Package 'RaggedExperiment'

Title: Representation of Sparse Experiments and Assays Across Samples
Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset.
Authors: Martin Morgan [aut], Marcel Ramos [aut, cre] , Lydia King [ctb]
Maintainer: Marcel Ramos <[email protected]>
License: Artistic-2.0
Version: 1.31.1
Built: 2024-11-22 20:29:11 UTC
Source: https://github.com/Bioconductor/RaggedExperiment

Help Index

RaggedExperiment: Range-based data representation package

Description

RaggedExperiment allows the user to represent, copy number, mutation, and other types of range-based data formats where optional information about samples can be provided. At the backbone of this package is the GRangesList class. The RaggedExperiment class uses this representation and presents the data in a couple of different ways:

  • rowRanges

  • colData

The rowRanges method will return the internal GRangesList representation of the dataset. A distinction between the SummarizedExperiment and the RaggedExperiment classes is that the RaggedExperiment class allows for ragged ranges, meaning that there may be a different number of ranges or rows per sample.

Author(s)

Maintainer: Marcel Ramos [email protected] (ORCID)

Authors:

Other contributors:

See Also

Useful links:

Create simplified representation of ragged assay data.

Description

These methods transform assay() from the default (i.e., sparseAssay()) representation to various forms of more dense representation. compactAssay() collapses identical ranges across samples into a single row. disjoinAssay() creates disjoint (non-overlapping) regions, simplifies values within each sample in a user-specified manner, and returns a matrix of disjoint regions x samples.

This method transforms assay() from the default (i.e., sparseAssay()) representation to a reduced representation summarizing each original range overlapping ranges in query. Reduction in each cell can be tailored to indivdual needs using the simplifyReduce functional argument.

Usage

sparseAssay(
  x,
  i = 1,
  withDimnames = TRUE,
  background = NA_integer_,
  sparse = FALSE
)

compactAssay(
  x,
  i = 1,
  withDimnames = TRUE,
  background = NA_integer_,
  sparse = FALSE
)

disjoinAssay(
  x,
  simplifyDisjoin,
  i = 1,
  withDimnames = TRUE,
  background = NA_integer_
)

qreduceAssay(
  x,
  query,
  simplifyReduce,
  i = 1,
  withDimnames = TRUE,
  background = NA_integer_
)

Arguments

x

A RaggedExperiment object
i

integer(1) or character(1) name of assay to be transformed.
withDimnames

logical(1) include dimnames on the returned matrix. When there are no explict rownames, these are manufactured with as.character(rowRanges(x)); rownames are always manufactured for compactAssay() and disjoinAssay().
background

A value (default NA) for the returned matrix after *Assay operations
sparse

logical(1) whether to return a sparseMatrix representation
simplifyDisjoin

A function / functional operating on a *List, where the elements of the list are all within-sample assay values from ranges overlapping each disjoint range. For instance, to use the simplifyDisjoin=mean of overlapping ranges, where ranges are characterized by integer-valued scores, the entries are calculated as 

                    a
    original: |-----------|
                        b
                   |----------|

                a    a, b   b
    disjoint: |----|------|---|

    values <- IntegerList(a, c(a, b), b)
    simplifyDisjoin(values)
query

GRanges providing regions over which reduction is to occur.
simplifyReduce

A function / functional accepting arguments score, range, and qrange:

  • score A *List, where each list element corresponds to a cell in the matrix to be returned by qreduceAssay. Vector elements correspond to ranges overlapping query. The *List objects support many vectorized mathematical operations, so simplifyReduce can be implemented efficiently.

  • range A GRangesList instance, 'parallel' to score. Each element of the list corresponds to a cell in the matrix to be returned by qreduceAssay. Each range in the element corresponds to the range for which the score element applies.

  • qrange A GRanges instance with the same length as unlist(score), providing the query range window to which the corresponding scores apply.

Value

sparseAssay(): A matrix() with dimensions dim(x). Elements contain the assay value for the ith range and jth sample. Use 'sparse=TRUE' to obtain a sparseMatrix assay representation.

compactAssay(): Samples with identical range are placed in the same row. Non-disjoint ranges are NOT collapsed. Use 'sparse=TRUE' to obtain a sparseMatrix assay representation.

disjoinAssay(): A matrix with number of rows equal to number of disjoint ranges across all samples. Elements of the matrix are summarized by applying simplifyDisjoin() to assay values of overlapping ranges

qreduceAssay(): A matrix() with dimensions length(query) x ncol(x). Elements contain assay values for the ith query range and jth sample, summarized according to the function simplifyReduce.

Examples

re4 <- RaggedExperiment(GRangesList(
    GRanges(c(A = "chr1:1-10:-", B = "chr1:8-14:-", C = "chr2:15-18:+"),
        score = 3:5),
    GRanges(c(D = "chr1:1-10:-", E = "chr2:11-18:+"), score = 1:2)
), colData = DataFrame(id = 1:2))

query <- GRanges(c("chr1:1-14:-", "chr2:11-18:+"))

weightedmean <- function(scores, ranges, qranges)
{
    ## weighted average score per query range
    ## the weight corresponds to the size of the overlap of each
    ## overlapping subject range with the corresponding query range
    isects <- pintersect(ranges, qranges)
    sum(scores * width(isects)) / sum(width(isects))
}

qreduceAssay(re4, query, weightedmean)

## Not run: 
    ## Extended example: non-silent mutations, summarized by genic
    ## region
    suppressPackageStartupMessages({
        library(TxDb.Hsapiens.UCSC.hg19.knownGene)
        library(org.Hs.eg.db)
        library(GenomeInfoDb)
        library(MultiAssayExperiment)
        library(curatedTCGAData)
        library(TCGAutils)
    })

    ## TCGA MultiAssayExperiment with RaggedExperiment data
    mae <- curatedTCGAData("ACC", c("RNASeq2GeneNorm", "CNASNP", "Mutation"),
        version = "1.1.38", dry.run = FALSE)

    ## genomic coordinates
    gn <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
    gn <- keepStandardChromosomes(granges(gn), pruning.mode="coarse")
    seqlevelsStyle(gn) <- "NCBI"
    genome(gn)
    gn <- unstrand(gn)

    ## reduce mutations, marking any genomic range with non-silent
    ## mutation as FALSE
    nonsilent <- function(scores, ranges, qranges)
        any(scores != "Silent")
    mre <- mae[["ACC_Mutation-20160128"]]
    seqlevelsStyle(rowRanges(mre)) <- "NCBI"
    ## hack to make genomes match
    genome(mre) <- paste0(correctBuild(unique(genome(mre)), "NCBI"), ".p13")
    mutations <- qreduceAssay(mre, gn, nonsilent, "Variant_Classification")
    genome(mre) <- correctBuild(unique(genome(mre)), "NCBI")

    ## reduce copy number
    re <- mae[["ACC_CNASNP-20160128"]]
    class(re)
    ## [1] "RaggedExperiment"
    seqlevelsStyle(re) <- "NCBI"
    genome(re) <- "GRCh37.p13"
    cn <- qreduceAssay(re, gn, weightedmean, "Segment_Mean")
    genome(re) <- "GRCh37"

    ## ALTERNATIVE
    ##
    ## TCGAutils helper function to convert RaggedExperiment objects to
    ## RangedSummarizedExperiment based on annotated gene ranges
    mae2 <- mae
    mae2[[1L]] <- re
    mae2[[2L]] <- mre
    qreduceTCGA(mae2)

## End(Not run)

RaggedExperiment objects

Description

The RaggedExperiment class is a container for storing range-based data, including but not limited to copy number data, and mutation data. It can store a collection of GRanges objects, as it is derived from the GenomicRangesList.

Usage

RaggedExperiment(..., colData = DataFrame(), metadata = list())

## S4 method for signature 'RaggedExperiment'
seqinfo(x)

## S4 replacement method for signature 'RaggedExperiment'
seqinfo(x, new2old = NULL, pruning.mode = c("error", "coarse", "fine", "tidy")) <- value

## S4 method for signature 'RaggedExperiment'
rowRanges(x, ...)

## S4 replacement method for signature 'RaggedExperiment,GRanges'
rowRanges(x, ...) <- value

## S4 method for signature 'RaggedExperiment'
mcols(x, use.names = FALSE, ...)

## S4 replacement method for signature 'RaggedExperiment'
mcols(x, ...) <- value

## S4 method for signature 'RaggedExperiment'
rowData(x, use.names = TRUE, ...)

## S4 replacement method for signature 'RaggedExperiment'
rowData(x, ...) <- value

## S4 method for signature 'RaggedExperiment'
dim(x)

## S4 method for signature 'RaggedExperiment'
dimnames(x)

## S4 replacement method for signature 'RaggedExperiment,list'
dimnames(x) <- value

## S4 replacement method for signature 'RaggedExperiment,ANY'
dimnames(x) <- value

## S4 method for signature 'RaggedExperiment'
length(x)

## S4 method for signature 'RaggedExperiment'
colData(x, ...)

## S4 replacement method for signature 'RaggedExperiment,DataFrame'
colData(x) <- value

## S4 method for signature 'RaggedExperiment,missing'
assay(x, i, withDimnames = TRUE, ...)

## S4 method for signature 'RaggedExperiment,ANY'
assay(x, i, withDimnames = TRUE, ...)

## S4 method for signature 'RaggedExperiment'
assays(x, withDimnames = TRUE, ...)

## S4 method for signature 'RaggedExperiment'
assayNames(x, ...)

## S4 method for signature 'RaggedExperiment'
show(object)

## S4 method for signature 'RaggedExperiment'
as.list(x, ...)

## S4 method for signature 'RaggedExperiment'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S4 method for signature 'RaggedExperiment'
x$name

## S4 method for signature 'RaggedExperiment,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'RaggedExperiment,Vector'
overlapsAny(
  query,
  subject,
  maxgap = 0L,
  minoverlap = 1L,
  type = c("any", "start", "end", "within", "equal"),
  ...
)

## S4 method for signature 'RaggedExperiment,Vector'
subsetByOverlaps(
  x,
  ranges,
  maxgap = -1L,
  minoverlap = 0L,
  type = c("any", "start", "end", "within", "equal"),
  invert = FALSE,
  ...
)

## S4 method for signature 'RaggedExperiment'
subset(x, subset, select, ...)

Arguments

...

Constructor: GRanges, list of GRanges, or GRangesList OR assay: Additional arguments for assay. See details for more information.
colData

A DataFrame describing samples. Length of rowRanges must equal the number of rows in colData
metadata

A list to include in the metadata. Any metadata included in the input objects are lost.
x

A RaggedExperiment object.
new2old

The new2old argument allows the user to rename, drop, add and/or reorder the "sequence levels" in x.

new2old can be NULL or an integer vector with one element per entry in Seqinfo object value (i.e. new2old and value must have the same length) describing how the "new" sequence levels should be mapped to the "old" sequence levels, that is, how the entries in value should be mapped to the entries in seqinfo(x). The values in new2old must be >= 1 and <= length(seqinfo(x)). NAs are allowed and indicate sequence levels that are being added. Old sequence levels that are not represented in new2old will be dropped, but this will fail if those levels are in use (e.g. if x is a GRanges object with ranges defined on those sequence levels) unless a pruning mode is specified via the pruning.mode argument (see below).

If new2old=NULL, then sequence levels can only be added to the existing ones, that is, value must have at least as many entries as seqinfo(x) (i.e. length(values) >= length(seqinfo(x))) and also seqlevels(values)[seq_len(length(seqlevels(x)))] must be identical to seqlevels(x).

Note that most of the times it's easier to proceed in 2 steps:

  1. First align the seqlevels on the left (seqlevels(x)) with the seqlevels on the right.

  2. Then call seqinfo(x) <- value. Because seqlevels(x) and seqlevels(value) now are identical, there's no need to specify new2old.

This 2-step approach will typically look like this: 

    seqlevels(x) <- seqlevels(value)  # align seqlevels
    seqinfo(x) <- seqinfo(value)  # guaranteed to work

Or, if x has seqlevels not in value, it will look like this: 

    seqlevels(x, pruning.mode="coarse") <- seqlevels(value)
    seqinfo(x) <- seqinfo(value)  # guaranteed to work

The pruning.mode argument will control what happens to x when some of its seqlevels get droppped. See below for more information.

pruning.mode

When some of the seqlevels to drop from x are in use (i.e. have ranges on them), the ranges on these sequences need to be removed before the seqlevels can be dropped. We call this pruning. The pruning.mode argument controls how to prune x. Four pruning modes are currently defined: "error", "coarse", "fine", and "tidy". "error" is the default. In this mode, no pruning is done and an error is raised. The other pruning modes do the following:

  • "coarse": Remove the elements in x where the seqlevels to drop are in use. Typically reduces the length of x. Note that if x is a list-like object (e.g. GRangesList, GAlignmentPairs, or GAlignmentsList), then any list element in x where at least one of the sequence levels to drop is in use is fully removed. In other words, when pruning.mode="coarse", the seqlevels setter will keep or remove full list elements and not try to change their content. This guarantees that the exact ranges (and their order) inside the individual list elements are preserved. This can be a desirable property when the list elements represent compound features like exons grouped by transcript (stored in a GRangesList object as returned by exonsBy( , by="tx")), or paired-end or fusion reads, etc...

  • "fine": Supported on list-like objects only. Removes the ranges that are on the sequences to drop. This removal is done within each list element of the original object x and doesn't affect its length or the order of its list elements. In other words, the pruned object is guaranteed to be parallel to the original object.

  • "tidy": Like the "fine" pruning above but also removes the list elements that become empty as the result of the pruning. Note that this pruning mode is particularly well suited on a GRangesList object that contains transcripts grouped by gene, as returned by transcriptsBy( , by="gene"). Finally note that, as a convenience, this pruning mode is supported on non list-like objects (e.g. GRanges or GAlignments objects) and, in this case, is equivalent to the "coarse" mode.

See the "B. DROP SEQLEVELS FROM A LIST-LIKE OBJECT" section in the examples below for an extensive illustration of these pruning modes.

value

  • dimnames: A list of dimension names

  • mcols: A DataFrame representing the assays

use.names

(logical default FALSE) whether to propagate rownames from the object to rownames of metadata DataFrame
i

logical(1), integer(1), or character(1) indicating the assay to be reported. For [, i can be any supported Vector object, e.g., GRanges.
withDimnames

logical (default TRUE) whether to use dimension names in the resulting object
object

A RaggedExperiment object.
row.names

NULL or a character vector giving the row names for the data frame. Missing values are not allowed.
optional

logical. If TRUE, setting row names and converting column names (to syntactic names: see make.names) is optional. Note that all of R's base package as.data.frame() methods use optional only for column names treatment, basically with the meaning of data.frame(*, check.names = !optional). See also the make.names argument of the matrix method.
name

a literal character string or a name (possibly backtick quoted). For extraction, this is normally (see under ‘Environments’) partially matched to the names of the object.

j

integer(), character(), or logical() index selecting columns from RaggedExperiment
drop

logical (default TRUE) whether to drop empty samples
query

A RaggedExperiment instance.
subject, ranges

Each of them can be an IntegerRanges (e.g. IRanges, Views) or IntegerRangesList (e.g. IRangesList, ViewsList) derivative. In addition, if subject or ranges is an IntegerRanges object, query or x can be an integer vector to be converted to length-one ranges.

If query (or x) is an IntegerRangesList object, then subject (or ranges) must also be an IntegerRangesList object.

If both arguments are list-like objects with names, each list element from the 2nd argument is paired with the list element from the 1st argument with the matching name, if any. Otherwise, list elements are paired by position. The overlap is then computed between the pairs as described below.

If subject is omitted, query is queried against itself. In this case, and only this case, the drop.self and drop.redundant arguments are allowed. By default, the result will contain hits for each range against itself, and if there is a hit from A to B, there is also a hit for B to A. If drop.self is TRUE, all self matches are dropped. If drop.redundant is TRUE, only one of A->B and B->A is returned.
maxgap

A single integer >= -1.

If type is set to "any", maxgap is interpreted as the maximum gap that is allowed between 2 ranges for the ranges to be considered as overlapping. The gap between 2 ranges is the number of positions that separate them. The gap between 2 adjacent ranges is 0. By convention when one range has its start or end strictly inside the other (i.e. non-disjoint ranges), the gap is considered to be -1.

If type is set to anything else, maxgap has a special meaning that depends on the particular type. See type below for more information.

minoverlap

A single non-negative integer.

Only ranges with a minimum of minoverlap overlapping positions are considered to be overlapping.

When type is "any", at least one of maxgap and minoverlap must be set to its default value.

type

By default, any overlap is accepted. By specifying the type parameter, one can select for specific types of overlap. The types correspond to operations in Allen's Interval Algebra (see references). If type is start or end, the intervals are required to have matching starts or ends, respectively. Specifying equal as the type returns the intersection of the start and end matches. If type is within, the query interval must be wholly contained within the subject interval. Note that all matches must additionally satisfy the minoverlap constraint described above.

The maxgap parameter has special meaning with the special overlap types. For start, end, and equal, it specifies the maximum difference in the starts, ends or both, respectively. For within, it is the maximum amount by which the subject may be wider than the query. If maxgap is set to -1 (the default), it's replaced internally by 0.

invert

If TRUE, keep only the ranges in x that do not overlap ranges.

subset

logical expression indicating elements or rows to keep: missing values are taken as false.
select

If query is an IntegerRanges derivative: When select is "all" (the default), the results are returned as a Hits object. Otherwise the returned value is an integer vector parallel to query (i.e. same length) containing the first, last, or arbitrary overlapping interval in subject, with NA indicating intervals that did not overlap any intervals in subject.

If query is an IntegerRangesList derivative: When select is "all" (the default), the results are returned as a HitsList object. Otherwise the returned value depends on the drop argument. When select != "all" && !drop, an IntegerList is returned, where each element of the result corresponds to a space in query. When select != "all" && drop, an integer vector is returned containing indices that are offset to align with the unlisted query.

Value

constructor returns a RaggedExperiment object

'rowRanges' returns a GRanges object summarizing ranges corresponding to assay() rows.

'rowRanges<-' returns a RaggedExperiment object with replaced ranges

'mcols' returns a DataFrame object of the metadata columns

'assays' returns a SimpleList

'overlapsAny' returns a logical vector of length equal to the number of rows in the query; TRUE when the copy number region overlaps the subject.

'subsetByOverlaps' returns a RaggedExperiment containing only copy number regions overlapping subject.

Methods (by generic)

  • seqinfo(RaggedExperiment): seqinfo accessor

  • seqinfo(RaggedExperiment) <- value: Replace seqinfo metadata of the ranges

  • rowRanges(RaggedExperiment): rowRanges accessor

  • rowRanges(x = RaggedExperiment) <- value: rowRanges replacement

  • mcols(RaggedExperiment): get the metadata columns of the ranges, rectangular representation of the 'assays'

  • mcols(RaggedExperiment) <- value: set the metadata columns of the ranges corresponding to the assays

  • rowData(RaggedExperiment): get the rowData or metadata for the ranges

  • rowData(RaggedExperiment) <- value: set the rowData or metadata for the ranges

  • dim(RaggedExperiment): get dimensions (number of sample-specific row ranges by number of samples)

  • dimnames(RaggedExperiment): get row (sample-specific) range names and sample names

  • dimnames(x = RaggedExperiment) <- value: set row (sample-specific) range names and sample names

  • dimnames(x = RaggedExperiment) <- value: set row range names and sample names to NULL

  • length(RaggedExperiment): get the length of row vectors in the object, similar to SummarizedExperiment

  • colData(RaggedExperiment): get column data

  • colData(x = RaggedExperiment) <- value: change the colData

  • assay(x = RaggedExperiment, i = missing): assay missing method uses first metadata column

  • assay(x = RaggedExperiment, i = ANY): assay numeric method.

  • assays(RaggedExperiment): assays

  • assayNames(RaggedExperiment): names in each assay

  • show(RaggedExperiment): show method

  • as.list(RaggedExperiment): Allow extraction of metadata columns as a plain list

  • as.data.frame(RaggedExperiment): Allow conversion to plain data.frame

  • $: Easily access the colData columns with the dollar sign operator

  • x[i: Subset a RaggedExperiment object

  • overlapsAny(query = RaggedExperiment, subject = Vector): Determine whether copy number ranges defined by query overlap ranges of subject.

  • subsetByOverlaps(x = RaggedExperiment, ranges = Vector): Subset the RaggedExperiment to contain only copy number ranges overlapping ranges of subject.

  • subset(RaggedExperiment): subset helper function for dividing by rowData and / or colData values

Constructors

RaggedExperiment(..., colData=DataFrame()): Creates a RaggedExperiment object using multiple GRanges objects or a list of GRanges objects. Additional column data may be provided as a DataFrame object.

Accessors

In the following, 'x' represents a RaggedExperiment object:

rowRanges(x):

Get the ranged data. Value is a GenomicRanges object.

assays(x):

Get the assays. Value is a SimpleList.

assay(x, i):

An alternative to assays(x)[[i]] to get the ith (default first) assay element.

mcols(x), mcols(x) <- value:

Get or set the metadata columns. For RaggedExperiment, the columns correspond to the assay ith elements.

rowData(x), rowData(x) <- value:

Get or set the row data. Value is a DataFrame object. Also corresponds to the mcols data.

Note for advanced users and developers. Both mcols and rowData setters may reduce the size of the internal RaggedExperiment data representation. Particularly after subsetting, the internal row index is modified and such setter operations will use the index to subset the data and reduce the "rows" of the internal data representation.

Subsetting

x[i, j]: Get ranges or elements (i and j, respectively) with optional metadata columns where i or j can be missing, an NA-free logical, numeric, or character vector.

Coercion

In the following, 'object' represents a RaggedExperiment object:

as(object, "GRangesList"):

Creates a GRangesList object from a RaggedExperiment.

as(from, "RaggedExperiment"):

Creates a RaggedExperiment object from a GRangesList, or GRanges object.

Examples

## Create an empty RaggedExperiment instance
re0 <- RaggedExperiment()
re0

## Create a couple of GRanges objects with row ranges names
sample1 <- GRanges(
    c(a = "chr1:1-10:-", b = "chr1:11-18:+"),
    score = 1:2)
sample2 <- GRanges(
    c(c = "chr2:1-10:-", d = "chr2:11-18:+"),
    score = 3:4)

## Include column data
colDat <- DataFrame(id = 1:2)

## Create a RaggedExperiment object from a couple of GRanges
re1 <- RaggedExperiment(sample1=sample1, sample2=sample2, colData = colDat)
re1

## With list of GRanges
lgr <- list(sample1 = sample1, sample2 = sample2)

## Create a RaggedExperiment from a list of GRanges
re2 <- RaggedExperiment(lgr, colData = colDat)

grl <- GRangesList(sample1 = sample1, sample2 = sample2)

## Create a RaggedExperiment from a GRangesList
re3 <- RaggedExperiment(grl, colData = colDat)

## Subset a RaggedExperiment
assay(re3[c(1, 3),])
subsetByOverlaps(re3, GRanges("chr1:1-5"))  # by ranges

Create SummarizedExperiment representations by transforming ragged assays to rectangular form.

Description

These methods transform RaggedExperiment objects to similar SummarizedExperiment objects. They do so by transforming assay data to more rectangular representations, following the rules outlined for similarly names transformations sparseAssay(), compactAssay(), disjoinAssay(), and qreduceAssay(). Because of the complexity of the transformation, ti usually only makes sense transform RaggedExperiment objects with a single assay; this is currently enforced at time of coercion.

Usage

sparseSummarizedExperiment(x, i = 1, withDimnames = TRUE, sparse = FALSE)

compactSummarizedExperiment(x, i = 1L, withDimnames = TRUE, sparse = FALSE)

disjoinSummarizedExperiment(x, simplifyDisjoin, i = 1L, withDimnames = TRUE)

qreduceSummarizedExperiment(
  x,
  query,
  simplifyReduce,
  i = 1L,
  withDimnames = TRUE
)

Arguments

x

RaggedExperiment
i

integer(1), character(1), or logical() selecting the assay to be transformed.
withDimnames

logical(1) default TRUE. propagate dimnames to SummarizedExperiment.
sparse

logical(1) whether to return a sparseMatrix representation
simplifyDisjoin

function of 1 argument, used to transform assays. See assay-functions.
query

GRanges provding regions over which reduction is to occur.
simplifyReduce

function of 3 arguments used to transform assays. See assay-functions.

Value

All functions return RangedSummarizedExperiment.

sparseSummarizedExperiment has rowRanges() identical to the row ranges of x, and assay() data as sparseAssay(). This is very space-inefficient representation of ragged data. Use 'sparse=TRUE' to obtain a sparseMatrix assay representation.

compactSummarizedExperiment has rowRanges() identical to the row ranges of x, and assay() data as compactAssay(). This is space-inefficient representation of ragged data when samples are primarily composed of different ranges. Use 'sparse=TRUE' to obtain a sparseMatrix assay representation.

disjoinSummarizedExperiment has rowRanges() identical to the disjoint row ranges of x, disjoint(rowRanges(x)), and assay() data as disjoinAssay().

qreduceSummarizedExperiment has rowRanges() identical to query, and assay() data as qreduceAssay().

sparseMatrix

Convert a dgCMatrix to a RaggedExperiment given that the rownames are coercible to GRanges.

In the following example, x is a dgCMatrix from the Matrix package. 

    `as(x, "RaggedExperiment")`

Examples

x <- RaggedExperiment(GRangesList(
    GRanges(c("A:1-5", "A:4-6", "A:10-15"), score=1:3),
    GRanges(c("A:1-5", "B:1-3"), score=4:5)
))

## sparseSummarizedExperiment

sse <- sparseSummarizedExperiment(x)
assay(sse)
rowRanges(sse)

## compactSummarizedExperiment

cse <- compactSummarizedExperiment(x)
assay(cse)
rowRanges(cse)

## disjoinSummarizedExperiment

disjoinAssay(x, lengths)
dse <- disjoinSummarizedExperiment(x, lengths)
assay(dse)
rowRanges(dse)

## qreduceSummarizedExperiment

x <- RaggedExperiment(GRangesList(
    GRanges(c("A:1-3", "A:4-5", "A:10-15"), score=1:3),
    GRanges(c("A:4-5", "B:1-3"), score=4:5)
))
query <- GRanges(c("A:1-2", "A:4-5", "B:1-5"))

weightedmean <- function(scores, ranges, qranges)
{
    ## weighted average score per query range
    ## the weight corresponds to the size of the overlap of each
    ## overlapping subject range with the corresponding query range
    isects <- pintersect(ranges, qranges)
    sum(scores * width(isects)) / sum(width(isects))
}

qreduceAssay(x, query, weightedmean)
qse <- qreduceSummarizedExperiment(x, query, weightedmean)
assay(qse)
rowRanges(qse)

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")