Utility function to map annotations from GRanges to rowData of SummarizedExperiment or to mcols of GRanges object. If multiple features overlap then they will be concatenated with the specified separtor string.
Arguments
- obj
RangedSummarizedExperiment or GRanges object
- gr
GRanges with annotations to map to obj
- cols_to_map
character vector of columns from GRanges to map to SummarizedExperiment. If the vector has names, the names will be the column names in the output.
- RLE
If TRUE, columns added will returned as
S4Vectors::Rle()vectors to reduce memory- sep
separator string, defaults to comma.
- ...
additional arguments to pass to
GenomicRanges::findOverlaps()
Value
Either a SummarizedExperiment or GRanges object with additional annotations provided by the supplied GRanges object.
Examples
library(SummarizedExperiment)
#> Loading required package: MatrixGenerics
#> Loading required package: matrixStats
#>
#> Attaching package: ‘MatrixGenerics’
#> The following objects are masked from ‘package:matrixStats’:
#>
#> colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
#> colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
#> colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#> colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
#> colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
#> colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
#> colWeightedMeans, colWeightedMedians, colWeightedSds,
#> colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
#> rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
#> rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
#> rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
#> rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#> rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#> rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#> rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: ‘BiocGenerics’
#> 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, setdiff, table, tapply,
#> union, unique, unsplit, which.max, which.min
#> Loading required package: S4Vectors
#>
#> Attaching package: ‘S4Vectors’
#> The following object is masked from ‘package:utils’:
#>
#> findMatches
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#> Loading required package: IRanges
#> Loading required package: GenomeInfoDb
#> 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")'.
#>
#> Attaching package: ‘Biobase’
#> The following object is masked from ‘package:MatrixGenerics’:
#>
#> rowMedians
#> The following objects are masked from ‘package:matrixStats’:
#>
#> anyMissing, rowMedians
rse_adar_ifn <- mock_rse()
gr <- GRanges(rep(c("SSR3", "SPCS3"), c(5, 15)),
IRanges(seq(1, 500, by = 25), width = 50),
strand = "+"
)
gr$feature <- sample(1:100, size = 20)
gr$id <- sample(LETTERS, size = 20)
rse <- annot_from_gr(rse_adar_ifn, gr, c(feature_set = "feature", "id"))
rowData(rse)
#> DataFrame with 74 rows and 6 columns
#> REF rpbz vdb sor feature_set id
#> <character> <numeric> <numeric> <numeric> <Rle> <Rle>
#> site_SSR3_102_2 T 1.489645 Inf 1.403164 NA NA
#> site_SSR3_125_2 C 0.356711 Inf 0.165499 NA NA
#> site_SSR3_156_2 C 1.073919 Inf 1.442924 NA NA
#> site_SSR3_176_2 A -0.387238 0.00686469 1.987570 NA NA
#> site_SSR3_198_2 A 1.040581 Inf 1.483784 NA NA
#> ... ... ... ... ... ... ...
#> site_DHFR_397_2 A -1.5715051 Inf 1.39896 NA NA
#> site_DHFR_399_2 G -0.1203878 Inf 0.09602 NA NA
#> site_DHFR_423_2 T -0.0468703 Inf 1.38985 NA NA
#> site_DHFR_430_2 A -1.5389404 Inf 1.39019 NA NA
#> site_DHFR_513_2 T -0.7160074 Inf 1.38637 NA NA