{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df2a2950",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:03.746195Z",
     "iopub.status.busy": "2025-03-25T06:40:03.746089Z",
     "iopub.status.idle": "2025-03-25T06:40:03.908281Z",
     "shell.execute_reply": "2025-03-25T06:40:03.907921Z"
    }
   },
   "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 = \"Asthma\"\n",
    "cohort = \"GSE123088\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Asthma\"\n",
    "in_cohort_dir = \"../../input/GEO/Asthma/GSE123088\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Asthma/GSE123088.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Asthma/gene_data/GSE123088.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Asthma/clinical_data/GSE123088.csv\"\n",
    "json_path = \"../../output/preprocess/Asthma/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d0f24e",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e4cbfc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:03.909735Z",
     "iopub.status.busy": "2025-03-25T06:40:03.909588Z",
     "iopub.status.idle": "2025-03-25T06:40:04.197389Z",
     "shell.execute_reply": "2025-03-25T06:40:04.197017Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Background Information:\n",
      "!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases\"\n",
      "!Series_summary\t\"This SuperSeries is composed of the SubSeries listed below.\"\n",
      "!Series_overall_design\t\"Refer to individual Series\"\n",
      "Sample Characteristics Dictionary:\n",
      "{0: ['cell type: CD4+ T cells'], 1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', 'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], 2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', 'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], 3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], 4: [nan, 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', 'age: 12', 'age: 27']}\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": "d376f47c",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1c2a70a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:04.198653Z",
     "iopub.status.busy": "2025-03-25T06:40:04.198528Z",
     "iopub.status.idle": "2025-03-25T06:40:04.211174Z",
     "shell.execute_reply": "2025-03-25T06:40:04.210857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of clinical features:\n",
      "{0: [1.0, 56.0, 1.0], 1: [0.0, nan, nan], 2: [0.0, 20.0, 0.0], 3: [0.0, 51.0, nan], 4: [0.0, 37.0, nan], 5: [0.0, 61.0, nan], 6: [0.0, 31.0, nan], 7: [0.0, 41.0, nan], 8: [0.0, 80.0, nan], 9: [0.0, 53.0, nan], 10: [0.0, 73.0, nan], 11: [0.0, 60.0, nan], 12: [0.0, 76.0, nan], 13: [0.0, 77.0, nan], 14: [0.0, 74.0, nan], 15: [0.0, 69.0, nan], 16: [nan, 81.0, nan], 17: [nan, 70.0, nan], 18: [nan, 82.0, nan], 19: [nan, 67.0, nan], 20: [nan, 78.0, nan], 21: [nan, 72.0, nan], 22: [nan, 66.0, nan], 23: [nan, 36.0, nan], 24: [nan, 45.0, nan], 25: [nan, 65.0, nan], 26: [nan, 48.0, nan], 27: [nan, 50.0, nan], 28: [nan, 24.0, nan], 29: [nan, 42.0, nan]}\n",
      "Clinical data saved to ../../output/preprocess/Asthma/clinical_data/GSE123088.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this appears to be a gene expression dataset from CD4+ T cells\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "# For trait (Asthma), row 1 contains 'primary diagnosis' which includes 'ASTHMA'\n",
    "trait_row = 1\n",
    "\n",
    "# For gender, row 2 and 3 contain 'Sex: Male' and 'Sex: Female'\n",
    "gender_row = 2  # This row seems to have more gender entries\n",
    "\n",
    "# For age, row 3 and 4 contain age information\n",
    "age_row = 3  # This row seems to have more age entries\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "def convert_trait(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    value = value.lower()\n",
    "    if 'diagnosis' not in value:\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    if 'asthma' in value.lower():\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def convert_gender(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    if 'sex' not in value.lower():\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip().lower()\n",
    "    if 'female' in value:\n",
    "        return 0\n",
    "    elif 'male' in value:\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    if 'age' not in value.lower():\n",
    "        return None\n",
    "    if ':' in value:\n",
    "        try:\n",
    "            age = int(value.split(':', 1)[1].strip())\n",
    "            return age\n",
    "        except:\n",
    "            return None\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata - initial filtering\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(\n",
    "    is_final=False,\n",
    "    cohort=cohort,\n",
    "    info_path=json_path,\n",
    "    is_gene_available=is_gene_available,\n",
    "    is_trait_available=is_trait_available\n",
    ")\n",
    "\n",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Create DataFrame from the sample characteristics dictionary\n",
    "    sample_characteristics_dict = {0: ['cell type: CD4+ T cells'], \n",
    "                                  1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', 'primary diagnosis: BREAST_CANCER', \n",
    "                                      'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', 'primary diagnosis: CROHN_DISEASE', \n",
    "                                      'primary diagnosis: ATOPIC_ECZEMA', 'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', \n",
    "                                      'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', 'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', \n",
    "                                      'primary diagnosis: TYPE_1_DIABETES', 'primary diagnosis: ACUTE_TONSILLITIS', \n",
    "                                      'primary diagnosis: ULCERATIVE_COLITIS', 'primary diagnosis: Breast cancer', 'primary diagnosis: Control'], \n",
    "                                  2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', \n",
    "                                      'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', 'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', \n",
    "                                      'diagnosis2: OBESITY'], \n",
    "                                  3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', 'age: 41', \n",
    "                                      'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', 'age: 74', 'age: 69', \n",
    "                                      'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', 'age: 72', 'age: 66', 'age: 36', \n",
    "                                      'age: 45', 'age: 65', 'age: 48', 'age: 50', 'age: 24', 'age: 42'], \n",
    "                                  4: [float('nan'), 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', 'age: 16', \n",
    "                                      'age: 12', 'age: 27']}\n",
    "    \n",
    "    clinical_data = pd.DataFrame.from_dict(sample_characteristics_dict, orient='index')\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 extracted clinical features\n",
    "    preview = preview_df(clinical_features)\n",
    "    print(\"Preview of clinical features:\")\n",
    "    print(preview)\n",
    "    \n",
    "    # Create directory if it doesn't exist and save the clinical features to a CSV file\n",
    "    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\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": "e6cb6e04",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b95d920d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:04.212191Z",
     "iopub.status.busy": "2025-03-25T06:40:04.212083Z",
     "iopub.status.idle": "2025-03-25T06:40:04.731481Z",
     "shell.execute_reply": "2025-03-25T06:40:04.731084Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Asthma/GSE123088/GSE123088_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (24166, 204)\n",
      "First 20 gene/probe identifiers:\n",
      "Index(['1', '2', '3', '9', '10', '12', '13', '14', '15', '16', '18', '19',\n",
      "       '20', '21', '22', '23', '24', '25', '26', '27'],\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": "c6be56df",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "77f1652d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:04.732834Z",
     "iopub.status.busy": "2025-03-25T06:40:04.732709Z",
     "iopub.status.idle": "2025-03-25T06:40:04.734675Z",
     "shell.execute_reply": "2025-03-25T06:40:04.734391Z"
    }
   },
   "outputs": [],
   "source": [
    "# The identifiers shown are not standard human gene symbols\n",
    "# They appear to be numeric indices or probe IDs that would need mapping to actual gene symbols\n",
    "# Standard human gene symbols would typically be formatted like BRCA1, TP53, etc.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f98ba288",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0abd3538",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:04.735822Z",
     "iopub.status.busy": "2025-03-25T06:40:04.735716Z",
     "iopub.status.idle": "2025-03-25T06:40:11.917849Z",
     "shell.execute_reply": "2025-03-25T06:40:11.917454Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Platform title found: Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Entrez Gene ID  version)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "{'ID': ['1', '2', '3', '9', '10', '12', '13', '14', '15', '16'], 'ENTREZ_GENE_ID': ['1', '2', '3', '9', '10', '12', '13', '14', '15', '16'], 'SPOT_ID': [1.0, 2.0, 3.0, 9.0, 10.0, 12.0, 13.0, 14.0, 15.0, 16.0]}\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",
    "# Check if there are any platforms defined in the SOFT file that might contain annotation data\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    soft_content = f.read()\n",
    "\n",
    "# Look for platform sections in the SOFT file\n",
    "platform_sections = re.findall(r'^!Platform_title\\s*=\\s*(.+)$', soft_content, re.MULTILINE)\n",
    "if platform_sections:\n",
    "    print(f\"Platform title found: {platform_sections[0]}\")\n",
    "\n",
    "# Try to extract more annotation data by reading directly from the SOFT file\n",
    "# Look for lines that might contain gene symbol mappings\n",
    "symbol_pattern = re.compile(r'ID_REF\\s+Symbol|ID\\s+Gene Symbol', re.IGNORECASE)\n",
    "annotation_lines = []\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for line in f:\n",
    "        if symbol_pattern.search(line):\n",
    "            annotation_lines.append(line)\n",
    "            # Collect the next few lines to see the annotation structure\n",
    "            for _ in range(10):\n",
    "                annotation_lines.append(next(f, ''))\n",
    "\n",
    "if annotation_lines:\n",
    "    print(\"Found potential gene symbol mappings:\")\n",
    "    for line in annotation_lines:\n",
    "        print(line.strip())\n",
    "\n",
    "# 2. Use the 'preview_df' function from the library to preview the data and print out the results.\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(preview_df(gene_annotation, n=10))\n",
    "\n",
    "# If we need an alternative source of mapping, check if there are any other annotation files in the cohort directory\n",
    "cohort_files = os.listdir(in_cohort_dir)\n",
    "annotation_files = [f for f in cohort_files if 'annotation' in f.lower() or 'platform' in f.lower()]\n",
    "if annotation_files:\n",
    "    print(\"\\nAdditional annotation files found in the cohort directory:\")\n",
    "    for file in annotation_files:\n",
    "        print(file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f50054b",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43665388",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T06:40:11.919215Z",
     "iopub.status.busy": "2025-03-25T06:40:11.919092Z",
     "iopub.status.idle": "2025-03-25T06:40:20.377129Z",
     "shell.execute_reply": "2025-03-25T06:40:20.376780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 'ID' to map to 'ENTREZ_GENE_ID'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping data shape: (4740924, 2)\n",
      "First few rows of mapping data:\n",
      "   ID Gene\n",
      "0   1    1\n",
      "1   2    2\n",
      "2   3    3\n",
      "3   9    9\n",
      "4  10   10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapped gene expression data shape: (0, 204)\n",
      "First few rows of gene expression data:\n",
      "Empty DataFrame\n",
      "Columns: [GSM3494884, GSM3494885, GSM3494886, GSM3494887, GSM3494888, GSM3494889, GSM3494890, GSM3494891, GSM3494892, GSM3494893, GSM3494894, GSM3494895, GSM3494896, GSM3494897, GSM3494898, GSM3494899, GSM3494900, GSM3494901, GSM3494902, GSM3494903, GSM3494904, GSM3494905, GSM3494906, GSM3494907, GSM3494908, GSM3494909, GSM3494910, GSM3494911, GSM3494912, GSM3494913, GSM3494914, GSM3494915, GSM3494916, GSM3494917, GSM3494918, GSM3494919, GSM3494920, GSM3494921, GSM3494922, GSM3494923, GSM3494924, GSM3494925, GSM3494926, GSM3494927, GSM3494928, GSM3494929, GSM3494930, GSM3494931, GSM3494932, GSM3494933, GSM3494934, GSM3494935, GSM3494936, GSM3494937, GSM3494938, GSM3494939, GSM3494940, GSM3494941, GSM3494942, GSM3494943, GSM3494944, GSM3494945, GSM3494946, GSM3494947, GSM3494948, GSM3494949, GSM3494950, GSM3494951, GSM3494952, GSM3494953, GSM3494954, GSM3494955, GSM3494956, GSM3494957, GSM3494958, GSM3494959, GSM3494960, GSM3494961, GSM3494962, GSM3494963, GSM3494964, GSM3494965, GSM3494966, GSM3494967, GSM3494968, GSM3494969, GSM3494970, GSM3494971, GSM3494972, GSM3494973, GSM3494974, GSM3494975, GSM3494976, GSM3494977, GSM3494978, GSM3494979, GSM3494980, GSM3494981, GSM3494982, GSM3494983, ...]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 204 columns]\n",
      "Gene expression data saved to ../../output/preprocess/Asthma/gene_data/GSE123088.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Determine which columns to use for mapping\n",
    "# From the previous output, I can see:\n",
    "# - The gene expression data uses numeric IDs as identifiers (e.g., '1', '2', '3')\n",
    "# - The gene annotation contains columns 'ID' and 'ENTREZ_GENE_ID'\n",
    "# - The annotation shows that 'ID' column contains the same identifiers seen in gene expression data\n",
    "# - 'ENTREZ_GENE_ID' contains gene IDs that we can use to map to gene symbols\n",
    "\n",
    "# First check if we have any additional mapping resources\n",
    "mapping_id_column = 'ID'  # This matches the identifiers in gene_data\n",
    "mapping_gene_column = 'ENTREZ_GENE_ID'  # We'll use this as the gene identifier\n",
    "\n",
    "print(f\"Using '{mapping_id_column}' to map to '{mapping_gene_column}'\")\n",
    "\n",
    "# 2. Extract the mapping data\n",
    "mapping_data = get_gene_mapping(gene_annotation, mapping_id_column, mapping_gene_column)\n",
    "print(f\"Mapping data shape: {mapping_data.shape}\")\n",
    "print(\"First few rows of mapping data:\")\n",
    "print(mapping_data.head())\n",
    "\n",
    "# 3. Apply the gene mapping to convert probe-level measurements to gene expression data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
    "print(f\"Mapped gene expression data shape: {gene_data.shape}\")\n",
    "print(\"First few rows of gene expression data:\")\n",
    "print(gene_data.head())\n",
    "\n",
    "# Save the mapped gene expression data to a CSV file\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}\")"
   ]
  }
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