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 the RaggedExperiment class is show below. There are three main operations for transforming the RaggedExperiment representation:

sparseAssay
compactAssay
qreduceAssay

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): id

Using 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): id

Using 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): id

Using a `List` of `GRanges` with metadata

Note: 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): id

Accessors

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 seqlengths

Dimension 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         2

Subsetting

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 in RaggedExperiment 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 2

Reduce(`*`, dim(ragexp))

## [1] 10

sparseAssay(ragexp)

##   sample1 sample2
## A       3      NA
## B       4      NA
## C       5      NA
## D      NA       1
## E      NA       2

length(sparseAssay(ragexp))

## [1] 10

Support 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 = TRUE argument.

sparseAssay(ragexp, sparse = TRUE)

## 5 x 2 sparse Matrix of class "dgCMatrix"
##   sample1 sample2
## A       3       .
## B       4       .
## C       5       .
## D       .       1
## E       .       2

This 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      NA

Similarly, 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       2

qreduceAssay

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 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 seqlengths

This 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 seqlengths

assay(ragexp, "score")

##   sample1 sample2
## A       3      NA
## B       4      NA
## C       5      NA
## D      NA       1
## E      NA       2

Here 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 seqlengths

The 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       2

See 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:

sparseSummarizedExperiment
compactSummarizedExperiment
disjoinSummarizedExperiment
qreduceSummarizedExperiment

See the documentation for details.

from dgCMatrix to RaggedExperiment

In the special case where the rownames of a sparseMatrix are coercible to GRanges, 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':
## 
##     expand

sm <- 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 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
## 
## 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       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] Matrix_1.7-1            RaggedExperiment_1.31.0 GenomicRanges_1.59.0   
## [4] GenomeInfoDb_1.43.0     IRanges_2.41.0          S4Vectors_0.44.0       
## [7] BiocGenerics_0.53.0     BiocStyle_2.35.0       
## 
## loaded via a namespace (and not attached):
##  [1] jsonlite_1.8.9              highr_0.11                 
##  [3] compiler_4.4.1              BiocManager_1.30.25        
##  [5] crayon_1.5.3                BiocBaseUtils_1.9.0        
##  [7] SummarizedExperiment_1.36.0 Biobase_2.67.0             
##  [9] jquerylib_0.1.4             yaml_2.3.10                
## [11] fastmap_1.2.0               lattice_0.22-6             
## [13] R6_2.5.1                    XVector_0.46.0             
## [15] S4Arrays_1.6.0              knitr_1.48                 
## [17] DelayedArray_0.33.1         MatrixGenerics_1.19.0      
## [19] maketools_1.3.1             GenomeInfoDbData_1.2.13    
## [21] bslib_0.8.0                 rlang_1.1.4                
## [23] cachem_1.1.0                xfun_0.48                  
## [25] sass_0.4.9                  sys_3.4.3                  
## [27] SparseArray_1.6.0           cli_3.6.3                  
## [29] zlibbioc_1.52.0             grid_4.4.1                 
## [31] digest_0.6.37               lifecycle_1.0.4            
## [33] evaluate_1.0.1              buildtools_1.0.0           
## [35] abind_1.4-8                 rmarkdown_2.28             
## [37] httr_1.4.7                  matrixStats_1.4.1          
## [39] tools_4.4.1                 htmltools_0.5.8.1          
## [41] UCSC.utils_1.2.0

References

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.

RaggedExperiment