geo-database

GEO Database

Overview

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.

When to Use This Skill

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.

Core Capabilities

1. Understanding GEO Data Organization

GEO organizes data hierarchically using different accession types:

Series (GSE): A complete experiment with a set of related samples

Example: GSE123456

Contains experimental design, samples, and overall study information

Largest organizational unit in GEO

Current count: 264,928+ series

Sample (GSM): A single experimental sample or biological replicate

Example: GSM987654

Contains individual sample data, protocols, and metadata

Linked to platforms and series

Current count: 8,068,632+ samples

Platform (GPL): The microarray or sequencing platform used

Example: GPL570 (Affymetrix Human Genome U133 Plus 2.0 Array)

Describes the technology and probe/feature annotations

Shared across multiple experiments

Current count: 27,739+ platforms

DataSet (GDS): Curated collections with consistent formatting

Example: GDS5678

Experimentally-comparable samples organized by study design

Processed for differential analysis

Subset of GEO data (4,348 curated datasets)

Ideal for quick comparative analyses

Profiles: Gene-specific expression data linked to sequence features

Queryable by gene name or annotation

Cross-references to Entrez Gene

Enables gene-centric searches across all studies

2. Searching GEO Data

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)

3. Retrieving GEO Data with GEOparse (Recommended)

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]

4. Using NCBI E-utilities for GEO Access

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)

5. Direct FTP Access for Data Files

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

6. Analyzing GEO Data

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')

7. Batch Processing Multiple Datasets

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)

Installation and Setup

Python Libraries

# 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

Configuration

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

Common Use Cases

Transcriptomics Research

Download gene expression data for specific conditions

Compare expression profiles across studies

Identify differentially expressed genes

Perform meta-analyses across multiple datasets

Drug Response Studies

Analyze gene expression changes after drug treatment

Identify biomarkers for drug response

Compare drug effects across cell lines or patients

Build predictive models for drug sensitivity

Disease Biology

Study gene expression in disease vs. normal tissues

Identify disease-associated expression signatures

Compare patient subgroups and disease stages

Correlate expression with clinical outcomes

Biomarker Discovery

Screen for diagnostic or prognostic markers

Validate biomarkers across independent cohorts

Compare marker performance across platforms

Integrate expression with clinical data

Key Concepts

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.

GEO2R Web Tool

For quick analysis without coding, use GEO2R:

Web-based statistical analysis tool integrated into GEO

Accessible at: https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSExxxxx

Performs differential expression analysis

Generates R scripts for reproducibility

Useful for exploratory analysis before downloading data

Rate Limiting and Best Practices

NCBI E-utilities Rate Limits:

Without API key: 3 requests per second

With API key: 10 requests per second

Implement delays between requests: time.sleep(0.34) (no API key) or time.sleep(0.1) (with API key)

FTP Access:

No rate limits for FTP downloads

Preferred method for bulk downloads

Can download entire directories with wget -r

GEOparse Caching:

GEOparse automatically caches downloaded files in destdir

Subsequent calls use cached data

Clean cache periodically to save disk space

Optimal Practices:

Use GEOparse for series-level access (easiest)

Use E-utilities for metadata searching and batch queries

Use FTP for direct file downloads and bulk operations

Cache data locally to avoid repeated downloads

Always set Entrez.email when using Biopython

Resources

references/geo_reference.md

Comprehensive reference documentation covering:

Detailed E-utilities API specifications and endpoints

Complete SOFT and MINiML file format documentation

Advanced GEOparse usage patterns and examples

FTP directory structure and file naming conventions

Data processing pipelines and normalization methods

Troubleshooting common issues and error handling

Platform-specific considerations and quirks

Consult this reference for in-depth technical details, complex query patterns, or when working with uncommon data formats.

Important Notes

Data Quality Considerations

GEO accepts user-submitted data with varying quality standards

Always check platform annotation and processing methods

Verify sample metadata and experimental design

Be cautious with batch effects across studies

Consider reprocessing raw data for consistency

File Size Warnings

Series matrix files can be large (>1 GB for large studies)

Supplementary files (e.g., CEL files) can be very large

Plan for adequate disk space before downloading

Consider downloading samples incrementally

Data Usage and Citation

GEO data is freely available for research use

Always cite original studies when using GEO data

Cite GEO database: Barrett et al. (2013) Nucleic Acids Research

Check individual dataset usage restrictions (if any)

Follow NCBI guidelines for programmatic access

Common Pitfalls

Different platforms use different probe IDs (requires annotation mapping)

Expression values may be raw, normalized, or log-transformed (check metadata)

Sample metadata can be inconsistently formatted across studies

Not all series have series matrix files (older submissions)

Platform annotations may be outdated (genes renamed, IDs deprecated)

Additional Resources

GEO Website: https://www.ncbi.nlm.nih.gov/geo/

GEO Submission Guidelines: https://www.ncbi.nlm.nih.gov/geo/info/submission.html

GEOparse Documentation: https://geoparse.readthedocs.io/

E-utilities Documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/

GEO FTP Site: ftp://ftp.ncbi.nlm.nih.gov/geo/

GEO2R Tool: https://www.ncbi.nlm.nih.gov/geo/geo2r/

NCBI API Keys: https://ncbiinsights.ncbi.nlm.nih.gov/2017/11/02/new-api-keys-for-the-e-utilities/

Biopython Tutorial: https://biopython.org/DIST/docs/tutorial/Tutorial.html

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