{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4786f9",
   "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 = \"Sjögrens_Syndrome\"\n",
    "cohort = \"GSE66795\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Sjögrens_Syndrome\"\n",
    "in_cohort_dir = \"../../input/GEO/Sjögrens_Syndrome/GSE66795\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Sjögrens_Syndrome/GSE66795.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Sjögrens_Syndrome/gene_data/GSE66795.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Sjögrens_Syndrome/clinical_data/GSE66795.csv\"\n",
    "json_path = \"../../output/preprocess/Sjögrens_Syndrome/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4ea364",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "464718dc",
   "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": "ac7cfbdf",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bdbb45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information and sample characteristics, this dataset likely contains gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# Trait (pSS) can be inferred from \"patient group\" (key 2)\n",
    "trait_row = 2  # 'patient group: Control', 'patient group: Patient'\n",
    "\n",
    "# Age is not available in the sample characteristics\n",
    "age_row = None  \n",
    "\n",
    "# Gender is available (key 3), but there's only one value ('gender: Female')\n",
    "# Since all subjects are female, this is a constant feature and not useful for association analysis\n",
    "gender_row = None  \n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert patient group to binary trait (Sjögren's Syndrome)\"\"\"\n",
    "    if isinstance(value, str) and \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "        if value.lower() == \"patient\":\n",
    "            return 1  # Has Sjögren's Syndrome\n",
    "        elif value.lower() == \"control\":\n",
    "            return 0  # Doesn't have Sjögren's Syndrome\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to continuous value\"\"\"\n",
    "    # Age data is not available\n",
    "    return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0=female, 1=male)\"\"\"\n",
    "    # Gender is constant (all female) so not useful for analysis\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available (trait_row is not None)\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Validate and save cohort info with is_final=False for initial filtering\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 (since trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Extract clinical features using the geo_select_clinical_features function\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 selected clinical features\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Preview of selected clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the selected clinical features as a CSV file\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",
    "    print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4b7bff",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84837bbb",
   "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": "b2e65f0c",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4fbc16b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis of gene identifiers\n",
    "# The gene identifiers shown (ILMN_xxxxxxx) are Illumina probe IDs, not human gene symbols.\n",
    "# These are microarray probe identifiers from Illumina BeadArray platforms\n",
    "# These need to be mapped to standard gene symbols for meaningful analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5624aa14",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55f18d39",
   "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": "68a59826",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8d0708",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Identify the relevant columns for gene mapping\n",
    "# From the preview, we can see:\n",
    "# - 'ID' column contains Illumina probe IDs (ILMN_xxxxxx) that match gene_data indices\n",
    "# - 'Symbol' column contains the gene symbols we need\n",
    "\n",
    "# 2. Extract the gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, 'ID', 'Symbol')\n",
    "print(f\"Gene mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"Gene mapping preview:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene expression\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene expression dataframe shape after mapping: {gene_data.shape}\")\n",
    "print(\"Gene expression data preview (first 5 genes, first 5 samples):\")\n",
    "print(gene_data.iloc[:5, :5])\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": "12faf072",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14d005e5",
   "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. Load the previously saved clinical data\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
    "print(clinical_df.head())\n",
    "\n",
    "# 3. Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df.T, normalized_gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "\n",
    "# Rename the unnamed index 0 to the trait name\n",
    "linked_data = linked_data.rename(index={0: trait})\n",
    "print(linked_data.head())\n",
    "\n",
    "# 4. 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",
    "# 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, 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 whole blood of pSS patients and healthy controls.\"\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.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da97eed0",
   "metadata": {},
   "source": [
    "### Step 8: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c767754",
   "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. Load the previously saved clinical data\n",
    "clinical_df = pd.read_csv(out_clinical_data_file)\n",
    "print(f\"Loaded clinical data shape: {clinical_df.shape}\")\n",
    "print(clinical_df.head())\n",
    "\n",
    "# 3. Transform clinical data to have trait as column\n",
    "sample_ids = clinical_df.columns\n",
    "trait_values = clinical_df.iloc[0].values\n",
    "clinical_df_restructured = pd.DataFrame({trait: trait_values}, index=sample_ids)\n",
    "print(f\"Restructured clinical data shape: {clinical_df_restructured.shape}\")\n",
    "print(clinical_df_restructured.head())\n",
    "\n",
    "# 4. Properly format gene expression data (transpose to have samples as rows)\n",
    "gene_expr_for_linking = normalized_gene_data.T\n",
    "print(f\"Transposed gene data shape: {gene_expr_for_linking.shape}\")\n",
    "\n",
    "# 5. Merge clinical and gene expression data on sample IDs\n",
    "linked_data = clinical_df_restructured.join(gene_expr_for_linking)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(linked_data.head(3))\n",
    "\n",
    "# 6. 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",
    "# 7. 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",
    "# 8. 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 whole blood samples of pSS patients and healthy controls.\"\n",
    ")\n",
    "\n",
    "# 9. 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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}