In vjcitn/MultiAssayExperiment: Software for the integration of multi-omics experiments in Bioconductor

suppressPackageStartupMessages({
    library(MultiAssayExperiment)
    library(GenomicRanges)
    library(SummarizedExperiment)
    library(RaggedExperiment)
})

Installation

source("https://bioconductor.org/biocLite.R")
BiocInstaller::biocLite("MultiAssayExperiment")

Loading the packages:

library(MultiAssayExperiment)
library(GenomicRanges)
library(SummarizedExperiment)
library(RaggedExperiment)

A Brief Description

MultiAssayExperiment offers a data structure for representing and analyzing multi-omics experiments: a biological analysis approach utilizing multiple types of observations, such as DNA mutations and abundance of RNA and proteins, in the same biological specimens.

Choosing the appropriate data structure

For assays with different numbers of rows and even columns, MultiAssayExperiment is recommended. For sets of assays with the same information across all rows (e.g., genes or genomic ranges), SummarizedExperiment is the recommended data structure.

Overview of the MultiAssayExperiment class

Here is an overview of the class and its constructors and extractors:

empty <- MultiAssayExperiment()
empty
slotNames(empty)

Components of the MultiAssayExperiment

ExperimentList: experimental data

The ExperimentList slot and class is the container workhorse for the MultiAssayExperiment class. It contains all the experimental data. It inherits from class S4Vectors::SimpleList with one element/component per data type.

class(experiments(empty)) # ExperimentList

The elements of the ExperimentList can contain ID-based and range-based data. Requirements for all classes in the ExperimentList are listed in the API.

The following base and Bioconductor classes are known to work as elements of the ExperimentList:

• base::matrix: the base class, can be used for ID-based datasets such as gene expression summarized per-gene, microRNA, metabolomics, or microbiome data.

• SummarizedExperiment::SummarizedExperiment: A richer representation compared to a ordinary matrix of ID-based datasets capable of storing additional assay- level metadata.

• Biobase::ExpressionSet: A legacy representation of ID-based datasets, supported for convenience and supplanted by SummarizedExperiment.

• 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.

• RaggedExperiment::RaggedExperiment: For range-based datasets, such as copy number and mutation data, the RaggedExperiment class can be used to represent measurements by genomic positions.

Class requirements within ExperimentList container

See the API section for details on requirements for using other data classes. In general, data classes meeting minimum requirements, including support for square bracket [ subsetting and dimnames() will work by default.

The datasets contained in elements of the ExperimentList can have:

• column names (required)
• row names (optional)

The column names correspond to samples, and are used to match assay data to specimen metadata stored in colData.

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. Note that the existence of "row" names does not mean the data must be rectangular or matrix-like.

Classes contained in the ExperimentList must support the following list of methods:

• [: single square bracket subsetting, with a single comma. It is assumed that values before the comma subset rows, and values after the comma subset columns.
• dimnames() : corresponding to features (such as genes, proteins, etc.) and experimental samples
• dim(): returns a vector of the number of rows and number of columns

colData: primary data

The MultiAssayExperiment keeps one set of "primary" metadata that describes the 'biological unit' which can refer to specimens, experimental subjects, patients, etc. In this vignette, we will refer to each experimental subject as a patient.

colData slot requirements

The colData dataset should be of class DataFrame but can accept a data.frame class object that will be coerced.

In order to relate metadata of the biological unit, the row names of the colData dataset 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

Note on the flexibility of the DataFrame

For many typical purposes the DataFrame and data.frame behave equivalently; but the Dataframe is more flexible as it allows any vector-like data type to be stored in its columns. The flexibility of the DataFrame permits, for example, storing multiple dose-response values for a single cell line, even if the number of doses and responses is not consistent across all cell lines. Doses could be stored in one column of colData as a SimpleList, and responses in another column, also as a SimpleList. Or, dose-response values could be stored in a single column of colData as a two-column matrix for each cell line.

sampleMap: relating colData to multiple assays

The sampleMap is a DataFrame that relates the "primary" data (colData) to the experimental assays:

class(sampleMap(empty)) # DataFrame

The sampleMap provides an unambiguous map from every experimental observation to one and only one row in colData. It is, however, permissible for a row of colData 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 colData, and a "one-to-any-number" mapping from rows of colData to experimental observations.

sampleMap structure

The sampleMap has three columns, with the following column names:

1. assay provides the names of the different experiments / assays performed. These are user-defined, with the only requirement that the names of the ExperimentList, where the experimental assays are stored, must be contained in this column.

2. primary provides the "primary" sample names. All values in this column must also be present in the rownames of colData(MultiAssayExperiment). In this example, allowable values in this column are "Jack", "Jill", "Barbara", and "Bob".

3. colname 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.

This design is motivated by the following situations:

1. It allows flexibility for any amount of technical replication and biological replication (such as tumor and matched normal for a single patient) of individual assays.
2. It allows missing observations (such as RNA-seq performed only for some of the patients).
3. It allows the use of different identifiers to be used for patients / specimens and for each assay. These different identifiers are matched unambiguously, and consistency between them is maintained during subsetting and re-ordering.

Instances where sampleMap isn't provided

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 the 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(experiments=doubleExp)
simpleMultiAssay

In the above example, the user did not provide the colData argument so the constructor function filled it with an empty DataFrame:

colData(simpleMultiAssay)

But the colData can be provided. Here, note that any assay sample (column) that cannot be mapped to a corresponding row in the provided colData gets dropped. This is part of ensuring internal validity of the MultiAssayExperiment.

simpleMultiAssay2 <- MultiAssayExperiment(experiments=doubleExp,
                                          colData=patient.data)
simpleMultiAssay2
colData(simpleMultiAssay2)

metadata

Metadata can be added at different levels of the MultiAssayExperiment.

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")

At the ExperimentList level, the metadata function would allow the user to enter metadata as a list.

metadata(experiments(empty))

At the individual assay level, certain classes may support metadata, for example, metadata and mcols for a SummarizedExperiment. It is recommended to use metadata at the ExperimentList level.

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Creating a MultiAssayExperiment object: a rich example

In this section we demonstrate all core supported data classes, using different sample ID conventions for each assay, with primary colData. The some supported classes such as, matrix, SummarizedExperiment, and RangedSummarizedExperiment.

Create toy datasets demonstrating all supported data types

We have three matrix-like datasets. First, let's represent expression data as a SummarizedExperiment:

(arraydat <- matrix(seq(101, 108), ncol=4,
    dimnames=list(c("ENST00000294241", "ENST00000355076"),
    c("array1", "array2", "array3", "array4"))))

coldat <- data.frame(slope53=rnorm(4),
    row.names=c("array1", "array2", "array3", "array4"))

exprdat <- SummarizedExperiment(arraydat, colData=coldat)
exprdat

The following map matches colData 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. Now, let's store this as a simple matrix which can 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 colData 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 matrix. It is also missing data for "Jill". We will 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 colData sample names to microdat sample names.

(micromap <- data.frame(primary = c("Jack", "Barbara", "Bob"),
                        assay = c("micro1", "micro2", "micro3"),
                        stringsAsFactors = FALSE))

Finally, we create a dataset of class RangedSummarizedExperiment:

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)

And we map the colData samples to the RangedSummarizedExperiment:

(rangemap <-
   data.frame(primary = c("Jack", "Jill", "Bob", "Barbara"),
              assay = c("mysnparray1", "mysnparray2", "mysnparray3",
                        "mysnparray4"), stringsAsFactors = FALSE))

sampleMap creation

The MultiAssayExperiment constructor function can create the sampleMap 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 colData, as a three-column data.frame or DataFrame with three columns named "assay", primary", and "colname". Here we start with a list:

listmap <- list(exprmap, methylmap, micromap, rangemap)
names(listmap) <- c("Affy", "Methyl 450k", "Mirna", "CNV gistic")
listmap

and use the convenience function listToMap to convert the list of data.frame objects to a valid object for the sampleMap:

dfmap <- listToMap(listmap)
dfmap

Note, dfmap can be reverted to a list with another provided function:

mapToList(dfmap, "assay")

Experimental data as a list()

Create an named list of experiments for the MultiAssayExperiment function. All of these names must be found within in the third column of dfmap:

objlist <- list("Affy" = exprdat, "Methyl 450k" = methyldat,
                "Mirna" = microdat, "CNV gistic" = rse)

Creation of the MultiAssayExperiment class object

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:

experiments(myMultiAssay)
colData(myMultiAssay)
sampleMap(myMultiAssay)
metadata(myMultiAssay)

Note that the ExperimentList class extends the SimpleList class to add some validity checks specific to MultiAssayExperiment. It can be used like a list.

Helper function to create a MultiAssayExperiment object

The prepMultiAssay function helps diagnose common problems when creating a MultiAssayExperiment object. It provides error messages and/or warnings in instances where names (either colnames or ExperimentList element names) are inconsistent with those found in the sampleMap. Input arguments are the same as those in the MultiAssayExperiment (i.e., ExperimentList, colData, sampleMap). The resulting output of the prepMultiAssay function is a list of inputs including a "metadata$drops" element for names that were not able to be matched.

Instances where ExperimentList is created without names will prompt an error from prepMultiAssay. Named ExperimentList elements are essential for checks in MultiAssayExperiment.

objlist3 <- objlist
(names(objlist3) <- NULL)
try(prepMultiAssay(objlist3, patient.data, dfmap)$experiments)

Non-matching names may also be present in the ExperimentList elements and the "assay" 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[, "assay"])
prepMultiAssay(objlist3, patient.data, dfmap)$experiments

When colnames in the ExperimentList cannot be matched back to the primary data (colData), these will be dropped and added to the drops element.

exampleMap <- sampleMap(simpleMultiAssay2)
sapply(doubleExp, colnames)
exampleMap
prepMultiAssay(doubleExp, patient.data, exampleMap)$metadata$drops

A similar operation is performed for checking "primary" sampleMap names and colData rownames. In this example, we add a row corresponding to "Joe" that does not have a match in the experimental data.

exMap <- rbind(dfmap,
               DataFrame(assay = "New methyl",
                         primary = "Joe",
                         colname = "Joe"))
invisible(prepMultiAssay(objlist, patient.data, exMap))

To create a MultiAssayExperiment from the results of the prepMultiAssay 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$experiments, prepped$colData,
                                     prepped$sampleMap, prepped$metadata)
preppedMulti

Alternatively, use the do.call function to easily create a MultiAssayExperiment from the output of prepMultiAssay function:

do.call(MultiAssayExperiment, prepped)

Helper functions to create Bioconductor classes from raw data

Recent updates to the GenomicRanges and SummarizedExperiment packages allow the user to create standard Bioconductor classes from raw data. Raw data read in as either data.frame or DataFrame can be converted to GRangesList or SummarizedExperiment classes depending on the type of data.

The function to create a GRangesList from a data.frame, called makeGRangesListFromDataFrame can be found in the GenomicRanges package. makeSummarizedExperimentFromDataFrame is available in the SummarizedExperiment package. It is also possible to create a RangedSummarizedExperiment class object from raw data when ranged data is available.

A simple example can be obtained from the function documentation in GenomicRanges:

grlls <- list(chr = rep("chr1", nrows), start = seq(11, 15),
              end = seq(12, 16), strand = c("+", "-", "+", "*", "*"),
              score = seq(1, 5), specimen = c("a", "a", "b", "b", "c"),
              gene_symbols = paste0("GENE", letters[seq_len(nrows)]))
grldf <- as.data.frame(grlls, stringsAsFactors = FALSE)
GRL <- makeGRangesListFromDataFrame(grldf, split.field = "specimen",
                             names.field = "gene_symbols")

This can then be converted to a RaggedExperiment object for a rectangular representation that will conform more easily to the MultiAssayExperiment API requirements.

RaggedExperiment(GRL)

Note. See the RaggedExperiment vignette for more details.

In the SummarizedExperiment package:

sels <- list(chr = rep("chr2", nrows), start = seq(11, 15),
             end = seq(12, 16), strand = c("+", "-", "+", "*", "*"),
             expr0 = seq(3, 7), expr1 = seq(8, 12), expr2 = seq(12, 16))
sedf <- as.data.frame(sels, row.names = paste0("GENE",
                                               letters[rev(seq_len(nrows))]),
                      stringsAsFactors = FALSE)
sedf
makeSummarizedExperimentFromDataFrame(sedf)

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Integrated subsetting across experiments

MultiAssayExperiment allows subsetting by rows, columns, and assays, rownames, and colnames, across all experiments simultaneously while guaranteeing continued matching of samples.

Subsetting can be done most compactly by the square bracket method, or more verbosely and potentially more flexibly by the subsetBy*() methods.

Subsetting by square bracket [

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). Note that when using the bracket method [, the drop argument is TRUE by default.

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). The following selects the ENST00000355076 gene across all samples, then the first two samples of each assay, and finally the Affy and Methyl 450k assays:

myMultiAssay["ENST00000355076", 1:2, c("Affy", "Methyl 450k")]

Subsetting by character, integer, and logical

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)]

the "drop" argument

Specify 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]

More on subsetting by columns

Experimental samples are stored in the rows of colData but the columns of elements of ExperimentList, 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 colData:

colData(myMultiAssay)

Subsetting by samples identifies the selected samples in rows of the colData DataFrame, then selects all columns of the ExperimentList corresponding to these rows. Here we use an integer to keep the first two rows of colData, and all experimental assays associated to those two primary samples:

myMultiAssay[, 1:2]

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.

Columns can be subset using a logical vector. Here the dollar sign operator ($) accesses one of the columns in colData.

malesMultiAssay <- myMultiAssay[, myMultiAssay$sex == "M"]
colData(malesMultiAssay)

Finally, for special use cases you can exert detailed control of row or column subsetting, by using a list or CharacterList to subset. The following creates a CharacterList of the column names of each assay:

allsamples <- colnames(myMultiAssay)
allsamples

Now let's get rid of three Methyl 450k arrays, those in positions 3, 4, and 5:

allsamples[["Methyl 450k"]] <- allsamples[["Methyl 450k"]][-3:-5]
myMultiAssay[, as.list(allsamples), ]
subsetByColumn(myMultiAssay,  as.list(allsamples))  #equivalent

Subsetting assays

You can select certain assays / experiments using subset, by providing a character, logical, or integer vector. An example using character:

myMultiAssay[, , c("Affy", "CNV gistic")]

You can subset assays also using logical or integer vectors:

is.cnv <- grepl("CNV", names(experiments(myMultiAssay)))
is.cnv
myMultiAssay[, , is.cnv]  #logical subsetting
myMultiAssay[, , which(is.cnv)] #integer subsetting

Subsetting rows (features) by IDs, integers, or logicals

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), they can be subset using any of the following:

• 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 ExperimentList 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 ExperimentList element. The most likely use of integer subsetting would be 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 list or List with element names matching those in the ExperimentList. Each element of the subsetting list will be passed on exactly to subset rows of the corresponding element of the ExperimentList.

Any list or List input allows for selective subsetting. The subsetting is applied only to the matching element names in the ExperimentList. For example, to only take the first two rows of the microRNA dataset, we use a named list to indicate what element we want to subset along with the drop = FALSE argument.

myMultiAssay[list(Mirna = 1:2), , ]
## equivalently
subsetByRow(myMultiAssay, list(Mirna = 1:2))

Again, these operations always return a MultiAssayExperiment class, unless drop=TRUE is passed to the [ backet subset, with any ExperimentList element not containing the feature having zero rows.

For example, return a MultiAssayExperiment where Affy and Methyl 450k contain only "ENST0000035076"" row, and "Mirna" and "CNV gistic" have zero rows (drop argument is set to FALSE by default in subsetBy*):

featSub0 <- subsetByRow(myMultiAssay, "ENST00000355076")
featSub1 <- myMultiAssay["ENST00000355076", , drop = FALSE] #equivalent
all.equal(featSub0, featSub1)
class(featSub1)
class(experiments(featSub1))
experiments(featSub1)

In the following, Affy SummarizedExperiment keeps both rows but with their order reversed, and Methyl 450k keeps only its second row.

featSubsetted <-
  subsetByRow(myMultiAssay, c("ENST00000355076", "ENST00000294241"))
assay(myMultiAssay, 1L)
assay(featSubsetted, 1L)

Subsetting rows (features) by GenomicRanges

For MultiAssayExperiment objects containing range-based objects (currently RangedSummarizedExperiment), these can be subset using a GRanges object, for example:

gr <- GRanges(seqnames = c("chr1", "chr1", "chr2"), strand = c("-", "+", "+"),
              ranges = IRanges(start = c(230602, 443625, 934533),
                               end = c(330701, 443724, 934632)))

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 subset, the rest passed on to IRanges::subsetByOverlaps through "...":

subsetted <- subsetByRow(myMultiAssay, gr, maxgap = 2L, type = "within")
experiments(subsetted)
rowRanges(subsetted[[4]])

Square bracket subsetting can still be used here, but passing on arguments to IRanges::subsetByOverlaps through "..." is simpler using subsetByRow().

Subsetting is endomorphic

subsetByRow, subsetByColumn, subsetByAssay, and square bracket subsetting are all "endomorphic" operations, in that they always return another MultiAssayExperiment object.

Double-bracket subsetting to select experiments

A double-bracket subset operation refers to an experiment, and will return the object contained within an ExperimentList element. It is not endomorphic. For example, the first ExperimentList element is called "Affy" and contains a SummarizedExperiment:

names(myMultiAssay)
myMultiAssay[[1]]
myMultiAssay[["Affy"]]

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Helpers for data clean-up and management

complete.cases

The complete.cases function returns a logical vector of colData rows identifying which primary units have data for all experiments. Recall that myMultiAssay provides data for four individuals:

colData(myMultiAssay)

Of these, only Jack has data for all 5 experiments:

complete.cases(myMultiAssay)

But all four have complete cases for Affy and Methyl 450k:

complete.cases(myMultiAssay[, , 1:2])

This output can be used to select individuals with complete data:

myMultiAssay[, complete.cases(myMultiAssay), ]

duplicated

The duplicated function identifies primary column values or biological units that have multiple observations per assay. It returns a list of LogicalLists that indicate what biological units have one or more duplicated measurements. This output is used for merging replicates by default.

duplicated(myMultiAssay)

intersectRows

The intersectRows function takes all common rownames across all experiments and returns a MultiAssayExperiment with those rows.

(ensmblMatches <- intersectRows(myMultiAssay[, , 1:2]))
rownames(ensmblMatches)

intersectColumns

A call to intersectColumns returns another MultiAssayExperiment where the columns of each element of the ExperimentList correspond exactly to the rows of colData. In many cases, this operation returns a 1-to-1 correspondence of samples to patients for each experiment assay unless replicates are present in the data.

intersectColumns(myMultiAssay)

mergeReplicates

The mergeReplicates function allows the user to specify a function (default: mean) for combining replicate columns in each assay element. This can be combined with intersectColumns to create a MultiAssayExperiment object with one measurement in each experiment per biological unit.

mergeReplicates(intersectColumns(myMultiAssay))

combine c

The combine c function allows the user to append an experiment to the list of experiments already present in MultiAssayExperiment. In the case that additional observations on the same set of samples were performed, the c function can conveniently be referenced to an existing assay that contains the same ordering of sample measurements.

The mapFrom argument indicates what experiment has the exact same organization of samples that will be introduced by the new experiment dataset. If the number of columns in the new experiment do not match those in the reference experiment, an error will be thrown.

Here we introduce a toy dataset created on the fly:

c(myMultiAssay, ExpScores = matrix(1:8, ncol = 4,
dim = list(c("ENSMBL0001", "ENSMBL0002"), paste0("pt", 1:4))),
mapFrom = 1L)

Note: Alternatively, a sampleMap for the additional dataset can be provided.

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Extractor functions

Extractor functions convert a MultiAssayExperiment into other forms that are convenient for analyzing. These would normally be called after any desired subsetting has been performed.

longFormat & wideFormat

Produces long (default) or wide DataFrame objects. The following produces a long DataFrame (the default) for the first two assays:

longFormat(myMultiAssay[, , 1:2])

This is especially useful for performing regression against patient or sample data from colData using the pDataCols argument:

longFormat(myMultiAssay[, , 1:2], colDataCols="age")

The "wide" format is useful for calculating correlations or performing regression against different genomic features. Wide format is in general not possible with replicate measurements, so we demonstrate on the cleaned MultiAssayExperiment for the first 5 columns:

maemerge <- mergeReplicates(intersectColumns(myMultiAssay))
wideFormat(maemerge, colDataCols="sex")[, 1:5]

assay / assays

The assay (singular) function takes a particular experiment and returns a matrix. By default, it will return the first experiment as a matrix.

assay(myMultiAssay)

The assays (plural) function returns a SimpleList of data matrices from the ExperimentList:

assays(myMultiAssay)

The Cancer Genome Atlas and MultiAssayExperiment

Our most recent efforts include the release of the experiment data package, curatedTCGAData. This package will allow users to selectively download cancer datasets from The Cancer Genome Atlas (TCGA) and represent the data as MultiAssayExperiment objects. Please see the package vignette for more details.

BiocInstaller::biocLite("curatedTCGAData")

Dimension names: rownames and colnames

rownames and colnames return a CharacterList of row names and column names across all the assays. A CharacterList is an efficient alternative to list used when each element contains a character vector. It also provides a nice show method:

rownames(myMultiAssay)
colnames(myMultiAssay)

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Requirements for support of additional data classes

Any data classes in the ExperimentList object must support the following methods:

• dimnames
• [
• dim()

Here is what happens if one of the methods doesn't:

objlist2 <- objlist
objlist2[[2]] <- as.vector(objlist2[[2]])
invalid.obj <- try(MultiAssayExperiment(objlist2, patient.data, dfmap))
invalid.obj

Application Programming Interface (API)

For more information on the formal API of MultiAssayExperiment, please see the API wiki document on GitHub. An API package is available for download on GitHub via biocLite("waldronlab/MultiAssayShiny"). It provides visual exploration of available methods in MultiAssayExperiment.

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Methods for MultiAssayExperiment

The following methods are defined for MultiAssayExperiment:

methods(class="MultiAssayExperiment")

Citing MultiAssayExperiment

We are excited to announce the official citation for MultiAssayExperiment in Cancer Research.

citation("MultiAssayExperiment")

sessionInfo()

sessionInfo()

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vjcitn/MultiAssayExperiment documentation built on May 3, 2019, 6:13 p.m.