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.,
## [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:
## setName: Sample
## geneIds: 31440_at, 31441_at, ..., 31489_at (total: 50)
## geneIdType: Annotation (hgu95av2)
## collectionType: ExpressionSet
## setIdentifier: 77766e74a47b:9347:Wed Oct 30 07:22:34 2024:1
## 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:
## 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:
## [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):
## 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
## [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
## 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.
## setName: chr16q24
## geneIds: GALNS, C16ORF44, ..., LOC646365 (total: 5)
## geneIdType: Symbol
## collectionType: Broad
## bcCategory: c1 (Positional)
## bcSubCategory: NA
## details: use 'details(object)'## 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
## 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
## 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.
## 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):
## GeneSetCollection
## names: GO:0000122, GO:0000209, ..., GO:1990837 (354 total)
## unique identifiers: 31480_f_at, 31473_s_at, ..., 31477_at (31 total)
## types in collection:
## geneIdType: AnnotationIdentifier (1 total)
## collectionType: GOCollection (1 total)## 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
## names: GO:0000122, GO:0000226, ..., GO:1902282 (123 total)
## unique identifiers: 31520_at, 31489_at, ..., 31487_at (27 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)## [1] "chr5q23" "chr16q24"Identifiers within a gene set collection can be mapped to a common type (provided maps are available) with, for example,
## GeneSetCollection
## names: GO:0000122, GO:0000209, ..., GO:1990837 (354 total)
## unique identifiers: 4111, 8658, ..., 7033 (27 total)
## types in collection:
## geneIdType: EntrezIdentifier (1 total)
## collectionType: GOCollection (1 total)## 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## 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
This concludes a brief tour of gene sets and gene set collections available in the GSEABase package.