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Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
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SOC 직업 분류 기준
| name | geo-database |
| description | Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis. |
The Gene Expression Omnibus (GEO) is NCBI's public repository for high-throughput gene expression and functional genomics data. GEO contains over 264,000 studies with more than 8 million samples from both array-based and sequence-based experiments.
This skill should be used when searching for gene expression datasets, retrieving experimental data, downloading raw and processed files, querying expression profiles, or integrating GEO data into computational analysis workflows.
GEO organizes data hierarchically using different accession types:
Series (GSE): A complete experiment with a set of related samples
Sample (GSM): A single experimental sample or biological replicate
Platform (GPL): The microarray or sequencing platform used
DataSet (GDS): Curated collections with consistent formatting
Profiles: Gene-specific expression data linked to sequence features
GEO DataSets Search:
Search for studies by keywords, organism, or experimental conditions:
from Bio import Entrez
# Configure Entrez (required)
Entrez.email = "your.email@example.com"
# Search for datasets
def search_geo_datasets(query, retmax=20):
"""Search GEO DataSets database"""
handle = Entrez.esearch(
db="gds",
term=query,
retmax=retmax,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
return results
# Example searches
results = search_geo_datasets("breast cancer[MeSH] AND Homo sapiens[Organism]")
print(f"Found {results['Count']} datasets")
# Search by specific platform
results = search_geo_datasets("GPL570[Accession]")
# Search by study type
results = search_geo_datasets("expression profiling by array[DataSet Type]")
GEO Profiles Search:
Find gene-specific expression patterns:
# Search for gene expression profiles
def search_geo_profiles(gene_name, organism="Homo sapiens", retmax=100):
"""Search GEO Profiles for a specific gene"""
query = f"{gene_name}[Gene Name] AND {organism}[Organism]"
handle = Entrez.esearch(
db="geoprofiles",
term=query,
retmax=retmax
)
results = Entrez.read(handle)
handle.close()
return results
# Find TP53 expression across studies
tp53_results = search_geo_profiles("TP53", organism="Homo sapiens")
print(f"Found {tp53_results['Count']} expression profiles for TP53")
Advanced Search Patterns:
# Combine multiple search terms
def advanced_geo_search(terms, operator="AND"):
"""Build complex search queries"""
query = f" {operator} ".join(terms)
return search_geo_datasets(query)
# Find recent high-throughput studies
search_terms = [
"RNA-seq[DataSet Type]",
"Homo sapiens[Organism]",
"2024[Publication Date]"
]
results = advanced_geo_search(search_terms)
# Search by author and condition
search_terms = [
"Smith[Author]",
"diabetes[Disease]"
]
results = advanced_geo_search(search_terms)
GEOparse is the primary Python library for accessing GEO data:
Installation:
uv pip install GEOparse
Basic Usage:
import GEOparse
# Download and parse a GEO Series
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Access series metadata
print(gse.metadata['title'])
print(gse.metadata['summary'])
print(gse.metadata['overall_design'])
# Access sample information
for gsm_name, gsm in gse.gsms.items():
print(f"Sample: {gsm_name}")
print(f" Title: {gsm.metadata['title'][0]}")
print(f" Source: {gsm.metadata['source_name_ch1'][0]}")
print(f" Characteristics: {gsm.metadata.get('characteristics_ch1', [])}")
# Access platform information
for gpl_name, gpl in gse.gpls.items():
print(f"Platform: {gpl_name}")
print(f" Title: {gpl.metadata['title'][0]}")
print(f" Organism: {gpl.metadata['organism'][0]}")
Working with Expression Data:
import GEOparse
import pandas as pd
# Get expression data from series
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Extract expression matrix
# Method 1: From series matrix file (fastest)
if hasattr(gse, 'pivot_samples'):
expression_df = gse.pivot_samples('VALUE')
print(expression_df.shape) # genes x samples
# Method 2: From individual samples
expression_data = {}
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'table'):
expression_data[gsm_name] = gsm.table['VALUE']
expression_df = pd.DataFrame(expression_data)
print(f"Expression matrix: {expression_df.shape}")
Accessing Supplementary Files:
import GEOparse
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Download supplementary files
gse.download_supplementary_files(
directory="./data/GSE123456_suppl",
download_sra=False # Set to True to download SRA files
)
# List available supplementary files
for gsm_name, gsm in gse.gsms.items():
if hasattr(gsm, 'supplementary_files'):
print(f"Sample {gsm_name}:")
for file_url in gsm.metadata.get('supplementary_file', []):
print(f" {file_url}")
Filtering and Subsetting Data:
import GEOparse
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
# Filter samples by metadata
control_samples = [
gsm_name for gsm_name, gsm in gse.gsms.items()
if 'control' in gsm.metadata.get('title', [''])[0].lower()
]
treatment_samples = [
gsm_name for gsm_name, gsm in gse.gsms.items()
if 'treatment' in gsm.metadata.get('title', [''])[0].lower()
]
print(f"Control samples: {len(control_samples)}")
print(f"Treatment samples: {len(treatment_samples)}")
# Extract subset expression matrix
expression_df = gse.pivot_samples('VALUE')
control_expr = expression_df[control_samples]
treatment_expr = expression_df[treatment_samples]
E-utilities provide lower-level programmatic access to GEO metadata:
Basic E-utilities Workflow:
from Bio import Entrez
import time
Entrez.email = "your.email@example.com"
# Step 1: Search for GEO entries
def search_geo(query, db="gds", retmax=100):
"""Search GEO using E-utilities"""
handle = Entrez.esearch(
db=db,
term=query,
retmax=retmax,
usehistory="y"
)
results = Entrez.read(handle)
handle.close()
return results
# Step 2: Fetch summaries
def fetch_geo_summaries(id_list, db="gds"):
"""Fetch document summaries for GEO entries"""
ids = ",".join(id_list)
handle = Entrez.esummary(db=db, id=ids)
summaries = Entrez.read(handle)
handle.close()
return summaries
# Step 3: Fetch full records
def fetch_geo_records(id_list, db="gds"):
"""Fetch full GEO records"""
ids = ",".join(id_list)
handle = Entrez.efetch(db=db, id=ids, retmode="xml")
records = Entrez.read(handle)
handle.close()
return records
# Example workflow
search_results = search_geo("breast cancer AND Homo sapiens")
id_list = search_results['IdList'][:5]
summaries = fetch_geo_summaries(id_list)
for summary in summaries:
print(f"GDS: {summary.get('Accession', 'N/A')}")
print(f"Title: {summary.get('title', 'N/A')}")
print(f"Samples: {summary.get('n_samples', 'N/A')}")
print()
Batch Processing with E-utilities:
from Bio import Entrez
import time
Entrez.email = "your.email@example.com"
def batch_fetch_geo_metadata(accessions, batch_size=100):
"""Fetch metadata for multiple GEO accessions"""
results = {}
for i in range(0, len(accessions), batch_size):
batch = accessions[i:i + batch_size]
# Search for each accession
for accession in batch:
try:
query = f"{accession}[Accession]"
search_handle = Entrez.esearch(db="gds", term=query)
search_results = Entrez.read(search_handle)
search_handle.close()
if search_results['IdList']:
# Fetch summary
summary_handle = Entrez.esummary(
db="gds",
id=search_results['IdList'][0]
)
summary = Entrez.read(summary_handle)
summary_handle.close()
results[accession] = summary[0]
# Be polite to NCBI servers
time.sleep(0.34) # Max 3 requests per second
except Exception as e:
print(f"Error fetching {accession}: {e}")
return results
# Fetch metadata for multiple datasets
gse_list = ["GSE100001", "GSE100002", "GSE100003"]
metadata = batch_fetch_geo_metadata(gse_list)
FTP URLs for GEO Data:
GEO data can be downloaded directly via FTP:
import ftplib
import os
def download_geo_ftp(accession, file_type="matrix", dest_dir="./data"):
"""Download GEO files via FTP"""
# Construct FTP path based on accession type
if accession.startswith("GSE"):
# Series files
gse_num = accession[3:]
base_num = gse_num[:-3] + "nnn"
ftp_path = f"/geo/series/GSE{base_num}/{accession}/"
if file_type == "matrix":
filename = f"{accession}_series_matrix.txt.gz"
elif file_type == "soft":
filename = f"{accession}_family.soft.gz"
elif file_type == "miniml":
filename = f"{accession}_family.xml.tgz"
# Connect to FTP server
ftp = ftplib.FTP("ftp.ncbi.nlm.nih.gov")
ftp.login()
ftp.cwd(ftp_path)
# Download file
os.makedirs(dest_dir, exist_ok=True)
local_file = os.path.join(dest_dir, filename)
with open(local_file, 'wb') as f:
ftp.retrbinary(f'RETR {filename}', f.write)
ftp.quit()
print(f"Downloaded: {local_file}")
return local_file
# Download series matrix file
download_geo_ftp("GSE123456", file_type="matrix")
# Download SOFT format file
download_geo_ftp("GSE123456", file_type="soft")
Using wget or curl for Downloads:
# Download series matrix file
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/matrix/GSE123456_series_matrix.txt.gz
# Download all supplementary files for a series
wget -r -np -nd ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/suppl/
# Download SOFT format family file
wget ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123456/soft/GSE123456_family.soft.gz
Quality Control and Preprocessing:
import GEOparse
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load dataset
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Check for missing values
print(f"Missing values: {expression_df.isnull().sum().sum()}")
# Log transformation (if needed)
if expression_df.min().min() > 0: # Check if already log-transformed
if expression_df.max().max() > 100:
expression_df = np.log2(expression_df + 1)
print("Applied log2 transformation")
# Distribution plots
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
expression_df.plot.box(ax=plt.gca())
plt.title("Expression Distribution per Sample")
plt.xticks(rotation=90)
plt.subplot(1, 2, 2)
expression_df.mean(axis=1).hist(bins=50)
plt.title("Gene Expression Distribution")
plt.xlabel("Average Expression")
plt.tight_layout()
plt.savefig("geo_qc.png", dpi=300, bbox_inches='tight')
Differential Expression Analysis:
import GEOparse
import pandas as pd
import numpy as np
from scipy import stats
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Define sample groups
control_samples = ["GSM1", "GSM2", "GSM3"]
treatment_samples = ["GSM4", "GSM5", "GSM6"]
# Calculate fold changes and p-values
results = []
for gene in expression_df.index:
control_expr = expression_df.loc[gene, control_samples]
treatment_expr = expression_df.loc[gene, treatment_samples]
# Calculate statistics
fold_change = treatment_expr.mean() - control_expr.mean()
t_stat, p_value = stats.ttest_ind(treatment_expr, control_expr)
results.append({
'gene': gene,
'log2_fold_change': fold_change,
'p_value': p_value,
'control_mean': control_expr.mean(),
'treatment_mean': treatment_expr.mean()
})
# Create results DataFrame
de_results = pd.DataFrame(results)
# Multiple testing correction (Benjamini-Hochberg)
from statsmodels.stats.multitest import multipletests
_, de_results['q_value'], _, _ = multipletests(
de_results['p_value'],
method='fdr_bh'
)
# Filter significant genes
significant_genes = de_results[
(de_results['q_value'] < 0.05) &
(abs(de_results['log2_fold_change']) > 1)
]
print(f"Significant genes: {len(significant_genes)}")
significant_genes.to_csv("de_results.csv", index=False)
Correlation and Clustering Analysis:
import GEOparse
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.cluster import hierarchy
from scipy.spatial.distance import pdist
gse = GEOparse.get_GEO(geo="GSE123456", destdir="./data")
expression_df = gse.pivot_samples('VALUE')
# Sample correlation heatmap
sample_corr = expression_df.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(sample_corr, cmap='coolwarm', center=0,
square=True, linewidths=0.5)
plt.title("Sample Correlation Matrix")
plt.tight_layout()
plt.savefig("sample_correlation.png", dpi=300, bbox_inches='tight')
# Hierarchical clustering
distances = pdist(expression_df.T, metric='correlation')
linkage = hierarchy.linkage(distances, method='average')
plt.figure(figsize=(12, 6))
hierarchy.dendrogram(linkage, labels=expression_df.columns)
plt.title("Hierarchical Clustering of Samples")
plt.xlabel("Samples")
plt.ylabel("Distance")
plt.xticks(rotation=90)
plt.tight_layout()
plt.savefig("sample_clustering.png", dpi=300, bbox_inches='tight')
Download and Process Multiple Series:
import GEOparse
import pandas as pd
import os
def batch_download_geo(gse_list, destdir="./geo_data"):
"""Download multiple GEO series"""
results = {}
for gse_id in gse_list:
try:
print(f"Processing {gse_id}...")
gse = GEOparse.get_GEO(geo=gse_id, destdir=destdir)
# Extract key information
results[gse_id] = {
'title': gse.metadata.get('title', ['N/A'])[0],
'organism': gse.metadata.get('organism', ['N/A'])[0],
'platform': list(gse.gpls.keys())[0] if gse.gpls else 'N/A',
'num_samples': len(gse.gsms),
'submission_date': gse.metadata.get('submission_date', ['N/A'])[0]
}
# Save expression data
if hasattr(gse, 'pivot_samples'):
expr_df = gse.pivot_samples('VALUE')
expr_df.to_csv(f"{destdir}/{gse_id}_expression.csv")
results[gse_id]['num_genes'] = len(expr_df)
except Exception as e:
print(f"Error processing {gse_id}: {e}")
results[gse_id] = {'error': str(e)}
# Save summary
summary_df = pd.DataFrame(results).T
summary_df.to_csv(f"{destdir}/batch_summary.csv")
return results
# Process multiple datasets
gse_list = ["GSE100001", "GSE100002", "GSE100003"]
results = batch_download_geo(gse_list)
Meta-Analysis Across Studies:
import GEOparse
import pandas as pd
import numpy as np
def meta_analysis_geo(gse_list, gene_of_interest):
"""Perform meta-analysis of gene expression across studies"""
results = []
for gse_id in gse_list:
try:
gse = GEOparse.get_GEO(geo=gse_id, destdir="./data")
# Get platform annotation
gpl = list(gse.gpls.values())[0]
# Find gene in platform
if hasattr(gpl, 'table'):
gene_probes = gpl.table[
gpl.table['Gene Symbol'].str.contains(
gene_of_interest,
case=False,
na=False
)
]
if not gene_probes.empty:
expr_df = gse.pivot_samples('VALUE')
for probe_id in gene_probes['ID']:
if probe_id in expr_df.index:
expr_values = expr_df.loc[probe_id]
results.append({
'study': gse_id,
'probe': probe_id,
'mean_expression': expr_values.mean(),
'std_expression': expr_values.std(),
'num_samples': len(expr_values)
})
except Exception as e:
print(f"Error in {gse_id}: {e}")
return pd.DataFrame(results)
# Meta-analysis for TP53
gse_studies = ["GSE100001", "GSE100002", "GSE100003"]
meta_results = meta_analysis_geo(gse_studies, "TP53")
print(meta_results)
# Primary GEO access library (recommended)
uv pip install GEOparse
# For E-utilities and programmatic NCBI access
uv pip install biopython
# For data analysis
uv pip install pandas numpy scipy
# For visualization
uv pip install matplotlib seaborn
# For statistical analysis
uv pip install statsmodels scikit-learn
Set up NCBI E-utilities access:
from Bio import Entrez
# Always set your email (required by NCBI)
Entrez.email = "your.email@example.com"
# Optional: Set API key for increased rate limits
# Get your API key from: https://www.ncbi.nlm.nih.gov/account/
Entrez.api_key = "your_api_key_here"
# With API key: 10 requests/second
# Without API key: 3 requests/second
SOFT (Simple Omnibus Format in Text): GEO's primary text-based format containing metadata and data tables. Easily parsed by GEOparse.
MINiML (MIAME Notation in Markup Language): XML format for GEO data, used for programmatic access and data exchange.
Series Matrix: Tab-delimited expression matrix with samples as columns and genes/probes as rows. Fastest format for getting expression data.
MIAME Compliance: Minimum Information About a Microarray Experiment - standardized annotation that GEO enforces for all submissions.
Expression Value Types: Different types of expression measurements (raw signal, normalized, log-transformed). Always check platform and processing methods.
Platform Annotation: Maps probe/feature IDs to genes. Essential for biological interpretation of expression data.
For quick analysis without coding, use GEO2R:
NCBI E-utilities Rate Limits:
time.sleep(0.34) (no API key) or time.sleep(0.1) (with API key)FTP Access:
GEOparse Caching:
Optimal Practices:
Comprehensive reference documentation covering:
Consult this reference for in-depth technical details, complex query patterns, or when working with uncommon data formats.