The GSEABase package implements data structures and methods to represent, manipulate, and analyze gene sets in the context of gene set enrichment analysis. This includes construction of gene sets from reference resources, ID mapping, coloring according to phenotype association, and storing in gene set collections.
GSEABase 1.61.0
Report issues on https://github.com/Bioconductor/GSEABase/issues
## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationDbi'A GeneSet stores a set of related gene identifiers. Important components of
the gene set are a vector of identifiers, general descriptive information about
the set, and information about how the gene set was constructed. To construct a
gene set, use GeneSet . For example, to create a gene set from the
identifiers in a subset of the sample ExpressionSet in the
Biobase package use
data(sample.ExpressionSet) # from Biobase
egs <- GeneSet(sample.ExpressionSet[201:250,], setName="Sample")
egs## setName: Sample
## geneIds: 31440_at, 31441_at, ..., 31489_at (total: 50)
## geneIdType: Annotation (hgu95av2)
## collectionType: ExpressionSet
## details: use 'details(object)'Each gene set may have a name. The gene set contains 50
identifiers (‘genes’) from the ExpressionSet. These are accessible using
geneIds, e.g.,
head(geneIds(egs))## [1] "31440_at" "31441_at" "31442_at" "31443_at" "31444_s_at"
## [6] "31445_at"The gene set records that the identifiers are probeset names from the
annotation package hgu95av2.db, and that the source of the gene
set was an ExpressionSet. Additional details are available:
details(egs)## setName: Sample
## geneIds: 31440_at, 31441_at, ..., 31489_at (total: 50)
## geneIdType: Annotation (hgu95av2)
## collectionType: ExpressionSet
## setIdentifier: nebbiolo1:1189125:Tue Apr 25 17:19:08 2023:1
## description: Smoking-Cancer Experiment
## (longDescription available)
## organism: Homo sapiens
## pubMedIds:
## urls: www.lab.not.exist
## contributor: Pierre Fermat
## setVersion: 0.0.1
## creationDate:The set identifier, set version, and creation date
provide mechanisms for carefully curating gene sets. Additional
information is automatically copied from the expression set used to
create egs.
## FIXME: GeneSet(AnnotationIdentifier("hgu95av2")) --> non-empty
## FIXME: GeneSet(AnnotationIdentifier("hgu95av2"),
## collectionType=GOCollection()) filters on GOCollection (or KEGG)View additional methods for creating gene sets with:
showMethods("GeneSet", inherited=FALSE)## Function: GeneSet (package GSEABase)
## type="BroadCollection"
## type="ExpressionSet"
## type="GOCollection"
## type="GeneIdentifierType"
## type="character"
## type="missing"These are described on the GeneSet help page.
The identifier type of gene sets created from expression sets is AnnotationIdentifier. Additional predefined identifiers are available:
names(slot(getClass("GeneIdentifierType"), "subclasses"))## [1] "NullIdentifier" "EnzymeIdentifier" "GenenameIdentifier"
## [4] "RefseqIdentifier" "SymbolIdentifier" "UniprotIdentifier"
## [7] "ENSEMBLIdentifier" "AnnotationIdentifier" "EntrezIdentifier"
## [10] "GOAllFrameIdentifier" "KEGGFrameIdentifier"It is possible to map between identifier types (provided the corresponding map in the annotation package exists):
mapIdentifiers(egs, EntrezIdentifier())## setName: Sample
## geneIds: 6932, 643332, ..., 4287 (total: 31)
## geneIdType: EntrezId (hgu95av2)
## collectionType: ExpressionSet
## details: use 'details(object)'mapIdentifiers consults the gene set to determine that annotation (probeset)
identifiers are to be converted to Entrez IDs based on the mapping in
the hgu95av2.db package.
The function mapIdentifiers can automatically determine many of the common
maps; it is also possible to provide as a third argument an environment
containing an arbitrary map. Use the verbose argument of mapIdentifiers to be informed when the
map between identifier types is not 1:1.
A gene set can be created with different types of identifier, e.g., to create a gene set with Entrez IDs, use
library(annotate) # getEG
eids <- unique(getEG(geneIds(egs), "hgu95av2"))
eids <- eids[!is.na(eids)]
GeneSet(EntrezIdentifier(), geneIds=as.character(eids))## setName: NA
## geneIds: 6932, 643332, ..., 4287 (total: 31)
## geneIdType: EntrezId
## collectionType: Null
## details: use 'details(object)'The collectionType of a gene set provides additional information about a gene set.
Available collection types are
names(slot(getClass("CollectionType"), "subclasses"))## [1] "NullCollection" "ExpressionSetCollection"
## [3] "ComputedCollection" "CollectionIdType"
## [5] "BroadCollection" "KEGGCollection"
## [7] "OMIMCollection" "PMIDCollection"
## [9] "ChrCollection" "ChrlocCollection"
## [11] "MapCollection" "PfamCollection"
## [13] "PrositeCollection" "GOCollection"
## [15] "OBOCollection"Collection types are most important when creating gene sets. For instance, the
GOCollection class allows creation of gene sets based on gene ontology (GO) terms.
The following command creates a gene set from two terms, including all
genes with a particular evidence code.
GeneSet(GOCollection(c("GO:0005488", "GO:0019825"),
evidenceCode="IDA"),
geneIdType=EntrezIdentifier("org.Hs.eg.db"),
setName="Sample GO Collection")## setName: Sample GO Collection
## geneIds: (total: 0)
## geneIdType: EntrezId (org.Hs.eg.db)
## collectionType: GO
## ids: GO:0005488, GO:0019825 (2 total)
## evidenceCode: IDA
## ontology: CC MF BP
## details: use 'details(object)'This creates a gene set by
consulting an object in the GO.db package.
A gene set created from an expression set, and with collection type GOCollection
consults the appropriate environment in the annotation package associated
with the expression set.
Gene sets encoded in XML following the schema and conventions of the Broad Institute can be read into R as follows:
fl <- system.file("extdata", "Broad1.xml", package="GSEABase")
bgs <- GeneSet(BroadCollection(), urls=fl)
bgs## setName: chr16q24
## geneIds: GALNS, C16ORF44, ..., TRAPPC2L (total: 129)
## geneIdType: Symbol
## collectionType: Broad
## bcCategory: c1 (Positional)
## bcSubCategory: NA
## details: use 'details(object)'The example above uses a local file, but the source for the gene set
might also be a web address accessible via http:// the protocol. The file name
is added to the url of the gene set. The set name and category of the
Broad collection indicate that the gene set contains gene symbols from
band 24 of the q arm of chromosome 16. The probe sets in chip hgu95av2
corresponding to these symbols can be determined by mapping identifiers
bgs1 <- mapIdentifiers(bgs, AnnotationIdentifier("hgu95av2"))
bgs1## setName: chr16q24
## geneIds: 32100_r_at, 32101_at, ..., 35807_at (total: 37)
## geneIdType: Annotation (hgu95av2)
## collectionType: Broad
## bcCategory: c1 (Positional)
## bcSubCategory: NA
## details: use 'details(object)'Subsetting creates sets with just the symbols identified. Subsetting can use indices or symbol names.
bgs[1:5]## setName: chr16q24
## geneIds: GALNS, C16ORF44, ..., LOC646365 (total: 5)
## geneIdType: Symbol
## collectionType: Broad
## bcCategory: c1 (Positional)
## bcSubCategory: NA
## details: use 'details(object)'bgs[c("GALNS", "LOC646365")]## setName: chr16q24
## geneIds: GALNS, LOC646365 (total: 2)
## geneIdType: Symbol
## collectionType: Broad
## bcCategory: c1 (Positional)
## bcSubCategory: NA
## details: use 'details(object)'Logical operations provide a convenient way to identify genes with particular properties. For instance, the intersection
egs & bgs1## setName: (Sample & chr16q24)
## geneIds: (total: 0)
## geneIdType: Annotation (hgu95av2)
## collectionType: Computed
## details: use 'details(object)'is empty (note that the identifiers in the two sets were of
the same type), indicating that none of the identifiers in egs are on 16q24.
Additional operations on sets include union (performed with |) and
difference (setdiff).
Methods exist to directly subset expression sets using gene sets
sample.ExpressionSet[bgs,]## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 2 features, 26 samples
## element names: exprs, se.exprs
## protocolData: none
## phenoData
## sampleNames: A B ... Z (26 total)
## varLabels: sex type score
## varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: hgu95av2Remember that sample.ExpressionSet contains just 500 probe sets, so the small size of the subset is not surprising. Note also that subsetting required mapping of symbol identifiers in bgs to AnnotationIdentifiers; this map used the annotation information in the expression set, and is not necessarily 1:1.
A GeneColorSet is a gene set with “coloring” to indicate how features of genes and
phenotypes are associated. The following sample data describes how
changes in expression levels of several genes (with Entrez and Symbol
names) influence cisplatin resistance phenotype.
tbl## Entrez.ID Gene.Symbol Expression.level Phenotype.response
## 1 1244 ABCC2 Increase Resistant
## 2 538 ATP7A Increase Resistant
## 3 540 ATP7B Increase Resistant
## 4 9961 MVP Increase Resistant
## 5 7507 XPA Increase Resistant
## 6 2067 ERCC1 Increase Resistant
## 7 672 BRCA1 Increase Resistant
## 8 3725 JUN Increase Resistant
## 9 2730 GCLM Increase ResistantNote that three different aspects of data influence coloring: the phenotype under consideration (cisplatin resistance), whether expression responses refer to increasing or decreasing levels of gene expression, and whether the phenotypic response represents greater resistance or sensitivity to cisplatin. Here is the resulting gene color set:
gcs <- GeneColorSet(EntrezIdentifier(),
setName="A color set",
geneIds=as.character(tbl$Entrez.ID),
phenotype="Cisplatin resistance",
geneColor=tbl$Expression.level,
phenotypeColor=tbl$Phenotype.response)
gcs## setName: A color set
## geneIds: 1244, 538, ..., 2730 (total: 9)
## geneIdType: EntrezId
## collectionType: Null
## phenotype: Cisplatin resistance
## geneColor: Increase, Increase, ..., Increase
## levels: Increase
## phenotypeColor: Resistant, Resistant, ..., Resistant
## levels: Resistant
## details: use 'details(object)'Gene color sets can be used in the same way as gene sets, e.g., for subsetting
expression sets (provided the map between identifiers is 1:1, so that coloring
corresponding to identifiers can be determined). The coloring method allows
access to the coloring information with a data frame interface; phenotype,
geneColor and phenotypeColor provide additional accessors.
A GeneSetCollection is a collection of gene sets. Sets in the collection must
have distinct setNames, but can be a mix of GeneSet and GeneColorSet. Two
convenient ways to create a gene set collection are by specifying a source of
identifiers (e.g., an ExpressionSet or AnnotationIdentifier) and how the
identifiers are to be induced into sets (e.g., by consulting the GO or KEGG
ontologies):
gsc <- GeneSetCollection(sample.ExpressionSet[201:250,], setType=GOCollection())
gsc## GeneSetCollection
## names: GO:0000122, GO:0000209, ..., GO:1990837 (352 total)
## unique identifiers: 31471_at, 31480_f_at, ..., 31477_at (31 total)
## types in collection:
## geneIdType: AnnotationIdentifier (1 total)
## collectionType: GOCollection (1 total)gsc[["GO:0005737"]]## setName: GO:0005737
## geneIds: 31450_s_at, 31451_at, ..., 31489_at (total: 10)
## geneIdType: Annotation (hgu95av2)
## collectionType: GO
## ids: GO:0005737 (1 total)
## evidenceCode: EXP IDA IPI IMP IGI IEP HTP HDA HMP HGI HEP ISS ISO ISA ISM IGC IBA IBD IKR IRD RCA TAS NAS IC ND IEA
## ontology: CC MF BP
## details: use 'details(object)'In this example, the annotation identifiers of the sample expression set are
organized into gene sets based on their presence in GO pathways. Providing
arguments such as evidenceCode to GOCollection act to select just those
pathways satisfying the GO collection constraint:
GeneSetCollection(sample.ExpressionSet[201:300,],
setType=GOCollection(evidenceCode="IMP"))## GeneSetCollection
## names: GO:0000122, GO:0000226, ..., GO:1902282 (120 total)
## unique identifiers: 31520_at, 31489_at, ..., 31487_at (26 total)
## types in collection:
## geneIdType: AnnotationIdentifier (1 total)
## collectionType: GOCollection (1 total)Sets in the collection are named after the GO terms, and can be accessed by numeric index or name.
A file or url containing several gene sets defined by Broad XML can be used to create a gene set collection, e.g.,
## FIXME: BroadCollection default to paste("c", 1:4, sep="")
## FIXME: GeneSetCollection(BroadCollection(), urls=fl); filters on bcCategory
fl <- system.file("extdata", "Broad.xml", package="GSEABase")
gss <- getBroadSets(fl)
gss## GeneSetCollection
## names: chr5q23, chr16q24 (2 total)
## unique identifiers: ZNF474, CCDC100, ..., TRAPPC2L (215 total)
## types in collection:
## geneIdType: SymbolIdentifier (1 total)
## collectionType: BroadCollection (1 total)names(gss)## [1] "chr5q23" "chr16q24"Identifiers within a gene set collection can be mapped to a common type (provided maps are available) with, for example,
gsc <- mapIdentifiers(gsc, EntrezIdentifier())
gsc## GeneSetCollection
## names: GO:0000122, GO:0000209, ..., GO:1990837 (352 total)
## unique identifiers: 641339, 4111, ..., 7033 (27 total)
## types in collection:
## geneIdType: EntrezIdentifier (1 total)
## collectionType: GOCollection (1 total)gsc[["GO:0005737"]]## setName: GO:0005737
## geneIds: 6014, 392, ..., 4287 (total: 7)
## geneIdType: EntrezId (hgu95av2)
## collectionType: GO
## ids: GO:0005737 (1 total)
## evidenceCode: EXP IDA IPI IMP IGI IEP HTP HDA HMP HGI HEP ISS ISO ISA ISM IGC IBA IBD IKR IRD RCA TAS NAS IC ND IEA
## ontology: CC MF BP
## details: use 'details(object)'A convenient way to visualize a GeneSetCollection is with the
ReportingTools package.
## 'interesting' gene sets
idx <- sapply(gsc, function(x) length(geneIds(x))) > 2
library(ReportingTools)## Loading required package: knitr## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationForge'## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## gscReport <- HTMLReport(
shortName="gsc_example",
title="GSEABase Vignette GeneSetCollection",
basePath=tempdir())
publish(gsc[idx], gscReport, annotation.db="org.Hs.eg")
url <- finish(gscReport)The report can be viewed with
browseURL(url)This concludes a brief tour of gene sets and gene set collections available in the GSEABase package.