{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9ed4b723",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:43.817050Z",
     "iopub.status.busy": "2025-03-25T06:13:43.816929Z",
     "iopub.status.idle": "2025-03-25T06:13:43.979757Z",
     "shell.execute_reply": "2025-03-25T06:13:43.979238Z"
    }
   },
   "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 = \"Polycystic_Ovary_Syndrome\"\n",
    "cohort = \"GSE151158\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Polycystic_Ovary_Syndrome\"\n",
    "in_cohort_dir = \"../../input/GEO/Polycystic_Ovary_Syndrome/GSE151158\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Polycystic_Ovary_Syndrome/GSE151158.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE151158.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE151158.csv\"\n",
    "json_path = \"../../output/preprocess/Polycystic_Ovary_Syndrome/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f881720",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6975f6d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:43.981476Z",
     "iopub.status.busy": "2025-03-25T06:13:43.981293Z",
     "iopub.status.idle": "2025-03-25T06:13:44.008949Z",
     "shell.execute_reply": "2025-03-25T06:13:44.008482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"Transcriptional analysis of non-fibrotic NAFLD progression\"\n",
      "!Series_summary\t\"Background & Aims:  Non-alcoholic steatohepatitis (NASH), a subtype of non-alcoholic fatty liver disease (NAFLD) that can lead to fibrosis, cirrhosis, and hepatocellular carcinoma, is characterized by hepatic inflammation.  Despite evolving therapies aimed to ameliorate inflammation in NASH, the transcriptional changes that lead to inflammation progression in NAFLD remain poorly understood.  The aim of this study is to define transcriptional changes in early, non-fibrotic NAFLD using a biopsy-proven non-fibrotic NAFLD cohort.  Methods:  We extracted RNA from liver tissue of 40 patients with biopsy-proven NAFLD based on NAFLD Activity Score (NAS) (23 with NAS ≤3, 17 with NAS ≥5) and 21 healthy controls and compared changes in expression of 594 genes involved in innate immune function.    Results:  Compared to healthy controls, NAFLD patients with NAS ≥5 had differential expression of 211 genes, while those with NAS ≤3 had differential expression of only 14 genes.  Notably, osteopontin (SPP1) (3.74-fold in NAS ≤3, 8.28-fold in NAS ≥5) and CXCL10 (2.27-fold in NAS ≤3, 8.28-fold in NAS ≥5) gene expression were significantly upregulated with histologic progression of NAFLD.\"\n",
      "!Series_overall_design\t\"We extracted RNA from liver tissue of 40 patients with biopsy-proven NAFLD based on NAFLD Activity Score (NAS) (23 with NAS ≤3, 17 with NAS ≥5) and 21 healthy controls (protocol biopsy obtained during living liver donation) and compared changes in expression of 594 genes involved in innate immune function\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: liver', 'sample type: blank'], 1: ['age: 53', 'age: 40', 'age: 51', 'age: 36', 'age: 44', 'age: 60', 'age: 31', 'age: 41', 'age: 55', 'age: 15', 'age: 57', 'age: 56', 'age: 34', 'age: 43', 'age: 49', 'age: 52', 'age: 35', 'age: 42', 'age: 33', 'age: 48', 'age: 47', 'age: 65', 'age: 59', 'age: 61', 'age: 28', 'age: 46', 'age: 25', 'age: 27', 'age: 54', 'age: 37'], 2: ['Sex: F', 'Sex: M', nan], 3: ['ethnicity: White', 'ethnicity: Hispanic', 'ethnicity: AA', nan], 4: ['bmi: 28.4', 'bmi: 37.8', 'bmi: 33.1', 'bmi: 39.6', 'bmi: 31.5', 'bmi: 29.9', 'bmi: 39.9', 'bmi: 33.3', 'bmi: 41.1', 'bmi: 62.9', 'bmi: 47.6', 'bmi: 31.7', 'bmi: 53.4', 'bmi: 31.4', 'bmi: 23.9', 'bmi: 22.4', 'bmi: 23.7', 'bmi: 28', 'bmi: 27.8', 'bmi: 37.7', 'bmi: 36.1', 'bmi: 36.7', 'bmi: 39.4', 'bmi: 36.8', 'bmi: 29.2', 'bmi: 35.2', 'bmi: 38.4', 'bmi: 30.8', 'bmi: 29', 'bmi: 47.8'], 5: ['dm2: N', 'dm2: Y', nan], 6: ['insulin: N', 'insulin: Y', nan], 7: ['hypertension: N', 'hypertension: Y', nan], 8: ['hyperlipidemia: N', 'hyperlipidemia: Y', nan], 9: ['statin/fibrate: N', 'statin/fibrate: Y', nan], 10: ['osa: N', 'osa: Y', nan], 11: ['pcos: N', nan], 12: ['hypothyroidism: N', 'hypothyroidism: Y', nan], 13: ['cardiovascular disease: N', 'cardiovascular disease: Y', nan], 14: ['ast (units/l): 77', 'ast (units/l): 66', 'ast (units/l): 64', 'ast (units/l): 68', 'ast (units/l): 21', 'ast (units/l): 51', 'ast (units/l): 174', 'ast (units/l): 58', 'ast (units/l): 45', 'ast (units/l): 19', 'ast (units/l): 41', 'ast (units/l): 24', 'ast (units/l): 49', 'ast (units/l): 26', 'ast (units/l): 16', 'ast (units/l): 15', 'ast (units/l): 17', 'ast (units/l): 20', 'ast (units/l): 27', 'ast (units/l): 305', 'ast (units/l): 43', 'ast (units/l): 75', 'ast (units/l): 67', 'ast (units/l): 118', 'ast (units/l): 69', 'ast (units/l): 59', 'ast (units/l): 31', 'ast (units/l): 18', 'ast (units/l): 33', 'ast (units/l): 37'], 15: ['alt (units/l): 129', 'alt (units/l): 123', 'alt (units/l): 84', 'alt (units/l): 120', 'alt (units/l): 28', 'alt (units/l): 88', 'alt (units/l): 429', 'alt (units/l): 66', 'alt (units/l): 26', 'alt (units/l): 40', 'alt (units/l): 46', 'alt (units/l): 94', 'alt (units/l): 72', 'alt (units/l): 17', 'alt (units/l): 12', 'alt (units/l): 27', 'alt (units/l): 3', 'alt (units/l): 16', 'alt (units/l): 70', 'alt (units/l): 301', 'alt (units/l): 6', 'alt (units/l): 102', 'alt (units/l): 97', 'alt (units/l): 110', 'alt (units/l): 89', 'alt (units/l): 44', 'alt (units/l): 42', 'alt (units/l): 33', 'alt (units/l): 52', 'alt (units/l): 31'], 16: ['alkaline phosphatase (units/l): 114', 'alkaline phosphatase (units/l): 60', 'alkaline phosphatase (units/l): 91', 'alkaline phosphatase (units/l): 130', 'alkaline phosphatase (units/l): 120', 'alkaline phosphatase (units/l): 58', 'alkaline phosphatase (units/l): 78', 'alkaline phosphatase (units/l): 83', 'alkaline phosphatase (units/l): 89', 'alkaline phosphatase (units/l): 95', 'alkaline phosphatase (units/l): 150', 'alkaline phosphatase (units/l): 131', 'alkaline phosphatase (units/l): 52', 'alkaline phosphatase (units/l): 72', 'alkaline phosphatase (units/l): 65', 'alkaline phosphatase (units/l): 94', 'alkaline phosphatase (units/l): 62', 'alkaline phosphatase (units/l): 105', 'alkaline phosphatase (units/l): 71', 'alkaline phosphatase (units/l): 76', 'alkaline phosphatase (units/l): 74', 'alkaline phosphatase (units/l): 90', 'nas: Steatosis: 2', 'alkaline phosphatase (units/l): 117', 'alkaline phosphatase (units/l): 48', 'alkaline phosphatase (units/l): 41', 'alkaline phosphatase (units/l): 93', 'alkaline phosphatase (units/l): 46', 'alkaline phosphatase (units/l): 67', 'alkaline phosphatase (units/l): 66'], 17: ['total bilirubin (mg/dl): 0.4', 'total bilirubin (mg/dl): 0.7', 'total bilirubin (mg/dl): 1.1', 'total bilirubin (mg/dl): 0.6', 'total bilirubin (mg/dl): 0.5', 'total bilirubin (mg/dl): 1', 'total bilirubin (mg/dl): 0.3', 'total bilirubin (mg/dl): 0.8', 'total bilirubin (mg/dl): 1.4', 'total bilirubin (mg/dl): 1.5', 'nas: Ballooning: 2', 'total bilirubin (mg/dl): 0.2', 'total bilirubin (mg/dl): 0.9', nan], 18: ['albumin (g/dl): 4.3', 'albumin (g/dl): 4.4', 'albumin (g/dl): 4.2', 'albumin (g/dl): 4.7', 'albumin (g/dl): 4', 'albumin (g/dl): 5.2', 'albumin (g/dl): 4.1', 'albumin (g/dl): 4.5', 'albumin (g/dl): 3.5', 'albumin (g/dl): 3.6', 'albumin (g/dl): 3.8', 'nas: Lobular inflammation: 1', 'albumin (g/dl): 3.9', 'albumin (g/dl): 3.7', 'albumin (g/dl): 3.2', 'albumin (g/dl): 4.9', 'albumin (g/dl): 4.6', nan], 19: ['total protein (g/dl): 8.2', 'total protein (g/dl): 7.7', 'total protein (g/dl): 7.2', 'total protein (g/dl): 8', 'total protein (g/dl): 7.6', 'total protein (g/dl): 8.7', 'total protein (g/dl): 7.1', 'total protein (g/dl): 7.9', 'total protein (g/dl): 6.8', 'total protein (g/dl): 6.5', 'total protein (g/dl): 7', 'total protein (g/dl): 7.3', 'total protein (g/dl): 6.6', 'total protein (g/dl): 7.5', 'nas: Total score: 5', 'total protein (g/dl): 7.8', 'total protein (g/dl): 7.4', 'total protein (g/dl): 6.9', 'total protein (g/dl): 8.1', 'total protein (g/dl): 8.4', 'total protein (g/dl): 6.3', 'total protein (g/dl): 6.7', nan], 20: ['nas: Steatosis: 1', 'nas: Steatosis: 2', 'nas: Steatosis: 3', 'nas: Steatosis: 0', nan], 21: ['nas: Ballooning: 2', 'nas: Ballooning: 1', 'nas: Ballooning: 0', nan], 22: ['nas: Lobular inflammation: 2', 'nas: Lobular inflammation: 1', 'nas: Lobular inflammation: 0', 'nas: Lobular inflammation: 3', nan], 23: ['nas: Total score: 5', 'nas: Total score: 6', 'nas: Total score: 3', 'nas: Total score: 0', nan, 'nas: Total score: 2']}\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": "3c88b184",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e0465093",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:44.010543Z",
     "iopub.status.busy": "2025-03-25T06:13:44.010398Z",
     "iopub.status.idle": "2025-03-25T06:13:44.016341Z",
     "shell.execute_reply": "2025-03-25T06:13:44.015894Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "\n",
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information and series title, this appears to be transcriptional analysis\n",
    "# which suggests gene expression data is available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "\n",
    "# 2.1 Data Availability\n",
    "# For Polycystic Ovary Syndrome (PCOS), I found row 11 with 'pcos: N' values\n",
    "trait_row = 11\n",
    "\n",
    "# For age, I found row 1 with age values\n",
    "age_row = 1\n",
    "\n",
    "# For gender, I found row 2 with 'Sex: F' and 'Sex: M' values\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion Functions\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert PCOS trait values to binary (0 or 1)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # PCOS: Y would be 1, PCOS: N would be 0\n",
    "    if value.upper() == 'Y':\n",
    "        return 1\n",
    "    elif value.upper() == 'N':\n",
    "        return 0\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age to a continuous value\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    try:\n",
    "        return float(value)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract value after colon\n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    if value.upper() == 'F':\n",
    "        return 0\n",
    "    elif value.upper() == 'M':\n",
    "        return 1\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available if trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# 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\n",
    "if trait_row is not None:\n",
    "    # Load the clinical data\n",
    "    clinical_data_path = os.path.join(in_cohort_dir, \"clinical_data.csv\")\n",
    "    if os.path.exists(clinical_data_path):\n",
    "        clinical_data = pd.read_csv(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=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the data\n",
    "        preview = preview_df(clinical_features)\n",
    "        print(\"Clinical Features Preview:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Create directory if it doesn't exist\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        \n",
    "        # Save as CSV\n",
    "        clinical_features.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to: {out_clinical_data_file}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55309289",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9540a2cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:44.017814Z",
     "iopub.status.busy": "2025-03-25T06:13:44.017702Z",
     "iopub.status.idle": "2025-03-25T06:13:44.031675Z",
     "shell.execute_reply": "2025-03-25T06:13:44.031202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Polycystic_Ovary_Syndrome/GSE151158/GSE151158_series_matrix.txt.gz\n",
      "Gene data shape: (618, 66)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['ABCB1', 'ABCF1', 'ABL1', 'ADA', 'AHR', 'AICDA', 'AIRE', 'ALAS1', 'APP',\n",
      "       'AREG', 'ARG1', 'ARG2', 'ARHGDIB', 'ATG10', 'ATG12', 'ATG16L1', 'ATG5',\n",
      "       'ATG7', 'ATM', 'B2M'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "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": "a499cb19",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ceebf6a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:44.033138Z",
     "iopub.status.busy": "2025-03-25T06:13:44.033028Z",
     "iopub.status.idle": "2025-03-25T06:13:44.035147Z",
     "shell.execute_reply": "2025-03-25T06:13:44.034721Z"
    }
   },
   "outputs": [],
   "source": [
    "# These identifiers (ABCB1, ABCF1, etc.) are indeed standard human gene symbols.\n",
    "# No additional mapping is required as they are already in the proper format for human genes.\n",
    "\n",
    "requires_gene_mapping = False\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403ee1a2",
   "metadata": {},
   "source": [
    "### Step 5: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "781ca83b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:13:44.036666Z",
     "iopub.status.busy": "2025-03-25T06:13:44.036558Z",
     "iopub.status.idle": "2025-03-25T06:13:44.263213Z",
     "shell.execute_reply": "2025-03-25T06:13:44.262644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data saved to: ../../output/preprocess/Polycystic_Ovary_Syndrome/clinical_data/GSE151158.csv\n",
      "Clinical data preview:\n",
      "{'GSM4567420': [0.0, 0.0], 'GSM4567421': [0.0, 0.0], 'GSM4567422': [0.0, 0.0], 'GSM4567423': [0.0, 0.0], 'GSM4567424': [0.0, 0.0], 'GSM4567425': [0.0, 0.0], 'GSM4567426': [0.0, 0.0], 'GSM4567427': [0.0, 0.0], 'GSM4567428': [0.0, 0.0], 'GSM4567429': [0.0, 0.0], 'GSM4567430': [0.0, 0.0], 'GSM4567431': [0.0, 0.0], 'GSM4567432': [0.0, 0.0], 'GSM4567433': [0.0, 0.0], 'GSM4567434': [0.0, 0.0], 'GSM4567435': [0.0, 0.0], 'GSM4567436': [0.0, 0.0], 'GSM4567437': [0.0, 0.0], 'GSM4567438': [0.0, 0.0], 'GSM4567439': [0.0, 0.0], 'GSM4567440': [0.0, 0.0], 'GSM4567441': [0.0, 0.0], 'GSM4567442': [0.0, 0.0], 'GSM4567443': [0.0, 0.0], 'GSM4567444': [0.0, 0.0], 'GSM4567445': [0.0, 0.0], 'GSM4567446': [0.0, 0.0], 'GSM4567447': [0.0, 0.0], 'GSM4567448': [0.0, 0.0], 'GSM4567449': [0.0, 0.0], 'GSM4567450': [0.0, 0.0], 'GSM4567451': [0.0, 0.0], 'GSM4567452': [0.0, 0.0], 'GSM4567453': [0.0, 0.0], 'GSM4567454': [0.0, 0.0], 'GSM4567455': [0.0, 0.0], 'GSM4567456': [0.0, 0.0], 'GSM4567457': [0.0, 0.0], 'GSM4567458': [0.0, 0.0], 'GSM4567459': [0.0, 0.0], 'GSM4567460': [0.0, 0.0], 'GSM4567461': [0.0, 0.0], 'GSM4567462': [0.0, 0.0], 'GSM4567463': [0.0, 0.0], 'GSM4567464': [0.0, 0.0], 'GSM4567465': [0.0, 0.0], 'GSM4567466': [0.0, 0.0], 'GSM4567467': [0.0, 0.0], 'GSM4567468': [0.0, 0.0], 'GSM4567469': [0.0, 0.0], 'GSM4567470': [0.0, 0.0], 'GSM4567471': [0.0, 0.0], 'GSM4567472': [0.0, 0.0], 'GSM4567473': [0.0, 0.0], 'GSM4567474': [0.0, 0.0], 'GSM4567475': [0.0, 0.0], 'GSM4567476': [0.0, 0.0], 'GSM4567477': [0.0, 0.0], 'GSM4567478': [0.0, 0.0], 'GSM4567479': [0.0, 0.0], 'GSM4567480': [0.0, 0.0], 'GSM4567481': [nan, 0.0], 'GSM4567482': [nan, 0.0], 'GSM4567483': [nan, 0.0], 'GSM4567484': [nan, 0.0], 'GSM4567485': [nan, 0.0]}\n",
      "\n",
      "Normalizing gene symbols...\n",
      "Gene data shape after normalization: (583, 66)\n",
      "First 10 normalized gene identifiers:\n",
      "Index(['ABCB1', 'ABCF1', 'ABL1', 'ACKR4', 'ADA', 'ADGRE2', 'AHR', 'AICDA',\n",
      "       'AIRE', 'ALAS1'],\n",
      "      dtype='object', name='ID')\n",
      "Normalized gene data saved to: ../../output/preprocess/Polycystic_Ovary_Syndrome/gene_data/GSE151158.csv\n",
      "\n",
      "Linking clinical and genetic data...\n",
      "Linked data shape: (66, 585)\n",
      "Linked data preview (first 5 rows, 5 columns):\n",
      "            Polycystic_Ovary_Syndrome  Gender    ABCB1   ABCF1    ABL1\n",
      "GSM4567420                        0.0     0.0  1264.86  697.01  435.43\n",
      "GSM4567421                        0.0     0.0  1958.63  665.75  398.78\n",
      "GSM4567422                        0.0     0.0  1592.80  592.85  458.84\n",
      "GSM4567423                        0.0     0.0  1421.80  659.55  493.41\n",
      "GSM4567424                        0.0     0.0  1661.67  737.55  562.18\n",
      "\n",
      "Handling missing values...\n",
      "Samples with missing trait values: 5 out of 66\n",
      "Genes with ≤20% missing values: 583 out of 583\n",
      "Samples with ≤5% missing gene values: 66 out of 66\n",
      "Linked data shape after handling missing values: (61, 585)\n",
      "\n",
      "Checking for bias in dataset features...\n",
      "Quartiles for 'Polycystic_Ovary_Syndrome':\n",
      "  25%: 0.0\n",
      "  50% (Median): 0.0\n",
      "  75%: 0.0\n",
      "Min: 0.0\n",
      "Max: 0.0\n",
      "The distribution of the feature 'Polycystic_Ovary_Syndrome' in this dataset is severely biased.\n",
      "\n",
      "For the feature 'Gender', the least common label is '0.0' with 61 occurrences. This represents 100.00% of the dataset.\n",
      "The distribution of the feature 'Gender' in this dataset is severely biased.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A new JSON file was created at: ../../output/preprocess/Polycystic_Ovary_Syndrome/cohort_info.json\n",
      "Dataset deemed not usable for associative studies. Linked data not saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. First, extract and save the clinical data since it's missing\n",
    "# Get the SOFT and matrix file paths\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# Get the background info and clinical data again\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",
    "# Define the conversion functions from Step 2\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert PCOS trait to binary (0 = control, 1 = PCOS)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if it exists\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Convert to binary\n",
    "    if 'PCOS' in value:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 = female, 1 = male)\n",
    "    Note: In this context, we're dealing with biological sex rather than gender identity\n",
    "    Female-to-male transsexuals are biologically female (0)\"\"\"\n",
    "    if pd.isna(value):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon if it exists\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Female is 0, Male is 1\n",
    "    if 'female' in value.lower():\n",
    "        return 0\n",
    "    elif 'male' in value.lower() and 'female to male' not in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return 0  # Female to male transsexuals are recorded as female (0) biologically\n",
    "\n",
    "# Extract clinical features with the correct row indices from previous steps\n",
    "trait_row = 1  # Contains \"disease state: PCOS\"\n",
    "gender_row = 0  # Contains gender information\n",
    "age_row = None  # Age information is not available in this dataset\n",
    "\n",
    "# Process and save clinical 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=None,\n",
    "    gender_row=gender_row,\n",
    "    convert_gender=convert_gender\n",
    ")\n",
    "\n",
    "# Create directory if it doesn't exist\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",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(selected_clinical_df))\n",
    "\n",
    "# 2. Normalize gene symbols using synonym information from NCBI\n",
    "print(\"\\nNormalizing gene symbols...\")\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Gene data shape after normalization: {gene_data.shape}\")\n",
    "print(\"First 10 normalized gene identifiers:\")\n",
    "print(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",
    "gene_data.to_csv(out_gene_data_file)\n",
    "print(f\"Normalized gene data saved to: {out_gene_data_file}\")\n",
    "\n",
    "# 3. Link clinical and genetic data\n",
    "print(\"\\nLinking clinical and genetic data...\")\n",
    "linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)\n",
    "print(f\"Linked data shape: {linked_data.shape}\")\n",
    "print(\"Linked data preview (first 5 rows, 5 columns):\")\n",
    "if linked_data.shape[0] > 0 and linked_data.shape[1] > 5:\n",
    "    print(linked_data.iloc[:5, :5])\n",
    "else:\n",
    "    print(linked_data)\n",
    "\n",
    "# 4. Handle missing values\n",
    "print(\"\\nHandling missing values...\")\n",
    "# First check how many samples have missing trait values\n",
    "if trait in linked_data.columns:\n",
    "    missing_trait = linked_data[trait].isna().sum()\n",
    "    print(f\"Samples with missing trait values: {missing_trait} out of {len(linked_data)}\")\n",
    "\n",
    "# Check gene missing value percentages\n",
    "gene_cols = [col for col in linked_data.columns if col not in [trait, 'Age', 'Gender']]\n",
    "gene_missing_pct = linked_data[gene_cols].isna().mean()\n",
    "genes_to_keep = gene_missing_pct[gene_missing_pct <= 0.2].index\n",
    "print(f\"Genes with ≤20% missing values: {len(genes_to_keep)} out of {len(gene_cols)}\")\n",
    "\n",
    "# Check sample missing value percentages\n",
    "if len(gene_cols) > 0:\n",
    "    sample_missing_pct = linked_data[gene_cols].isna().mean(axis=1)\n",
    "    samples_to_keep = sample_missing_pct[sample_missing_pct <= 0.05].index\n",
    "    print(f\"Samples with ≤5% missing gene values: {len(samples_to_keep)} out of {len(linked_data)}\")\n",
    "\n",
    "# Apply missing value handling\n",
    "linked_data_clean = handle_missing_values(linked_data, trait)\n",
    "print(f\"Linked data shape after handling missing values: {linked_data_clean.shape}\")\n",
    "\n",
    "# 5. Check for bias in the dataset\n",
    "print(\"\\nChecking for bias in dataset features...\")\n",
    "trait_biased, linked_data_clean = judge_and_remove_biased_features(linked_data_clean, trait)\n",
    "\n",
    "# 6. Conduct final quality validation\n",
    "note = \"This dataset contains gene expression data from ovary biopsies of women with PCOS and female-to-male transsexual individuals, focusing on LH-induced gene expression.\"\n",
    "is_gene_available = len(gene_data) > 0\n",
    "is_trait_available = trait in linked_data.columns\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=trait_biased,\n",
    "    df=linked_data_clean,\n",
    "    note=note\n",
    ")\n",
    "\n",
    "# 7. Save the linked data if it's usable\n",
    "if is_usable and linked_data_clean.shape[0] > 0:\n",
    "    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)\n",
    "    linked_data_clean.to_csv(out_data_file, index=True)\n",
    "    print(f\"Linked data saved to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset deemed not usable for associative studies. Linked data not saved.\")"
   ]
  }
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