{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0809c98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.223357Z",
     "iopub.status.busy": "2025-03-25T06:02:22.223128Z",
     "iopub.status.idle": "2025-03-25T06:02:22.387996Z",
     "shell.execute_reply": "2025-03-25T06:02:22.387659Z"
    }
   },
   "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 = \"Ovarian_Cancer\"\n",
    "cohort = \"GSE103737\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Ovarian_Cancer\"\n",
    "in_cohort_dir = \"../../input/GEO/Ovarian_Cancer/GSE103737\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Ovarian_Cancer/GSE103737.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Ovarian_Cancer/gene_data/GSE103737.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Ovarian_Cancer/clinical_data/GSE103737.csv\"\n",
    "json_path = \"../../output/preprocess/Ovarian_Cancer/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f5bc8a",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48ff45c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.389378Z",
     "iopub.status.busy": "2025-03-25T06:02:22.389234Z",
     "iopub.status.idle": "2025-03-25T06:02:22.563350Z",
     "shell.execute_reply": "2025-03-25T06:02:22.563011Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptional correlates of high norepinephrine content in ovarian carcinomas\"\n",
      "!Series_summary\t\"Genome-wide transcriptome profiling was conducted on 97 Stage I-IV ovarian carcinomas classified according to tissue norepinephrine content.\"\n",
      "!Series_overall_design\t\"To characterize the impact of norepinephrine on ovarian carcinoma biology, we conducted genome-wide transcriptome profiling of 97 clinical ovarian tumors.  Tissue norepinephrine content was assessed by high performance liquid chromatogrphy (0=below median value of 1.05 pg/ml;1=above median).  Covariates included age (years), body mass index (kg / m^2), tumor stage (1-4), and tumor grade (0=low;1=high).\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['subject age: 70', 'subject age: 49', 'subject age: 87', 'subject age: 65', 'subject age: 63', 'subject age: 50', 'subject age: 71', 'subject age: 62', 'subject age: 51', 'subject age: 68', 'subject age: 48', 'subject age: 61', 'subject age: 79', 'subject age: 73', 'subject age: 76', 'subject age: 59', 'subject age: 69', 'subject age: 39', 'subject age: 56', 'subject age: 47', 'subject age: 53', 'subject age: 58', 'subject age: 77', 'subject age: 80', 'subject age: 40', 'subject age: 41', 'subject age: 44', 'subject age: 60', 'subject age: 64', 'subject age: 33'], 1: ['bmi: 23.92', 'bmi: 55.06', 'bmi: 26.47', 'bmi: 35.82', 'bmi: 32.91', 'bmi: 32.15', 'bmi: 31.83', 'bmi: 22.66', 'bmi: 33.66', 'bmi: 24.65', 'bmi: 28.25', 'bmi: 28.26', 'bmi: 35.83', 'bmi: 28.83', 'bmi: 49.28', 'bmi: 29.58', 'bmi: 31.6', 'bmi: 26.29', 'bmi: 32.2', 'bmi: 21.18', 'bmi: 26.59', 'bmi: 24.06', 'bmi: 20.01', 'bmi: 34.57', 'bmi: 24.37', 'bmi: 35.42', 'bmi: 24.54', 'bmi: 19.64', 'bmi: 36.52', 'bmi: 24.5'], 2: ['tissue: ovarian carcinoma'], 3: ['tumor stage: 3', 'tumor stage: 2', 'tumor stage: 4', 'tumor stage: 1'], 4: ['tumor grade (0=low;1=high): 1', 'tumor grade (0=low;1=high): 0'], 5: ['norepinephrine content (0=below median value of 1.05 pg/ml;1=above median): 0', 'norepinephrine content (0=below median value of 1.05 pg/ml;1=above median): 1']}\n"
     ]
    }
   ],
   "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": "1caf1f03",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3dc3fc20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.564639Z",
     "iopub.status.busy": "2025-03-25T06:02:22.564536Z",
     "iopub.status.idle": "2025-03-25T06:02:22.569088Z",
     "shell.execute_reply": "2025-03-25T06:02:22.568805Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on series title and overall design, this appears to be gene expression data\n",
    "is_gene_available = True\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# From the sample characteristics dictionary:\n",
    "# - For trait: norepinephrine content is in position 5\n",
    "# - For age: subject age is in position 0\n",
    "# - There is no gender information\n",
    "\n",
    "trait_row = 5  # norepinephrine content\n",
    "age_row = 0    # subject age\n",
    "gender_row = None  # gender not available\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert norepinephrine content to binary values (0 or 1).\n",
    "    Format: \"norepinephrine content (0=below median value of 1.05 pg/ml;1=above median): 0/1\"\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    parts = value.split(':')\n",
    "    if len(parts) < 2:\n",
    "        return None\n",
    "    \n",
    "    value_str = parts[1].strip()\n",
    "    try:\n",
    "        # Convert to integer (already binary)\n",
    "        return int(value_str)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age to continuous values.\n",
    "    Format: \"subject age: XX\"\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    parts = value.split(':')\n",
    "    if len(parts) < 2:\n",
    "        return None\n",
    "    \n",
    "    value_str = parts[1].strip()\n",
    "    try:\n",
    "        # Convert to float\n",
    "        return float(value_str)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# Gender conversion function not needed as gender data is not available\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Determine if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "initial_validation = 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 (only if trait_row is not None)\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data from the previous step\n",
    "    clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.pkl\")\n",
    "    if os.path.exists(clinical_data_path):\n",
    "        clinical_data = pd.read_pickle(clinical_data_path)\n",
    "        \n",
    "        # Extract clinical features\n",
    "        clinical_features = 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=None  # No gender data\n",
    "        )\n",
    "        \n",
    "        # Preview the extracted features\n",
    "        preview = preview_df(clinical_features)\n",
    "        print(\"Preview of clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save to CSV file\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 features saved to {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf824b6a",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a556ea42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.570213Z",
     "iopub.status.busy": "2025-03-25T06:02:22.570113Z",
     "iopub.status.idle": "2025-03-25T06:02:22.871473Z",
     "shell.execute_reply": "2025-03-25T06:02:22.871102Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['7A5', 'A1BG', 'A1CF', 'A26C3', 'A2BP1', 'A2LD1', 'A2M', 'A2ML1',\n",
      "       'A3GALT2', 'A4GALT', 'A4GNT', 'AAA1', 'AAAS', 'AACS', 'AACSL', 'AADAC',\n",
      "       'AADACL1', 'AADACL2', 'AADACL3', 'AADACL4'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "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": "3383977d",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6eccc489",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.872762Z",
     "iopub.status.busy": "2025-03-25T06:02:22.872646Z",
     "iopub.status.idle": "2025-03-25T06:02:22.874522Z",
     "shell.execute_reply": "2025-03-25T06:02:22.874249Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers based on the provided preview\n",
    "# These appear to be proper human gene symbols. For example:\n",
    "# A1BG (Alpha-1-B Glycoprotein)\n",
    "# A2M (Alpha-2-Macroglobulin)\n",
    "# AAAS (Aladin WD Repeat Nucleoporin)\n",
    "# AACS (Acetoacetyl-CoA Synthetase)\n",
    "\n",
    "# Some might be older gene symbols that have been updated, but they are gene symbols nonetheless,\n",
    "# not probe IDs that would require mapping.\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f048969e",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "301fc7d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:02:22.875658Z",
     "iopub.status.busy": "2025-03-25T06:02:22.875556Z",
     "iopub.status.idle": "2025-03-25T06:02:37.553528Z",
     "shell.execute_reply": "2025-03-25T06:02:37.552985Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Ovarian_Cancer/gene_data/GSE103737.csv\n",
      "Clinical features saved to ../../output/preprocess/Ovarian_Cancer/clinical_data/GSE103737.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the feature 'Ovarian_Cancer', the least common label is '0.0' with 46 occurrences. This represents 47.42% of the dataset.\n",
      "The distribution of the feature 'Ovarian_Cancer' in this dataset is fine.\n",
      "\n",
      "Quartiles for 'Age':\n",
      "  25%: 50.0\n",
      "  50% (Median): 60.0\n",
      "  75%: 68.0\n",
      "Min: 29.0\n",
      "Max: 87.0\n",
      "The distribution of the feature 'Age' in this dataset is fine.\n",
      "\n",
      "A new JSON file was created at: ../../output/preprocess/Ovarian_Cancer/cohort_info.json\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linked data saved to ../../output/preprocess/Ovarian_Cancer/GSE103737.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "normalized_gene_data = normalize_gene_symbols_in_index(gene_data)\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. Re-extract clinical features since they weren't properly saved in a previous step\n",
    "# Define the conversion functions again\n",
    "def convert_trait(value):\n",
    "    \"\"\"\n",
    "    Convert norepinephrine content to binary values (0 or 1).\n",
    "    Format: \"norepinephrine content (0=below median value of 1.05 pg/ml;1=above median): 0/1\"\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    parts = value.split(':')\n",
    "    if len(parts) < 2:\n",
    "        return None\n",
    "    \n",
    "    value_str = parts[1].strip()\n",
    "    try:\n",
    "        # Convert to integer (already binary)\n",
    "        return int(value_str)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"\n",
    "    Convert age to continuous values.\n",
    "    Format: \"subject age: XX\"\n",
    "    \"\"\"\n",
    "    if pd.isna(value) or not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    parts = value.split(':')\n",
    "    if len(parts) < 2:\n",
    "        return None\n",
    "    \n",
    "    value_str = parts[1].strip()\n",
    "    try:\n",
    "        # Convert to float\n",
    "        return float(value_str)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "# Extract clinical features\n",
    "clinical_features = geo_select_clinical_features(\n",
    "    clinical_df=clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=5,  # norepinephrine content is in position 5\n",
    "    convert_trait=convert_trait,\n",
    "    age_row=0,    # subject age is in position 0\n",
    "    convert_age=convert_age,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "# Save clinical features\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 features saved to {out_clinical_data_file}\")\n",
    "\n",
    "# Link the clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)\n",
    "\n",
    "# 3. Handle missing values in the linked data\n",
    "linked_data = handle_missing_values(linked_data, trait)\n",
    "\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, 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=\"Cohort contains ovarian cancer patients with norepinephrine content data.\"\n",
    ")\n",
    "\n",
    "# 6. If the linked data is usable, save it as a CSV file\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(\"Data quality check failed. Linked data not saved.\")"
   ]
  }
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