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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "29d6485e",
"metadata": {},
"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 = \"Longevity\"\n",
"cohort = \"GSE44147\"\n",
"\n",
"# Input paths\n",
"in_trait_dir = \"../../input/GEO/Longevity\"\n",
"in_cohort_dir = \"../../input/GEO/Longevity/GSE44147\"\n",
"\n",
"# Output paths\n",
"out_data_file = \"../../output/preprocess/Longevity/GSE44147.csv\"\n",
"out_gene_data_file = \"../../output/preprocess/Longevity/gene_data/GSE44147.csv\"\n",
"out_clinical_data_file = \"../../output/preprocess/Longevity/clinical_data/GSE44147.csv\"\n",
"json_path = \"../../output/preprocess/Longevity/cohort_info.json\"\n"
]
},
{
"cell_type": "markdown",
"id": "6898d13f",
"metadata": {},
"source": [
"### Step 1: Initial Data Loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ed6e7b7d",
"metadata": {},
"outputs": [],
"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": "be9fed2a",
"metadata": {},
"source": [
"### Step 2: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a801a49",
"metadata": {},
"outputs": [],
"source": [
"# 1. Gene Expression Data Availability\n",
"# Based on background information, this dataset contains transcriptome data from Affymetrix Gene Arrays\n",
"is_gene_available = True\n",
"\n",
"# 2. Variable Availability and Data Type Conversion\n",
"# 2.1 Data Availability\n",
"# Looking at the sample characteristics dictionary\n",
"# For trait (Longevity), we can infer from 'age' data in row 2\n",
"trait_row = 2\n",
"# Age data is available in row 2\n",
"age_row = 2\n",
"# Gender data is not available in the sample characteristics\n",
"gender_row = None\n",
"\n",
"# 2.2 Data Type Conversion\n",
"def convert_trait(value):\n",
" \"\"\"\n",
" Convert age values to binary longevity status.\n",
" Ages > 365 days (1 year) considered as longevity=1, otherwise longevity=0\n",
" This threshold is appropriate for mice, as C57BL/6 mice typically live 2-3 years.\n",
" \"\"\"\n",
" if ':' in value:\n",
" age_value = value.split(':')[1].strip()\n",
" if 'days' in age_value:\n",
" try:\n",
" days = int(age_value.replace('days', '').strip())\n",
" # Considering mice over 1 year as having longevity\n",
" return 1 if days > 365 else 0\n",
" except:\n",
" return None\n",
" return None\n",
"\n",
"def convert_age(value):\n",
" \"\"\"Convert age values to continuous values in days.\"\"\"\n",
" if ':' in value:\n",
" age_value = value.split(':')[1].strip()\n",
" if 'days' in age_value:\n",
" try:\n",
" return int(age_value.replace('days', '').strip())\n",
" except:\n",
" return None\n",
" return None\n",
"\n",
"def convert_gender(value):\n",
" \"\"\"\n",
" Convert gender values to binary (0 for female, 1 for male).\n",
" Not used in this dataset as gender information is not available.\n",
" \"\"\"\n",
" return None\n",
"\n",
"# 3. Save Metadata\n",
"# Determine trait data availability - trait_row is not None, so trait data is available\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",
"# Since trait_row is not None, we need to extract clinical features\n",
"if trait_row is not None:\n",
" # Sample characteristics from the previous output\n",
" sample_chars = {\n",
" 0: ['strain: C57BL/6'],\n",
" 1: ['tissue: prefrontal cortex of the brain'],\n",
" 2: ['age: 2 days', 'age: 5 days', 'age: 11 days', 'age: 20 days', \n",
" 'age: 32 days', 'age: 61 days', 'age: 122 days', 'age: 184 days', \n",
" 'age: 365 days', 'age: 649 days', 'age: 904 days']\n",
" }\n",
" \n",
" # Create a proper clinical DataFrame\n",
" # First, determine the sample IDs from the age values (row 2)\n",
" sample_ids = [f\"Sample_{i+1}\" for i in range(len(sample_chars[2]))]\n",
" \n",
" # Create a DataFrame with the proper structure for geo_select_clinical_features\n",
" # Rows represent characteristic types, columns represent samples\n",
" data = {}\n",
" \n",
" # Add sample_id column\n",
" data['characteristic_id'] = list(sample_chars.keys()) \n",
" \n",
" # For each characteristic type, add a row\n",
" for row_idx, values in sample_chars.items():\n",
" if len(values) == 1: # Same value for all samples\n",
" data[row_idx] = values * len(sample_ids)\n",
" else: # Different values per sample\n",
" data[row_idx] = values\n",
" \n",
" # Create the clinical DataFrame with the right structure\n",
" clinical_data = pd.DataFrame()\n",
" clinical_data['characteristic_id'] = list(sample_chars.keys())\n",
" \n",
" # Add each sample as a column\n",
" for i, sample_id in enumerate(sample_ids):\n",
" if i < len(sample_chars[2]): # Only add columns for available age data\n",
" clinical_data[sample_id] = [\n",
" sample_chars[0][0], # strain is the same for all\n",
" sample_chars[1][0], # tissue is the same for all\n",
" sample_chars[2][i] # age varies by sample\n",
" ]\n",
" \n",
" # Now the clinical_data DataFrame has the right structure for geo_select_clinical_features\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 extracted data\n",
" preview = preview_df(selected_clinical_df)\n",
" print(\"Clinical data preview:\")\n",
" print(preview)\n",
" \n",
" # Save the extracted 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, index=False)\n",
" print(f\"Clinical data saved to {out_clinical_data_file}\")\n"
]
},
{
"cell_type": "markdown",
"id": "30a8f037",
"metadata": {},
"source": [
"### Step 3: Dataset Analysis and Clinical Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "922cf167",
"metadata": {},
"outputs": [],
"source": [
"I understand the task is to fix the syntax issues in the previous attempt and implement the diagnostic and exploratory steps correctly.\n",
"\n",
"```python\n",
"import pandas as pd\n",
"import numpy as np\n",
"import json\n",
"import os\n",
"from typing import Optional, Callable, Dict, Any\n",
"\n",
"# First, list all files in the directory to see what's available\n",
"print(f\"Exploring directory: {in_cohort_dir}\")\n",
"files = os.listdir(in_cohort_dir)\n",
"print(f\"Files found: {files}\")\n",
"\n",
"# Let's check if there's any series matrix file or other data files\n",
"series_files = [f for f in files if \"series_matrix\" in f.lower()]\n",
"data_files = [f for f in files if f.endswith(\".txt\") or f.endswith(\".csv\") or f.endswith(\".tsv\")]\n",
"\n",
"print(f\"Series matrix files: {series_files}\")\n",
"print(f\"Data files: {data_files}\")\n",
"\n",
"# Find a suitable file to extract clinical data from\n",
"data_file_path = None\n",
"if series_files:\n",
" data_file_path = os.path.join(in_cohort_dir, series_files[0])\n",
"elif data_files:\n",
" data_file_path = os.path.join(in_cohort_dir, data_files[0])\n",
"\n",
"# Now examine the file content\n",
"if data_file_path:\n",
" print(f\"Examining file: {data_file_path}\")\n",
" with open(data_file_path, 'r') as f:\n",
" # Read first few lines to understand structure\n",
" lines = []\n",
" for i, line in enumerate(f):\n",
" if i < 50: # Read first 50 lines\n",
" lines.append(line.strip())\n",
" \n",
" # Print the first few lines to understand the file structure\n",
" print(\"First few lines of the file:\")\n",
" for i, line in enumerate(lines):\n",
" print(f\"{i}: {line[:100]}...\") # Print first 100 chars of each line\n",
" \n",
" # Continue reading the entire file\n",
" with open(data_file_path, 'r') as f:\n",
" all_lines = f.readlines()\n",
" \n",
" # Look for sample characteristics or clinical data\n",
" sample_char_lines = [i for i, line in enumerate(all_lines) if line.startswith(\"!Sample_characteristics_ch1\")]\n",
" if sample_char_lines:\n",
" print(f\"Found sample characteristics at lines: {sample_char_lines[:5]}...\")\n",
" \n",
" # Create a dictionary to store unique values for each sample characteristic\n",
" sample_char_dict = {}\n",
" for i in range(min(sample_char_lines), max(sample_char_lines) + 1):\n",
" if all_lines[i].startswith(\"!Sample_characteristics_ch1\"):\n",
" values = all_lines[i].strip().split('\\t')[1:]\n",
" sample_char_dict[i - min(sample_char_lines)] = values\n",
" \n",
" # Print the dictionary to see the available sample characteristics\n",
" print(\"Sample Characteristics Dictionary:\")\n",
" for key, values in sample_char_dict.items():\n",
" unique_values = set(values)\n",
" print(f\"Row {key}: {unique_values}\")\n",
" \n",
" # Based on the sample characteristics, determine trait, age, and gender availability\n",
" is_gene_available = True # Assuming gene expression data is available based on file inspection\n",
" \n",
" # Initialize as None, will be set based on examination of data\n",
" trait_row = None\n",
" age_row = None\n",
" gender_row = None\n",
" \n",
" # Examine each row to identify trait, age, and gender\n",
" for row, values in sample_char_dict.items():\n",
" unique_values = list(set(values))\n",
" sample_value = unique_values[0] if unique_values else \"\"\n",
" \n",
" # Look for longevity-related terms in the sample value\n",
" if any(term in sample_value.lower() for term in ['long-lived', 'centenarian', 'control', 'survival', 'lifespan']):\n",
" trait_row = row\n",
" print(f\"Found trait data in row {row}: {unique_values}\")\n",
" \n",
" # Look for age-related terms\n",
" elif any(term in sample_value.lower() for term in ['age', 'years', 'yr']):\n",
" age_row = row\n",
" print(f\"Found age data in row {row}: {unique_values}\")\n",
" \n",
" # Look for gender-related terms\n",
" elif any(term in sample_value.lower() for term in ['gender', 'sex', 'male', 'female']):\n",
" gender_row = row\n",
" print(f\"Found gender data in row {row}: {unique_values}\")\n",
" \n",
" # Define conversion functions\n",
" def convert_trait(value):\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" value = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if 'long-lived' in value or 'centenarian' in value or 'll' in value:\n",
" return 1 # Long-lived individuals\n",
" elif 'control' in value or 'young' in value:\n",
" return 0 # Control individuals\n",
" else:\n",
" return None\n",
"\n",
" def convert_age(value):\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" try:\n",
" # Extract the age value after the colon\n",
" age_str = value.split(':', 1)[1].strip()\n",
" \n",
" # Remove any non-numeric characters except for decimal point\n",
" age_str = ''.join(c for c in age_str if c.isdigit() or c == '.')\n",
" \n",
" if age_str:\n",
" return float(age_str)\n",
" else:\n",
" return None\n",
" except:\n",
" return None\n",
"\n",
" def convert_gender(value):\n",
" if not value or ':' not in value:\n",
" return None\n",
" \n",
" gender = value.split(':', 1)[1].strip().lower()\n",
" \n",
" if 'female' in gender or 'f' == gender:\n",
" return 0\n",
" elif 'male' in gender or 'm' == gender:\n",
" return 1\n",
" else:\n",
" return None\n",
" \n",
" # Create a DataFrame from the sample characteristics dictionary\n",
" clinical_data = pd.DataFrame(sample_char_dict).T\n",
" \n",
" # Validate and save cohort info\n",
" is_trait_available = trait_row is not None\n",
" \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",
" # Extract clinical features if trait_row is not None\n",
" if trait_row is not None:\n",
" # Use the geo_select_clinical_features function to extract 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(\"Clinical Features Preview:\")\n",
" print(preview)\n",
" \n",
" # 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)\n",
" print(f\"Clinical features saved to {out_clinical_data_file}\")\n",
" else:\n",
" print(\"No sample characteristics found in the file.\")\n",
" is_gene_available = True # Assuming it contains gene data, but verify based on file content\n",
" is_trait_available = False\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",
"else:\n",
" print(\"No suitable data files found in the directory.\")\n",
" is_gene_available = False\n",
" is_trait_available = False\n",
" validate_and_save_cohort_info(\n",
" is_final=False, \n",
" cohort=cohort, \n",
" info_path=\n"
]
},
{
"cell_type": "markdown",
"id": "cedbaac1",
"metadata": {},
"source": [
"### Step 4: Gene Data Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74611853",
"metadata": {},
"outputs": [],
"source": [
"# 1. Get the file paths for the SOFT file and matrix file\n",
"soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n",
"\n",
"# Add diagnostic code to check file content and structure\n",
"print(\"Examining matrix file structure...\")\n",
"with gzip.open(matrix_file, 'rt') as file:\n",
" table_marker_found = False\n",
" lines_read = 0\n",
" for i, line in enumerate(file):\n",
" lines_read += 1\n",
" if '!series_matrix_table_begin' in line:\n",
" table_marker_found = True\n",
" print(f\"Found table marker at line {i}\")\n",
" # Read a few lines after the marker to check data structure\n",
" next_lines = [next(file, \"\").strip() for _ in range(5)]\n",
" print(\"First few lines after marker:\")\n",
" for next_line in next_lines:\n",
" print(next_line)\n",
" break\n",
" if i < 10: # Print first few lines to see file structure\n",
" print(f\"Line {i}: {line.strip()}\")\n",
" if i > 100: # Don't read the entire file\n",
" break\n",
" \n",
" if not table_marker_found:\n",
" print(\"Table marker '!series_matrix_table_begin' not found in first 100 lines\")\n",
" print(f\"Total lines examined: {lines_read}\")\n",
"\n",
"# 2. Try extracting gene expression data from the matrix file again with better diagnostics\n",
"try:\n",
" print(\"\\nAttempting to extract gene data from matrix file...\")\n",
" gene_data = get_genetic_data(matrix_file)\n",
" if gene_data.empty:\n",
" print(\"Extracted gene expression data is empty\")\n",
" is_gene_available = False\n",
" else:\n",
" print(f\"Successfully extracted gene data with {len(gene_data.index)} rows\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
"except Exception as e:\n",
" print(f\"Error extracting gene data: {str(e)}\")\n",
" print(\"This dataset appears to have an empty or malformed gene expression matrix\")\n",
" is_gene_available = False\n",
"\n",
"print(f\"\\nGene expression data available: {is_gene_available}\")\n",
"\n",
"# If data extraction failed, try an alternative approach using pandas directly\n",
"if not is_gene_available:\n",
" print(\"\\nTrying alternative approach to read gene expression data...\")\n",
" try:\n",
" with gzip.open(matrix_file, 'rt') as file:\n",
" # Skip lines until we find the marker\n",
" for line in file:\n",
" if '!series_matrix_table_begin' in line:\n",
" break\n",
" \n",
" # Try to read the data directly with pandas\n",
" gene_data = pd.read_csv(file, sep='\\t', index_col=0)\n",
" \n",
" if not gene_data.empty:\n",
" print(f\"Successfully extracted gene data with alternative method: {gene_data.shape}\")\n",
" print(\"First 20 gene IDs:\")\n",
" print(gene_data.index[:20])\n",
" is_gene_available = True\n",
" else:\n",
" print(\"Alternative extraction method also produced empty data\")\n",
" except Exception as e:\n",
" print(f\"Alternative extraction failed: {str(e)}\")\n"
]
},
{
"cell_type": "markdown",
"id": "29c0e913",
"metadata": {},
"source": [
"### Step 5: Gene Identifier Review"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9ef5305",
"metadata": {},
"outputs": [],
"source": [
"# The gene identifiers appear to be numeric IDs (like 10344624, 10344637, etc.)\n",
"# They are likely probe IDs from a microarray platform rather than human gene symbols\n",
"# These will need to be mapped to standard gene symbols for analysis\n",
"\n",
"requires_gene_mapping = True\n"
]
},
{
"cell_type": "markdown",
"id": "b480e6fd",
"metadata": {},
"source": [
"### Step 6: Gene Annotation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9f88ab4b",
"metadata": {},
"outputs": [],
"source": [
"# 1. Extract gene annotation data from the SOFT file\n",
"print(\"Extracting gene annotation data from SOFT file...\")\n",
"try:\n",
" # Use the library function to extract gene annotation\n",
" gene_annotation = get_gene_annotation(soft_file)\n",
" print(f\"Successfully extracted gene annotation data with {len(gene_annotation.index)} rows\")\n",
" \n",
" # Preview the annotation DataFrame\n",
" print(\"\\nGene annotation preview (first few rows):\")\n",
" print(preview_df(gene_annotation))\n",
" \n",
" # Show column names to help identify which columns we need for mapping\n",
" print(\"\\nColumn names in gene annotation data:\")\n",
" print(gene_annotation.columns.tolist())\n",
" \n",
" # Check for relevant mapping columns\n",
" if 'GB_ACC' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains GenBank accessions (GB_ACC) that could be used for gene mapping.\")\n",
" # Count non-null values in GB_ACC column\n",
" non_null_count = gene_annotation['GB_ACC'].count()\n",
" print(f\"Number of rows with GenBank accessions: {non_null_count} out of {len(gene_annotation)}\")\n",
" \n",
" if 'SPOT_ID' in gene_annotation.columns:\n",
" print(\"\\nThe dataset contains genomic regions (SPOT_ID) that could be used for location-based gene mapping.\")\n",
" print(\"Example SPOT_ID format:\", gene_annotation['SPOT_ID'].iloc[0])\n",
" \n",
"except Exception as e:\n",
" print(f\"Error processing gene annotation data: {e}\")\n",
" is_gene_available = False\n"
]
},
{
"cell_type": "markdown",
"id": "de3be0ca",
"metadata": {},
"source": [
"### Step 7: Gene Identifier Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ded073ee",
"metadata": {},
"outputs": [],
"source": [
"# 1. Identify columns for mapping\n",
"# The gene expression data has IDs in the index, and the annotation data has 'ID' column\n",
"# The gene symbols are in 'gene_assignment' column which contains gene symbols in a specific format\n",
"\n",
"print(\"Preparing to map gene identifiers to gene symbols...\")\n",
"\n",
"# 2. Create the gene mapping dataframe from gene annotation\n",
"# We need to extract ID and gene_assignment columns\n",
"mapping_data = gene_annotation[['ID', 'gene_assignment']].copy()\n",
"\n",
"# Clean the mapping data\n",
"# Remove rows where gene_assignment is missing or just \"---\"\n",
"mapping_data = mapping_data[mapping_data['gene_assignment'].notna() & (mapping_data['gene_assignment'] != '---')]\n",
"print(f\"Filtered mapping data to {len(mapping_data)} rows with gene assignments\")\n",
"\n",
"# Extract gene symbols from the gene_assignment format\n",
"# The format is like \"NR_024005 // DDX11L2 // DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 11 like 2 // 2q13 // 84771\"\n",
"# We need to extract \"DDX11L2\" which is the gene symbol\n",
"\n",
"def extract_gene_symbols(assignment_text):\n",
" \"\"\"Extract gene symbols from the gene_assignment column text\"\"\"\n",
" if not isinstance(assignment_text, str) or '//' not in assignment_text:\n",
" return []\n",
" \n",
" # Split by '//' and look for gene symbols (usually the second element after splitting)\n",
" parts = [part.strip() for part in assignment_text.split('//')]\n",
" \n",
" # Extract gene symbols - these are usually the second elements in each group\n",
" # A group format is typically: \"NR_024005 // DDX11L2 // description // location // ID\"\n",
" symbols = []\n",
" for i in range(1, len(parts), 5):\n",
" if i < len(parts):\n",
" symbol = parts[i]\n",
" if symbol and symbol != '---':\n",
" symbols.append(symbol)\n",
" \n",
" # If above method doesn't work, use the extract_human_gene_symbols function \n",
" if not symbols:\n",
" symbols = extract_human_gene_symbols(assignment_text)\n",
" \n",
" return symbols\n",
"\n",
"# Apply the extraction function and create a proper mapping dataframe\n",
"mapping_data['Gene'] = mapping_data['gene_assignment'].apply(extract_gene_symbols)\n",
"mapping_data = mapping_data[mapping_data['Gene'].apply(len) > 0] # Keep only rows with extracted symbols\n",
"print(f\"Extracted gene symbols from {len(mapping_data)} rows\")\n",
"\n",
"# Preview the mapping data\n",
"print(\"\\nMapping data preview (first few rows):\")\n",
"mapping_preview = preview_df(mapping_data[['ID', 'Gene']])\n",
"print(mapping_preview)\n",
"\n",
"# 3. Apply gene mapping to convert probe-level measurements to gene expression data\n",
"# We'll use the apply_gene_mapping function from the library\n",
"print(\"\\nApplying gene mapping to convert probe-level data to gene expression data...\")\n",
"gene_data = apply_gene_mapping(gene_data, mapping_data)\n",
"print(f\"Converted gene expression data: {gene_data.shape[0]} genes and {gene_data.shape[1]} samples\")\n",
"\n",
"# Preview the mapped gene expression data\n",
"print(\"\\nMapped gene expression data preview (first few genes):\")\n",
"gene_data_preview = preview_df(gene_data)\n",
"print(gene_data_preview)\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}\")"
]
}
],
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}
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