{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2b2361c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:58.498405Z",
     "iopub.status.busy": "2025-03-25T04:08:58.497847Z",
     "iopub.status.idle": "2025-03-25T04:08:58.679154Z",
     "shell.execute_reply": "2025-03-25T04:08:58.678828Z"
    }
   },
   "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 = \"Thymoma\"\n",
    "cohort = \"GSE131027\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Thymoma\"\n",
    "in_cohort_dir = \"../../input/GEO/Thymoma/GSE131027\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Thymoma/GSE131027.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Thymoma/gene_data/GSE131027.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Thymoma/clinical_data/GSE131027.csv\"\n",
    "json_path = \"../../output/preprocess/Thymoma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8de0de72",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6769b12f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:58.680652Z",
     "iopub.status.busy": "2025-03-25T04:08:58.680507Z",
     "iopub.status.idle": "2025-03-25T04:08:59.015570Z",
     "shell.execute_reply": "2025-03-25T04:08:59.015220Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files in the directory:\n",
      "['GSE131027_family.soft.gz', 'GSE131027_series_matrix.txt.gz']\n",
      "SOFT file: ../../input/GEO/Thymoma/GSE131027/GSE131027_family.soft.gz\n",
      "Matrix file: ../../input/GEO/Thymoma/GSE131027/GSE131027_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"High frequency of pathogenic germline variants in genes associated with homologous recombination repair in patients with advanced solid cancers\"\n",
      "!Series_summary\t\"We identified pathogenic and likely pathogenic variants in 17.8% of the patients within a wide range of cancer types. In particular, mesothelioma, ovarian cancer, cervical cancer, urothelial cancer, and cancer of unknown primary origin displayed high frequencies of pathogenic variants. In total, 22 BRCA1 and BRCA2 germline variant were identified in 12 different cancer types, of which 10 (45%) variants were not previously identified in these patients. Pathogenic germline variants were predominantly found in DNA repair pathways; approximately half of the variants were within genes involved in homologous recombination repair. Loss of heterozygosity and somatic second hits were identified in several of these genes, supporting possible causality for cancer development. A potential treatment target based on pathogenic germline variant could be suggested in 25 patients (4%).\"\n",
      "!Series_overall_design\t\"investigation of expression features related to Class 4 and 5 germline mutations in cancer patients\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['tissue: tumor biopsy'], 1: ['cancer: Breast cancer', 'cancer: Colorectal cancer', 'cancer: Bile duct cancer', 'cancer: Mesothelioma', 'cancer: Urothelial cancer', 'cancer: Pancreatic cancer', 'cancer: Melanoma', 'cancer: Hepatocellular carcinoma', 'cancer: Ovarian cancer', 'cancer: Cervical cancer', 'cancer: Head and Neck cancer', 'cancer: Sarcoma', 'cancer: Prostate cancer', 'cancer: Adenoid cystic carcinoma', 'cancer: NSCLC', 'cancer: Oesophageal cancer', 'cancer: Thymoma', 'cancer: Others', 'cancer: CUP', 'cancer: Renal cell carcinoma', 'cancer: Gastric cancer', 'cancer: Neuroendocrine cancer', 'cancer: vulvovaginal'], 2: ['mutated gene: ATR', 'mutated gene: FAN1', 'mutated gene: ERCC3', 'mutated gene: FANCD2', 'mutated gene: BAP1', 'mutated gene: DDB2', 'mutated gene: TP53', 'mutated gene: ATM', 'mutated gene: CHEK1', 'mutated gene: BRCA1', 'mutated gene: WRN', 'mutated gene: CHEK2', 'mutated gene: BRCA2', 'mutated gene: XPC', 'mutated gene: PALB2', 'mutated gene: ABRAXAS1', 'mutated gene: NBN', 'mutated gene: BLM', 'mutated gene: FAM111B', 'mutated gene: FANCA', 'mutated gene: MLH1', 'mutated gene: BRIP1', 'mutated gene: IPMK', 'mutated gene: RECQL', 'mutated gene: RAD50', 'mutated gene: FANCM', 'mutated gene: GALNT12', 'mutated gene: SMAD9', 'mutated gene: ERCC2', 'mutated gene: FANCC'], 3: ['predicted: HRDEXP: HRD', 'predicted: HRDEXP: NO_HRD'], 4: ['parp predicted: kmeans-2: PARP sensitive', 'parp predicted: kmeans-2: PARP insensitive']}\n"
     ]
    }
   ],
   "source": [
    "# 1. Check what files are actually in the directory\n",
    "import os\n",
    "print(\"Files in the directory:\")\n",
    "files = os.listdir(in_cohort_dir)\n",
    "print(files)\n",
    "\n",
    "# 2. Find appropriate files with more flexible pattern matching\n",
    "soft_file = None\n",
    "matrix_file = None\n",
    "\n",
    "for file in files:\n",
    "    file_path = os.path.join(in_cohort_dir, file)\n",
    "    # Look for files that might contain SOFT or matrix data with various possible extensions\n",
    "    if 'soft' in file.lower() or 'family' in file.lower() or file.endswith('.soft.gz'):\n",
    "        soft_file = file_path\n",
    "    if 'matrix' in file.lower() or file.endswith('.txt.gz') or file.endswith('.tsv.gz'):\n",
    "        matrix_file = file_path\n",
    "\n",
    "if not soft_file:\n",
    "    print(\"Warning: Could not find a SOFT file. Using the first .gz file as fallback.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if gz_files:\n",
    "        soft_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "if not matrix_file:\n",
    "    print(\"Warning: Could not find a matrix file. Using the second .gz file as fallback if available.\")\n",
    "    gz_files = [f for f in files if f.endswith('.gz')]\n",
    "    if len(gz_files) > 1 and soft_file != os.path.join(in_cohort_dir, gz_files[1]):\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[1])\n",
    "    elif len(gz_files) == 1 and not soft_file:\n",
    "        matrix_file = os.path.join(in_cohort_dir, gz_files[0])\n",
    "\n",
    "print(f\"SOFT file: {soft_file}\")\n",
    "print(f\"Matrix file: {matrix_file}\")\n",
    "\n",
    "# 3. Read files if found\n",
    "if soft_file and matrix_file:\n",
    "    # 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",
    "    \n",
    "    try:\n",
    "        background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "        \n",
    "        # Obtain the sample characteristics dictionary from the clinical dataframe\n",
    "        sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "        \n",
    "        # 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",
    "    except Exception as e:\n",
    "        print(f\"Error processing files: {e}\")\n",
    "        # Try swapping files if first attempt fails\n",
    "        print(\"Trying to swap SOFT and matrix files...\")\n",
    "        temp = soft_file\n",
    "        soft_file = matrix_file\n",
    "        matrix_file = temp\n",
    "        try:\n",
    "            background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)\n",
    "            sample_characteristics_dict = get_unique_values_by_row(clinical_data)\n",
    "            print(\"Background Information:\")\n",
    "            print(background_info)\n",
    "            print(\"Sample Characteristics Dictionary:\")\n",
    "            print(sample_characteristics_dict)\n",
    "        except Exception as e:\n",
    "            print(f\"Still error after swapping: {e}\")\n",
    "else:\n",
    "    print(\"Could not find necessary files for processing.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85c3dbe7",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4371165b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:59.016976Z",
     "iopub.status.busy": "2025-03-25T04:08:59.016867Z",
     "iopub.status.idle": "2025-03-25T04:08:59.027163Z",
     "shell.execute_reply": "2025-03-25T04:08:59.026855Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A new JSON file was created at: ../../output/preprocess/Thymoma/cohort_info.json\n",
      "Clinical Data Preview:\n",
      "{0: [0.0], 1: [0.0], 2: [0.0], 3: [0.0], 4: [0.0], 5: [0.0], 6: [0.0], 7: [0.0], 8: [0.0], 9: [0.0], 10: [0.0], 11: [0.0], 12: [0.0], 13: [0.0], 14: [0.0], 15: [0.0], 16: [1.0], 17: [0.0], 18: [0.0], 19: [0.0], 20: [0.0], 21: [0.0], 22: [0.0], 23: [nan], 24: [nan], 25: [nan], 26: [nan], 27: [nan], 28: [nan], 29: [nan]}\n",
      "Clinical data saved to ../../output/preprocess/Thymoma/clinical_data/GSE131027.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# This dataset appears to contain genetic mutation data rather than gene expression data\n",
    "is_gene_available = False\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For Thymoma, we need to check the cancer type list in row 1\n",
    "trait_row = 1  # cancer type data is in row 1\n",
    "\n",
    "# Age is not available in the sample characteristics\n",
    "age_row = None\n",
    "\n",
    "# Gender is not available in the sample characteristics\n",
    "gender_row = None\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(x):\n",
    "    \"\"\"Convert trait data to binary format (1 for Thymoma, 0 for others)\"\"\"\n",
    "    if pd.isna(x) or x is None:\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after colon and strip whitespace\n",
    "    if isinstance(x, str) and ':' in x:\n",
    "        x = x.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Check if it's Thymoma (case insensitive)\n",
    "    if x.lower() == 'thymoma':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "# Define these functions even though we don't have the data\n",
    "def convert_age(x):\n",
    "    \"\"\"Convert age data to continuous format\"\"\"\n",
    "    if pd.isna(x) or x is None:\n",
    "        return None\n",
    "    \n",
    "    if isinstance(x, str) and ':' in x:\n",
    "        value = x.split(':', 1)[1].strip()\n",
    "        try:\n",
    "            return float(value)\n",
    "        except ValueError:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "def convert_gender(x):\n",
    "    \"\"\"Convert gender data to binary format (0 for female, 1 for male)\"\"\"\n",
    "    if pd.isna(x) or x is None:\n",
    "        return None\n",
    "    \n",
    "    if isinstance(x, str) and ':' in x:\n",
    "        value = x.split(':', 1)[1].strip().lower()\n",
    "        if 'female' in value or 'f' == value:\n",
    "            return 0\n",
    "        elif 'male' in value or 'm' == value:\n",
    "            return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Trait data is available since trait_row is not None\n",
    "is_trait_available = trait_row is not None\n",
    "\n",
    "# Conduct initial filtering and save metadata\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",
    "# Since trait_row is not None, we need to extract clinical features\n",
    "if trait_row is not None:\n",
    "    # Create a DataFrame from the sample characteristics dictionary\n",
    "    # This dictionary was provided in the \"Output of a previous step\"\n",
    "    sample_chars_dict = {\n",
    "        0: ['tissue: tumor biopsy'], \n",
    "        1: ['cancer: Breast cancer', 'cancer: Colorectal cancer', 'cancer: Bile duct cancer', \n",
    "            'cancer: Mesothelioma', 'cancer: Urothelial cancer', 'cancer: Pancreatic cancer', \n",
    "            'cancer: Melanoma', 'cancer: Hepatocellular carcinoma', 'cancer: Ovarian cancer', \n",
    "            'cancer: Cervical cancer', 'cancer: Head and Neck cancer', 'cancer: Sarcoma', \n",
    "            'cancer: Prostate cancer', 'cancer: Adenoid cystic carcinoma', 'cancer: NSCLC', \n",
    "            'cancer: Oesophageal cancer', 'cancer: Thymoma', 'cancer: Others', 'cancer: CUP', \n",
    "            'cancer: Renal cell carcinoma', 'cancer: Gastric cancer', 'cancer: Neuroendocrine cancer', \n",
    "            'cancer: vulvovaginal'], \n",
    "        2: ['mutated gene: ATR', 'mutated gene: FAN1', 'mutated gene: ERCC3', 'mutated gene: FANCD2', \n",
    "            'mutated gene: BAP1', 'mutated gene: DDB2', 'mutated gene: TP53', 'mutated gene: ATM', \n",
    "            'mutated gene: CHEK1', 'mutated gene: BRCA1', 'mutated gene: WRN', 'mutated gene: CHEK2', \n",
    "            'mutated gene: BRCA2', 'mutated gene: XPC', 'mutated gene: PALB2', 'mutated gene: ABRAXAS1', \n",
    "            'mutated gene: NBN', 'mutated gene: BLM', 'mutated gene: FAM111B', 'mutated gene: FANCA', \n",
    "            'mutated gene: MLH1', 'mutated gene: BRIP1', 'mutated gene: IPMK', 'mutated gene: RECQL', \n",
    "            'mutated gene: RAD50', 'mutated gene: FANCM', 'mutated gene: GALNT12', 'mutated gene: SMAD9', \n",
    "            'mutated gene: ERCC2', 'mutated gene: FANCC'], \n",
    "        3: ['predicted: HRDEXP: HRD', 'predicted: HRDEXP: NO_HRD'], \n",
    "        4: ['parp predicted: kmeans-2: PARP sensitive', 'parp predicted: kmeans-2: PARP insensitive']\n",
    "    }\n",
    "    \n",
    "    # Convert to DataFrame format\n",
    "    clinical_data = pd.DataFrame.from_dict(sample_chars_dict, orient='index')\n",
    "    \n",
    "    # Extract 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 if age_row is not None else None,\n",
    "        gender_row=gender_row,\n",
    "        convert_gender=convert_gender if gender_row is not None else None\n",
    "    )\n",
    "    \n",
    "    # Preview the dataframe\n",
    "    preview = preview_df(selected_clinical_df)\n",
    "    print(\"Clinical Data Preview:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Save the clinical data\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e544e9",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2dcb28cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:59.028219Z",
     "iopub.status.busy": "2025-03-25T04:08:59.028110Z",
     "iopub.status.idle": "2025-03-25T04:08:59.597928Z",
     "shell.execute_reply": "2025-03-25T04:08:59.597555Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\n",
      "No subseries references found in the first 1000 lines of the SOFT file.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene data extraction result:\n",
      "Number of rows: 54675\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at', '1294_at',\n",
      "       '1316_at', '1320_at', '1405_i_at', '1431_at', '1438_at', '1487_at',\n",
      "       '1494_f_at', '1552256_a_at', '1552257_a_at', '1552258_at', '1552261_at',\n",
      "       '1552263_at', '1552264_a_at', '1552266_at'],\n",
      "      dtype='object', name='ID')\n"
     ]
    }
   ],
   "source": [
    "# 1. First get the path to the soft and matrix files\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "\n",
    "# 2. Looking more carefully at the background information\n",
    "# This is a SuperSeries which doesn't contain direct gene expression data\n",
    "# Need to investigate the soft file to find the subseries\n",
    "print(\"This appears to be a SuperSeries. Looking at the SOFT file to find potential subseries:\")\n",
    "\n",
    "# Open the SOFT file to try to identify subseries\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    subseries_lines = []\n",
    "    for i, line in enumerate(f):\n",
    "        if 'Series_relation' in line and 'SuperSeries of' in line:\n",
    "            subseries_lines.append(line.strip())\n",
    "        if i > 1000:  # Limit search to first 1000 lines\n",
    "            break\n",
    "\n",
    "# Display the subseries found\n",
    "if subseries_lines:\n",
    "    print(\"Found potential subseries references:\")\n",
    "    for line in subseries_lines:\n",
    "        print(line)\n",
    "else:\n",
    "    print(\"No subseries references found in the first 1000 lines of the SOFT file.\")\n",
    "\n",
    "# Despite trying to extract gene data, we expect it might fail because this is a SuperSeries\n",
    "try:\n",
    "    gene_data = get_genetic_data(matrix_file)\n",
    "    print(\"\\nGene data extraction result:\")\n",
    "    print(\"Number of rows:\", len(gene_data))\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",
    "    print(\"This confirms the dataset is a SuperSeries without direct gene expression data.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02f08728",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d0acc0ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:59.599236Z",
     "iopub.status.busy": "2025-03-25T04:08:59.599128Z",
     "iopub.status.idle": "2025-03-25T04:08:59.601131Z",
     "shell.execute_reply": "2025-03-25T04:08:59.600841Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reviewing the gene identifiers from the previous step\n",
    "# The format \"1007_s_at\", \"1053_at\", etc. indicates these are Affymetrix probe IDs,\n",
    "# not standard HGNC gene symbols\n",
    "# These probe IDs need to be mapped to gene symbols for proper analysis\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c78ab4",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "74f5a936",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:08:59.602246Z",
     "iopub.status.busy": "2025-03-25T04:08:59.602148Z",
     "iopub.status.idle": "2025-03-25T04:09:07.504611Z",
     "shell.execute_reply": "2025-03-25T04:09:07.504238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene annotation preview:\n",
      "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'GB_ACC': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'SPOT_ID': [nan, nan, nan, nan, nan], 'Species Scientific Name': ['Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens', 'Homo sapiens'], 'Annotation Date': ['Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014', 'Oct 6, 2014'], 'Sequence Type': ['Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence', 'Exemplar sequence'], 'Sequence Source': ['Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database', 'GenBank', 'Affymetrix Proprietary Database'], 'Target Description': ['U48705 /FEATURE=mRNA /DEFINITION=HSU48705 Human receptor tyrosine kinase DDR gene, complete cds', 'M87338 /FEATURE= /DEFINITION=HUMA1SBU Human replication factor C, 40-kDa subunit (A1) mRNA, complete cds', \"X51757 /FEATURE=cds /DEFINITION=HSP70B Human heat-shock protein HSP70B' gene\", 'X69699 /FEATURE= /DEFINITION=HSPAX8A H.sapiens Pax8 mRNA', 'L36861 /FEATURE=expanded_cds /DEFINITION=HUMGCAPB Homo sapiens guanylate cyclase activating protein (GCAP) gene exons 1-4, complete cds'], 'Representative Public ID': ['U48705', 'M87338', 'X51757', 'X69699', 'L36861'], 'Gene Title': ['discoidin domain receptor tyrosine kinase 1 /// microRNA 4640', 'replication factor C (activator 1) 2, 40kDa', \"heat shock 70kDa protein 6 (HSP70B')\", 'paired box 8', 'guanylate cyclase activator 1A (retina)'], 'Gene Symbol': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A'], 'ENTREZ_GENE_ID': ['780 /// 100616237', '5982', '3310', '7849', '2978'], 'RefSeq Transcript ID': ['NM_001202521 /// NM_001202522 /// NM_001202523 /// NM_001954 /// NM_013993 /// NM_013994 /// NR_039783 /// XM_005249385 /// XM_005249386 /// XM_005249387 /// XM_005249389 /// XM_005272873 /// XM_005272874 /// XM_005272875 /// XM_005272877 /// XM_005275027 /// XM_005275028 /// XM_005275030 /// XM_005275031 /// XM_005275162 /// XM_005275163 /// XM_005275164 /// XM_005275166 /// XM_005275457 /// XM_005275458 /// XM_005275459 /// XM_005275461 /// XM_006715185 /// XM_006715186 /// XM_006715187 /// XM_006715188 /// XM_006715189 /// XM_006715190 /// XM_006725501 /// XM_006725502 /// XM_006725503 /// XM_006725504 /// XM_006725505 /// XM_006725506 /// XM_006725714 /// XM_006725715 /// XM_006725716 /// XM_006725717 /// XM_006725718 /// XM_006725719 /// XM_006725720 /// XM_006725721 /// XM_006725722 /// XM_006725827 /// XM_006725828 /// XM_006725829 /// XM_006725830 /// XM_006725831 /// XM_006725832 /// XM_006726017 /// XM_006726018 /// XM_006726019 /// XM_006726020 /// XM_006726021 /// XM_006726022 /// XR_427836 /// XR_430858 /// XR_430938 /// XR_430974 /// XR_431015', 'NM_001278791 /// NM_001278792 /// NM_001278793 /// NM_002914 /// NM_181471 /// XM_006716080', 'NM_002155', 'NM_003466 /// NM_013951 /// NM_013952 /// NM_013953 /// NM_013992', 'NM_000409 /// XM_006715073'], 'Gene Ontology Biological Process': ['0001558 // regulation of cell growth // inferred from electronic annotation /// 0001952 // regulation of cell-matrix adhesion // inferred from electronic annotation /// 0006468 // protein phosphorylation // inferred from electronic annotation /// 0007155 // cell adhesion // traceable author statement /// 0007169 // transmembrane receptor protein tyrosine kinase signaling pathway // inferred from electronic annotation /// 0007565 // female pregnancy // inferred from electronic annotation /// 0007566 // embryo implantation // inferred from electronic annotation /// 0007595 // lactation // inferred from electronic annotation /// 0008285 // negative regulation of cell proliferation // inferred from electronic annotation /// 0010715 // regulation of extracellular matrix disassembly // inferred from mutant phenotype /// 0014909 // smooth muscle cell migration // inferred from mutant phenotype /// 0016310 // phosphorylation // inferred from electronic annotation /// 0018108 // peptidyl-tyrosine phosphorylation // inferred from electronic annotation /// 0030198 // extracellular matrix organization // traceable author statement /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from direct assay /// 0038063 // collagen-activated tyrosine kinase receptor signaling pathway // inferred from mutant phenotype /// 0038083 // peptidyl-tyrosine autophosphorylation // inferred from direct assay /// 0043583 // ear development // inferred from electronic annotation /// 0044319 // wound healing, spreading of cells // inferred from mutant phenotype /// 0046777 // protein autophosphorylation // inferred from direct assay /// 0060444 // branching involved in mammary gland duct morphogenesis // inferred from electronic annotation /// 0060749 // mammary gland alveolus development // inferred from electronic annotation /// 0061302 // smooth muscle cell-matrix adhesion // inferred from mutant phenotype', '0000278 // mitotic cell cycle // traceable author statement /// 0000722 // telomere maintenance via recombination // traceable author statement /// 0000723 // telomere maintenance // traceable author statement /// 0006260 // DNA replication // traceable author statement /// 0006271 // DNA strand elongation involved in DNA replication // traceable author statement /// 0006281 // DNA repair // traceable author statement /// 0006283 // transcription-coupled nucleotide-excision repair // traceable author statement /// 0006289 // nucleotide-excision repair // traceable author statement /// 0006297 // nucleotide-excision repair, DNA gap filling // traceable author statement /// 0015979 // photosynthesis // inferred from electronic annotation /// 0015995 // chlorophyll biosynthetic process // inferred from electronic annotation /// 0032201 // telomere maintenance via semi-conservative replication // traceable author statement', '0000902 // cell morphogenesis // inferred from electronic annotation /// 0006200 // ATP catabolic process // inferred from direct assay /// 0006950 // response to stress // inferred from electronic annotation /// 0006986 // response to unfolded protein // traceable author statement /// 0034605 // cellular response to heat // inferred from direct assay /// 0042026 // protein refolding // inferred from direct assay /// 0070370 // cellular heat acclimation // inferred from mutant phenotype', '0001655 // urogenital system development // inferred from sequence or structural similarity /// 0001656 // metanephros development // inferred from electronic annotation /// 0001658 // branching involved in ureteric bud morphogenesis // inferred from expression pattern /// 0001822 // kidney development // inferred from expression pattern /// 0001823 // mesonephros development // inferred from sequence or structural similarity /// 0003337 // mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from expression pattern /// 0006351 // transcription, DNA-templated // inferred from direct assay /// 0006355 // regulation of transcription, DNA-templated // inferred from electronic annotation /// 0007275 // multicellular organismal development // inferred from electronic annotation /// 0007417 // central nervous system development // inferred from expression pattern /// 0009653 // anatomical structure morphogenesis // traceable author statement /// 0030154 // cell differentiation // inferred from electronic annotation /// 0030878 // thyroid gland development // inferred from expression pattern /// 0030878 // thyroid gland development // inferred from mutant phenotype /// 0038194 // thyroid-stimulating hormone signaling pathway // traceable author statement /// 0039003 // pronephric field specification // inferred from sequence or structural similarity /// 0042472 // inner ear morphogenesis // inferred from sequence or structural similarity /// 0042981 // regulation of apoptotic process // inferred from sequence or structural similarity /// 0045893 // positive regulation of transcription, DNA-templated // inferred from direct assay /// 0045893 // positive regulation of transcription, DNA-templated // inferred from sequence or structural similarity /// 0045944 // positive regulation of transcription from RNA polymerase II promoter // inferred from direct assay /// 0048793 // pronephros development // inferred from sequence or structural similarity /// 0071371 // cellular response to gonadotropin stimulus // inferred from direct assay /// 0071599 // otic vesicle development // inferred from expression pattern /// 0072050 // S-shaped body morphogenesis // inferred from electronic annotation /// 0072073 // kidney epithelium development // inferred from electronic annotation /// 0072108 // positive regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis // inferred from sequence or structural similarity /// 0072164 // mesonephric tubule development // inferred from electronic annotation /// 0072207 // metanephric epithelium development // inferred from expression pattern /// 0072221 // metanephric distal convoluted tubule development // inferred from sequence or structural similarity /// 0072278 // metanephric comma-shaped body morphogenesis // inferred from expression pattern /// 0072284 // metanephric S-shaped body morphogenesis // inferred from expression pattern /// 0072289 // metanephric nephron tubule formation // inferred from sequence or structural similarity /// 0072305 // negative regulation of mesenchymal cell apoptotic process involved in metanephric nephron morphogenesis // inferred from sequence or structural similarity /// 0072307 // regulation of metanephric nephron tubule epithelial cell differentiation // inferred from sequence or structural similarity /// 0090190 // positive regulation of branching involved in ureteric bud morphogenesis // inferred from sequence or structural similarity /// 1900212 // negative regulation of mesenchymal cell apoptotic process involved in metanephros development // inferred from sequence or structural similarity /// 1900215 // negative regulation of apoptotic process involved in metanephric collecting duct development // inferred from sequence or structural similarity /// 1900218 // negative regulation of apoptotic process involved in metanephric nephron tubule development // inferred from sequence or structural similarity /// 2000594 // positive regulation of metanephric DCT cell differentiation // inferred from sequence or structural similarity /// 2000611 // positive regulation of thyroid hormone generation // inferred from mutant phenotype /// 2000612 // regulation of thyroid-stimulating hormone secretion // inferred from mutant phenotype', '0007165 // signal transduction // non-traceable author statement /// 0007601 // visual perception // inferred from electronic annotation /// 0007602 // phototransduction // inferred from electronic annotation /// 0007603 // phototransduction, visible light // traceable author statement /// 0016056 // rhodopsin mediated signaling pathway // traceable author statement /// 0022400 // regulation of rhodopsin mediated signaling pathway // traceable author statement /// 0030828 // positive regulation of cGMP biosynthetic process // inferred from electronic annotation /// 0031282 // regulation of guanylate cyclase activity // inferred from electronic annotation /// 0031284 // positive regulation of guanylate cyclase activity // inferred from electronic annotation /// 0050896 // response to stimulus // inferred from electronic annotation'], 'Gene Ontology Cellular Component': ['0005576 // extracellular region // inferred from electronic annotation /// 0005615 // extracellular space // inferred from direct assay /// 0005886 // plasma membrane // traceable author statement /// 0005887 // integral component of plasma membrane // traceable author statement /// 0016020 // membrane // inferred from electronic annotation /// 0016021 // integral component of membrane // inferred from electronic annotation /// 0043235 // receptor complex // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay', '0005634 // nucleus // inferred from electronic annotation /// 0005654 // nucleoplasm // traceable author statement /// 0005663 // DNA replication factor C complex // inferred from direct assay', '0005737 // cytoplasm // inferred from direct assay /// 0005814 // centriole // inferred from direct assay /// 0005829 // cytosol // inferred from direct assay /// 0008180 // COP9 signalosome // inferred from direct assay /// 0070062 // extracellular vesicular exosome // inferred from direct assay /// 0072562 // blood microparticle // inferred from direct assay', '0005634 // nucleus // inferred from direct assay /// 0005654 // nucleoplasm // inferred from sequence or structural similarity /// 0005730 // nucleolus // inferred from direct assay', '0001750 // photoreceptor outer segment // inferred from electronic annotation /// 0001917 // photoreceptor inner segment // inferred from electronic annotation /// 0005578 // proteinaceous extracellular matrix // inferred from electronic annotation /// 0005886 // plasma membrane // inferred from direct assay /// 0016020 // membrane // inferred from electronic annotation /// 0097381 // photoreceptor disc membrane // traceable author statement'], 'Gene Ontology Molecular Function': ['0000166 // nucleotide binding // inferred from electronic annotation /// 0004672 // protein kinase activity // inferred from electronic annotation /// 0004713 // protein tyrosine kinase activity // inferred from electronic annotation /// 0004714 // transmembrane receptor protein tyrosine kinase activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0005518 // collagen binding // inferred from direct assay /// 0005518 // collagen binding // inferred from mutant phenotype /// 0005524 // ATP binding // inferred from electronic annotation /// 0016301 // kinase activity // inferred from electronic annotation /// 0016740 // transferase activity // inferred from electronic annotation /// 0016772 // transferase activity, transferring phosphorus-containing groups // inferred from electronic annotation /// 0038062 // protein tyrosine kinase collagen receptor activity // inferred from direct assay /// 0046872 // metal ion binding // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0003677 // DNA binding // inferred from electronic annotation /// 0005515 // protein binding // inferred from physical interaction /// 0005524 // ATP binding // inferred from electronic annotation /// 0016851 // magnesium chelatase activity // inferred from electronic annotation /// 0017111 // nucleoside-triphosphatase activity // inferred from electronic annotation', '0000166 // nucleotide binding // inferred from electronic annotation /// 0005524 // ATP binding // inferred from electronic annotation /// 0019899 // enzyme binding // inferred from physical interaction /// 0031072 // heat shock protein binding // inferred from physical interaction /// 0042623 // ATPase activity, coupled // inferred from direct assay /// 0051082 // unfolded protein binding // inferred from direct assay', '0000979 // RNA polymerase II core promoter sequence-specific DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from direct assay /// 0003677 // DNA binding // inferred from mutant phenotype /// 0003700 // sequence-specific DNA binding transcription factor activity // inferred from direct assay /// 0004996 // thyroid-stimulating hormone receptor activity // traceable author statement /// 0005515 // protein binding // inferred from physical interaction /// 0044212 // transcription regulatory region DNA binding // inferred from direct assay', '0005509 // calcium ion binding // inferred from electronic annotation /// 0008048 // calcium sensitive guanylate cyclase activator activity // inferred from electronic annotation /// 0030249 // guanylate cyclase regulator activity // inferred from electronic annotation /// 0046872 // metal ion binding // inferred from electronic annotation']}\n"
     ]
    }
   ],
   "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": "fdeb4256",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4f4c96c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:09:07.506230Z",
     "iopub.status.busy": "2025-03-25T04:09:07.506091Z",
     "iopub.status.idle": "2025-03-25T04:09:09.754153Z",
     "shell.execute_reply": "2025-03-25T04:09:09.753795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene mapping dataframe shape: (45782, 2)\n",
      "Sample of gene mapping dataframe:\n",
      "{'ID': ['1007_s_at', '1053_at', '117_at', '121_at', '1255_g_at'], 'Gene': ['DDR1 /// MIR4640', 'RFC2', 'HSPA6', 'PAX8', 'GUCA1A']}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression dataframe shape: (21278, 92)\n",
      "First few gene symbols in gene expression data:\n",
      "Index(['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1', 'A2ML1', 'A2MP1',\n",
      "       'A4GALT', 'A4GNT', 'AA06'],\n",
      "      dtype='object', name='Gene')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene expression data saved to ../../output/preprocess/Thymoma/gene_data/GSE131027.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify which columns in the gene annotation data correspond to \n",
    "# gene identifiers and gene symbols\n",
    "# Looking at the preview, we can see:\n",
    "# - 'ID' column contains probe IDs like \"1007_s_at\" which match the gene expression data\n",
    "# - 'Gene Symbol' column contains the human gene symbols we need to map to\n",
    "\n",
    "# 2. Get gene mapping dataframe with the identified columns\n",
    "gene_mapping = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')\n",
    "\n",
    "# Print some information about the mapping\n",
    "print(\"Gene mapping dataframe shape:\", gene_mapping.shape)\n",
    "print(\"Sample of gene mapping dataframe:\")\n",
    "print(preview_df(gene_mapping, n=5))\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
    "# First, get the gene expression data from the matrix file\n",
    "genetic_data = get_genetic_data(matrix_file)\n",
    "\n",
    "# Then apply the mapping to convert probe IDs to gene symbols\n",
    "gene_data = apply_gene_mapping(expression_df=genetic_data, mapping_df=gene_mapping)\n",
    "\n",
    "# Print information about the generated gene expression data\n",
    "print(\"Gene expression dataframe shape:\", gene_data.shape)\n",
    "print(\"First few gene symbols in gene expression data:\")\n",
    "print(gene_data.index[:10])\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": "f2d62084",
   "metadata": {},
   "source": [
    "### Step 7: Data Normalization and Linking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c9528e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T04:09:09.755767Z",
     "iopub.status.busy": "2025-03-25T04:09:09.755648Z",
     "iopub.status.idle": "2025-03-25T04:09:17.502650Z",
     "shell.execute_reply": "2025-03-25T04:09:17.502027Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of gene data after normalization: (19845, 92)\n",
      "First few gene symbols: ['A1BG', 'A1BG-AS1', 'A1CF', 'A2M', 'A2M-AS1']\n",
      "Sample IDs in gene data: ['GSM3759992', 'GSM3759993', 'GSM3759994', 'GSM3759995', 'GSM3759996']...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized gene data saved to ../../output/preprocess/Thymoma/gene_data/GSE131027.csv\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Clinical data preview:\n",
      "{'GSM3759992': [1.0], 'GSM3759993': [1.0], 'GSM3759994': [1.0], 'GSM3759995': [1.0], 'GSM3759996': [1.0], 'GSM3759997': [1.0], 'GSM3759998': [1.0], 'GSM3759999': [1.0], 'GSM3760000': [1.0], 'GSM3760001': [1.0], 'GSM3760002': [1.0], 'GSM3760003': [1.0], 'GSM3760004': [1.0], 'GSM3760005': [1.0], 'GSM3760006': [1.0], 'GSM3760007': [1.0], 'GSM3760008': [1.0], 'GSM3760009': [1.0], 'GSM3760010': [1.0], 'GSM3760011': [1.0], 'GSM3760012': [1.0], 'GSM3760013': [1.0], 'GSM3760014': [1.0], 'GSM3760015': [1.0], 'GSM3760016': [1.0], 'GSM3760017': [1.0], 'GSM3760018': [1.0], 'GSM3760019': [1.0], 'GSM3760020': [1.0], 'GSM3760021': [1.0], 'GSM3760022': [1.0], 'GSM3760023': [1.0], 'GSM3760024': [1.0], 'GSM3760025': [1.0], 'GSM3760026': [1.0], 'GSM3760027': [1.0], 'GSM3760028': [1.0], 'GSM3760029': [1.0], 'GSM3760030': [1.0], 'GSM3760031': [1.0], 'GSM3760032': [1.0], 'GSM3760033': [1.0], 'GSM3760034': [1.0], 'GSM3760035': [1.0], 'GSM3760036': [1.0], 'GSM3760037': [1.0], 'GSM3760038': [1.0], 'GSM3760039': [1.0], 'GSM3760040': [1.0], 'GSM3760041': [1.0], 'GSM3760042': [1.0], 'GSM3760043': [1.0], 'GSM3760044': [1.0], 'GSM3760045': [1.0], 'GSM3760046': [1.0], 'GSM3760047': [1.0], 'GSM3760048': [1.0], 'GSM3760049': [1.0], 'GSM3760050': [1.0], 'GSM3760051': [1.0], 'GSM3760052': [1.0], 'GSM3760053': [1.0], 'GSM3760054': [1.0], 'GSM3760055': [1.0], 'GSM3760056': [1.0], 'GSM3760057': [1.0], 'GSM3760058': [1.0], 'GSM3760059': [1.0], 'GSM3760060': [1.0], 'GSM3760061': [1.0], 'GSM3760062': [1.0], 'GSM3760063': [1.0], 'GSM3760064': [1.0], 'GSM3760065': [1.0], 'GSM3760066': [1.0], 'GSM3760067': [1.0], 'GSM3760068': [1.0], 'GSM3760069': [1.0], 'GSM3760070': [1.0], 'GSM3760071': [1.0], 'GSM3760072': [1.0], 'GSM3760073': [1.0], 'GSM3760074': [1.0], 'GSM3760075': [1.0], 'GSM3760076': [1.0], 'GSM3760077': [1.0], 'GSM3760078': [1.0], 'GSM3760079': [1.0], 'GSM3760080': [1.0], 'GSM3760081': [1.0], 'GSM3760082': [1.0], 'GSM3760083': [1.0]}\n",
      "Clinical data saved to ../../output/preprocess/Thymoma/clinical_data/GSE131027.csv\n",
      "Shape of linked data: (92, 19846)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of linked data after handling missing values: (92, 19846)\n",
      "Quartiles for 'Thymoma':\n",
      "  25%: 1.0\n",
      "  50% (Median): 1.0\n",
      "  75%: 1.0\n",
      "Min: 1.0\n",
      "Max: 1.0\n",
      "The distribution of the feature 'Thymoma' in this dataset is severely biased.\n",
      "\n",
      "Dataset validation failed. Final linked data not saved.\n"
     ]
    }
   ],
   "source": [
    "# 1. Normalize gene symbols in the gene expression data\n",
    "gene_data = normalize_gene_symbols_in_index(gene_data)\n",
    "print(f\"Shape of gene data after normalization: {gene_data.shape}\")\n",
    "print(f\"First few gene symbols: {gene_data.index[:5].tolist()}\")\n",
    "print(f\"Sample IDs in gene data: {gene_data.columns[:5].tolist()}...\")\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",
    "# 2. Reload clinical data and properly process it for this Thymoma dataset\n",
    "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
    "background_info, clinical_data = get_background_and_clinical_data(matrix_file)\n",
    "\n",
    "# Define conversion functions specifically for the Thymoma dataset\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert to binary: 0 for cell lines, 1 for tumor samples\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    if \":\" in value:\n",
    "        value = value.split(\":\", 1)[1].strip()\n",
    "    \n",
    "    # Cell lines are encoded as \"CL\"\n",
    "    if value == \"CL\":\n",
    "        return 0\n",
    "    # All other types are tumor samples\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "# Extract the clinical data using the appropriate rows based on our analysis\n",
    "clinical_df = geo_select_clinical_features(\n",
    "    clinical_data,\n",
    "    trait=trait,\n",
    "    trait_row=1,  # Row for tumor type (from the sample characteristics dictionary)\n",
    "    convert_trait=convert_trait,\n",
    "    # No age or gender data available in this dataset\n",
    "    age_row=None,\n",
    "    convert_age=None,\n",
    "    gender_row=None,\n",
    "    convert_gender=None\n",
    ")\n",
    "\n",
    "print(\"Clinical data preview:\")\n",
    "print(preview_df(clinical_df))\n",
    "\n",
    "# Save the clinical data\n",
    "os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "clinical_df.to_csv(out_clinical_data_file)\n",
    "print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "\n",
    "# 3. Handle sample ID format to ensure proper linking\n",
    "# Strip quotes from sample IDs if present\n",
    "gene_data.columns = gene_data.columns.str.strip('\"')\n",
    "clinical_df.columns = clinical_df.columns.str.strip('\"')\n",
    "\n",
    "# Link clinical and genetic data\n",
    "linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)\n",
    "print(f\"Shape of linked data: {linked_data.shape}\")\n",
    "\n",
    "# 4. Handle missing values in the linked data\n",
    "linked_data_cleaned = handle_missing_values(linked_data, trait)\n",
    "print(f\"Shape of linked data after handling missing values: {linked_data_cleaned.shape}\")\n",
    "\n",
    "# 5. Check if the trait and demographic features are biased\n",
    "is_trait_biased, unbiased_linked_data = judge_and_remove_biased_features(linked_data_cleaned, trait)\n",
    "\n",
    "# 6. Validate the dataset and save cohort information\n",
    "note = \"Dataset contains gene expression data from thymic tumors. The trait variable represents tumor type (0=Cell line, 1=Thymic tumor).\"\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=note\n",
    ")\n",
    "\n",
    "# 7. 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",
    "    unbiased_linked_data.to_csv(out_data_file)\n",
    "    print(f\"Saved processed linked data to {out_data_file}\")\n",
    "else:\n",
    "    print(\"Dataset validation failed. Final linked data not saved.\")"
   ]
  }
 ],
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