RaggedExperiment (original) (raw)
Introduction
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.
Installation
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("RaggedExperiment")Loading the package:
library(RaggedExperiment)
library(GenomicRanges)Citing RaggedExperiment
Your citations are crucial in keeping our software free and open source. To cite our package see the citation (Ramos et al. (2023)) in the Reference section. You may also browse to the publication at the link here.
RaggedExperiment class overview
A schematic showing the class geometry and supported transformations of theRaggedExperiment class is show below. There are three main operations for transforming the RaggedExperiment representation:
sparseAssaycompactAssayqreduceAssay
Figure 1: RaggedExperiment object schematic
Rows and columns represent genomic ranges and samples, respectively. Assay operations can be performed with (from left to right) compactAssay, qreduceAssay, and sparseAssay.
Constructing a RaggedExperiment object
We 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)Using GRanges objects
ragexp <- 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): idUsing a GRangesList instance
grl <- 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): idUsing a list of GRanges
rangeList <- 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): idAccessors
Range data
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 seqlengthsDimension names
dimnames(ragexp)## [[1]]
## [1] "A" "B" "C" "D" "E"
##
## [[2]]
## [1] "sample1" "sample2"colData
colData(ragexp)## DataFrame with 2 rows and 1 column
## id
## <integer>
## sample1 1
## sample2 2Subsetting
by dimension
Subsetting 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.
by genomic ranges
The overlapsAny and subsetByOverlaps functionalities are available for use for RaggedExperiment. Please see the corresponding documentation inRaggedExperiment and GenomicRanges.
*Assay functions
RaggedExperiment package provides several different functions for representing ranged data in a rectangular matrix via the *Assay methods.
sparseAssay
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] 10Support for sparse matrix output
We provide sparse matrix representations with the help of the Matrix package. To obtain a sparse representation, the user can use the sparse = TRUEargument.
sparseAssay(ragexp, sparse = TRUE)## 5 x 2 sparse Matrix of class "dgCMatrix"
## sample1 sample2
## A 3 .
## B 4 .
## C 5 .
## D . 1
## E . 2This representation is of class dgCMatrix see the Matrix documentation for more details.
compactAssay
Samples 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 NASimilarly, to sparseAssay the compactAssay function provides the option to obtain a sparse matrix representation with the sparse = TRUE argument. This will return a dgCMatrix class from the Matrix package.
compactAssay(ragexp, sparse = TRUE)## 4 x 2 sparse Matrix of class "dgCMatrix"
## sample1 sample2
## chr1:8-14:+ 4 .
## chr1:1-10:- 3 1
## chr2:11-18:+ . 2
## chr2:15-18:+ 5 .disjoinAssay
This 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 2qreduceAssay
The 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 thesimplifyReduce functional argument.
For demonstration purposes, we can have a look at the original GRangesListand 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 2See the schematic for a visual representation.
Coercion
The 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:
sparseSummarizedExperimentcompactSummarizedExperimentdisjoinSummarizedExperimentqreduceSummarizedExperiment
See the documentation for details.
from dgCMatrix to RaggedExperiment
In the special case where the rownames of a sparseMatrix are coercible toGRanges, RaggedExperiment provides the facility to convert sparse matrices into RaggedExperiment. This can be done using the as coercion method. The example below first creates an example sparse dgCMatrix class and then shows the as method usage to this end.
library(Matrix)##
## Attaching package: 'Matrix'## The following object is masked from 'package:S4Vectors':
##
## expandsm <- Matrix::sparseMatrix(
i = c(2, 3, 4, 3, 4, 3, 4),
j = c(1, 1, 1, 3, 3, 4, 4),
x = c(2L, 4L, 2L, 2L, 2L, 4L, 2L),
dims = c(4, 4),
dimnames = list(
c("chr2:1-10", "chr2:2-10", "chr2:3-10", "chr2:4-10"),
LETTERS[1:4]
)
)
as(sm, "RaggedExperiment")## class: RaggedExperiment
## dim: 7 3
## assays(1): counts
## rownames: NULL
## colnames(3): A C D
## colData names(0):Session Information
sessionInfo()## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] Matrix_1.7-3 RaggedExperiment_1.33.2 GenomicRanges_1.61.0
## [4] GenomeInfoDb_1.45.3 IRanges_2.43.0 S4Vectors_0.47.0
## [7] BiocGenerics_0.55.0 generics_0.1.3 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] jsonlite_2.0.0 compiler_4.5.0
## [3] BiocManager_1.30.25 crayon_1.5.3
## [5] BiocBaseUtils_1.11.0 SummarizedExperiment_1.39.0
## [7] Biobase_2.69.0 jquerylib_0.1.4
## [9] yaml_2.3.10 fastmap_1.2.0
## [11] lattice_0.22-7 R6_2.6.1
## [13] XVector_0.49.0 S4Arrays_1.9.0
## [15] knitr_1.50 DelayedArray_0.35.1
## [17] bookdown_0.43 MatrixGenerics_1.21.0
## [19] bslib_0.9.0 rlang_1.1.6
## [21] cachem_1.1.0 xfun_0.52
## [23] sass_0.4.10 SparseArray_1.9.0
## [25] cli_3.6.5 digest_0.6.37
## [27] grid_4.5.0 lifecycle_1.0.4
## [29] evaluate_1.0.3 abind_1.4-8
## [31] rmarkdown_2.29 httr_1.4.7
## [33] matrixStats_1.5.0 tools_4.5.0
## [35] htmltools_0.5.8.1 UCSC.utils_1.5.0References
Ramos, Marcel, Martin Morgan, Ludwig Geistlinger, Vincent J Carey, and Levi Waldron. 2023. “RaggedExperiment: the missing link between genomic ranges and matrices in Bioconductor.” Bioinformatics 39 (6): btad330. https://doi.org/10.1093/bioinformatics/btad330.