The NCBI Gene Expression Omnibus (GEO) serves as a public repository for a wide range of high-throughput experimental data. These data include single and dual channel microarray-based experiments measuring mRNA, genomic DNA, and protein abundance, as well as non-array techniques such as serial analysis of gene expression (SAGE), mass spectrometry proteomic data, and high-throughput sequencing data.
At the most basic level of organization of GEO, there are four basic entity types. The first three (Sample, Platform, and Series) are supplied by users; the fourth, the dataset, is compiled and curated by GEO staff from the user-submitted data. See the GEO home page for more information.
A Platform record describes the list of elements on the array (e.g., cDNAs, oligonucleotide probesets, ORFs, antibodies) or the list of elements that may be detected and quantified in that experiment (e.g., SAGE tags, peptides). Each Platform record is assigned a unique and stable GEO accession number (GPLxxx). A Platform may reference many Samples that have been submitted by multiple submitters.
A Sample record describes the conditions under which an individual Sample was handled, the manipulations it underwent, and the abundance measurement of each element derived from it. Each Sample record is assigned a unique and stable GEO accession number (GSMxxx). A Sample entity must reference only one Platform and may be included in multiple Series.
A Series record defines a set of related Samples considered to be part of a group, how the Samples are related, and if and how they are ordered. A Series provides a focal point and description of the experiment as a whole. Series records may also contain tables describing extracted data, summary conclusions, or analyses. Each Series record is assigned a unique and stable GEO accession number (GSExxx). Series records are available in a couple of formats which are handled by GEOquery independently. The smaller and new GSEMatrix files are quite fast to parse; a simple flag is used by GEOquery to choose to use GSEMatrix files (see below).
GEO DataSets (GDSxxx) are curated sets of GEO Sample data. A GDS record represents a collection of biologically and statistically comparable GEO Samples and forms the basis of GEO’s suite of data display and analysis tools. Samples within a GDS refer to the same Platform, that is, they share a common set of probe elements. Value measurements for each Sample within a GDS are assumed to be calculated in an equivalent manner, that is, considerations such as background processing and normalization are consistent across the dataset. Information reflecting experimental design is provided through GDS subsets.
Getting data from GEO is really quite easy. There is only one command
that is needed, getGEO. This one function interprets its
input to determine how to get the data from GEO and then parse the data
into useful R data structures. Usage is quite simple. This loads the
GEOquery library.
Now, we are free to access any GEO accession. Note that in the following, I use a file packaged with the GEOquery package. In general, you will use only the GEO accession, as noted in the code comments.
# If you have network access, the more typical way to do this
# would be to use this:
# gds <- getGEO("GDS507")
gds <- getGEO(filename=system.file("extdata/GDS507.soft.gz",package="GEOquery"))Now, gds contains the R data structure (of class
GDS) that represents the GDS507 entry from GEO. You’ll note
that the filename used to store the download was output to the screen
(but not saved anywhere) for later use to a call to
getGEO(filename=...).
We can do the same with any other GEO accession, such as
GSM11805, a GEO sample.
The GEOquery data structures really come in two forms. The first,
comprising GDS, GPL, and GSM all
behave similarly and accessors have similar effects on each. The fourth
GEOquery data structure, GSE is a composite data type made
up of a combination of GSM and GPL objects. I
will explain the first three together first.
Each of these classes is comprised of a metadata header (taken nearly
verbatim from the SOFT format header) and a GEODataTable. The
GEODataTable has two simple parts, a Columns part which describes the
column headers on the Table part. There is also a show
method for each class. For example, using the gsm from above:
## $channel_count
## [1] "1"
##
## $comment
## [1] "Raw data provided as supplementary file"
##
## $contact_address
## [1] "715 Albany Street, E613B"
##
## $contact_city
## [1] "Boston"
##
## $contact_country
## [1] "USA"
##
## $contact_department
## [1] "Genetics and Genomics"# Look at data associated with the GSM:
# but restrict to only first 5 rows, for brevity
Table(gsm)[1:5,]## ID_REF VALUE ABS_CALL
## 1 AFFX-BioB-5_at 953.9 P
## 2 AFFX-BioB-M_at 2982.8 P
## 3 AFFX-BioB-3_at 1657.9 P
## 4 AFFX-BioC-5_at 2652.7 P
## 5 AFFX-BioC-3_at 2019.5 P## Column
## 1
## 2 VALUE
## 3 ABS_CALL
## Description
## 1 ID_REF =
## 2 MAS 5.0 Statistical Algorithm (mean scaled to 500)
## 3 MAS 5.0 Absent, Marginal, Present call with Alpha1 = 0.05, Alpha2 = 0.065The GPL class behaves exactly as the GSM
class. However, the GDS class has a bit more information
associated with the Columns method:
## 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 4The GSE entity is the most confusing of the GEO
entities. A GSE entry can represent an arbitrary number of samples run
on an arbitrary number of platforms. The GSE class has a
metadata section, just like the other classes. However, it doesn’t have
a GEODataTable. Instead, it contains two lists, accessible using the
GPLList and GSMList methods, that are each
lists of GPL and GSM objects. To show an
example:
# Again, with good network access, one would do:
# gse <- getGEO("GSE781",GSEMatrix=FALSE)
gse <- getGEO(filename=system.file("extdata/GSE781_family.soft.gz",package="GEOquery"))
head(Meta(gse))## $contact_address
## [1] "715 Albany Street, E613B"
##
## $contact_city
## [1] "Boston"
##
## $contact_country
## [1] "USA"
##
## $contact_department
## [1] "Genetics and Genomics"
##
## $contact_email
## [1] "[email protected]"
##
## $contact_fax
## [1] "617-414-1646"## [1] "GSM11805" "GSM11810" "GSM11814" "GSM11815" "GSM11823" "GSM11827"
## [7] "GSM11830" "GSM11832" "GSM12067" "GSM12069" "GSM12075" "GSM12078"
## [13] "GSM12079" "GSM12083" "GSM12098" "GSM12099" "GSM12100" "GSM12101"
## [19] "GSM12105" "GSM12106" "GSM12268" "GSM12269" "GSM12270" "GSM12274"
## [25] "GSM12283" "GSM12287" "GSM12298" "GSM12299" "GSM12300" "GSM12301"
## [31] "GSM12399" "GSM12412" "GSM12444" "GSM12448"## An object of class "GSM"
## channel_count
## [1] "1"
## comment
## [1] "Raw data provided as supplementary file"
## contact_address
## [1] "715 Albany Street, E613B"
## contact_city
## [1] "Boston"
## contact_country
## [1] "USA"
## contact_department
## [1] "Genetics and Genomics"
## contact_email
## [1] "[email protected]"
## contact_fax
## [1] "617-414-1646"
## contact_institute
## [1] "Boston University School of Medicine"
## contact_name
## [1] "Marc,E.,Lenburg"
## contact_phone
## [1] "617-414-1375"
## contact_state
## [1] "MA"
## contact_web_link
## [1] "http://gg.bu.edu"
## contact_zip/postal_code
## [1] "02130"
## data_row_count
## [1] "22283"
## description
## [1] "Age = 70; Gender = Female; Right Kidney; Adjacent Tumor Type = clear cell; Adjacent Tumor Fuhrman Grade = 3; Adjacent Tumor Capsule Penetration = true; Adjacent Tumor Perinephric Fat Invasion = true; Adjacent Tumor Renal Sinus Invasion = false; Adjacent Tumor Renal Vein Invasion = true; Scaling Target = 500; Scaling Factor = 7.09; Raw Q = 2.39; Noise = 2.60; Background = 55.24."
## [2] "Keywords = kidney"
## [3] "Keywords = renal"
## [4] "Keywords = RCC"
## [5] "Keywords = carcinoma"
## [6] "Keywords = cancer"
## [7] "Lot batch = 2004638"
## geo_accession
## [1] "GSM11805"
## last_update_date
## [1] "May 28 2005"
## molecule_ch1
## [1] "total RNA"
## organism_ch1
## [1] "Homo sapiens"
## platform_id
## [1] "GPL96"
## series_id
## [1] "GSE781"
## source_name_ch1
## [1] "Trizol isolation of total RNA from normal tissue adjacent to Renal Cell Carcinoma"
## status
## [1] "Public on Nov 25 2003"
## submission_date
## [1] "Oct 20 2003"
## supplementary_file
## [1] "ftp://ftp.ncbi.nih.gov/pub/geo/DATA/supplementary/samples/GSM11nnn/GSM11805/GSM11805.CEL.gz"
## title
## [1] "N035 Normal Human Kidney U133A"
## type
## [1] "RNA"
## An object of class "GEODataTable"
## ****** Column Descriptions ******
## Column
## 1
## 2 VALUE
## 3 ABS_CALL
## Description
## 1 ID_REF =
## 2 MAS 5.0 Statistical Algorithm (mean scaled to 500)
## 3 MAS 5.0 Absent, Marginal, Present call with Alpha1 = 0.05, Alpha2 = 0.065
## ****** Data Table ******## [1] "GPL96" "GPL97"See below for an additional, preferred method of obtaining GSE information.
GEO datasets are (unlike some of the other GEO entities), quite
similar to the limma data structure MAList and
to the Biobase data structure ExpressionSet.
Therefore, there are two functions, GDS2MA and
GDS2eSet that accomplish that task.
GEO Series are collections of related experiments. In addition to
being available as SOFT format files, which are quite large, NCBI GEO
has prepared a simpler format file based on tab-delimited text. The
getGEO function can handle this format and will parse very
large GSEs quite quickly. The data structure returned from this parsing
is a list of ExpressionSets. As an example, we download and parse
GSE2553.
## $GSE2553_series_matrix.txt.gz
## 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## title type
## GSM48681 Patient sample ST18, Dermatofibrosarcoma RNA
## GSM48682 Patient sample ST410, Ewing Sarcoma RNA
## GSM48683 Patient sample ST130, Sarcoma, NOS RNA
## GSM48684 Patient sample ST293, Malignant Peripheral Nerve Sheath Tumor RNA
## GSM48685 Patient sample ST367, Liposarcoma RNA
## source_name_ch1
## GSM48681 Dermatofibrosarcoma
## GSM48682 Ewing Sarcoma
## GSM48683 Sarcoma, NOS
## GSM48684 Malignant Peripheral Nerve Sheath Tumor
## GSM48685 LiposarcomaTaking our gds object from above, we can simply do:
Now, eset is an ExpressionSet that contains
the same information as in the GEO dataset, including the sample
information, which we can see here:
## 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:## sample disease.state individual
## GSM11815 GSM11815 RCC 035
## GSM11832 GSM11832 RCC 023
## GSM12069 GSM12069 RCC 001
## GSM12083 GSM12083 RCC 005
## GSM12101 GSM12101 RCC 011
## GSM12106 GSM12106 RCC 032
## GSM12274 GSM12274 RCC 2
## GSM12299 GSM12299 RCC 3
## GSM12412 GSM12412 RCC 4
## GSM11810 GSM11810 normal 035
## GSM11827 GSM11827 normal 023
## GSM12078 GSM12078 normal 001
## GSM12099 GSM12099 normal 005
## GSM12269 GSM12269 normal 1
## GSM12287 GSM12287 normal 2
## GSM12301 GSM12301 normal 3
## GSM12448 GSM12448 normal 4No annotation information (called platform information by GEO) was
retrieved from because ExpressionSet does not contain slots
for gene information, typically. However, it is easy to obtain this
information. First, we need to know what platform this GDS used. Then,
another call to getGEO will get us what we need.
## [1] "GPL97"#So use this information in a call to getGEO
gpl <- getGEO(filename=system.file("extdata/GPL97.annot.gz",package="GEOquery"))So, gpl now contains the information for GPL5 from GEO.
Unlike ExpressionSet, the limma MAList does
store gene annotation information, so we can use our newly created
gpl of class GPL in a call to
GDS2MA like so:
## [1] "MAList"
## attr(,"package")
## [1] "limma"Now, MA is of class MAList and contains not
only the data, but the sample information and gene information
associated with GDS507.
First, make sure that using the method described above in the section ``Getting GSE Series Matrix files as an ExpressionSet’’ for using GSE Series Matrix files is not sufficient for the task, as it is much faster and simpler. If it is not (i.e., other columns from each GSM are needed), then this method will be needed.
Converting a GSE object to an ExpressionSet
object currently takes a bit of R data manipulation due to the varied
data that can be stored in a GSE and the underlying
GSM and GPL objects. However, using a simple
example will hopefully be illustrative of the technique.
First, we need to make sure that all of the GSMs are
from the same platform:
## $GSM11805
## [1] "GPL96"
##
## $GSM11810
## [1] "GPL97"
##
## $GSM11814
## [1] "GPL96"
##
## $GSM11815
## [1] "GPL97"
##
## $GSM11823
## [1] "GPL96"
##
## $GSM11827
## [1] "GPL97"Indeed, there are two GPLs, GPL96 and GPL97, as their platforms
(which we could have determined by looking at the GPLList for
gse). We can filter the original GSMList to include only
those GSMs with the GPL96 platform and use this list for further
processing
## [1] 17So, now we would like to know what column represents the data that we
would like to extract. Looking at the first few rows of the Table of a
single GSM will likely give us an idea (and by the way, GEO uses a
convention that the column that contains the single measurement for each
array is called the VALUE column, which we could use if we
don’t know what other column is most relevant).
## ID_REF VALUE ABS_CALL
## 1 AFFX-BioB-5_at 953.9 P
## 2 AFFX-BioB-M_at 2982.8 P
## 3 AFFX-BioB-3_at 1657.9 P
## 4 AFFX-BioC-5_at 2652.7 P
## 5 AFFX-BioC-3_at 2019.5 P## Column
## 1
## 2 VALUE
## 3 ABS_CALL
## NA <NA>
## NA.1 <NA>
## Description
## 1 ID_REF =
## 2 MAS 5.0 Statistical Algorithm (mean scaled to 500)
## 3 MAS 5.0 Absent, Marginal, Present call with Alpha1 = 0.05, Alpha2 = 0.065
## NA <NA>
## NA.1 <NA>We will indeed use the VALUE column. We then want to
make a matrix of these values like so:
# get the probeset ordering
probesets <- Table(GPLList(gse)[[1]])$ID
# make the data matrix from the VALUE columns from each GSM
# being careful to match the order of the probesets in the platform
# with those in the GSMs
data.matrix <- do.call('cbind',lapply(gsmlist,function(x)
{tab <- Table(x)
mymatch <- match(probesets,tab$ID_REF)
return(tab$VALUE[mymatch])
}))
data.matrix <- apply(data.matrix,2,function(x) {as.numeric(as.character(x))})
data.matrix <- log2(data.matrix)
data.matrix[1:5,]## GSM11805 GSM11814 GSM11823 GSM11830 GSM12067 GSM12075 GSM12079
## [1,] 10.926963 11.105254 11.275019 11.438636 11.424376 11.222795 11.469845
## [2,] 5.749534 7.908092 7.093814 7.514122 7.901470 6.407693 5.165912
## [3,] 7.066089 7.750205 7.244126 7.962896 7.337176 6.569856 7.477354
## [4,] 12.660353 12.479755 12.215897 11.458355 11.397568 12.529870 12.240046
## [5,] 6.195741 6.061776 6.565293 6.583459 6.877744 6.652486 3.981853
## GSM12098 GSM12100 GSM12105 GSM12268 GSM12270 GSM12283 GSM12298
## [1,] 10.823367 10.835971 10.810893 11.062653 10.323055 11.181028 11.566387
## [2,] 6.556123 8.207014 6.816344 6.563768 7.353147 5.770829 6.912889
## [3,] 7.708739 7.428779 7.754888 7.126188 8.742815 7.339850 7.602142
## [4,] 12.336534 11.762839 11.237509 12.412490 11.213408 12.678380 12.232901
## [5,] 5.501439 6.247928 6.017922 6.525129 6.683696 5.918863 5.837943
## GSM12300 GSM12399 GSM12444
## [1,] 11.078151 11.535178 11.105450
## [2,] 4.812498 7.471675 7.488644
## [3,] 7.383704 7.432959 7.381110
## [4,] 12.090939 11.421802 12.172834
## [5,] 6.281698 5.419539 5.469235Note that we do a match to make sure that the values and
the platform information are in the same order. Finally, to make the
ExpressionSet object:
require(Biobase)
# go through the necessary steps to make a compliant ExpressionSet
rownames(data.matrix) <- probesets
colnames(data.matrix) <- names(gsmlist)
pdata <- data.frame(samples=names(gsmlist))
rownames(pdata) <- names(gsmlist)
pheno <- as(pdata,"AnnotatedDataFrame")
eset2 <- new('ExpressionSet',exprs=data.matrix,phenoData=pheno)
eset2## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 22283 features, 17 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM11805 GSM11814 ... GSM12444 (17 total)
## varLabels: samples
## varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation:So, using a combination of lapply on the GSMList, one
can extract as many columns of interest as necessary to build the data
structure of choice. Because the GSM data from the GEO website are fully
downloaded and included in the GSE object, one can extract
foreground and background as well as quality for two-channel arrays, for
example. Getting array annotation is also a bit more complicated, but by
replacing ``platform’’ in the lapply call to get platform information
for each array, one can get other information associated with each
array.
NCBI GEO accepts (but has not always required) raw data such as .CEL
files, .CDF files, images, etc. Sometimes, it is useful to get quick
access to such data. A single function, getGEOSuppFiles,
can take as an argument a GEO accession and will download all the raw
data associate with that accession. By default, the function will create
a directory in the current working directory to store the raw data for
the chosen GEO accession. Combining a simple sapply
statement or other loop structure with getGEOSuppFiles
makes for a very simple way to get gobs of raw data quickly and easily
without needing to know the specifics of GEO raw data URLs.
GEOquery can be quite powerful for gathering a lot of data quickly. A few examples can be useful to show how this might be done for data mining purposes.
For data mining purposes, it is sometimes useful to be able to pull all the GSE records for a given platform. GEOquery makes this very easy, but a little bit of knowledge of the GPL record is necessary to get started. The GPL record contains both the GSE and GSM accessions that reference it. Some code is useful to illustrate the point:
## [1] "[HG-U133B] Affymetrix Human Genome U133B Array"## [1] "GSE362" "GSE473" "GSE620" "GSE674" "GSE781" "GSE907"## [1] 165## [1] "GSM3922" "GSM3924" "GSM3926" "GSM3928" "GSM3930" "GSM3932"## [1] 7917The code above loads the GPL97 record into R. The Meta method
extracts a list of header information from the GPL record. The
title gives the human name of the platform. The
series_id gives a vector of series ids. Note that there are
165 series associated with this platform and 7917 samples. Code like the
following could be used to download all the samples or series. I show
only the first 5 samples as an example:
## [1] "GSM3922" "GSM3924" "GSM3926" "GSM3928" "GSM3930"The GEOquery package provides a bridge to the vast array resources contained in the NCBI GEO repositories. By maintaining the full richness of the GEO data rather than focusing on getting only the ``numbers’’, it is possible to integrate GEO data into current Bioconductor data structures and to perform analyses on that data quite quickly and easily. These tools will hopefully open GEO data more fully to the array community at large.
Please consider citing GEOquery if used in support of your own research:
## 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},
## }If you run into problems using GEOquery, the Bioconductor Support site is a good first place to ask for help. If you are convinced that there is a bug in GEOquery (this is pretty unusual, but not unheard of), feel free to submit an issue on the GEOquery github site or file a bug report directly from R (will open a new github issue):
The following package and versions were used in the production of this vignette.
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] GEOquery_2.75.0 Biobase_2.67.0 BiocGenerics_0.53.0
## [4] knitr_1.48
##
## loaded via a namespace (and not attached):
## [1] SummarizedExperiment_1.35.5 httr2_1.0.5
## [3] xfun_0.48 bslib_0.8.0
## [5] lattice_0.22-6 tzdb_0.4.0
## [7] vctrs_0.6.5 tools_4.4.1
## [9] generics_0.1.3 curl_5.2.3
## [11] stats4_4.4.1 tibble_3.2.1
## [13] fansi_1.0.6 pkgconfig_2.0.3
## [15] R.oo_1.26.0 Matrix_1.7-1
## [17] data.table_1.16.2 rentrez_1.2.3
## [19] S4Vectors_0.43.2 lifecycle_1.0.4
## [21] GenomeInfoDbData_1.2.13 compiler_4.4.1
## [23] statmod_1.5.0 GenomeInfoDb_1.41.2
## [25] htmltools_0.5.8.1 sys_3.4.3
## [27] buildtools_1.0.0 sass_0.4.9
## [29] yaml_2.3.10 pillar_1.9.0
## [31] crayon_1.5.3 jquerylib_0.1.4
## [33] tidyr_1.3.1 R.utils_2.12.3
## [35] DelayedArray_0.31.14 cachem_1.1.0
## [37] limma_3.61.12 abind_1.4-8
## [39] tidyselect_1.2.1 digest_0.6.37
## [41] dplyr_1.1.4 purrr_1.0.2
## [43] maketools_1.3.1 fastmap_1.2.0
## [45] grid_4.4.1 cli_3.6.3
## [47] SparseArray_1.5.45 magrittr_2.0.3
## [49] S4Arrays_1.5.11 XML_3.99-0.17
## [51] utf8_1.2.4 readr_2.1.5
## [53] rappdirs_0.3.3 UCSC.utils_1.1.0
## [55] rmarkdown_2.28 XVector_0.45.0
## [57] httr_1.4.7 matrixStats_1.4.1
## [59] R.methodsS3_1.8.2 hms_1.1.3
## [61] evaluate_1.0.1 GenomicRanges_1.57.2
## [63] IRanges_2.39.2 rlang_1.1.4
## [65] glue_1.8.0 xml2_1.3.6
## [67] jsonlite_1.8.9 R6_2.5.1
## [69] MatrixGenerics_1.17.1 zlibbioc_1.51.2