Using the GEOquery Package • GEOquery

1 Introduction to GEO and GEOquery

The NCBI Gene Expression Omnibus (GEO) was established in 2000 as a public repository for high-throughput molecular abundance data, primarily microarray data at the time. Today, GEO hosts a diverse array of data types including gene expression, genomic DNA, and protein abundance measurements from various technologies like microarrays, next-generation sequencing, and mass spectrometry.

GEOquery was created to bridge the gap between this vast resource and Bioconductor’s analytical tools. First released in 2007, GEOquery has evolved alongside GEO itself, adapting to new data types and formats over time.

1.1 Why GEOquery?

Before GEOquery, researchers would need to:

Manually download data from the GEO website
Parse complex SOFT format files
Construct data structures suitable for analysis
Integrate metadata with expression data

GEOquery automates this entire process, allowing researchers to focus on analysis rather than data acquisition and formatting. GEOquery also facilitates automation and reproducibility by incorporating data acquisition into workflows, scripts, or documents.

1.2 GEO Data Organization

Understanding GEO’s data organization is essential for effective use of GEOquery:

Platform (GPL): Describes array design, probes, or detectable elements
Sample (GSM): Contains individual experiment measurements
Series (GSE): Groups related samples together, typically representing a complete study
Dataset (GDS): Curated by GEO staff, represents biologically and statistically comparable samples

2 Getting Started with GEOquery

Before working with GEOquery (or any R or Bioconductor package), one must first install the package. Installation is a one-time (or at least not repeated often) operation. To install GEOquery, ensure that you have installed R and Bioconductor.

Then, to install GEOquery:

BiocManager::install('GEOquery')

Before using GEOquery, we need to load the GEOquery library. Loading the GEOquery library must be done each time you start a new R session.

library(GEOquery)

2.1 Downloading a GEO Series

The most common use case is downloading a GEO Series (GSE), which typically represents a complete study:

# Download GSE2553
gse <- getGEO("GSE2553")

Found 1 file(s)

GSE2553_series_matrix.txt.gz

class(gse)

[1] "list"

Notice that getGEO returns a list. This is because a single GSE can contain experiments from multiple platforms. Each element of the list is an ExpressionSet containing data from one platform:

length(gse)

[1] 1

gse[[1]]

ExpressionSet (storageMode: lockedEnvironment)
assayData: 12600 features, 181 samples
  element names: exprs
protocolData: none
phenoData
  sampleNames: GSM48681 GSM48682 ... GSM48861 (181 total)
  varLabels: title geo_accession ... data_row_count (30 total)
  varMetadata: labelDescription
featureData
  featureNames: 1 2 ... 12600 (12600 total)
  fvarLabels: ID PenAt ... Chimeric_Cluster_IDs (13 total)
  fvarMetadata: Column Description labelDescription
experimentData: use 'experimentData(object)'
  pubMedIds: 16230383
Annotation: GPL1977

2.2 Historical Context: SOFT Format vs GSEMatrix Files

GEO originally provided data in SOFT (Simple Omnibus Format in Text) format, which contained extensive information but was slow to parse for large datasets. In response to community needs, GEO introduced GSEMatrix files—a more efficient, tab-delimited format.

GEOquery defaults to using GSEMatrix files (GSEMatrix=TRUE) because: 1. Parsing is substantially faster (often by 10-100x) 2. Memory usage is more efficient 3. The resulting ExpressionSet objects are directly usable with Bioconductor tools

3 Searching GEO Programmatically

While GEO’s web interface is powerful, programmatic searches enable automated data discovery and retrieval. GEOquery provides direct access to GEO’s search capabilities:

# What fields can we search?
fields <- searchFieldsGEO()
kable(fields)

Table 1: Available GEO search fields.

Name	FullName	Description	TermCount	IsDate	IsNumerical	SingleToken	Hierarchy	IsHidden
ALL	All Fields	All term….	44306002	N	N	N	N	N
UID	UID	Unique n….	0	N	Y	Y	N	Y
FILT	Filter	Limits t….	71	N	N	Y	N	N
ORGN	Organism	exploded….	74819	N	N	Y	Y	N
ACCN	GEO Acce….	accessio….	18890676	N	N	Y	N	N
TITL	Title	Words in….	9773698	N	N	Y	N	N
DESC	Description	Text fro….	10315188	N	N	Y	N	N
SFIL	Suppleme….	Suppleme….	255	N	N	Y	N	N
ETYP	Entry Type	Entry ty….	4	N	N	Y	N	N
STYP	Sample Type	Sample type	9	N	N	Y	N	N
VTYP	Sample V….	type of ….	7	N	N	Y	N	N
PTYP	Platform….	Platform….	17	N	N	Y	N	N
GTYP	DataSet Type	type of ….	27	N	N	Y	N	N
NSAM	Number o….	Number o….	2129	N	Y	Y	N	N
SRC	Sample S….	sample s….	458896	N	N	Y	N	N
AUTH	Author	author o….	1279657	N	N	Y	N	N
INST	Submitte….	institut….	24895	N	N	Y	N	N
NPRO	Number o….	number o….	7248	N	Y	Y	N	N
SSTP	Subset V….	subset v….	24	N	N	Y	N	N
SSDE	Subset D….	subset d….	7535	N	N	Y	N	N
GEID	Reporter….	name or ….	2840498	N	N	Y	N	N
PDAT	Publicat….	publicat….	8350	Y	N	Y	N	N
UDAT	Update Date	date	7468	Y	N	Y	N	N
TAGL	Tag Length	Tag/Sign….	9	N	N	Y	N	N
RGSE	Related ….	Related ….	29875	N	N	Y	N	N
RGPL	Related ….	Related ….	264683	N	N	Y	N	N
MESH	MeSH Terms	Medical ….	17589	N	N	Y	Y	N
PROJ	Project	Project	10	N	N	Y	N	N
ATNM	Attribut….	Attribut….	48809	N	N	Y	N	N
ATTR	Attribute	Attribute	2732197	N	N	Y	N	N
PROP	Properties	Properties	3	N	N	Y	N	N

GEO uses a specific search syntax with field identifiers in square brackets:

# Find RNA-seq studies related to COVID-19 in humans
results <- searchGEO('covid-19[All Fields] AND "rnaseq counts"[Filter] AND Homo sapiens[ORGN]')
results |>
  dplyr::mutate(Summary=paste(strtrim(Summary,120), '...')) |>
  dplyr::mutate(Title = paste(strtrim(Title, 120), '...')) |>
  head() |>
  kable()

Table 2: Top search results for studies related to COVID-19 in humans with GEO-calculated RNA-seq counts available.

Title	Summary	Organism	Type	Platforms	Contains	FTP download	Series Accession	ID	SRA Run Selector
Comparative Analysis of Host Responses Between Sepsis and COVID-19: A Prospective Observational Study of Whole Blood Tra …	This prospective observational study conducted at Osaka University Graduate School of Medicine aimed to compare host res …	Homo sapiens	Expression profiling by high throughput sequencing	GPL30209	72 Samples	GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE243nnn/GSE243217/	GSE243217	200243217	NA
An aberrant immune-epithelial progenitor niche drives post-viral lung sequelae [human] …	Respiratory viral infections are being increasingly recognized not just for their acute impact but also as potential tri …	Homo sapiens	Other	GPL24676	5 Samples	GEO (H5, PNG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE267nnn/GSE267226/	GSE267226	200267226	NA
Longitudinal transcriptomic analysis reveals persistent enrichment of iron homeostasis and erythrocyte function pathways …	The acute respiratory distress syndrome (ARDS) is a common complications of severe COVID-19 and contributes to patient m …	Homo sapiens	Expression profiling by high throughput sequencing	GPL34284	49 Samples	GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE273nnn/GSE273149/	GSE273149	200273149	NA
Features of chronic urticaria after COVID-19 mRNA vaccine, a real-life cohort study …	New onsets of chronic urticaria (CU) have been reported after repeated immunizations, mainly with the Moderna mRNA-1273 …	Homo sapiens	Expression profiling by high throughput sequencing	GPL20301	32 Samples	GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE272nnn/GSE272645/	GSE272645	200272645	NA
Transcriptomic Profiling of Neutrophils and Low-Density Granulocytes in COVID-19 Patients …	The severity of COVID-19 is linked to excessive inflammation. Neutrophils represent a critical arm of the innate immune …	Homo sapiens	Expression profiling by high throughput sequencing	GPL24676	36 Samples	GEO (TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE272nnn/GSE272381/	GSE272381	200272381	NA
Effects of envelope- or membrane-protein segments of SARS-CoV-2 on gene expression of HUVEC cells …	Exterior segments of E-proteins bound onto and modulated gene expression in human vascular endothelial cells in vitro Th …	Homo sapiens	Expression profiling by high throughput sequencing	GPL28038	9 Samples	GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE268nnn/GSE268369/	GSE268369	200268369	NA

The search capabilities mirror GEO’s web interface but allow for integration with R workflows.

4 RNA-seq Quantifications: NCBI’s Solution to Reanalysis Challenges

4.1 The Challenge of RNA-seq Reanalysis

A major barrier to exploiting the massive volume of public RNA-seq data has been the computational cost and expertise required to consistently process raw reads into usable expression values. Different processing pipelines can produce different results, making cross-study comparisons challenging.

4.2 NCBI’s RNA-seq Quantification Pipeline

To address this challenge, in 2020-2021, the NCBI SRA and GEO teams developed a standardized pipeline that precomputes RNA-seq gene expression counts for human and mouse datasets. As described in their documentation, this pipeline:

Processes RNA-seq data from SRA using the HISAT2 aligner
Generates gene expression counts using the featureCounts program
Provides consistent annotation based on current genome builds
Makes counts available in standardized formats

GEOquery provides direct access to these precomputed counts:

# Check if RNA-seq quantifications are available
has_quant <- hasRNASeqQuantifications("GSE164073")
has_quant

[1] TRUE

# Get genome build and species information
genome_info <- getRNASeqQuantGenomeInfo("GSE164073")
genome_info

# Download and construct a SummarizedExperiment
se <- getRNASeqData("GSE164073")
se

This feature saves researchers significant time and computational resources while ensuring standardized processing across datasets.

5 Understanding Supplementary Files in GEO

GEO accessions often include supplementary files containing raw data, processing scripts, or additional results not captured in the standard GEO formats. These files are invaluable for:

Accessing raw data (e.g., CEL files, FASTQ files)
Understanding custom processing pipelines
Retrieving additional metadata or results

GEOquery makes accessing these files straightforward:

# List available supplementary files without downloading
supp_files <- getGEOSuppFiles('GSE63137', fetch_files = FALSE)
head(supp_files)

                                                             fname
1                  GSE63137_ATAC-seq_PV_neurons_HOMER_peaks.bed.gz
2                 GSE63137_ATAC-seq_VIP_neurons_HOMER_peaks.bed.gz
3          GSE63137_ATAC-seq_excitatory_neurons_HOMER_peaks.bed.gz
4  GSE63137_ChIP-seq_H3K27ac_excitatory_neurons_SICER_peaks.bed.gz
5 GSE63137_ChIP-seq_H3K27me3_excitatory_neurons_SICER_peaks.bed.gz
6  GSE63137_ChIP-seq_H3K4me1_excitatory_neurons_SICER_peaks.bed.gz
                                                                                                                                url
1                  https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ATAC-seq_PV_neurons_HOMER_peaks.bed.gz
2                 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ATAC-seq_VIP_neurons_HOMER_peaks.bed.gz
3          https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ATAC-seq_excitatory_neurons_HOMER_peaks.bed.gz
4  https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ChIP-seq_H3K27ac_excitatory_neurons_SICER_peaks.bed.gz
5 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ChIP-seq_H3K27me3_excitatory_neurons_SICER_peaks.bed.gz
6  https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_ChIP-seq_H3K4me1_excitatory_neurons_SICER_peaks.bed.gz

You can filter files by pattern to find specific file types:

# Find all text files
txt_files <- getGEOSuppFiles('GSE63137', fetch_files = FALSE, 
                             filter_regex = 'txt')
head(txt_files)

                                                     fname
1                GSE63137_MethylC-seq_DMRs_methylpy.txt.gz
2         GSE63137_MethylC-seq_PV_neurons_UMRs_LMRs.txt.gz
3        GSE63137_MethylC-seq_VIP_neurons_UMRs_LMRs.txt.gz
4 GSE63137_MethylC-seq_excitatory_neurons_UMRs_LMRs.txt.gz
                                                                                                                        url
1                https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_MethylC-seq_DMRs_methylpy.txt.gz
2         https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_MethylC-seq_PV_neurons_UMRs_LMRs.txt.gz
3        https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_MethylC-seq_VIP_neurons_UMRs_LMRs.txt.gz
4 https://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63137/suppl//GSE63137_MethylC-seq_excitatory_neurons_UMRs_LMRs.txt.gz

And download specific files or all supplementary files:

# Download all supplementary files for a sample
getGEOSuppFiles('GSM15789') # Files saved to a new directory

6 Navigating Between R and the GEO Web Interface

Sometimes you may want to examine a GEO record in its web interface. GEOquery provides convenience functions for this:

# Get the URL for a GEO accession
url <- urlForAccession("GSE262484")
url

# Open a browser to the GEO page
browseGEOAccession("GSE262484")

For RNA-seq datasets specifically, there’s a convenience function to search for RNA-seq counts on the GEO website:

browseWebsiteRNASeqSearch()

These functions bridge the programmatic and web interfaces to GEO, allowing seamless transitions between analytical and exploratory modes.

7 Working with GDS Datasets

GEO DataSets (GDS) are curated collections of samples, processed and normalized to be directly comparable. While less common in modern workflows, they remain available and GEOquery supports them:

# Download a GDS dataset
gds <- getGEO("GDS507")
gds

An object of class "GDS"
channel_count
[1] "1"
dataset_id
 [1] "GDS507" "GDS507" "GDS507" "GDS507" "GDS507" "GDS507" "GDS507" "GDS507"
 [9] "GDS507" "GDS507" "GDS507" "GDS507"
description
 [1] "Investigation into mechanisms of renal clear cell carcinogenesis (RCC). Comparison of renal clear cell tumor tissue and adjacent normal tissue isolated from the same surgical samples."
 [2] "RCC"
 [3] "normal"
 [4] "035"
 [5] "023"
 [6] "001"
 [7] "005"
 [8] "011"
 [9] "032"
[10] "1"
[11] "2"
[12] "3"
[13] "4"
email
[1] "geo@ncbi.nlm.nih.gov"
feature_count
[1] "22645"
institute
[1] "NCBI NLM NIH"
name
[1] "Gene Expression Omnibus (GEO)"
order
[1] "none"
platform
[1] "GPL97"
platform_organism
[1] "Homo sapiens"
platform_technology_type
[1] "in situ oligonucleotide"
pubmed_id
[1] "14641932"
ref
[1] "Nucleic Acids Res. 2005 Jan 1;33 Database Issue:D562-6"
reference_series
[1] "GSE781"
sample_count
[1] "17"
sample_id
 [1] "GSM11815,GSM11832,GSM12069,GSM12083,GSM12101,GSM12106,GSM12274,GSM12299,GSM12412"
 [2] "GSM11810,GSM11827,GSM12078,GSM12099,GSM12269,GSM12287,GSM12301,GSM12448"
 [3] "GSM11810,GSM11815"
 [4] "GSM11827,GSM11832"
 [5] "GSM12069,GSM12078"
 [6] "GSM12083,GSM12099"
 [7] "GSM12101"
 [8] "GSM12106"
 [9] "GSM12269"
[10] "GSM12274,GSM12287"
[11] "GSM12299,GSM12301"
[12] "GSM12412,GSM12448"
sample_organism
[1] "Homo sapiens"
sample_type
[1] "RNA"
title
[1] "Renal clear cell carcinoma (HG-U133B)"
type
 [1] "Expression profiling by array" "disease state"
 [3] "disease state"                 "individual"
 [5] "individual"                    "individual"
 [7] "individual"                    "individual"
 [9] "individual"                    "individual"
[11] "individual"                    "individual"
[13] "individual"
update_date
[1] "Mar 04 2004"
value_type
[1] "count"
web_link
[1] "http://www.ncbi.nlm.nih.gov/geo"
An object of class "GEODataTable"
****** Column Descriptions ******
     sample disease.state individual
1  GSM11815           RCC        035
2  GSM11832           RCC        023
3  GSM12069           RCC        001
4  GSM12083           RCC        005
5  GSM12101           RCC        011
6  GSM12106           RCC        032
7  GSM12274           RCC          2
8  GSM12299           RCC          3
9  GSM12412           RCC          4
10 GSM11810        normal        035
11 GSM11827        normal        023
12 GSM12078        normal        001
13 GSM12099        normal        005
14 GSM12269        normal          1
15 GSM12287        normal          2
16 GSM12301        normal          3
17 GSM12448        normal          4
                                                                                                                                       description
1             Value for GSM11815: C035 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
2             Value for GSM11832: C023 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
3             Value for GSM12069: C001 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
4             Value for GSM12083: C005 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
5             Value for GSM12101: C011 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
6             Value for GSM12106: C032 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
7               Value for GSM12274: C2 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
8               Value for GSM12299: C3 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
9               Value for GSM12412: C4 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from Renal Clear Cell Carcinoma tissue
10      Value for GSM11810: N035 Normal Human Kidney U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
11      Value for GSM11827: N023 Normal Human Kidney U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
12      Value for GSM12078: N001 Normal Human Kidney U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
13      Value for GSM12099: N005 Normal Human Kidney U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
14        Value for GSM12269: N1 Normal Human Kidney U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
15 Value for GSM12287: N2 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
16 Value for GSM12301: N3 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
17 Value for GSM12448: N4 Renal Clear Cell Carcinoma U133B; src: Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma
****** Data Table ******

GDS objects can be converted to Bioconductor data structures:

# Convert to ExpressionSet (with log2 transformation)
eset <- GDS2eSet(gds, do.log2=TRUE)
eset

ExpressionSet (storageMode: lockedEnvironment)
assayData: 22645 features, 17 samples
  element names: exprs
protocolData: none
phenoData
  sampleNames: GSM11815 GSM11832 ... GSM12448 (17 total)
  varLabels: sample disease.state individual description
  varMetadata: labelDescription
featureData
  featureNames: 200000_s_at 200001_at ... AFFX-TrpnX-M_at (22645 total)
  fvarLabels: ID Gene title ... GO:Component ID (21 total)
  fvarMetadata: Column labelDescription
experimentData: use 'experimentData(object)'
  pubMedIds: 14641932
Annotation:

# Or to a limma MAList
malist <- GDS2MA(gds)
class(malist)

[1] "MAList"
attr(,"package")
[1] "limma"

These conversions are particularly useful for integrating older GEO datasets into modern analytical workflows.

8 Advanced Features

8.1 Getting GSE Data Tables

Some GSE records contain data tables with important metadata not captured in the standard GSE structure:

# Get data tables from GSE3494
dt_list <- getGSEDataTables("GSE3494")

Rows: 251 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (6): X1, X2, X5, X6, X7, X10
dbl (6): X3, X4, X8, X9, X11, X12

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 502 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (3): X1, X2, X3

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

names(dt_list)

NULL

head(dt_list[[1]])

# A tibble: 6 × 12
  `INDEX (ID)` p53 seq mut status (p53+=mutant; p53-=wt…¹ p53 DLDA classifier …²
  <chr>        <chr>                                                       <dbl>
1 X101B88      p53+                                                            1
2 X102B06      p53+                                                            1
3 X104B91      p53+                                                            0
4 X110B34      p53+                                                            1
5 X111B51      p53+                                                            1
6 X127B00      p53+                                                            1
# ℹ abbreviated names: ¹`p53 seq mut status (p53+=mutant; p53-=wt)`,
#   ²`p53 DLDA classifier result (0=wt-like, 1=mt-like)`
# ℹ 9 more variables: `DLDA error (1=yes, 0=no)` <dbl>,
#   `Elston histologic grade` <chr>, `ER status` <chr>, `PgR status` <chr>,
#   `age at diagnosis` <dbl>, `tumor size (mm)` <dbl>,
#   `Lymph node status` <chr>,
#   `DSS TIME (Disease-Specific Survival Time in years)` <dbl>, …

8.2 Working with GPL Platforms

Platform records (GPL) contain important probe annotations:

# Get a platform record
gpl <- getGEO("GPL96")
head(Table(gpl)[, 1:5])

         ID GB_ACC SPOT_ID Species Scientific Name Annotation Date
1 1007_s_at U48705                    Homo sapiens     Oct 6, 2014
2   1053_at M87338                    Homo sapiens     Oct 6, 2014
3    117_at X51757                    Homo sapiens     Oct 6, 2014
4    121_at X69699                    Homo sapiens     Oct 6, 2014
5 1255_g_at L36861                    Homo sapiens     Oct 6, 2014
6   1294_at L13852                    Homo sapiens     Oct 6, 2014

When retrieving GSE records, GEOquery can automatically include GPL annotation:

# Get GSE with GPL annotation
gse_with_gpl <- getGEO("GSE2553", AnnotGPL=TRUE)
head(fData(gse_with_gpl[[1]]))

9 Reporting Bugs and Contributing

As GEO continues to evolve, GEOquery adapts to support new features and data types. If you encounter issues:

Check the Bioconductor Support site
Report bugs on GitHub
Consider contributing via pull requests

10 Citing GEOquery

If you use GEOquery in your research, please cite:

citation("GEOquery")

Please cite the following if utilizing the GEOquery software:

  Davis S, Meltzer P (2007). "GEOquery: a bridge between the Gene
  Expression Omnibus (GEO) and BioConductor." _Bioinformatics_, *14*,
  1846-1847. doi:10.1093/bioinformatics/btm254
  <https://doi.org/10.1093/bioinformatics/btm254>.

A BibTeX entry for LaTeX users is

  @Article{,
    author = {Sean Davis and Paul Meltzer},
    title = {GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor},
    journal = {Bioinformatics},
    year = {2007},
    volume = {14},
    pages = {1846--1847},
    doi = {10.1093/bioinformatics/btm254},
  }

11 Session Information

sessionInfo()

R version 4.5.1 (2025-06-13)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C

time zone: UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] knitr_1.50          GEOquery_2.77.2     Biobase_2.68.0
[4] BiocGenerics_0.54.0 generics_0.1.4

loaded via a namespace (and not attached):
 [1] SummarizedExperiment_1.38.1 xfun_0.52
 [3] httr2_1.2.1                 lattice_0.22-7
 [5] tzdb_0.5.0                  vctrs_0.6.5
 [7] tools_4.5.1                 parallel_4.5.1
 [9] stats4_4.5.1                curl_6.4.0
[11] tibble_3.3.0                pkgconfig_2.0.3
[13] R.oo_1.27.1                 Matrix_1.7-3
[15] data.table_1.17.8           rentrez_1.2.4
[17] S4Vectors_0.46.0            lifecycle_1.0.4
[19] GenomeInfoDbData_1.2.14     compiler_4.5.1
[21] stringr_1.5.1               statmod_1.5.0
[23] GenomeInfoDb_1.44.1         htmltools_0.5.8.1
[25] yaml_2.3.10                 pillar_1.11.0
[27] crayon_1.5.3                tidyr_1.3.1
[29] R.utils_2.13.0              DelayedArray_0.34.1
[31] limma_3.64.3                abind_1.4-8
[33] tidyselect_1.2.1            rvest_1.0.4
[35] digest_0.6.37               stringi_1.8.7
[37] dplyr_1.1.4                 purrr_1.1.0
[39] fastmap_1.2.0               grid_4.5.1
[41] cli_3.6.5                   SparseArray_1.8.1
[43] magrittr_2.0.3              S4Arrays_1.8.1
[45] utf8_1.2.6                  XML_3.99-0.18
[47] withr_3.0.2                 readr_2.1.5
[49] UCSC.utils_1.4.0            rappdirs_0.3.3
[51] bit64_4.6.0-1               rmarkdown_2.29
[53] XVector_0.48.0              httr_1.4.7
[55] matrixStats_1.5.0           bit_4.6.0
[57] R.methodsS3_1.8.2           hms_1.1.3
[59] evaluate_1.0.4              GenomicRanges_1.60.0
[61] IRanges_2.42.0              rlang_1.1.6
[63] glue_1.8.0                  selectr_0.4-2
[65] xml2_1.3.8                  vroom_1.6.5
[67] jsonlite_2.0.0              R6_2.6.1
[69] MatrixGenerics_1.20.0