topGOdata-class: Class "topGOdata"
In topGO: Enrichment Analysis for Gene Ontology
Description
Objects from the Class
Slots
Methods
Author(s)
See Also
Examples
Description
TODO: The node attributes are environments containing the genes/probes
annotated to the respective node
If genes is a numeric vector than this should represent the gene's
score. If it is factor it should discriminate the genes in
interesting genes and the rest
TODO: it will be a good idea to replace the allGenes and allScore with an
ExpressionSet class. In this way we can use tests like global test, globalAncova....
– ALL variables starting with . are just for internal class usage (private)
Objects from the Class
Objects can be created by calls of the form new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...).
~~ describe objects here ~~
Slots
description:Object of class "character" ~~
ontology:Object of class "character" ~~
allGenes:Object of class "character" ~~
allScores:Object of class "ANY" ~~
geneSelectionFun:Object of class "function" ~~
feasible:Object of class "logical" ~~
nodeSize:Object of class "integer" ~~
graph:Object of class "graphNEL" ~~
expressionMatrix:Object of class "matrix" ~~
phenotype:Object of class "factor" ~~
Methods
- allGenes
signature(object = "topGOdata"): ...
- attrInTerm
signature(object = "topGOdata", attr = "character", whichGO = "character"): ...
- attrInTerm
signature(object = "topGOdata", attr = "character", whichGO = "missing"): ...
- countGenesInTerm
signature(object = "topGOdata", whichGO = "character"): ...
- countGenesInTerm
signature(object = "topGOdata", whichGO = "missing"): ...
- description<-
signature(object = "topGOdata"): ...
- description
signature(object = "topGOdata"): ...
- feasible<-
signature(object = "topGOdata"): ...
- feasible
signature(object = "topGOdata"): ...
- geneScore
signature(object = "topGOdata"): ...
- geneSelectionFun<-
signature(object = "topGOdata"): ...
- geneSelectionFun
signature(object = "topGOdata"): ...
- genes
signature(object = "topGOdata"): A method for
obtaining the list of genes, as a characther vector, which will be
used in the further analysis.
- numGenes
signature(object = "topGOdata"): A method for
obtaining the number of genes, which will be used in the further
analysis. It has the same effect as: lenght(genes(object)).
- sigGenes
signature(object = "topGOdata"): A method for
obtaining the list of significant genes, as a charachter vector.
- genesInTerm
signature(object = "topGOdata", whichGO = "character"): ...
- genesInTerm
signature(object = "topGOdata", whichGO = "missing"): ...
- getSigGroups
signature(object = "topGOdata", test.stat = "classicCount"): ...
- getSigGroups
signature(object = "topGOdata", test.stat = "classicScore"): ...
- graph<-
signature(object = "topGOdata"): ...
- graph
signature(object = "topGOdata"): ...
- initialize
signature(.Object = "topGOdata"): ...
- ontology<-
signature(object = "topGOdata"): ...
- ontology
signature(object = "topGOdata"): ...
- termStat
signature(object = "topGOdata", whichGO = "character"): ...
- termStat
signature(object = "topGOdata", whichGO = "missing"): ...
- updateGenes
signature(object = "topGOdata", geneList = "numeric", geneSelFun = "function"): ...
- updateGenes
signature(object = "topGOdata", geneList = "factor", geneSelFun = "missing"): ...
- updateTerm<-
signature(object = "topGOdata", attr = "character"): ...
- usedGO
signature(object = "topGOdata"): ...
Author(s)
Adrian Alexa
See Also
Examples
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## load the dataset
data(geneList)
library(package = affyLib, character.only = TRUE)
## the distribution of the adjusted p-values
hist(geneList, 100)
## how many differentially expressed genes are:
sum(topDiffGenes(geneList))
## build the topGOdata class
GOdata <- new("topGOdata",
ontology = "BP",
allGenes = geneList,
geneSel = topDiffGenes,
description = "GO analysis of ALL data: Differential Expression between B-cell and T-cell",
annot = annFUN.db,
affyLib = affyLib)
## display the GOdata object
GOdata
##########################################################
## Examples on how to use the methods
##########################################################
## description of the experiment
description(GOdata)
## obtain the genes that will be used in the analysis
a <- genes(GOdata)
str(a)
numGenes(GOdata)
## obtain the score (p-value) of the genes
selGenes <- names(geneList)[sample(1:length(geneList), 10)]
gs <- geneScore(GOdata, whichGenes = selGenes)
print(gs)
## if we want an unnamed vector containing all the feasible genes
gs <- geneScore(GOdata, use.names = FALSE)
str(gs)
## the list of significant genes
sg <- sigGenes(GOdata)
str(sg)
numSigGenes(GOdata)
## to update the gene list
.geneList <- geneScore(GOdata, use.names = TRUE)
GOdata ## more available genes
GOdata <- updateGenes(GOdata, .geneList, topDiffGenes)
GOdata ## the available genes are now the feasible genes
## the available GO terms (all the nodes in the graph)
go <- usedGO(GOdata)
length(go)
## to list the genes annotated to a set of specified GO terms
sel.terms <- sample(go, 10)
ann.genes <- genesInTerm(GOdata, sel.terms)
str(ann.genes)
## the score for these genes
ann.score <- scoresInTerm(GOdata, sel.terms)
str(ann.score)
## to see the number of annotated genes
num.ann.genes <- countGenesInTerm(GOdata)
str(num.ann.genes)
## to summarise the statistics
termStat(GOdata, sel.terms)
Example output
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: graph
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: GO.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
Attaching package: 'topGO'
The following object is masked from 'package:IRanges':
members
Loading required package: org.Hs.eg.db
[1] 50
Building most specific GOs .....
( 1555 GO terms found. )
Build GO DAG topology ..........
( 4396 GO terms and 10270 relations. )
Annotating nodes ...............
( 310 genes annotated to the GO terms. )
Warning message:
In rsqlite_fetch(res@ptr, n = n) :
Don't need to call dbFetch() for statements, only for queries
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
323 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 50 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
[1] "GO analysis of ALL data: Differential Expression between B-cell and T-cell"
chr [1:310] "1095_s_at" "1130_at" "1196_at" "1329_s_at" "1340_s_at" ...
[1] 310
258_at 36047_at 32641_at 40726_at 39630_at 33266_at 1095_s_at
1.00000000 0.16212923 0.13557561 0.35745356 0.66562728 1.00000000 1.00000000
41717_at 36839_at 41869_at
0.12531451 0.01390908 1.00000000
num [1:310] 1 1 0.622 0.541 1 ...
chr [1:46] "1347_at" "1792_g_at" "31864_at" "32074_at" "32861_s_at" ...
[1] 46
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
323 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 50 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
310 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
[1] 4396
List of 10
$ GO:0043312: chr [1:19] "1793_at" "307_at" "32837_at" "33153_at" ...
$ GO:0031124: chr [1:2] "1945_at" "34736_at"
$ GO:0070371: chr [1:18] "1130_at" "1408_at" "1542_at" "1634_s_at" ...
$ GO:0072677: chr [1:2] "1574_s_at" "33981_at"
$ GO:0045190: chr [1:7] "1574_s_at" "1634_s_at" "1830_s_at" "32861_s_at" ...
$ GO:0060148: chr [1:5] "1130_at" "1634_s_at" "1830_s_at" "1844_s_at" ...
$ GO:0060389: chr [1:4] "1634_s_at" "1830_s_at" "40421_at" "41445_at"
$ GO:1903055: chr [1:5] "1634_s_at" "1830_s_at" "38711_at" "39838_at" ...
$ GO:0007067: chr [1:171] "1095_s_at" "1196_at" "1329_s_at" "1340_s_at" ...
$ GO:0051702: chr [1:2] "32241_at" "34763_at"
List of 10
$ GO:0043312: num [1:19] 1 1 1 1 1 ...
$ GO:0031124: num [1:2] 0.522 0.383
$ GO:0070371: num [1:18] 1 0.43 1 1 1 ...
$ GO:0072677: num [1:2] 0.1222 0.0416
$ GO:0045190: num [1:7] 0.122156 1 1 0.000395 0.041561 ...
$ GO:0060148: num [1:5] 1 1 1 1 1
$ GO:0060389: num [1:4] 1 1 0.158 1
$ GO:1903055: num [1:5] 1 1 0.02424 0.00121 1
$ GO:0007067: num [1:171] 1 0.622 0.541 1 1 ...
$ GO:0051702: num [1:2] 0.4903 0.0263
Named int [1:4396] 83 7 2 1 1 1 1 9 52 37 ...
- attr(*, "names")= chr [1:4396] "GO:0000003" "GO:0000018" "GO:0000022" "GO:0000045" ...
Annotated Significant Expected
GO:0043312 19 5 2.82
GO:0031124 2 0 0.30
GO:0070371 18 1 2.67
GO:0072677 2 0 0.30
GO:0045190 7 1 1.04
GO:0060148 5 0 0.74
GO:0060389 4 0 0.59
GO:1903055 5 1 0.74
GO:0007067 171 17 25.37
GO:0051702 2 0 0.30
topGO documentation built on Nov. 8, 2020, 6:55 p.m.
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Description Objects from the Class Slots Methods Author(s) See Also Examples
Description
TODO: The node attributes are environments containing the genes/probes annotated to the respective node
If genes is a numeric vector than this should represent the gene's score. If it is factor it should discriminate the genes in interesting genes and the rest
TODO: it will be a good idea to replace the allGenes and allScore with an ExpressionSet class. In this way we can use tests like global test, globalAncova.... – ALL variables starting with . are just for internal class usage (private)
Objects from the Class
Objects can be created by calls of the form new("topGOdata", ontology, allGenes, geneSelectionFun, description, annotationFun, ...).
~~ describe objects here ~~
Slots
description:Object of class
"character"~~ontology:Object of class
"character"~~allGenes:Object of class
"character"~~allScores:Object of class
"ANY"~~geneSelectionFun:Object of class
"function"~~feasible:Object of class
"logical"~~nodeSize:Object of class
"integer"~~graph:Object of class
"graphNEL"~~expressionMatrix:Object of class
"matrix"~~phenotype:Object of class
"factor"~~
Methods
- allGenes
signature(object = "topGOdata"): ...- attrInTerm
signature(object = "topGOdata", attr = "character", whichGO = "character"): ...- attrInTerm
signature(object = "topGOdata", attr = "character", whichGO = "missing"): ...- countGenesInTerm
signature(object = "topGOdata", whichGO = "character"): ...- countGenesInTerm
signature(object = "topGOdata", whichGO = "missing"): ...- description<-
signature(object = "topGOdata"): ...- description
signature(object = "topGOdata"): ...- feasible<-
signature(object = "topGOdata"): ...- feasible
signature(object = "topGOdata"): ...- geneScore
signature(object = "topGOdata"): ...- geneSelectionFun<-
signature(object = "topGOdata"): ...- geneSelectionFun
signature(object = "topGOdata"): ...- genes
signature(object = "topGOdata"): A method for obtaining the list of genes, as a characther vector, which will be used in the further analysis.- numGenes
signature(object = "topGOdata"): A method for obtaining the number of genes, which will be used in the further analysis. It has the same effect as:lenght(genes(object)).- sigGenes
signature(object = "topGOdata"): A method for obtaining the list of significant genes, as a charachter vector.- genesInTerm
signature(object = "topGOdata", whichGO = "character"): ...- genesInTerm
signature(object = "topGOdata", whichGO = "missing"): ...- getSigGroups
signature(object = "topGOdata", test.stat = "classicCount"): ...- getSigGroups
signature(object = "topGOdata", test.stat = "classicScore"): ...- graph<-
signature(object = "topGOdata"): ...- graph
signature(object = "topGOdata"): ...- initialize
signature(.Object = "topGOdata"): ...- ontology<-
signature(object = "topGOdata"): ...- ontology
signature(object = "topGOdata"): ...- termStat
signature(object = "topGOdata", whichGO = "character"): ...- termStat
signature(object = "topGOdata", whichGO = "missing"): ...- updateGenes
signature(object = "topGOdata", geneList = "numeric", geneSelFun = "function"): ...- updateGenes
signature(object = "topGOdata", geneList = "factor", geneSelFun = "missing"): ...- updateTerm<-
signature(object = "topGOdata", attr = "character"): ...- usedGO
signature(object = "topGOdata"): ...
Author(s)
Adrian Alexa
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ## load the dataset
data(geneList)
library(package = affyLib, character.only = TRUE)
## the distribution of the adjusted p-values
hist(geneList, 100)
## how many differentially expressed genes are:
sum(topDiffGenes(geneList))
## build the topGOdata class
GOdata <- new("topGOdata",
ontology = "BP",
allGenes = geneList,
geneSel = topDiffGenes,
description = "GO analysis of ALL data: Differential Expression between B-cell and T-cell",
annot = annFUN.db,
affyLib = affyLib)
## display the GOdata object
GOdata
##########################################################
## Examples on how to use the methods
##########################################################
## description of the experiment
description(GOdata)
## obtain the genes that will be used in the analysis
a <- genes(GOdata)
str(a)
numGenes(GOdata)
## obtain the score (p-value) of the genes
selGenes <- names(geneList)[sample(1:length(geneList), 10)]
gs <- geneScore(GOdata, whichGenes = selGenes)
print(gs)
## if we want an unnamed vector containing all the feasible genes
gs <- geneScore(GOdata, use.names = FALSE)
str(gs)
## the list of significant genes
sg <- sigGenes(GOdata)
str(sg)
numSigGenes(GOdata)
## to update the gene list
.geneList <- geneScore(GOdata, use.names = TRUE)
GOdata ## more available genes
GOdata <- updateGenes(GOdata, .geneList, topDiffGenes)
GOdata ## the available genes are now the feasible genes
## the available GO terms (all the nodes in the graph)
go <- usedGO(GOdata)
length(go)
## to list the genes annotated to a set of specified GO terms
sel.terms <- sample(go, 10)
ann.genes <- genesInTerm(GOdata, sel.terms)
str(ann.genes)
## the score for these genes
ann.score <- scoresInTerm(GOdata, sel.terms)
str(ann.score)
## to see the number of annotated genes
num.ann.genes <- countGenesInTerm(GOdata)
str(num.ann.genes)
## to summarise the statistics
termStat(GOdata, sel.terms)
|
Example output
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Loading required package: graph
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: GO.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following object is masked from 'package:base':
expand.grid
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
groupGOTerms: GOBPTerm, GOMFTerm, GOCCTerm environments built.
Attaching package: 'topGO'
The following object is masked from 'package:IRanges':
members
Loading required package: org.Hs.eg.db
[1] 50
Building most specific GOs .....
( 1555 GO terms found. )
Build GO DAG topology ..........
( 4396 GO terms and 10270 relations. )
Annotating nodes ...............
( 310 genes annotated to the GO terms. )
Warning message:
In rsqlite_fetch(res@ptr, n = n) :
Don't need to call dbFetch() for statements, only for queries
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
323 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 50 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
[1] "GO analysis of ALL data: Differential Expression between B-cell and T-cell"
chr [1:310] "1095_s_at" "1130_at" "1196_at" "1329_s_at" "1340_s_at" ...
[1] 310
258_at 36047_at 32641_at 40726_at 39630_at 33266_at 1095_s_at
1.00000000 0.16212923 0.13557561 0.35745356 0.66562728 1.00000000 1.00000000
41717_at 36839_at 41869_at
0.12531451 0.01390908 1.00000000
num [1:310] 1 1 0.622 0.541 1 ...
chr [1:46] "1347_at" "1792_g_at" "31864_at" "32074_at" "32861_s_at" ...
[1] 46
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
323 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 50 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
------------------------- topGOdata object -------------------------
Description:
- GO analysis of ALL data: Differential Expression between B-cell and T-cell
Ontology:
- BP
310 available genes (all genes from the array):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
310 feasible genes (genes that can be used in the analysis):
- symbol: 1095_s_at 1130_at 1196_at 1329_s_at 1340_s_at ...
- score : 1 1 0.62238 0.541224 1 ...
- 46 significant genes.
GO graph (nodes with at least 1 genes):
- a graph with directed edges
- number of nodes = 4396
- number of edges = 10270
------------------------- topGOdata object -------------------------
[1] 4396
List of 10
$ GO:0043312: chr [1:19] "1793_at" "307_at" "32837_at" "33153_at" ...
$ GO:0031124: chr [1:2] "1945_at" "34736_at"
$ GO:0070371: chr [1:18] "1130_at" "1408_at" "1542_at" "1634_s_at" ...
$ GO:0072677: chr [1:2] "1574_s_at" "33981_at"
$ GO:0045190: chr [1:7] "1574_s_at" "1634_s_at" "1830_s_at" "32861_s_at" ...
$ GO:0060148: chr [1:5] "1130_at" "1634_s_at" "1830_s_at" "1844_s_at" ...
$ GO:0060389: chr [1:4] "1634_s_at" "1830_s_at" "40421_at" "41445_at"
$ GO:1903055: chr [1:5] "1634_s_at" "1830_s_at" "38711_at" "39838_at" ...
$ GO:0007067: chr [1:171] "1095_s_at" "1196_at" "1329_s_at" "1340_s_at" ...
$ GO:0051702: chr [1:2] "32241_at" "34763_at"
List of 10
$ GO:0043312: num [1:19] 1 1 1 1 1 ...
$ GO:0031124: num [1:2] 0.522 0.383
$ GO:0070371: num [1:18] 1 0.43 1 1 1 ...
$ GO:0072677: num [1:2] 0.1222 0.0416
$ GO:0045190: num [1:7] 0.122156 1 1 0.000395 0.041561 ...
$ GO:0060148: num [1:5] 1 1 1 1 1
$ GO:0060389: num [1:4] 1 1 0.158 1
$ GO:1903055: num [1:5] 1 1 0.02424 0.00121 1
$ GO:0007067: num [1:171] 1 0.622 0.541 1 1 ...
$ GO:0051702: num [1:2] 0.4903 0.0263
Named int [1:4396] 83 7 2 1 1 1 1 9 52 37 ...
- attr(*, "names")= chr [1:4396] "GO:0000003" "GO:0000018" "GO:0000022" "GO:0000045" ...
Annotated Significant Expected
GO:0043312 19 5 2.82
GO:0031124 2 0 0.30
GO:0070371 18 1 2.67
GO:0072677 2 0 0.30
GO:0045190 7 1 1.04
GO:0060148 5 0 0.74
GO:0060389 4 0 0.59
GO:1903055 5 1 0.74
GO:0007067 171 17 25.37
GO:0051702 2 0 0.30
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