---
name: geo-database
description: Work with the Gene Expression Omnibus (GEO) database to search, retrieve, download, and analyze high-throughput gene expression and functional genomics data. Use this skill for microarray data analysis, RNA-seq datasets, gene expression profiling, accessing GEO accessions (GSE series, GSM samples, GPL platforms, GDS datasets), downloading SOFT/MINiML/Matrix files, querying expression experiments, performing differential expression analysis, accessing GEO metadata, batch processing multiple datasets, quality control of expression data, correlation analysis, clustering, meta-analysis across studies, biomarker discovery, drug response studies, disease biology research, transcriptomics analysis, or when needing programmatic access to functional genomics repositories. This skill covers GEOparse library usage, NCBI E-utilities API, FTP downloads, data preprocessing, statistical analysis, visualization, and integration with downstream analysis workflows.
---

# GEO Database

## Overview

This skill provides tools and guidance for working with the Gene Expression Omnibus (GEO), 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. Use this skill to search for gene expression datasets, retrieve experimental data, download raw and processed files, query expression profiles, and integrate 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:

```python
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:

```python
# 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:**

```python
# 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:**
```bash
pip install GEOparse
```

**Basic Usage:**

```python
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:**

```python
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:**

```python
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:**

```python
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:**

```python
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:**

```python
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:

```python
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:**

```bash
# 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:**

```python
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:**

```python
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:**

```python
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:**

```python
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:**

```python
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

```bash
# Primary GEO access library (recommended)
pip install GEOparse

# For E-utilities and programmatic NCBI access
pip install biopython

# For data analysis
pip install pandas numpy scipy

# For visualization
pip install matplotlib seaborn

# For statistical analysis
pip install statsmodels scikit-learn
```

### Configuration

Set up NCBI E-utilities access:

```python
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
