library(MultiAssayExperiment) empty <- MultiAssayExperiment() empty slotNames(empty)
Elist slot and class is the container workhorse for the
It contains all the experiment data. It inherits from class
one element/component per experiment type.
The elements of the
Elist can contain ID-based and range-based data.
Requirements for all classes in the
Elist are listed in the API, see
API() for details.
These familiar base and Bioconductor classes are supported:
matrix: the base class, can be used for ID-based datasets such as gene expression
summarized per-gene, microRNA, metabolomics, or microbiome data.
Biobase::ExpressionSet: A richer representation of ID-based datasets with additional
SummarizedExperiment::SummarizedExperiment: Also provides a rich representation of ID-based
datasets matrix-like datasets.
SummarizedExperiment::RangedSummarizedExperiment: For rectangular range-based datasets,
one set of genomic ranges are assayed for multiple samples. It can be used for gene
expression, methylation, or other data types that refer to genomic positions.
MultiAssayExperiment::RangedRaggedAssay: inherits from
GRangesList, for ranged-based
ragged arrays, meaning that a potentially different set of genomic ranges are assayed
for each sample. A typical example would be segmented copy number, where
segmentation of copy number alterations occurs and different genomic
locations in each sample.
The datasets contained in elements of the
Elist must have:
The column names correspond to samples, and are used to match assay data to specimen
metadata stored in
The row names can correspond to a variety of features in the data including but not limited to gene names, probe IDs, proteins, and named ranges.
Classes contained in the
Elist must support the following list of methods:
[: standard square bracket subsetting, with a single comma. It is assumed that values before the comma subset rows, and values after the comma subset columns.
colnames(): corresponding to experimental samples
rownames(): corresponding to features such as genes, proteins, etc.
dim(): returns a vector of the number of rows and number of columns
MultiAssayExperiment keeps one set of "primary" metadata that describes
the 'biological unit' which can refer to specimens, experiment subject, patients,
etc. In this tutorial, we will refer to each experiment subject as a patient.
pData dataset should be of class
DataFrame but can accept a
object that will subsequently be coerced.
In order to relate metadata of the biological unit, the row names of the
must contain patient identifiers.
patient.data <- data.frame(sex=c("M", "F", "M", "F"), age=38:41, row.names=c("Jack", "Jill", "Bob", "Barbara")) patient.data
sampleMap is a
DataFrame that provides a map between the "primary" data
pData) and the experimental assays:
sampleMap provides an unambiguous map from every experimental
observation to one and only one row in
pData. It is, however, permissible
for a row of
pData to be associated with multiple experimental observations
or no observations at all. In other words, there is a "many-to-one" mapping
from experimental observations to rows of
pData, and a "one-to-any-number"
mapping from rows of
pData to experimental observations.
pData has three columns, with the following column names:
primary provides the "primary" sample names. All values in this column must also be present in the rownames of pData(MultiAssayExperiment). In this example, allowable values in this column are "Jack", "Jill", "Barbara", and "Bob".
assay provides the sample names used by experimental datasets, which in practice are often different than the primary sample names. For each assay, all column names must be found in this column. Otherwise, those assays would be orphaned: it would be impossible to match them up to samples in the overall experiment. As mentioned above, duplicated values are allowed, to represent replicates with the same overall experiment-level annotation.
assayname provides the names of the different experiments / assays
performed. These are user-defined, with the only requirement that the names
Elist, where the experimental assays are stored, must be contained
in this column.
If each assay uses the same colnames (i.e., if the same sample identifiers are
used for each experiment), a simple list of these datasets is sufficient for
MultiAssayExperiment() constructor function. It is not necessary for
them to have the same rownames or colnames:
exprss1 <- matrix(rnorm(16), ncol = 4, dimnames = list(sprintf("ENST00000%i", sample(288754:290000, 4)), c("Jack", "Jill", "Bob", "Bobby"))) exprss2 <- matrix(rnorm(12), ncol = 3, dimnames = list(sprintf("ENST00000%i", sample(288754:290000, 4)), c("Jack", "Jane", "Bob"))) doubleExp <- list("methyl 2k" = exprss1, "methyl 3k" = exprss2) simpleMultiAssay <- MultiAssayExperiment(Elist=doubleExp) simpleMultiAssay
In the above example, the user did not provide the
pData argument so the
constructor function filled it with a trivial DataFrame:
pData can be provided. Here, note that any assay sample (column) that
cannot be mapped to a corresponding row in the provided
pData gets dropped.
This is part of ensuring internal validity of the
simpleMultiAssay2 <- MultiAssayExperiment(Elist=doubleExp, pData=patient.data) simpleMultiAssay2 pData(simpleMultiAssay2)
Can be of ANY class, for storing study-wide metadata, such as citation information.
For an empty
MultiAssayExperiment object, it is NULL.
class(metadata(empty)) # NULL (class "ANY")
MultiAssayExperimentobject: a rich example
In this section we demonstrate all core supported data classes, using different
sample ID conventions for each assay, with primary pData. The some
supported classes such as,
We have three matrix-like datasets. First let's say expression data,
which in this example we represent as an
library(Biobase) (arraydat <- matrix(seq(101, 108), ncol=4, dimnames=list(c("ENST00000294241", "ENST00000355076"), c("array1", "array2", "array3", "array4")))) arraypdat <- as(data.frame(slope53=rnorm(4), row.names=c("array1", "array2", "array3", "array4")), "AnnotatedDataFrame") exprdat <- ExpressionSet(assayData=arraydat, phenoData=arraypdat) exprdat
The following map matches pData sample names to exprdata sample names. Note that row orders aren't initially matched up, and this is OK.
(exprmap <- data.frame(primary=rownames(patient.data)[c(1, 2, 4, 3)], assay=c("array1", "array2", "array3", "array4"), stringsAsFactors = FALSE))
Now methylation data, which we will represent as a
matrix. It uses
gene identifiers also, but measures a partially overlapping set of genes.
For fun, let's store this as a simple matrix. Also, it contains a replicate
for one of the patients.
(methyldat <- matrix(1:10, ncol=5, dimnames=list(c("ENST00000355076", "ENST00000383706"), c("methyl1", "methyl2", "methyl3", "methyl4", "methyl5"))))
The following map matches pData sample names to methyldat sample names.
(methylmap <- data.frame(primary = c("Jack", "Jack", "Jill", "Barbara", "Bob"), assay = c("methyl1", "methyl2", "methyl3", "methyl4", "methyl5"), stringsAsFactors = FALSE))
Now we have a microRNA platform, which has no common identifiers with the
other datasets, and which we also represent as a
is also missing data for Jill. Just for fun, let's use the same
sample naming convention as we did for arrays.
(microdat <- matrix(201:212, ncol=3, dimnames=list(c("hsa-miR-21", "hsa-miR-191", "hsa-miR-148a", "hsa-miR148b"), c("micro1", "micro2", "micro3"))))
And the following map matches pData sample names to microdat sample names.
(micromap <- data.frame(primary = c("Jack", "Barbara", "Bob"), assay = c("micro1", "micro2", "micro3"), stringsAsFactors = FALSE))
Let's include a
RangedRaggedAssay, which is defined in this package and
GRangesList. This is intended for data such as segmented copy
number, which provide genomic ranges that may be different for each sample.
We start with a
GRangesList, which will later be converted automatically
MultiAssayExperiment constructor function.
suppressPackageStartupMessages(library(GenomicRanges)) ## completely encompasses ENST00000355076 gr1 <- GRanges(seqnames = "chr3", ranges = IRanges(58000000, 59502360), strand = "+", score = 5L, GC = 0.45) ## first is within ENST0000035076 gr2 <- GRanges(seqnames = c("chr3", "chr3"), ranges = IRanges(c(58493000, 3), width=9000), strand = c("+", "-"), score = 3:4, GC = c(0.3, 0.5)) gr3 <- GRanges(seqnames = c("chr1", "chr2"), ranges = IRanges(c(1, 4), c(3, 9)), strand = c("-", "-"), score = c(6L, 2L), GC = c(0.4, 0.1)) grl <- GRangesList("gr1" = gr1, "gr2" = gr2, "gr3" = gr3) names(grl) <- c("snparray1", "snparray2", "snparray3") grl
data.frame matches pData sample to the
(rangemap <- data.frame(primary = c("Jack", "Jill", "Jill"), assay = c("snparray1", "snparray2", "snparray3"), stringsAsFactors = FALSE))
Finally, we create a dataset of class
library(SummarizedExperiment) nrows <- 5; ncols <- 4 counts <- matrix(runif(nrows * ncols, 1, 1e4), nrows) rowRanges <- GRanges(rep(c("chr1", "chr2"), c(2, nrows - 2)), IRanges(floor(runif(nrows, 1e5, 1e6)), width=100), strand=sample(c("+", "-"), nrows, TRUE), feature_id=sprintf("ID\\%03d", 1:nrows)) names(rowRanges) <- letters[1:5] colData <- DataFrame(Treatment=rep(c("ChIP", "Input"), 2), row.names= c("mysnparray1", "mysnparray2", "mysnparray3", "mysnparray4")) rse <- SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=colData)
(rangemap2 <- data.frame(primary = c("Jack", "Jill", "Bob", "Barbara"), assay = c("mysnparray1", "mysnparray2", "mysnparray3", "mysnparray4"), stringsAsFactors = FALSE))
MultiAssayExperiment constructor function can create the
automatically if a single naming convention is used, but in this example
it cannot because we used platform-specific sample identifiers
(e.g. mysnparray1, etc). So we must provide an ID map that matches the
samples of each experiment back to the
pData, as a three-column
DataFrame with three columns named "primary", "assay",
and "assayname". Here we start with a list:
listmap <- list(exprmap, methylmap, micromap, rangemap, rangemap2) names(listmap) <- c("Affy", "Methyl 450k", "Mirna", "CNV gistic", "CNV gistic2") listmap
and use the convenience function
listToMap to convert the list of
objects to a valid object for the
dfmap <- listToMap(listmap) dfmap
dfmap can be reverted to a list with another provided function:
Create an named list of experiments for the MultiAssay function. All of these
names must be found within in the third column of
objlist <- list("Affy" = exprdat, "Methyl 450k" = methyldat, "Mirna" = microdat, "CNV gistic" = grl, "CNV gistic2" = rse)
We recommend using the
MultiAssayExperiment() constructor function:
myMultiAssay <- MultiAssayExperiment(objlist, patient.data, dfmap) myMultiAssay
The following extractor functions can be used to get extract data from the object:
Elist(myMultiAssay) pData(myMultiAssay) sampleMap(myMultiAssay) metadata(myMultiAssay)
Note that the
Elist class extends the
SimpleList class to add some
validity checks specific to
MultiAssayExperiment. It can be used like
PrepMultiAssay function helps diagnose common problems when creating a
MultiAssayExperiment object. It provides error messages and/or warnings in
instances where names (either
Elist element names) are
inconsistent with those found in the sampleMap. Input arguments are the same
as those in the
The resulting output of the
PrepMultiAssay function is a list of inputs
including a "drops" element for names that were not able to be matched.
Elist is created without names will prompt an error
Elist elements are essential for checks in
objlist3 <- objlist (names(objlist3) <- NULL) try(PrepMultiAssay(objlist3, patient.data, dfmap)$Elist)
Non-matching names may also be present in the
Elist elements and the
"assayname" column of the
sampleMap. If names only differ by case and are
identical and unique, names will be standardized to lower case and replaced.
names(objlist3) <- toupper(names(objlist)) names(objlist3) unique(dfmap[, "assayname"]) PrepMultiAssay(objlist3, patient.data, dfmap)$Elist
colnames in the
Elist cannot be matched back to the primary data
pData), these will be dropped and added to the drops element.
exampleMap <- sampleMap(simpleMultiAssay2) sapply(doubleExp, colnames) exampleMap PrepMultiAssay(doubleExp, patient.data, exampleMap)$drops
A similar operation is performed for checking "primary" sampleMap names and
pData rownames. In this example, we add a row corresponding to "Joe" that
does not have a match in the experiment data.
exMap <- rbind(dfmap, DataFrame(primary = "Joe", assay = "Joe", assayname = "New methyl")) PrepMultiAssay(objlist, patient.data, exMap)$drops
To create a
MultiAssayExperiment from the results of the
function, take each corresponding element from the resulting list and enter
them as arguments to the
MultiAssayExperiment constructor function.
prepped <- PrepMultiAssay(objlist, patient.data, exMap) preppedMulti <- MultiAssayExperiment(prepped$Elist, prepped$pData, prepped$sampleMap) preppedMulti
Note that the GRangesList got converted to a
RangedRaggedAssay, a class
intended for data such as segmented copy number that is provides different
genomic ranges for each sample.
RangedRaggedAssay is defined by this
package and inherits from
methods(class="RangedRaggedAssay") getMethod("colnames", "RangedRaggedAssay")
It has some additional methods that are required for any data class contained
class(Elist(myMultiAssay)[]) rownames(Elist(myMultiAssay)[]) colnames(Elist(myMultiAssay)[])
One of the requirements for the
assay method (specifically for this
Elist element) is that the metadata have a
from which to obtain values for the resulting assay matrix. Here we add
ficticious values to such column contained within list elements. See
metadata(Elist(myMultiAssay)[]) <- list(snparray1 = DataFrame(score = 1), snparray2 = DataFrame(score = 1), snparray3 = DataFrame(score = 3)) assay(Elist(myMultiAssay)[], background = 2)
The core functionality of
MultiAssayExperiment is to allow subsetting by
assay, rownames, and colnames, across all experiments simultaneously while
guaranteeing continued matching of samples.
Experimental samples are stored in the rows of pData but the columns of elements of Elist, so when we refer to subsetting by columns, we are referring to columns of the experimental assays. Subsetting by samples / columns will be more obvious after recalling the pData:
Subsetting by samples identifies the selected samples in rows of the pData DataFrame, then selects all columns of the Elist corresponding to these rows. Here we use an integer to keep the first two rows of pData, and all experimental assays associated to those two primary samples:
Note that the above operation keeps different numbers of columns / samples from each assay, reflecting the reality that some samples may not have been assayed in all experiments, and may have replicates in some.
Subsetting the primary identifiers using a character vector corresponding to some rownames of pData returns the same result:
subsetByColumn(myMultiAssay, c("Jack", "Jill"))
Columns can be subset using a logical:
malesMultiAssay <- subsetByColumn(myMultiAssay, pData(myMultiAssay)$sex=="M") pData(malesMultiAssay)
Note that selecting male patients from all assays could have been accomplished equivalently using the square bracket:
myMultiAssay[, pData(myMultiAssay)$sex=="M", ]
Finally, for special use cases you can exert detail control of which samples
to select using a
CharacterList, which is just a convenient form
of a list containing character vectors.
allsamples <- colnames(myMultiAssay) allsamples
Now let's get rid of the Methyl 450k arrays 3-5, a couple different but equivalent ways:
allsamples[["Methyl 450k"]] <- allsamples[["Methyl 450k"]][-3:-5] myMultiAssay[, as.list(allsamples), ]
You can select certain assays / experiments using subset, by providing a character, logical, or integer vector. An example using character:
subsetByAssay(myMultiAssay, c("Affy", "CNV gistic"))
Examples using logical and integer:
is.cnv = grepl("CNV", names(Elist(myMultiAssay))) is.cnv subsetByAssay(myMultiAssay, is.cnv) subsetByAssay(myMultiAssay, which(is.cnv))
subsetByAssay are endogenous operations,
in that it always returns another
assay(myMultiAssay) to retrieve the experimental data in an ordinary
list of datasets as their original classes.
Rows of the assays correspond to assay features or measurements, such as genes. Regardless of whether the assay is ID-based (e.g. matrix, ExpressionSet) or range-based (e.g. RangedSummarizedExperiment, RangedRaggedAssay), they can be subset using any of:
a character vector of IDs that will be matched to rownames in each assay
an integer vector that will select rows of this position from each assay. This probably doesn't make sense unless every Elist element represents the same measurements in the same order and will generate an error if any of the integer elements exceeds the number of rows in any Elist element. The most likely use of integer subsetting would as a "head()" function, for example to look at the first 6 rows of each assay.
a logical vector that will be passed directly to the row subsetting operation for each assay. A warning is issued if this results in recycling for any of the assays.
a list or CharacterList of the same length as Elist. Each element of the subsetting list will be passed on exactly to subset rows of the corresponding element of Elist.
Again, this operation always returns a
MultiAssayExperiment class, unless
"drop=TRUE" is passed to subset, with any
Elist element not containing the
feature having zero rows.
For example, return a MultiAssayExperiment where
contain only ENST0000035076 row, and "Mirna" and "CNV gistic" have zero rows:
drop argument is set to
TRUE by default)
featSubsetted0 <- subsetByRow(myMultiAssay, "ENST00000355076") class(featSubsetted0) class(Elist(featSubsetted0)) Elist(featSubsetted0)
In the following,
Affy ExpressionSet keeps both rows but with their
order reversed, and
Methyl 450k keeps only its second row.
featSubsetted <- subsetByRow(myMultiAssay, c("ENST00000355076", "ENST00000294241")) exprs(Elist(myMultiAssay)[]) exprs(Elist(featSubsetted)[])
MultiAssayExperiment objects containing range-based objects (currently
RangedRaggedAssay), these can be subset
GRanges object, for example:
gr <- GRanges(seqnames = c("chr1"), strand = c("-", "+", "-"), ranges = IRanges(start = c(1, 4, 6), width = 3))
Now do the subsetting. The function doing the work here is
IRanges::subsetByOverlaps - see its arguments for flexible types of
subsetting by range. The first three arguments here are for
rest passed on to
IRanges::subsetByOverlaps through "...":
subsetted <- subsetByRow(myMultiAssay, gr, maxgap = 2L, type = "within") Elist(subsetted)
The bracket method for the
MultiAssayExperiment is equivalent but more
compact than the
subsetBy*() methods. The three positions within the bracket
operator indicate rows, columns, and assays, respectively (pseudocode):
myMultiAssay[rows, columns, assays]
For example, to select the gene ENST00000355076:
myMultiAssay["ENST00000355076", , ]
The above operation works across all types of assays, whether ID-based (e.g. matrix, ExpressionSet, SummarizedExperiment) or range-based (e.g. RangedSummarizedExperiment, RangedRaggedAssay).
You can subset by rows, columns, and assays in a single bracket operation, and they will be performed in that order (rows, then columns, then assays):
myMultiAssay["ENST00000355076", 1:2, c("Affy", "Methyl 450k")]
By columns - character, integer, and logical are all allowed, for example:
myMultiAssay[, "Jack", ] myMultiAssay[, 1, ] myMultiAssay[, c(TRUE, FALSE, FALSE, FALSE), ]
By assay - character, integer, and logical are allowed:
myMultiAssay[, , "Mirna"] myMultiAssay[, , 3] myMultiAssay[, , c(FALSE, FALSE, TRUE, FALSE, FALSE)]
drop=FALSE to keep assays with zero rows or zero columns, e.g.:
myMultiAssay["ENST00000355076", , , drop=FALSE]
Using the default
drop=TRUE, assays with no rows or no columns are removed:
myMultiAssay["ENST00000355076", , , drop=TRUE]
rownames and colnames return a
CharacterList of rownames and colnames across
all the assays. A
CharacterList is just an alternative to
list when each
element contains a character vector, that provides a nice show method:
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