The RaggedExperiment package provides a flexible data representation for copy number, mutation and other ragged array schema for genomic location data. It aims to provide a framework for a set of samples that have differing numbers of genomic ranges.
The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. The row and column dimensions of the RaggedExperiment correspond to the number of ranges in the entire dataset and the number of samples represented in the data, respectively.
source("https://bioconductor.org/biocLite.R")
BiocInstaller::biocLite("RaggedExperiment")Loading the package:
library(RaggedExperiment)RaggedExperiment objectWe start with a couple of GRanges objects, each representing an individual sample:
sample1 <- GRanges(
c(A = "chr1:1-10:-", B = "chr1:8-14:+", C = "chr2:15-18:+"),
score = 3:5)
sample2 <- GRanges(
c(D = "chr1:1-10:-", E = "chr2:11-18:+"),
score = 1:2)Include column data colData to describe the samples:
colDat <- DataFrame(id = 1:2)GRanges objectsragexp <- RaggedExperiment(sample1 = sample1,
sample2 = sample2,
colData = colDat)
ragexp## class: RaggedExperiment
## dim: 5 2
## assays(1): score
## rownames(5): A B C D E
## colnames(2): sample1 sample2
## colData names(1): idGRangesList instancegrl <- GRangesList(sample1 = sample1, sample2 = sample2)
RaggedExperiment(grl, colData = colDat)## class: RaggedExperiment
## dim: 5 2
## assays(1): score
## rownames(5): A B C D E
## colnames(2): sample1 sample2
## colData names(1): idlist of GRangesrangeList <- list(sample1 = sample1, sample2 = sample2)
RaggedExperiment(rangeList, colData = colDat)## class: RaggedExperiment
## dim: 5 2
## assays(1): score
## rownames(5): A B C D E
## colnames(2): sample1 sample2
## colData names(1): idList of GRanges with metadataNote: In cases where a SimpleGenomicRangesList is provided along with accompanying metadata (accessed by mcols), the metadata is used as the colData for the RaggedExperiment.
grList <- List(sample1 = sample1, sample2 = sample2)
mcols(grList) <- colDat
RaggedExperiment(grList)## class: RaggedExperiment
## dim: 5 2
## assays(1): score
## rownames(5): A B C D E
## colnames(2): sample1 sample2
## colData names(1): idrowRanges(ragexp)## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## A chr1 [ 1, 10] -
## B chr1 [ 8, 14] +
## C chr2 [15, 18] +
## D chr1 [ 1, 10] -
## E chr2 [11, 18] +
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsdimnames(ragexp)## [[1]]
## [1] "A" "B" "C" "D" "E"
##
## [[2]]
## [1] "sample1" "sample2"colDatacolData(ragexp)## DataFrame with 2 rows and 1 column
## id
## <integer>
## sample1 1
## sample2 2Subsetting a RaggedExperiment is akin to subsetting a matrix object. Rows correspond to genomic ranges and columns to samples or specimen. It is possible to subset using integer, character, and logical indices.
The overlapsAny and subsetByOverlaps functionalities are available for use for RaggedExperiment. Please see the corresponding documentation in RaggedExperiment and GenomicRanges.
RaggedExperiment package provides several different functions for representing ranged data in a rectangular matrix via the *Assay methods.
The most straightforward matrix representation of a RaggedExperiment will return a matrix of dimensions equal to the product of the number of ranges and samples.
dim(ragexp)## [1] 5 2Reduce(`*`, dim(ragexp))## [1] 10sparseAssay(ragexp)## sample1 sample2
## A 3 NA
## B 4 NA
## C 5 NA
## D NA 1
## E NA 2length(sparseAssay(ragexp))## [1] 10Samples with identical ranges are placed in the same row. Non-disjoint ranges are not collapsed.
compactAssay(ragexp)## sample1 sample2
## chr1:8-14:+ 4 NA
## chr1:1-10:- 3 1
## chr2:11-18:+ NA 2
## chr2:15-18:+ 5 NAThis function returns a matrix of disjoint ranges across all samples. Elements of the matrix are summarized by applying the simplifyDisjoin functional argument to assay values of overlapping ranges.
disjoinAssay(ragexp, simplifyDisjoin = mean)## sample1 sample2
## chr1:8-14:+ 4 NA
## chr1:1-10:- 3 1
## chr2:11-14:+ NA 2
## chr2:15-18:+ 5 2The qreduceAssay function works with a query parameter that highlights a window of ranges for the resulting matrix. The returned matrix will have dimensions length(query) by ncol(x). Elements contain assay values for the i th query range and the j th sample, summarized according to the simplifyReduce functional argument.
For demonstration purposes, we can have a look at the original GRangesList and the associated scores from which the current ragexp object is derived:
unlist(grl, use.names = FALSE)## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## A chr1 [ 1, 10] - | 3
## B chr1 [ 8, 14] + | 4
## C chr2 [15, 18] + | 5
## D chr1 [ 1, 10] - | 1
## E chr2 [11, 18] + | 2
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThis data is represented as rowRanges and assays in RaggedExperiment:
rowRanges(ragexp)## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## A chr1 [ 1, 10] -
## B chr1 [ 8, 14] +
## C chr2 [15, 18] +
## D chr1 [ 1, 10] -
## E chr2 [11, 18] +
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsassay(ragexp, "score")## sample1 sample2
## A 3 NA
## B 4 NA
## C 5 NA
## D NA 1
## E NA 2Here we provide the “query” region of interest:
(query <- GRanges(c("chr1:1-14:-", "chr2:11-18:+")))## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## [1] chr1 [ 1, 14] -
## [2] chr2 [11, 18] +
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe simplifyReduce argument in qreduceAssay allows the user to summarize overlapping regions with a custom method for the given “query” region of interest. We provide one for calculating a weighted average score per query range, where the weight is proportional to the overlap widths between overlapping ranges and a query range.
Note that there are three arguments to this function. See the documentation for additional details.
weightedmean <- function(scores, ranges, qranges)
{
isects <- pintersect(ranges, qranges)
sum(scores * width(isects)) / sum(width(isects))
}A call to qreduceAssay involves the RaggedExperiment, the GRanges query and the simplifyReduce functional argument.
qreduceAssay(ragexp, query, simplifyReduce = weightedmean)## sample1 sample2
## chr1:1-14:- 3 1
## chr2:11-18:+ 5 2The RaggedExperiment provides a family of parallel functions for coercing to the SummarizedExperiment class. By selecting a particular assay index (i), a parallel assay coercion method can be achieved.
Here is the list of function names:
sparseSummarizedExperimentcompactSummarizedExperimentdisjoinSummarizedExperimentqreduceSummarizedExperimentSee the documentation for details.
sessionInfo()## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] RaggedExperiment_1.2.5 GenomicRanges_1.30.0 GenomeInfoDb_1.14.0
## [4] IRanges_2.12.0 S4Vectors_0.16.0 BiocGenerics_0.24.0
## [7] BiocStyle_2.6.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.14 knitr_1.17
## [3] XVector_0.18.0 magrittr_1.5
## [5] zlibbioc_1.24.0 lattice_0.20-35
## [7] stringr_1.2.0 tools_3.4.3
## [9] grid_3.4.3 SummarizedExperiment_1.8.0
## [11] Biobase_2.38.0 matrixStats_0.52.2
## [13] htmltools_0.3.6 yaml_2.1.16
## [15] rprojroot_1.2 digest_0.6.12
## [17] bookdown_0.5 Matrix_1.2-12
## [19] GenomeInfoDbData_0.99.1 bitops_1.0-6
## [21] RCurl_1.95-4.8 evaluate_0.10.1
## [23] rmarkdown_1.8 DelayedArray_0.4.1
## [25] stringi_1.1.6 compiler_3.4.3
## [27] backports_1.1.2