_data.shape}\")\n",
    "            \n",
    "            # Print the first 20 gene/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",
    "    else:\n",
    "        print(\"No files found in the input directory.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfa072a",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d4935c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These identifiers (\"1007_s_at\", \"1053_at\", etc.) are Affymetrix probe IDs from a microarray platform,\n",
    "# not standard human gene symbols. They need to be mapped to official gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efc29dbe",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69b41cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Use the 'get_gene_annotation' function from the library to get gene annotation data from the SOFT file.\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\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",
    "# Look more closely at columns that might contain gene information\n",
    "print(\"\\nExamining potential gene mapping columns:\")\n",
    "potential_gene_columns = ['gene_assignment', 'mrna_assignment', 'swissprot', 'unigene']\n",
    "for col in potential_gene_columns:\n",
    "    if col in gene_annotation.columns:\n",
    "        print(f\"\\nSample values from '{col}' column:\")\n",
    "        print(gene_annotation[col].head(3).tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aadb4c4f",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62835c6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify the columns for mapping\n",
    "# From the gene annotation preview, I can see that:\n",
    "# - 'ID' column contains the probe identifiers (e.g., '1007_s_at')\n",
    "# - 'Gene Symbol' column contains the gene symbols (e.g., 'DDR1 /// MIR4640')\n",
    "\n",
    "# 1. Get the mapping between probe IDs and gene symbols\n",
    "mapping_df = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# Print mapping dataframe info\n",
    "print(\"\\nGene mapping dataframe info:\")\n",
    "print(f\"Shape: {mapping_df.shape}\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 2. Apply the mapping to convert probe-level measurements to gene-level expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "\n",
    "# 3. Normalize gene symbols (handle synonyms and merge duplicate symbols)\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "\n",
    "# Print gene expression data info\n",
    "print(\"\\nGene expression data info after mapping:\")\n",
    "print(f\"Shape: {gene_data.shape}\")\n",
    "print(f\"First 5 gene symbols: {gene_data.index[:5].tolist()}\")\n",
    "print(f\"First 5 sample IDs: {gene_data.columns[:5].tolist()}\")\n",
    "\n",
    "# Save the gene expression 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": "3c223b69",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acf55804",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the clinical data from the file we saved in step 2\n",
    "clinical_data_file = out_clinical_data_file\n",
    "if os.path.exists(clinical_data_file):\n",
    "    selected_clinical_df = pd.read_csv(clinical_data_file)\n",
    "    print(f\"Loaded clinical data from {clinical_data_file}\")\n",
    "    print(f\"Clinical data shape: {selected_clinical_df.shape}\")\n",
    "    print(\"Clinical data preview:\")\n",
    "    print(selected_clinical_df.head())\n",
    "else:\n",
    "    print(f\"Clinical data file {clinical_data_file} not found. Re-extracting 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",
    "    print(\"Re-extracted clinical data preview:\")\n",
    "    print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 1. Normalize gene symbols in the index\n",
    "print(\"\\nNormalizing gene symbols...\")\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {normalized_gene_data.shape}\")\n",
    "print(\"First 10 gene identifiers after normalization:\")\n",
    "print(normalized_gene_data.index[:10].tolist())\n",
    "\n",
    "# Save the normalized gene data to CSV\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",
    "print(\"\\nLinking clinical and genetic data...\")\n",
    "# Since we read clinical data with a standard index (0, 1, 2...), need to transpose before linking\n",
    "if 'Liver_Cancer' in selected_clinical_df.columns:\n",
    "    selected_clinical_df.set_index('Liver_Cancer', inplace=True)\n",
    "    selected_clinical_df = selected_clinical_df.T\n",
    "else:\n",
    "    # Transpose to get samples as rows and trait as column\n",
    "    selected_clinical_df = selected_clinical_df.T\n",
    "    selected_clinical_df.columns = [trait]\n",
    "\n",
    "linked_data = pd.concat([selected_clinical_df, normalized_gene_data.T], axis=1)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, first 5 columns):\")\n",
    "print(linked_data.iloc[:5, :5])\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "print(\"\\nHandling 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 if the trait and demographic features are biased\n",
    "print(\"\\nChecking for bias in trait and demographic features...\")\n",
    "is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)\n",
    "\n",
    "# 5. Conduct final quality validation and save relevant information\n",
    "print(\"\\nConducting final quality validation...\")\n",
    "is_gene_available = len(normalized_gene_data) > 0\n",
    "is_trait_available = True  # We've confirmed trait data is available in previous steps\n",
    "\n",
    "note = \"This dataset contains gene expression data from skin biopsies of patients with alopecia areata, comparing lesional and non-lesional samples. The dataset is actually about alopecia areata, not liver cancer.\"\n",
    "\n",
    "is_usable = validate_and_save_cohort_info(\n",
    "    is_final=True,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available,\n",
    "    is_biased=is_biased,\n",
    "    df=linked_data,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 6. Save the linked data if it's 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(\"Linked data not saved as dataset is not usable for the current trait study.\")"
   ]
  }
 ],
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}