code/Eczema/GSE123086.ipynb

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   "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 = \"Eczema\"\n",
    "cohort = \"GSE123086\"\n",
    "\n",
    "# Input paths\n",
    "in_trait_dir = \"../../input/GEO/Eczema\"\n",
    "in_cohort_dir = \"../../input/GEO/Eczema/GSE123086\"\n",
    "\n",
    "# Output paths\n",
    "out_data_file = \"../../output/preprocess/Eczema/GSE123086.csv\"\n",
    "out_gene_data_file = \"../../output/preprocess/Eczema/gene_data/GSE123086.csv\"\n",
    "out_clinical_data_file = \"../../output/preprocess/Eczema/clinical_data/GSE123086.csv\"\n",
    "json_path = \"../../output/preprocess/Eczema/cohort_info.json\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4dd6f2f",
   "metadata": {},
   "source": [
    "### Step 1: Initial Data Loading"
   ]
  },
  {
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   "id": "5e01943d",
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     "name": "stdout",
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     "text": [
      "Background Information:\n",
      "!Series_title\t\"A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases [study of 13 diseases]\"\n",
      "!Series_summary\t\"We conducted prospective clinical studies to validate the importance of CD4+ T cells in 13 diseases from the following ICD-10-CM chapters: Neoplasms (breast cancer, chronic lymphocytic leukemia); endocrine, nutritional and metabolic diseases (type I diabetes, obesity); diseases of the circulatory system (atherosclerosis); diseases of the respiratory system (acute tonsillitis, influenza, seasonal allergic rhinitis, asthma); diseases of the digestive system (Crohn’s disease [CD], ulcerative colitis [UC]); and diseases of the skin and subcutaneous tissue (atopic eczema, psoriatic diseases).\"\n",
      "!Series_summary\t\"Study participants were recruited by clinical specialists based on diagnostic criteria defined by organizations representing each specialist’s discipline. Age and gender matched healthy controls (n = 127 and 39, respectively) were recruited in the Southeast region of Sweden from outpatient clinics at the University Hospital, Linköping; Ryhov County Hospital, Jönköping, a primary health care center in Jönköping; and a medical specialist unit for children in Värnamo. Study participants represented both urban and rural populations with an age range of 8–94 years. Patients with type I diabetes and obesity had an age range of 8–18 years. 12 patients had more than one diagnosis.\"\n",
      "!Series_overall_design\t\"Total RNA was extracted using the AllPrep DNA/RNA Micro kit (Qiagen, Hilden, Germany; cat. no. 80284) according to the manufacturer’s instructions. RNA concentration and integrity were evaluated using the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, California, USA; cat. no. 5067-1511) on an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, California, USA). Microarrays were then further computationally processed as described in One-Color Microarray-Based Gene Expression Analysis Low Input Quick Amp Labeling protocol (Agilent Technologies, Santa Clara, California, USA).\"\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'], 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": "ca40a92c",
   "metadata": {},
   "source": [
    "### Step 2: Dataset Analysis and Clinical Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69111922",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preview of selected clinical features:\n",
      "{0: [nan, 56.0, 1.0], 1: [nan, nan, nan], 2: [nan, 20.0, 0.0], 3: [nan, 51.0, nan], 4: [nan, 37.0, nan], 5: [1.0, 61.0, nan], 6: [0.0, 31.0, nan], 7: [nan, 41.0, nan], 8: [nan, 80.0, nan], 9: [nan, 53.0, nan], 10: [nan, 73.0, nan], 11: [nan, 60.0, nan], 12: [nan, 76.0, nan], 13: [nan, 77.0, nan], 14: [nan, 74.0, nan], 15: [nan, 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/Eczema/clinical_data/GSE123086.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Gene Expression Data Availability\n",
    "# Based on the background information, this study used microarrays to analyze gene expression\n",
    "# from CD4+ T cells, so gene expression data should be available\n",
    "is_gene_available = True\n",
    "\n",
    "# 2. Variable Availability and Data Type Conversion\n",
    "# 2.1 Data Availability\n",
    "\n",
    "# For trait (eczema):\n",
    "# Looking at the sample characteristics, primary diagnosis is at index 1\n",
    "# and includes \"ATOPIC_ECZEMA\" as one of the possible values\n",
    "trait_row = 1\n",
    "\n",
    "# For age:\n",
    "# Age information appears to be available at indices 3 and 4\n",
    "age_row = 3\n",
    "\n",
    "# For gender:\n",
    "# Gender information (Sex) appears to be at index 2\n",
    "gender_row = 2\n",
    "\n",
    "# 2.2 Data Type Conversion\n",
    "\n",
    "def convert_trait(value):\n",
    "    \"\"\"Convert trait value to binary (1 for Eczema, 0 for control)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Map values to binary\n",
    "    if 'ATOPIC_ECZEMA' in value:\n",
    "        return 1\n",
    "    elif 'HEALTHY_CONTROL' in value:\n",
    "        return 0\n",
    "    return None\n",
    "\n",
    "def convert_age(value):\n",
    "    \"\"\"Convert age value to continuous numeric value\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Try to convert to float\n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return None\n",
    "\n",
    "def convert_gender(value):\n",
    "    \"\"\"Convert gender to binary (0 for female, 1 for male)\"\"\"\n",
    "    if not isinstance(value, str):\n",
    "        return None\n",
    "    \n",
    "    # Extract the value after the colon\n",
    "    if ':' in value:\n",
    "        value = value.split(':', 1)[1].strip()\n",
    "    \n",
    "    # Map values to binary\n",
    "    if value.upper() == 'FEMALE':\n",
    "        return 0\n",
    "    elif value.upper() == 'MALE':\n",
    "        return 1\n",
    "    return None\n",
    "\n",
    "# 3. Save Metadata\n",
    "# Check if trait data is available\n",
    "is_trait_available = trait_row is not None\n",
    "validate_and_save_cohort_info(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",
    "# 4. Clinical Feature Extraction\n",
    "if trait_row is not None:\n",
    "    # Use the sample characteristics dictionary to create a DataFrame\n",
    "    # This dictionary is assumed to be available from the previous step\n",
    "    sample_chars_dict = {0: ['cell type: CD4+ T cells'], \n",
    "                         1: ['primary diagnosis: ASTHMA', 'primary diagnosis: ATHEROSCLEROSIS', \n",
    "                             'primary diagnosis: BREAST_CANCER', 'primary diagnosis: CHRONIC_LYMPHOCYTIC_LEUKEMIA', \n",
    "                             'primary diagnosis: CROHN_DISEASE', 'primary diagnosis: ATOPIC_ECZEMA', \n",
    "                             'primary diagnosis: HEALTHY_CONTROL', 'primary diagnosis: INFLUENZA', \n",
    "                             'primary diagnosis: OBESITY', 'primary diagnosis: PSORIASIS', \n",
    "                             'primary diagnosis: SEASONAL_ALLERGIC_RHINITIS', 'primary diagnosis: TYPE_1_DIABETES', \n",
    "                             'primary diagnosis: ACUTE_TONSILLITIS', 'primary diagnosis: ULCERATIVE_COLITIS'], \n",
    "                         2: ['Sex: Male', 'diagnosis2: ATOPIC_ECZEMA', 'Sex: Female', 'diagnosis2: ATHEROSCLEROSIS', \n",
    "                             'diagnosis2: ASTHMA_OBESITY', 'diagnosis2: ASTHMA', \n",
    "                             'diagnosis2: ASTMHA_SEASONAL_ALLERGIC_RHINITIS', 'diagnosis2: OBESITY'], \n",
    "                         3: ['age: 56', 'Sex: Male', 'age: 20', 'age: 51', 'age: 37', 'age: 61', 'age: 31', \n",
    "                             'age: 41', 'age: 80', 'age: 53', 'age: 73', 'age: 60', 'age: 76', 'age: 77', \n",
    "                             'age: 74', 'age: 69', 'age: 81', 'age: 70', 'age: 82', 'age: 67', 'age: 78', \n",
    "                             'age: 72', 'age: 66', 'age: 36', 'age: 45', 'age: 65', 'age: 48', 'age: 50', \n",
    "                             'age: 24', 'age: 42'], \n",
    "                         4: [float('nan'), 'age: 63', 'age: 74', 'age: 49', 'age: 60', 'age: 68', 'age: 38', \n",
    "                             'age: 16', 'age: 12', 'age: 27']}\n",
    "    \n",
    "    clinical_data = pd.DataFrame.from_dict(sample_chars_dict, orient='index')\n",
    "    \n",
    "    try:\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,\n",
    "            gender_row=gender_row,\n",
    "            convert_gender=convert_gender\n",
    "        )\n",
    "        \n",
    "        # Preview the data\n",
    "        preview = preview_df(selected_clinical_df)\n",
    "        print(\"Preview of selected clinical features:\")\n",
    "        print(preview)\n",
    "        \n",
    "        # Save to CSV\n",
    "        os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)\n",
    "        selected_clinical_df.to_csv(out_clinical_data_file, index=False)\n",
    "        print(f\"Clinical data saved to {out_clinical_data_file}\")\n",
    "    \n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred during clinical feature extraction: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17a943e2",
   "metadata": {},
   "source": [
    "### Step 3: Gene Data Extraction"
   ]
  },
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   "id": "bce8a868",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix file found: ../../input/GEO/Eczema/GSE123086/GSE123086_series_matrix.txt.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene data shape: (22881, 166)\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": "6db78aec",
   "metadata": {},
   "source": [
    "### Step 4: Gene Identifier Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2d13c964",
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   },
   "outputs": [],
   "source": [
    "# These identifiers are not human gene symbols. They appear to be numeric probe identifiers \n",
    "# from a microarray platform, which need to be mapped to actual gene symbols.\n",
    "\n",
    "requires_gene_mapping = True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4358bcc5",
   "metadata": {},
   "source": [
    "### Step 5: Gene Annotation"
   ]
  },
  {
   "cell_type": "code",
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   "id": "60d55c42",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Gene annotation preview:\n",
      "Columns in gene annotation: ['ID', 'ENTREZ_GENE_ID', 'SPOT_ID']\n",
      "{'ID': ['1', '2', '3', '9', '10'], 'ENTREZ_GENE_ID': ['1', '2', '3', '9', '10'], 'SPOT_ID': [1.0, 2.0, 3.0, 9.0, 10.0]}\n",
      "\n",
      "Exploring SOFT file more thoroughly for gene information:\n",
      "!Series_platform_id = GPL25864\n",
      "!Platform_title = Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Entrez Gene ID  version)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "No explicit gene info patterns found\n",
      "\n",
      "Analyzing ENTREZ_GENE_ID column:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries where ENTREZ_GENE_ID differs from ID: 3798412\n",
      "Some ENTREZ_GENE_ID values differ from probe IDs - this could be useful for mapping\n",
      "           ID ENTREZ_GENE_ID  SPOT_ID\n",
      "24166  ID_REF          VALUE      NaN\n",
      "24167    3553    15.35998289      NaN\n",
      "24168    1609    10.05521694      NaN\n",
      "24169   10112     4.22140954      NaN\n",
      "24170   57827    8.437124629      NaN\n",
      "\n",
      "Looking for alternative annotation approaches:\n",
      "- Checking for platform ID or accession number in SOFT file\n",
      "\n",
      "Preparing provisional gene mapping using ENTREZ_GENE_ID:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Provisional mapping data shape: (3822578, 2)\n",
      "{'ID': ['1', '2', '3', '9', '10'], 'Gene': ['1', '2', '3', '9', '10']}\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. Analyze the gene annotation dataframe to identify which columns contain the gene identifiers and gene symbols\n",
    "print(\"\\nGene annotation preview:\")\n",
    "print(f\"Columns in gene annotation: {gene_annotation.columns.tolist()}\")\n",
    "print(preview_df(gene_annotation, n=5))\n",
    "\n",
    "# Let's explore the SOFT file more thoroughly to find gene symbols\n",
    "print(\"\\nExploring SOFT file more thoroughly for gene information:\")\n",
    "gene_info_patterns = []\n",
    "entrez_to_symbol = {}\n",
    "\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for i, line in enumerate(f):\n",
    "        if i < 1000:  # Check header section for platform info\n",
    "            if '!Series_platform_id' in line or '!Platform_title' in line:\n",
    "                print(line.strip())\n",
    "                \n",
    "        # Look for gene-related columns and patterns in the file\n",
    "        if 'GENE_SYMBOL' in line or 'gene_symbol' in line or 'Symbol' in line:\n",
    "            gene_info_patterns.append(line.strip())\n",
    "            \n",
    "        # Extract a mapping using ENTREZ_GENE_ID if available\n",
    "        if len(gene_info_patterns) < 2 and 'ENTREZ_GENE_ID' in line and '\\t' in line:\n",
    "            parts = line.strip().split('\\t')\n",
    "            if len(parts) >= 2:\n",
    "                try:\n",
    "                    # Attempt to add to mapping - assuming ENTREZ_GENE_ID could help with lookup\n",
    "                    entrez_id = parts[1]\n",
    "                    probe_id = parts[0]\n",
    "                    if entrez_id.isdigit() and entrez_id != probe_id:\n",
    "                        entrez_to_symbol[probe_id] = entrez_id\n",
    "                except:\n",
    "                    pass\n",
    "        \n",
    "        if i > 10000 and len(gene_info_patterns) > 0:  # Limit search but ensure we found something\n",
    "            break\n",
    "\n",
    "# Show some of the patterns found\n",
    "if gene_info_patterns:\n",
    "    print(\"\\nFound gene-related patterns:\")\n",
    "    for pattern in gene_info_patterns[:5]:\n",
    "        print(pattern)\n",
    "else:\n",
    "    print(\"\\nNo explicit gene info patterns found\")\n",
    "\n",
    "# Let's try to match the ENTREZ_GENE_ID to the probe IDs\n",
    "print(\"\\nAnalyzing ENTREZ_GENE_ID column:\")\n",
    "if 'ENTREZ_GENE_ID' in gene_annotation.columns:\n",
    "    # Check if ENTREZ_GENE_ID contains actual Entrez IDs (different from probe IDs)\n",
    "    gene_annotation['ENTREZ_GENE_ID'] = gene_annotation['ENTREZ_GENE_ID'].astype(str)\n",
    "    different_ids = (gene_annotation['ENTREZ_GENE_ID'] != gene_annotation['ID']).sum()\n",
    "    print(f\"Number of entries where ENTREZ_GENE_ID differs from ID: {different_ids}\")\n",
    "    \n",
    "    if different_ids > 0:\n",
    "        print(\"Some ENTREZ_GENE_ID values differ from probe IDs - this could be useful for mapping\")\n",
    "        # Show examples of differing values\n",
    "        diff_examples = gene_annotation[gene_annotation['ENTREZ_GENE_ID'] != gene_annotation['ID']].head(5)\n",
    "        print(diff_examples)\n",
    "    else:\n",
    "        print(\"ENTREZ_GENE_ID appears to be identical to probe ID - not useful for mapping\")\n",
    "\n",
    "# Search for additional annotation information in the dataset\n",
    "print(\"\\nLooking for alternative annotation approaches:\")\n",
    "print(\"- Checking for platform ID or accession number in SOFT file\")\n",
    "\n",
    "platform_id = None\n",
    "with gzip.open(soft_file, 'rt') as f:\n",
    "    for i, line in enumerate(f):\n",
    "        if '!Platform_geo_accession' in line:\n",
    "            platform_id = line.split('=')[1].strip().strip('\"')\n",
    "            print(f\"Found platform GEO accession: {platform_id}\")\n",
    "            break\n",
    "        if i > 200:\n",
    "            break\n",
    "\n",
    "# If we don't find proper gene symbol mappings, prepare to use the ENTREZ_GENE_ID as is\n",
    "if 'ENTREZ_GENE_ID' in gene_annotation.columns:\n",
    "    print(\"\\nPreparing provisional gene mapping using ENTREZ_GENE_ID:\")\n",
    "    mapping_data = gene_annotation[['ID', 'ENTREZ_GENE_ID']].copy()\n",
    "    mapping_data.rename(columns={'ENTREZ_GENE_ID': 'Gene'}, inplace=True)\n",
    "    print(f\"Provisional mapping data shape: {mapping_data.shape}\")\n",
    "    print(preview_df(mapping_data, n=5))\n",
    "else:\n",
    "    print(\"\\nWarning: No suitable mapping column found for gene symbols\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3487c987",
   "metadata": {},
   "source": [
    "### Step 6: Gene Identifier Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fed35a2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-25T08:40:25.191261Z",
     "iopub.status.busy": "2025-03-25T08:40:25.191142Z",
     "iopub.status.idle": "2025-03-25T08:40:32.374731Z",
     "shell.execute_reply": "2025-03-25T08:40:32.374207Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mapping dataframe shape: (3822578, 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": [
      "Number of unique probe IDs: 24167\n",
      "Number of unique gene symbols: 1275651\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gene-level data shape: (0, 166)\n",
      "First few gene symbols:\n",
      "Index([], dtype='object', name='Gene')\n",
      "Gene data saved to ../../output/preprocess/Eczema/gene_data/GSE123086.csv\n"
     ]
    }
   ],
   "source": [
    "# 1. Identify the column names for gene identifiers and gene symbols\n",
    "# From the preview, we saw that ID is the probe identifier and ENTREZ_GENE_ID contains the gene IDs\n",
    "id_column = 'ID'  # Column with probe identifiers that match gene_data index\n",
    "gene_column = 'ENTREZ_GENE_ID'  # Column with gene identifiers (Entrez IDs in this case)\n",
    "\n",
    "# 2. Get gene mapping dataframe\n",
    "mapping_df = get_gene_mapping(gene_annotation, id_column, gene_column)\n",
    "print(f\"Mapping dataframe shape: {mapping_df.shape}\")\n",
    "print(\"First few rows of mapping data:\")\n",
    "print(mapping_df.head())\n",
    "\n",
    "# Check how many unique probe IDs and gene symbols are in the mapping\n",
    "unique_probes = mapping_df['ID'].nunique()\n",
    "unique_genes = mapping_df['Gene'].nunique()\n",
    "print(f\"Number of unique probe IDs: {unique_probes}\")\n",
    "print(f\"Number of unique gene symbols: {unique_genes}\")\n",
    "\n",
    "# 3. Apply gene mapping to convert probe-level data to gene-level data\n",
    "gene_data = apply_gene_mapping(gene_data, mapping_df)\n",
    "print(f\"Gene-level data shape: {gene_data.shape}\")\n",
    "print(\"First few gene symbols:\")\n",
    "print(gene_data.index[:10])\n",
    "\n",
    "# Save the processed 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\"Gene data saved to {out_gene_data_file}\")"
   ]
  }
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