These functions help to create a TidySet object from
data.frame, list, matrix, and GO3AnnDbBimap.
They can create both fuzzy and standard sets.
Usage
# S3 method for class 'GeneSetCollection'
tidySet(relations)
# S3 method for class 'GeneColorSet'
tidySet(relations)
# S3 method for class 'GeneSet'
tidySet(relations)
tidySet(relations)
# S3 method for class 'data.frame'
tidySet(relations)
# S3 method for class 'list'
tidySet(relations)
# S3 method for class 'matrix'
tidySet(relations)
# S3 method for class 'Go3AnnDbBimap'
tidySet(relations)
# S3 method for class 'TidySet'
tidySet(relations)Methods (by class)
tidySet(GeneSetCollection): Converts to a tidySet given a GeneSetCollectiontidySet(GeneColorSet): Converts to a tidySet given a GeneColorSettidySet(GeneSet): Converts to a tidySet given a GeneSettidySet(data.frame): Given the relations in a data.frametidySet(list): Convert to a TidySet from a list.tidySet(matrix): Convert an incidence matrix into a TidySettidySet(Go3AnnDbBimap): Convert Go3AnnDbBimap into a TidySet object.tidySet(TidySet): Convert TidySet into a TidySet object.
Examples
# Needs GSEABase package from Bioconductor
if (requireNamespace("GSEABase", quietly = TRUE)) {
library("GSEABase")
gs <- GeneSet()
gs
tidySet(gs)
fl <- system.file("extdata", "Broad.xml", package="GSEABase")
gs2 <- getBroadSets(fl) # actually, a list of two gene sets
TS <- tidySet(gs2)
dim(TS)
sets(TS)
}
#> Loading required package: BiocGenerics
#>
#> Attaching package: ‘BiocGenerics’
#> The following object is masked from ‘package:BaseSet’:
#>
#> union
#> 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, aperm, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
#> tapply, union, unique, unsplit, which.max, which.min
#> 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: annotate
#> 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:BaseSet’:
#>
#> active
#> The following object is masked from ‘package:utils’:
#>
#> findMatches
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#>
#> Attaching package: ‘AnnotationDbi’
#> The following object is masked from ‘package:BaseSet’:
#>
#> select
#> Loading required package: XML
#> Loading required package: graph
#>
#> Attaching package: ‘graph’
#> The following object is masked from ‘package:XML’:
#>
#> addNode
#> The following objects are masked from ‘package:BaseSet’:
#>
#> complement, intersection
#>
#> Attaching package: ‘GSEABase’
#> The following object is masked from ‘package:BaseSet’:
#>
#> incidence
#> sets Identifier shortDescripton
#> 1 chr5q23 c1:101 Genes in cytogenetic band chr5q23
#> 2 chr16q24 c1:102 Genes in cytogenetic band chr16q24
#> longDescription organism
#> 1 Genes in cytogenetic band chr5q23 Human
#> 2 Genes in cytogenetic band chr16q24 Human
#> urls.getBroadSet
#> 1 file://usr/local/lib/R/site-library/GSEABase/extdata/Broad.xml
#> 2 file://usr/local/lib/R/site-library/GSEABase/extdata/Broad.xml
#> urls2 contributor type
#> 1 http://www.broad.mit.edu/gsea/msigdb/cards/chr5q23.xml Broad Institute Broad
#> 2 http://genome.ucsc.edu/cgi-bin/hgTracks?position=16q24 Broad Institute Broad
#> urls3
#> 1 http://genome.ucsc.edu/cgi-bin/hgTracks?position=5q23
#> 2 <NA>
relations <- data.frame(
sets = c(rep("a", 5), "b"),
elements = letters[seq_len(6)]
)
tidySet(relations)
#> elements sets fuzzy
#> 1 a a 1
#> 2 b a 1
#> 3 c a 1
#> 4 d a 1
#> 5 e a 1
#> 6 f b 1
relations2 <- data.frame(
sets = c(rep("A", 5), "B"),
elements = letters[seq_len(6)],
fuzzy = runif(6)
)
tidySet(relations2)
#> elements sets fuzzy
#> 1 a A 0.1258563
#> 2 b A 0.9381527
#> 3 c A 0.8012751
#> 4 d A 0.7580536
#> 5 e A 0.5325652
#> 6 f B 0.5468048
# A
x <- list("A" = letters[1:5], "B" = LETTERS[3:7])
tidySet(x)
#> elements sets fuzzy
#> 1 a A 1
#> 2 b A 1
#> 3 c A 1
#> 4 d A 1
#> 5 e A 1
#> 6 C B 1
#> 7 D B 1
#> 8 E B 1
#> 9 F B 1
#> 10 G B 1
# A fuzzy set taken encoded as a list
A <- runif(5)
names(A) <- letters[1:5]
B <- runif(5)
names(B) <- letters[3:7]
relations <- list(A, B)
tidySet(relations)
#> elements sets fuzzy
#> 1 a Set2 0.0959265
#> 2 b Set2 0.3883498
#> 3 c Set2 0.1723519
#> 4 d Set2 0.6907258
#> 5 e Set2 0.6752085
#> 6 c <NA> 0.9462948
#> 7 d <NA> 0.1962195
#> 8 e <NA> 0.9686375
#> 9 f <NA> 0.3870963
#> 10 g <NA> 0.6503439
# Will error
# x <- list("A" = letters[1:5], "B" = LETTERS[3:7], "c" = runif(5))
# a <- tidySet(x) # Only characters or factors are allowed as elements.
M <- matrix(c(1, 0.5, 1, 0), ncol = 2,
dimnames = list(c("A", "B"), c("a", "b")))
tidySet(M)
#> elements sets fuzzy
#> 1 A a 1.0
#> 2 B a 0.5
#> 3 A b 1.0