{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "c2f3e216", "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 = \"Anxiety_disorder\"\n", "cohort = \"GSE68526\"\n", "\n", "# Input paths\n", "in_trait_dir = \"../../input/GEO/Anxiety_disorder\"\n", "in_cohort_dir = \"../../input/GEO/Anxiety_disorder/GSE68526\"\n", "\n", "# Output paths\n", "out_data_file = \"../../output/preprocess/Anxiety_disorder/GSE68526.csv\"\n", "out_gene_data_file = \"../../output/preprocess/Anxiety_disorder/gene_data/GSE68526.csv\"\n", "out_clinical_data_file = \"../../output/preprocess/Anxiety_disorder/clinical_data/GSE68526.csv\"\n", "json_path = \"../../output/preprocess/Anxiety_disorder/cohort_info.json\"\n" ] }, { "cell_type": "markdown", "id": "a0d41705", "metadata": {}, "source": [ "### Step 1: Initial Data Loading" ] }, { "cell_type": "code", "execution_count": null, "id": "c7435175", "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": "676e3e8c", "metadata": {}, "source": [ "### Step 2: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "679846f6", "metadata": {}, "outputs": [], "source": [ "I'll provide a correctly formatted code solution for this step:\n", "\n", "```python\n", "# 1. Determine if gene expression data is available\n", "# The background information mentions \"Gene expression profiling was carried out on peripheral blood RNA samples\"\n", "# This suggests the dataset contains gene expression data, not just miRNA or methylation data\n", "is_gene_available = True\n", "\n", "# 2. Identify and convert clinical data\n", "\n", "# 2.1. Trait (Anxiety disorder)\n", "# The sample characteristics include \"anxiety\" scores at index 13\n", "# This is from the Beck Anxiety Inventory mentioned in the description\n", "trait_row = 13\n", "\n", "def convert_trait(value):\n", " if 'missing' in str(value).lower():\n", " return None\n", " try:\n", " # Extract the numeric part after the colon\n", " parts = value.split(':', 1)\n", " if len(parts) > 1:\n", " anxiety_score = float(parts[1].strip())\n", " # Convert to binary based on a threshold\n", " # Beck Anxiety Inventory: higher values indicate greater anxiety\n", " # Using threshold of 2.0 (moderate anxiety)\n", " return 1 if anxiety_score >= 2.0 else 0\n", " return None\n", " except:\n", " return None\n", "\n", "# 2.2. Age\n", "# Age is recorded at index 0\n", "age_row = 0\n", "\n", "def convert_age(value):\n", " try:\n", " # Extract the numeric part after the colon\n", " parts = value.split(':', 1)\n", " if len(parts) > 1:\n", " age = float(parts[1].strip())\n", " return age\n", " return None\n", " except:\n", " return None\n", "\n", "# 2.3. Gender\n", "# Gender is recorded at index 1 as \"female: 0\" or \"female: 1\"\n", "gender_row = 1\n", "\n", "def convert_gender(value):\n", " try:\n", " # Extract the numeric part after the colon\n", " parts = value.split(':', 1)\n", " if len(parts) > 1:\n", " female = int(parts[1].strip())\n", " # Convert to standard format where 0=female, 1=male\n", " # In the data, female=1 means it's a female, female=0 means it's a male\n", " return 1 - female # Reverse the coding to match our standard\n", " return None\n", " except:\n", " return None\n", "\n", "# 3. Perform initial filtering and save metadata\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. Extract clinical features if trait_row is not None\n", "if trait_row is not None:\n", " # Create a DataFrame from the sample characteristics dictionary provided\n", " sample_characteristics = {0: ['age (yrs): 79', 'age (yrs): 76', 'age (yrs): 70', 'age (yrs): 65', 'age (yrs): 64', 'age (yrs): 75', 'age (yrs): 66', 'age (yrs): 93', 'age (yrs): 69', 'age (yrs): 67', 'age (yrs): 77', 'age (yrs): 74', 'age (yrs): 73', 'age (yrs): 80', 'age (yrs): 68', 'age (yrs): 83', 'age (yrs): 87', 'age (yrs): 81', 'age (yrs): 84', 'age (yrs): 55', 'age (yrs): 62', 'age (yrs): 58', 'age (yrs): 60', 'age (yrs): 56', 'age (yrs): 86', 'age (yrs): 78', 'age (yrs): 48', 'age (yrs): 82', 'age (yrs): 95', 'age (yrs): 71'], 1: ['female: 0', 'female: 1'], 2: ['black: 0', 'black: 1'], 3: ['hispanic: 0', 'hispanic: 1'], 4: ['bmi: 22.7', 'bmi: 29.1', 'bmi: 25.8', 'bmi: 24.8', 'bmi: 42.1', 'bmi: 29.6', 'bmi: 21.4', 'bmi: 32.7', 'bmi: 30.7', 'bmi: 29.2', 'bmi: 34.0', 'bmi: 44.3', 'bmi: 28.7', 'bmi: 27.4', 'bmi: 30.6', 'bmi: 31.3', 'bmi: 30.0', 'bmi: 25.1', 'bmi: 26.4', 'bmi: 21.6', 'bmi: 18.6', 'bmi: 24.1', 'bmi: 22.9', 'bmi: 28.6', 'bmi: 25.0', 'bmi: 27.5', 'bmi: 25.5', 'bmi: 23.7', 'bmi: 23.0', 'bmi: 28.5'], 5: ['diabcvdcastr: 1', 'diabcvdcastr: 0'], 6: ['ln_hh_income: 16.03', 'ln_hh_income: 15.49', 'ln_hh_income: 15.34', 'ln_hh_income: 15.52', 'ln_hh_income: 16.41', 'ln_hh_income: 14.20', 'ln_hh_income: 18.09', 'ln_hh_income: 13.57', 'ln_hh_income: 16.29', 'ln_hh_income: 14.64', 'ln_hh_income: 15.28', 'ln_hh_income: 15.30', 'ln_hh_income: 15.93', 'ln_hh_income: 15.02', 'ln_hh_income: 16.93', 'ln_hh_income: 14.44', 'ln_hh_income: 16.10', 'ln_hh_income: 14.22', 'ln_hh_income: 13.18', 'ln_hh_income: 14.90', 'ln_hh_income: 12.97', 'ln_hh_income: 16.36', 'ln_hh_income: 16.14', 'ln_hh_income: 14.91', 'ln_hh_income: 13.71', 'ln_hh_income: 15.99', 'ln_hh_income: 13.77', 'ln_hh_income: 15.46', 'ln_hh_income: 13.86', 'ln_hh_income: 16.42'], 7: ['smoke: 1', 'smoke: 0'], 8: ['alcohol: 0', 'alcohol: 1', 'alcohol: missing'], 9: ['loneliness: 1.00', 'loneliness: 2.00', 'loneliness: 1.67', 'loneliness: 1.33', 'loneliness: 2.67', 'loneliness: missing', 'loneliness: 2.33', 'loneliness: 3.00'], 10: ['pwb_purpose: 4.14', 'pwb_purpose: 6.00', 'pwb_purpose: 5.00', 'pwb_purpose: 5.43', 'pwb_purpose: 3.29', 'pwb_purpose: 5.14', 'pwb_purpose: 4.71', 'pwb_purpose: 5.33', 'pwb_purpose: 4.83', 'pwb_purpose: 2.67', 'pwb_purpose: 5.71', 'pw\n" ] }, { "cell_type": "markdown", "id": "e9f6ca24", "metadata": {}, "source": [ "### Step 3: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "724b4e54", "metadata": {}, "outputs": [], "source": [ "I'll implement code that works with the available files without relying on GEOparse.\n", "\n", "```python\n", "# Step 1: Analyze the dataset to determine gene expression data availability\n", "import os\n", "import pandas as pd\n", "import json\n", "import numpy as np\n", "import gzip\n", "import re\n", "\n", "# First, let's check what files are available in the cohort directory\n", "cohort_files = os.listdir(in_cohort_dir)\n", "print(f\"Files in cohort directory: {cohort_files}\")\n", "\n", "# Let's check if there are matrix files that might contain gene expression data\n", "matrix_files = [f for f in cohort_files if 'matrix' in f.lower()]\n", "print(f\"Matrix files: {matrix_files}\")\n", "\n", "# Load the series matrix file to check for gene expression data\n", "series_matrix_path = os.path.join(in_cohort_dir, matrix_files[0] if matrix_files else cohort_files[0])\n", "\n", "# Function to check if file contains gene expression data\n", "def analyze_matrix_file(file_path):\n", " # Check if the file contains gene expression data by reading header lines\n", " with gzip.open(file_path, 'rt', encoding='utf-8') as f:\n", " header_lines = [next(f) for _ in range(100) if '!' in next(f, '')]\n", " \n", " # Check if the file contains gene expression data\n", " is_gene_expression = any(['gene' in line.lower() for line in header_lines]) or \\\n", " any(['expression' in line.lower() for line in header_lines])\n", " \n", " # Check if it's miRNA or methylation only\n", " is_mirna_only = any(['mirna' in line.lower() for line in header_lines]) and not is_gene_expression\n", " is_methylation_only = any(['methylation' in line.lower() for line in header_lines]) and not is_gene_expression\n", " \n", " return not (is_mirna_only or is_methylation_only)\n", "\n", "# Function to parse sample characteristics from series matrix file\n", "def parse_clinical_data(file_path):\n", " clinical_data = None\n", " characteristic_lines = []\n", " sample_ids = []\n", " \n", " with gzip.open(file_path, 'rt', encoding='utf-8') as f:\n", " in_header = True\n", " for line in f:\n", " if in_header:\n", " if line.startswith('!Sample_geo_accession'):\n", " sample_ids = line.strip().split('\\t')[1:]\n", " elif line.startswith('!Sample_characteristics_ch'):\n", " characteristic_lines.append(line.strip().split('\\t')[1:])\n", " elif line.startswith('!series_matrix_table_begin'):\n", " in_header = False\n", " else:\n", " break\n", " \n", " # Create a dataframe with the clinical data\n", " if sample_ids and characteristic_lines:\n", " df_dict = {f'char_{i}': chars for i, chars in enumerate(characteristic_lines)}\n", " clinical_data = pd.DataFrame(df_dict, index=sample_ids)\n", " \n", " return clinical_data\n", "\n", "# Determine if gene expression data is available\n", "try:\n", " is_gene_available = analyze_matrix_file(series_matrix_path)\n", " print(f\"Is gene expression data available: {is_gene_available}\")\n", "except Exception as e:\n", " print(f\"Error analyzing gene expression: {e}\")\n", " is_gene_available = False\n", "\n", "# Get clinical data from the series matrix file\n", "try:\n", " clinical_data = parse_clinical_data(series_matrix_path)\n", " \n", " if clinical_data is not None:\n", " print(\"Clinical data found with shape:\", clinical_data.shape)\n", " print(\"Sample characteristics:\")\n", " for i, col in enumerate(clinical_data.columns):\n", " unique_values = clinical_data[col].unique()\n", " if len(unique_values) < 10: # Only print if there are few unique values\n", " print(f\"Row {i}: {col} - Unique values: {unique_values}\")\n", " else:\n", " print(f\"Row {i}: {col} - {len(unique_values)} unique values\")\n", " else:\n", " print(\"Clinical data not found in the file.\")\n", "except Exception as e:\n", " print(f\"Error parsing clinical data: {e}\")\n", " clinical_data = pd.DataFrame()\n", "\n", "# Functions to convert trait, age, and gender data\n", "def convert_trait(value):\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (0: control, 1: anxiety disorder)\n", " value = str(value).lower()\n", " if 'control' in value or 'healthy' in value or 'normal' in value:\n", " return 0\n", " elif 'anxiety' in value or 'ptsd' in value or 'stress' in value or 'disorder' in value:\n", " return 1\n", " else:\n", " return None\n", "\n", "def convert_age(value):\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Try to convert to float\n", " try:\n", " age = float(value)\n", " return age\n", " except (ValueError, TypeError):\n", " # If age is given as a range (e.g., \"25-30\"), take the average\n", " if isinstance(value, str) and '-' in value:\n", " try:\n", " parts = value.split('-')\n", " return (float(parts[0]) + float(parts[1])) / 2\n", " except (ValueError, IndexError):\n", " pass\n", " return None\n", "\n", "def convert_gender(value):\n", " if pd.isna(value) or value is None:\n", " return None\n", " \n", " # Extract value after colon if present\n", " if isinstance(value, str) and ':' in value:\n", " value = value.split(':', 1)[1].strip()\n", " \n", " # Convert to binary (0: female, 1: male)\n", " value = str(value).lower()\n", " if 'female' in value or 'f' == value:\n", " return 0\n", " elif 'male' in value or 'm' == value:\n", " return 1\n", " else:\n", " return None\n", "\n", "# Based on the analysis, determine which rows contain trait, age, and gender\n", "# Setting default values as None (not available)\n", "trait_row = None\n", "age_row = None\n", "gender_row = None\n", "\n", "# Analyze clinical data to find relevant rows\n", "if clinical_data is not None and not clinical_data.empty:\n", " for i, col in enumerate(clinical_data.columns):\n", " # Get string representation of column values\n", " col_str = ' '.join(str(v) for v in clinical_data[col].unique())\n", " \n", " # Look for trait indicators\n", " if ('diagnosis' in col_str.lower() or 'disease' in col_str.lower() or \n", " 'condition' in col_str.lower() or 'group' in col_str.lower() or \n", " 'anxiety' in col_str.lower() or 'control' in col_str.lower()):\n", " # Check if this column could be trait data\n", " unique_values = clinical_data[col].unique()\n", " # Verify if the values correspond to trait data (should have at least 2 groups)\n", " if len(unique_values) > 1:\n", " trait_row = i\n", " \n", " # Look for age indicators\n", " if 'age' in col_str.lower():\n", " unique_values = clinical_data[col].unique()\n", " if len(unique_values) > 1: # Should have variation in age\n", " age_row = i\n", " \n", " # Look for gender indicators\n", " if 'gender' in col_str.lower() or 'sex' in col_str.lower():\n", " unique_values = clinical_data[col].unique()\n", " if len(unique_values) > 1: # Should have both males and females\n", " gender_row = i\n", "\n", "# Determine trait data availability\n", "is_trait_available = trait_row is not None\n", "\n", "print(f\"Trait row: {trait_row}\")\n", "print(f\"Age row: {age_row}\")\n", "print(f\"Gender row: {gender_row}\")\n", "print(f\"Is trait available: {is_trait\n" ] }, { "cell_type": "markdown", "id": "6fc60ab9", "metadata": {}, "source": [ "### Step 4: Dataset Analysis and Clinical Feature Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "244b8b4f", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import os\n", "import re\n", "import json\n", "import gzip\n", "import io\n", "from typing import Optional, Callable, Dict, Any, List\n", "\n", "def get_feature_data(clinical_df, row_index, feature_name, convert_func):\n", " \"\"\"Helper function to extract and convert feature data from a row.\"\"\"\n", " feature_series = clinical_df.iloc[row_index].copy()\n", " feature_series.name = feature_name\n", " if convert_func is not None:\n", " feature_series = feature_series.apply(convert_func)\n", " return pd.DataFrame(feature_series).T\n", "\n", "# First, check what files are available in the cohort directory\n", "files = os.listdir(in_cohort_dir)\n", "print(f\"Available files: {files}\")\n", "\n", "# Determine if gene expression data is available\n", "# GEO series matrix files typically contain gene expression data\n", "is_gene_available = any('series_matrix' in f for f in files)\n", "\n", "# Extract clinical data from the series matrix file\n", "clinical_data = None\n", "series_matrix_file = [f for f in files if 'series_matrix' in f.lower()][0]\n", "file_path = os.path.join(in_cohort_dir, series_matrix_file)\n", "\n", "# Parse the GEO series matrix file to extract sample characteristics\n", "sample_char_lines = []\n", "with gzip.open(file_path, 'rt') as f:\n", " in_sample_char_section = False\n", " sample_ids = []\n", " \n", " for line in f:\n", " line = line.strip()\n", " \n", " # Extract sample IDs\n", " if line.startswith('!Sample_geo_accession'):\n", " sample_ids = line.split('\\t')[1:]\n", " \n", " # Collect sample characteristics lines\n", " elif line.startswith('!Sample_characteristics_ch'):\n", " sample_char_lines.append(line.split('\\t')[1:])\n", " \n", " # Check if we're done with the characteristics section\n", " elif line.startswith('!Sample_') and sample_char_lines:\n", " continue\n", " elif line.startswith('!series_matrix_table_begin'):\n", " break\n", "\n", "# Create clinical dataframe if sample characteristics were found\n", "if sample_char_lines and sample_ids:\n", " clinical_data = pd.DataFrame(sample_char_lines, columns=sample_ids)\n", " print(f\"Clinical data shape: {clinical_data.shape}\")\n", " print(\"First few rows of clinical data:\")\n", " print(clinical_data.head(10))\n", "\n", " # Look for trait (anxiety disorder), age, and gender data in the characteristics\n", " trait_row = None\n", " age_row = None\n", " gender_row = None\n", " \n", " # Print unique values for each row to help identify relevant rows\n", " for i in range(len(clinical_data.index)):\n", " unique_vals = clinical_data.iloc[i, :].unique()\n", " print(f\"Row {i} unique values: {unique_vals}\")\n", " \n", " # Check if this row might contain trait data\n", " row_str = ' '.join(str(val).lower() for val in unique_vals)\n", " if ('anxiety' in row_str or 'patient' in row_str or 'diagnosis' in row_str or \n", " 'disorder' in row_str or 'case' in row_str or 'control' in row_str):\n", " print(f\"Potential trait row: {i}\")\n", " trait_row = i\n", " \n", " # Check if this row might contain age data\n", " if 'age' in row_str or 'years' in row_str:\n", " print(f\"Potential age row: {i}\")\n", " age_row = i\n", " \n", " # Check if this row might contain gender data\n", " if 'gender' in row_str or 'sex' in row_str or 'male' in row_str or 'female' in row_str:\n", " print(f\"Potential gender row: {i}\")\n", " gender_row = i\n", "\n", " # Define conversion functions based on observed data patterns\n", " def convert_trait(value):\n", " if pd.isnull(value):\n", " return None\n", " value_str = str(value).lower()\n", " \n", " # Extract value after colon if present\n", " if \":\" in value_str:\n", " value_str = value_str.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary: 1 for anxiety, 0 for control/healthy\n", " if re.search(r'anxi|patient|disorder|case', value_str):\n", " return 1\n", " elif re.search(r'control|healthy|normal', value_str):\n", " return 0\n", " return None\n", "\n", " def convert_age(value):\n", " if pd.isnull(value):\n", " return None\n", " value_str = str(value).lower()\n", " \n", " # Extract value after colon if present\n", " if \":\" in value_str:\n", " value_str = value_str.split(\":\", 1)[1].strip()\n", " \n", " # Try to extract numeric age value\n", " age_match = re.search(r'(\\d+(?:\\.\\d+)?)', value_str)\n", " if age_match:\n", " return float(age_match.group(1))\n", " return None\n", "\n", " def convert_gender(value):\n", " if pd.isnull(value):\n", " return None\n", " value_str = str(value).lower()\n", " \n", " # Extract value after colon if present\n", " if \":\" in value_str:\n", " value_str = value_str.split(\":\", 1)[1].strip()\n", " \n", " # Convert to binary: 0 for female, 1 for male\n", " if re.search(r'female|f\\b|woman|women', value_str):\n", " return 0\n", " elif re.search(r'male|m\\b|man|men', value_str):\n", " return 1\n", " return None\n", "\n", " # Check if trait data is available\n", " is_trait_available = trait_row is not None\n", "\n", " # Save initial 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", " # Extract clinical features if trait data is available\n", " if is_trait_available:\n", " print(\"\\nExtracting clinical features...\")\n", " selected_clinical_data = 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 selected clinical data\n", " preview = preview_df(selected_clinical_data)\n", " print(\"\\nSelected 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_data.to_csv(out_clinical_data_file)\n", " print(f\"Clinical data saved to {out_clinical_data_file}\")\n", " else:\n", " print(\"\\nNo suitable trait data found in the clinical information.\")\n", "else:\n", " print(\"No sample characteristics found in the series matrix file.\")\n", " is_trait_available = False\n", " \n", " # Save the negative result\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=False\n", " )\n" ] }, { "cell_type": "markdown", "id": "35adae85", "metadata": {}, "source": [ "### Step 5: Gene Data Extraction" ] }, { "cell_type": "code", "execution_count": null, "id": "40f614d6", "metadata": {}, "outputs": [], "source": [ "# 1. Re-identify the SOFT and matrix files to ensure we have the correct paths\n", "soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)\n", "\n", "# 2. Extract the gene expression data from the matrix file\n", "gene_data = get_genetic_data(matrix_file)\n", "\n", "# 3. Print the first 20 row IDs (gene or probe identifiers)\n", "print(\"\\nFirst 20 gene/probe identifiers:\")\n", "print(gene_data.index[:20])\n", "\n", "# 4. Print the dimensions of the gene expression data\n", "print(f\"\\nGene data dimensions: {gene_data.shape[0]} genes × {gene_data.shape[1]} samples\")\n", "\n", "# Note: we keep is_gene_available as True since we successfully extracted gene expression data\n", "is_gene_available = True\n" ] }, { "cell_type": "markdown", "id": "e1b1321a", "metadata": {}, "source": [ "### Step 6: Gene Identifier Review" ] }, { "cell_type": "code", "execution_count": null, "id": "f5ee5479", "metadata": {}, "outputs": [], "source": [ "# The identifiers in the gene expression data appear to be human gene symbols.\n", "# This can be determined because we see standard gene symbols like A1BG, A2M, AAAS, etc.\n", "# These are official HGNC (HUGO Gene Nomenclature Committee) gene symbols for human genes.\n", "\n", "# Since the data is already using human gene symbols, we don't need to perform mapping.\n", "requires_gene_mapping = False\n", "\n", "# Printing the result for clarification (though the variable assignment above is sufficient)\n", "print(f\"Gene mapping requirement: {requires_gene_mapping}\")" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }