MultiAssayExperiment Application Programming Interface (API)MultiAssayExperiment classMultiAssayExperiment object: a rich exampleRangedRaggedAssay classA built html version of this vignette is available.
MultiAssayExperiment Application Programming Interface (API)To see the API wiki document on GitHub, type:
API()A Shiny app that browses the package API is also available via:
API(shiny=TRUE)MultiAssayExperiment classHere is an overview of the class and its constructors and extractors:
empty <- MultiAssayExperiment()
empty## A MultiAssayExperiment object of 0 listed
## experiments with no user-defined names and respective classes.
## Containing an ExperimentList class object of length 0:
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()slotNames(empty)## [1] "ExperimentList" "pData" "sampleMap" "metadata"
## [5] "drops"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## [1] "ExperimentList"
## attr(,"package")
## [1] "MultiAssayExperiment"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.
See API() for details on using data classes not listed here.
The following base and Bioconductor classes are supported:
base::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 capable of storing additional assay-level metadata.
SummarizedExperiment::SummarizedExperiment: Also provides a rich representation of ID-based 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 ExperimentList must have:
The column names correspond to samples, and are used to match assay data to specimen metadata stored in pData.
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 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.colnames(): corresponding to experimental samplesrownames(): corresponding to features such as genes, proteins, etc.dim(): returns a vector of the number of rows and number of columnsThe 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.
The pData 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 pData 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## sex age
## Jack M 38
## Jill F 39
## Bob M 40
## Barbara F 41DataFrameFor 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 pData as a SimpleList, and responses in another column, also as a SimpleList. Or, dose-response values could be stored in a single column of pData as a two-column matrix for each cell line.
The sampleMap is a DataFrame that relates the “primary” data (pData) to the experimental assays:
class(sampleMap(empty)) # DataFrame## [1] "DataFrame"
## attr(,"package")
## [1] "S4Vectors"The 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.
The sampleMap has three columns, with the following column names:
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.
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”.
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:
sampleMap isn’t providedIf 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## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] methyl 2k: matrix with 4 rows and 4 columns
## [2] methyl 3k: matrix with 4 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()In the above example, the user did not provide the pData argument so the constructor function filled it with an empty DataFrame:
pData(simpleMultiAssay)## DataFrame with 5 rows and 0 columnsBut the 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 MultiAssayExperiment.
simpleMultiAssay2 <- MultiAssayExperiment(experiments=doubleExp,
pData=patient.data)## Warning in .generateMap(pData, experiments): Data from rows:
## NA - Bobby
## NA - Jane
## dropped due to missing phenotype data## harmonizing input:
## removing 1 pData rownames not in sampleMap 'primary'simpleMultiAssay2## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] methyl 2k: matrix with 4 rows and 3 columns
## [2] methyl 3k: matrix with 4 rows and 2 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()pData(simpleMultiAssay2)## DataFrame with 3 rows and 2 columns
## sex age
## <factor> <integer>
## Jack M 38
## Jill F 39
## Bob M 40Can 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")## [1] "NULL"MultiAssayExperiment object: a rich exampleIn 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, matrix, ExpressionSet, SummarizedExperiment, RangedSummarizedExperiment, and RangedRaggedAssay.
We have three matrix-like datasets. First, let’s represent expression data as an ExpressionSet:
library(Biobase)
(arraydat <- matrix(seq(101, 108), ncol=4,
dimnames=list(c("ENST00000294241", "ENST00000355076"),
c("array1", "array2", "array3", "array4"))))## array1 array2 array3 array4
## ENST00000294241 101 103 105 107
## ENST00000355076 102 104 106 108arraypdat <- as(data.frame(slope53=rnorm(4),
row.names=c("array1", "array2", "array3",
"array4")), "AnnotatedDataFrame")
exprdat <- ExpressionSet(assayData=arraydat, phenoData=arraypdat)
exprdat## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 2 features, 4 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: array1 array2 array3 array4
## varLabels: slope53
## varMetadata: labelDescription
## featureData: none
## experimentData: use 'experimentData(object)'
## Annotation: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))## primary assay
## 1 Jack array1
## 2 Jill array2
## 3 Barbara array3
## 4 Bob array4Now 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"))))## methyl1 methyl2 methyl3 methyl4 methyl5
## ENST00000355076 1 3 5 7 9
## ENST00000383706 2 4 6 8 10The 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))## primary assay
## 1 Jack methyl1
## 2 Jack methyl2
## 3 Jill methyl3
## 4 Barbara methyl4
## 5 Bob methyl5Now 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"))))## micro1 micro2 micro3
## hsa-miR-21 201 205 209
## hsa-miR-191 202 206 210
## hsa-miR-148a 203 207 211
## hsa-miR148b 204 208 212And 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))## primary assay
## 1 Jack micro1
## 2 Barbara micro2
## 3 Bob micro3Let’s include a RangedRaggedAssay, which is defined in this package and extends GRangesList. This is intended for data such as segmented copy number, which provides genomic ranges that may be different for each sample. We start with a GRangesList, which will later be converted automatically by the 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## GRangesList object of length 3:
## $snparray1
## GRanges object with 1 range and 2 metadata columns:
## seqnames ranges strand | score GC
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## [1] chr3 [58000000, 59502360] + | 5 0.45
##
## $snparray2
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## [1] chr3 [58493000, 58501999] + | 3 0.3
## [2] chr3 [ 3, 9002] - | 4 0.5
##
## $snparray3
## GRanges object with 2 ranges and 2 metadata columns:
## seqnames ranges strand | score GC
## [1] chr1 [1, 3] - | 6 0.4
## [2] chr2 [4, 9] - | 2 0.1
##
## -------
## seqinfo: 3 sequences from an unspecified genome; no seqlengthsThe following data.frame matches pData samples to the GRangesList:
(rangemap <- data.frame(primary = c("Jack", "Jill", "Jill"),
assay = c("snparray1", "snparray2", "snparray3"),
stringsAsFactors = FALSE))## primary assay
## 1 Jack snparray1
## 2 Jill snparray2
## 3 Jill snparray3Finally, we create a dataset of class RangedSummarizedExperiment:
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)And we map the pData samples to the RangedSummarizedExperiment:
(rangemap2 <-
data.frame(primary = c("Jack", "Jill", "Bob", "Barbara"),
assay = c("mysnparray1", "mysnparray2", "mysnparray3",
"mysnparray4"), stringsAsFactors = FALSE))## primary assay
## 1 Jack mysnparray1
## 2 Jill mysnparray2
## 3 Bob mysnparray3
## 4 Barbara mysnparray4sampleMap creationThe 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 pData, 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, rangemap2)
names(listmap) <- c("Affy", "Methyl 450k", "Mirna", "CNV gistic", "CNV gistic2")
listmap## $Affy
## primary assay
## 1 Jack array1
## 2 Jill array2
## 3 Barbara array3
## 4 Bob array4
##
## $`Methyl 450k`
## primary assay
## 1 Jack methyl1
## 2 Jack methyl2
## 3 Jill methyl3
## 4 Barbara methyl4
## 5 Bob methyl5
##
## $Mirna
## primary assay
## 1 Jack micro1
## 2 Barbara micro2
## 3 Bob micro3
##
## $`CNV gistic`
## primary assay
## 1 Jack snparray1
## 2 Jill snparray2
## 3 Jill snparray3
##
## $`CNV gistic2`
## primary assay
## 1 Jack mysnparray1
## 2 Jill mysnparray2
## 3 Bob mysnparray3
## 4 Barbara mysnparray4and use the convenience function listToMap to convert the list of data.frame objects to a valid object for the sampleMap:
dfmap <- listToMap(listmap)
dfmap## DataFrame with 19 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 Affy Jack array1
## 2 Affy Jill array2
## 3 Affy Barbara array3
## 4 Affy Bob array4
## 5 Methyl 450k Jack methyl1
## ... ... ... ...
## 15 CNV gistic Jill snparray3
## 16 CNV gistic2 Jack mysnparray1
## 17 CNV gistic2 Jill mysnparray2
## 18 CNV gistic2 Bob mysnparray3
## 19 CNV gistic2 Barbara mysnparray4Note, dfmap can be reverted to a list with another provided function:
mapToList(dfmap, "assay")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" = grl, "CNV gistic2" = rse)MultiAssayExperiment class objectWe recommend using the MultiAssayExperiment constructor function:
myMultiAssay <- MultiAssayExperiment(objlist, patient.data, dfmap)
myMultiAssay## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 4 columns
## [2] Methyl 450k: matrix with 2 rows and 5 columns
## [3] Mirna: matrix with 4 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()The following extractor functions can be used to get extract data from the object:
experiments(myMultiAssay)## ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 4 columns
## [2] Methyl 450k: matrix with 2 rows and 5 columns
## [3] Mirna: matrix with 4 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columnspData(myMultiAssay)## DataFrame with 4 rows and 2 columns
## sex age
## <factor> <integer>
## Jack M 38
## Jill F 39
## Bob M 40
## Barbara F 41sampleMap(myMultiAssay)## DataFrame with 19 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 Affy Jack array1
## 2 Affy Jill array2
## 3 Affy Barbara array3
## 4 Affy Bob array4
## 5 Methyl 450k Jack methyl1
## ... ... ... ...
## 15 CNV gistic Jill snparray3
## 16 CNV gistic2 Jack mysnparray1
## 17 CNV gistic2 Jill mysnparray2
## 18 CNV gistic2 Bob mysnparray3
## 19 CNV gistic2 Barbara mysnparray4metadata(myMultiAssay)## NULLNote that the ExperimentList class extends the SimpleList class to add some validity checks specific to MultiAssayExperiment. It can be used like a list.
MultiAssayExperiment objectThe 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, pData, sampleMap). 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.
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)## NULLtry(PrepMultiAssay(objlist3, patient.data, dfmap)$ExperimentList)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)## [1] "AFFY" "METHYL 450K" "MIRNA" "CNV GISTIC" "CNV GISTIC2"unique(dfmap[, "assay"])## [1] Affy Methyl 450k Mirna CNV gistic CNV gistic2
## Levels: Affy Methyl 450k Mirna CNV gistic CNV gistic2PrepMultiAssay(objlist3, patient.data, dfmap)$ExperimentList## Names in the ExperimentList do not match sampleMap assaynames
## standardizing will be attempted...## - names set to lowercase## ExperimentList class object of length 5:
## [1] affy: ExpressionSet with 2 rows and 4 columns
## [2] methyl 450k: matrix with 2 rows and 5 columns
## [3] mirna: matrix with 4 rows and 3 columns
## [4] cnv gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] cnv gistic2: RangedSummarizedExperiment with 5 rows and 4 columnsWhen colnames in the ExperimentList 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)## $`methyl 2k`
## [1] "Jack" "Jill" "Bob" "Bobby"
##
## $`methyl 3k`
## [1] "Jack" "Jane" "Bob"exampleMap## DataFrame with 5 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 methyl 2k Jack Jack
## 2 methyl 2k Jill Jill
## 3 methyl 2k Bob Bob
## 4 methyl 3k Jack Jack
## 5 methyl 3k Bob BobPrepMultiAssay(doubleExp, patient.data, exampleMap)$drops## Not all colnames in the ExperimentList are found in the
## sampleMap, dropping samples from ExperimentList...## $`methyl 2k`
## [1] "Bobby"
##
## $`methyl 3k`
## [1] "Jane"## $`columns.methyl 2k`
## [1] "Bobby"
##
## $`columns.methyl 3k`
## [1] "Jane"A similar operation is performed for checking “primary” sampleMap names and pData rownames. In this example, we add a row corresponding to “Joe” that does not have a match in the experimental data.
exMap <- rbind(dfmap,
DataFrame(assay = "New methyl",
primary = "Joe",
colname = "Joe"))
PrepMultiAssay(objlist, patient.data, exMap)$drops## Warning in PrepMultiAssay(objlist, patient.data, exMap): Lengths of names
## in the ExperimentList and sampleMap are not equal## Not all names in the primary column of the sampleMap
## could be matched to the pData rownames; see $drops## DataFrame with 1 row and 3 columns
## assay primary colname
## <character> <character> <character>
## 1 New methyl Joe Joe## $sampleMap_rows
## DataFrame with 1 row and 3 columns
## assay primary colname
## <character> <character> <character>
## 1 New methyl Joe JoeTo 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)## Warning in PrepMultiAssay(objlist, patient.data, exMap): Lengths of names
## in the ExperimentList and sampleMap are not equal## Not all names in the primary column of the sampleMap
## could be matched to the pData rownames; see $drops## DataFrame with 1 row and 3 columns
## assay primary colname
## <character> <character> <character>
## 1 New methyl Joe JoepreppedMulti <- MultiAssayExperiment(prepped$ExperimentList, prepped$pData,
prepped$sampleMap)
preppedMulti## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 4 columns
## [2] Methyl 450k: matrix with 2 rows and 5 columns
## [3] Mirna: matrix with 4 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Bioconductor classes from raw dataRecent 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:
library(GenomicRanges)
grldf <- structure(
list(chr = structure(c(1L, 1L, 1L, 1L, 1L),
class = "factor", .Label = "chr1"),
start = 11:15, end = 12:16,
strand = structure(c(4L, 1L, 4L, 3L, 2L),
.Label = c("-", ".", "*", "+"),
class = "factor"),
score = 1:5, specimen =
structure(c(1L, 1L, 2L, 2L, 3L),
.Label = c("a", "b", "c"),
class = "factor"),
gene_symbols = structure(1:5, .Label = c("GENEa", "GENEb",
"GENEc", "GENEd",
"GENEe"),
class = "factor")),
.Names = c("chr", "start", "end", "strand", "score", "specimen",
"gene_symbols"),
row.names = c(NA, -5L), class = "data.frame")
makeGRangesListFromDataFrame(grldf, split.field = "specimen",
names.field = "gene_symbols")## GRangesList object of length 3:
## $a
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## GENEa chr1 [11, 12] +
## GENEb chr1 [12, 13] -
##
## $b
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## GENEc chr1 [13, 14] +
## GENEd chr1 [14, 15] *
##
## $c
## GRanges object with 1 range and 0 metadata columns:
## seqnames ranges strand
## GENEe chr1 [15, 16] *
##
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsIn the SummarizedExperiment package:
library(SummarizedExperiment)
sedf <- structure(list(chr = structure(c(1L, 1L, 1L, 1L, 1L),
class = "factor", .Label = "chr2"),
start = 11:15, end = 12:16,
strand = structure(c(4L, 1L, 4L, 3L, 2L),
.Label = c("-", ".", "*", "+"),
class = "factor"),
expr0 = 3:7, expr1 = 8:12, expr2 = 12:16),
.Names = c("chr", "start", "end", "strand",
"expr0", "expr1", "expr2"),
row.names = c("GENEe", "GENEd", "GENEc", "GENEb", "GENEa"),
class = "data.frame")
sedf## chr start end strand expr0 expr1 expr2
## GENEe chr2 11 12 + 3 8 12
## GENEd chr2 12 13 - 4 9 13
## GENEc chr2 13 14 + 5 10 14
## GENEb chr2 14 15 * 6 11 15
## GENEa chr2 15 16 . 7 12 16makeSummarizedExperimentFromDataFrame(sedf)## class: RangedSummarizedExperiment
## dim: 5 3
## metadata(0):
## assays(1): ''
## rownames(5): GENEe GENEd GENEc GENEb GENEa
## rowData names(0):
## colnames(3): expr0 expr1 expr2
## colData names(0):RangedRaggedAssay classNote 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 GRangesList:
methods(class="RangedRaggedAssay")## [1] ! != $
## [4] $<- %in% <
## [7] <= == >
## [10] >= Filter NROW
## [13] ROWNAMES Reduce [
## [16] [<- [[ [[<-
## [19] aggregate anyNA append
## [22] as.character as.complex as.data.frame
## [25] as.env as.integer as.list
## [28] as.logical as.matrix as.numeric
## [31] as.raw assay by
## [34] c cbind classNameForDisplay
## [37] coerce coerce<- countOverlaps
## [40] coverage dim dimnames
## [43] dimnames<- disjoin do.call
## [46] droplevels duplicated elementMetadata
## [49] elementMetadata<- elementNROWS elementType
## [52] end end<- endoapply
## [55] eval expand expand.grid
## [58] extractROWS findOverlaps flank
## [61] getHits getListElement head
## [64] ifelse intersect is.na
## [67] is.unsorted isDisjoint isEmpty
## [70] lapply length lengths
## [73] match mcols mcols<-
## [76] mendoapply merge metadata
## [79] metadata<- mstack names
## [82] names<- narrow ncol
## [85] nrow order overlapsAny
## [88] parallelSlotNames parallelVectorNames pcompare
## [91] pcompareRecursively pintersect promoters
## [94] psetdiff punion range
## [97] ranges ranges<- rank
## [100] rbind reduce relist
## [103] rename rep rep.int
## [106] replaceROWS resize restrict
## [109] rev revElements rowRanges<-
## [112] sapply score score<-
## [115] seqinfo seqinfo<- seqlevelsInUse
## [118] seqnames seqnames<- setdiff
## [121] setequal shift shiftApply
## [124] show sort split
## [127] split<- stack start
## [130] start<- strand strand<-
## [133] subset subsetByColumn subsetByOverlaps
## [136] subsetByRow table tail
## [139] tapply union unique
## [142] unlist unsplit updateObject
## [145] values values<- width
## [148] width<- window window<-
## [151] with within xtabs
## [154] xtfrm zipdown
## see '?methods' for accessing help and source codegetMethod("dimnames", "RangedRaggedAssay")## Method Definition:
##
## function (x)
## {
## dgr <- names(unlist(x, use.names = FALSE))
## list(dgr, names(x))
## }
## <environment: namespace:MultiAssayExperiment>
##
## Signatures:
## x
## target "RangedRaggedAssay"
## defined "RangedRaggedAssay"It has some additional methods that are required for any data class contained in a MultiAssayExperiment:
class(experiments(myMultiAssay)[[4]])## [1] "RangedRaggedAssay"
## attr(,"package")
## [1] "MultiAssayExperiment"rownames(experiments(myMultiAssay)[[4]])## [1] "chr3:58000000-59502360:+" "chr3:58493000-58501999:+"
## [3] "chr3:3-9002:-" "chr1:1-3:-"
## [5] "chr2:4-9:-"colnames(experiments(myMultiAssay)[[4]])## [1] "snparray1" "snparray2" "snparray3"One of the requirements for the assay method (specifically for this RangedRaggedAssay ExperimentList element) is that the metadata have a score column from which to obtain values for the resulting assay matrix. Here we have added ficticious values to such column contained within list elements. See assay,RangedRaggedAssay,ANY-method documentation.
assay(experiments(myMultiAssay)[[4]], background = 2)## Note: method with signature 'RangedRaggedAssay#ANY' chosen for function 'assay',
## target signature 'RangedRaggedAssay#missing'.
## "ANY#missing" would also be valid## snparray1 snparray2 snparray3
## chr3:58000000-59502360:+ 5 2 2
## chr3:58493000-58501999:+ 2 4 2
## chr3:3-9002:- 2 3 2
## chr1:1-3:- 2 2 2
## chr2:4-9:- 2 2 6assay methodThe assay method uses the “inner” metadata columns to obtain a score value from which to create the assay matrix.
rra <- experiments(myMultiAssay)[[4]]
mcols(rra[[1]])## DataFrame with 1 row and 2 columns
## score GC
## <integer> <numeric>
## 1 5 0.45assay(rra, background = 2)## snparray1 snparray2 snparray3
## chr3:58000000-59502360:+ 5 2 2
## chr3:58493000-58501999:+ 2 4 2
## chr3:3-9002:- 2 3 2
## chr1:1-3:- 2 2 2
## chr2:4-9:- 2 2 6The 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 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 pData:
pData(myMultiAssay)## DataFrame with 4 rows and 2 columns
## sex age
## <factor> <integer>
## Jack M 38
## Jill F 39
## Bob M 40
## Barbara F 41Subsetting by samples identifies the selected samples in rows of the pData DataFrame, then selects all columns of the ExperimentList 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:
subsetByColumn(myMultiAssay, 1:2)## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 2 columns
## [2] Methyl 450k: matrix with 2 rows and 3 columns
## [3] Mirna: matrix with 4 rows and 1 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 2 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()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"))## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 2 columns
## [2] Methyl 450k: matrix with 2 rows and 3 columns
## [3] Mirna: matrix with 4 rows and 1 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 2 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Columns can be subset using a logical vector. Here the dollar sign operator ($) accesses one of the columns in pData.
malesMultiAssay <- subsetByColumn(myMultiAssay, myMultiAssay$sex == "M")
pData(malesMultiAssay)## DataFrame with 2 rows and 2 columns
## sex age
## <factor> <integer>
## Jack M 38
## Bob M 40Note that selecting male patients from all assays could have been accomplished equivalently using the square bracket:
myMultiAssay[, myMultiAssay$sex == "M", ]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 2 columns
## [2] Methyl 450k: matrix with 2 rows and 3 columns
## [3] Mirna: matrix with 4 rows and 2 columns
## [4] CNV gistic: RangedRaggedAssay with 1 rows and 1 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 2 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Finally, for special use cases you can exert detail control of which samples to select using a list or CharacterList, which is just a convenient form of a list containing character vectors.
allsamples <- colnames(myMultiAssay)
allsamples## CharacterList of length 5
## [["Affy"]] array1 array2 array3 array4
## [["Methyl 450k"]] methyl1 methyl2 methyl3 methyl4 methyl5
## [["Mirna"]] micro1 micro2 micro3
## [["CNV gistic"]] snparray1 snparray2 snparray3
## [["CNV gistic2"]] mysnparray1 mysnparray2 mysnparray3 mysnparray4Now let’s get rid of the Methyl 450k arrays 3 to 5, a couple different but equivalent ways:
allsamples[["Methyl 450k"]] <- allsamples[["Methyl 450k"]][-3:-5]
myMultiAssay[, as.list(allsamples), ]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 4 columns
## [2] Methyl 450k: matrix with 2 rows and 2 columns
## [3] Mirna: matrix with 4 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()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"))## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] Affy: ExpressionSet with 2 rows and 4 columns
## [2] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Examples using logical and integer:
is.cnv = grepl("CNV", names(experiments(myMultiAssay)))
is.cnv## [1] FALSE FALSE FALSE TRUE TRUEsubsetByAssay(myMultiAssay, is.cnv)## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [2] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()subsetByAssay(myMultiAssay, which(is.cnv))## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] CNV gistic: RangedRaggedAssay with 5 rows and 3 columns
## [2] CNV gistic2: RangedSummarizedExperiment with 5 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()subsetByRow, subsetByColumn, and subsetByAssay are endogenous operations, in that it always returns another MultiAssayExperiment object.
Use assay(myMultiAssay) to retrieve the experimental data in an ordinary list of numeric matrices from the different data classes (in progress).
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 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 CharacterList of the same length as ExperimentList. Each element of the subsetting list will be passed on exactly to subset rows of the corresponding element of the ExperimentList.
Again, this operation always returns 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*):
featSubsetted0 <- subsetByRow(myMultiAssay, "ENST00000355076")
class(featSubsetted0)## [1] "MultiAssayExperiment"
## attr(,"package")
## [1] "MultiAssayExperiment"class(experiments(featSubsetted0))## [1] "ExperimentList"
## attr(,"package")
## [1] "MultiAssayExperiment"experiments(featSubsetted0)## ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 1 rows and 4 columns
## [2] Methyl 450k: matrix with 1 rows and 5 columns
## [3] Mirna: matrix with 0 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 0 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 0 rows and 4 columnsIn 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(experiments(myMultiAssay)[[1]])## array1 array2 array3 array4
## ENST00000294241 101 103 105 107
## ENST00000355076 102 104 106 108exprs(experiments(featSubsetted)[[1]])## array1 array2 array3 array4
## ENST00000355076 102 104 106 108
## ENST00000294241 101 103 105 107GenomicRangesFor MultiAssayExperiment objects containing range-based objects (currently RangedSummarizedExperiment and RangedRaggedAssay), these can be subset using a 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 subset, the rest passed on to IRanges::subsetByOverlaps through “…”:
subsetted <- subsetByRow(myMultiAssay, gr, maxgap = 2L, type = "within")
experiments(subsetted)## ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 0 rows and 4 columns
## [2] Methyl 450k: matrix with 0 rows and 5 columns
## [3] Mirna: matrix with 0 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 1 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 0 rows and 4 columns[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", , ]## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] Affy: ExpressionSet with 1 rows and 4 columns
## [2] Methyl 450k: matrix with 1 rows and 5 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()The above operation works across all types of assays, whether ID-based (e.g. matrix, ExpressionSet, SummarizedExperiment) or range-based (e.g. RangedSummarizedExperiment, RangedRaggedAssay). 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):
myMultiAssay["ENST00000355076", 1:2, c("Affy", "Methyl 450k")]## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] Affy: ExpressionSet with 1 rows and 2 columns
## [2] Methyl 450k: matrix with 1 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()By columns - character, integer, and logical are all allowed, for example:
myMultiAssay[, "Jack", ]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 1 columns
## [2] Methyl 450k: matrix with 2 rows and 2 columns
## [3] Mirna: matrix with 4 rows and 1 columns
## [4] CNV gistic: RangedRaggedAssay with 1 rows and 1 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 1 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()myMultiAssay[, 1, ]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 1 columns
## [2] Methyl 450k: matrix with 2 rows and 2 columns
## [3] Mirna: matrix with 4 rows and 1 columns
## [4] CNV gistic: RangedRaggedAssay with 1 rows and 1 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 1 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()myMultiAssay[, c(TRUE, FALSE, FALSE, FALSE), ]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 2 rows and 1 columns
## [2] Methyl 450k: matrix with 2 rows and 2 columns
## [3] Mirna: matrix with 4 rows and 1 columns
## [4] CNV gistic: RangedRaggedAssay with 1 rows and 1 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 5 rows and 1 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()By assay - character, integer, and logical are allowed:
myMultiAssay[, , "Mirna"]## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] Mirna: matrix with 4 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()myMultiAssay[, , 3]## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] Mirna: matrix with 4 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()myMultiAssay[, , c(FALSE, FALSE, TRUE, FALSE, FALSE)]## A MultiAssayExperiment object of 1 listed
## experiment with a user-defined name and respective class.
## Containing an ExperimentList class object of length 1:
## [1] Mirna: matrix with 4 rows and 3 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Specify drop=FALSE to keep assays with zero rows or zero columns, e.g.:
myMultiAssay["ENST00000355076", , , drop=FALSE]## A MultiAssayExperiment object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 5:
## [1] Affy: ExpressionSet with 1 rows and 4 columns
## [2] Methyl 450k: matrix with 1 rows and 5 columns
## [3] Mirna: matrix with 0 rows and 3 columns
## [4] CNV gistic: RangedRaggedAssay with 0 rows and 3 columns
## [5] CNV gistic2: RangedSummarizedExperiment with 0 rows and 4 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()Using the default drop=TRUE, assays with no rows or no columns are removed:
myMultiAssay["ENST00000355076", , , drop=TRUE]## A MultiAssayExperiment object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 2:
## [1] Affy: ExpressionSet with 1 rows and 4 columns
## [2] Methyl 450k: matrix with 1 rows and 5 columns
## To access:
## experiments() - to obtain the ExperimentList instance
## pData() - for the primary/phenotype DataFrame
## sampleMap() - for the sample availability DataFrame
## metadata() - for the metadata object of ANY class
## See also: subsetByAssay(), subsetByRow(), subsetByColumn()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:
rownames(myMultiAssay)## CharacterList of length 5
## [["Affy"]] ENST00000294241 ENST00000355076
## [["Methyl 450k"]] ENST00000355076 ENST00000383706
## [["Mirna"]] hsa-miR-21 hsa-miR-191 hsa-miR-148a hsa-miR148b
## [["CNV gistic"]] chr3:58000000-59502360:+ ... chr2:4-9:-
## [["CNV gistic2"]] a b c d ecolnames(myMultiAssay)## CharacterList of length 5
## [["Affy"]] array1 array2 array3 array4
## [["Methyl 450k"]] methyl1 methyl2 methyl3 methyl4 methyl5
## [["Mirna"]] micro1 micro2 micro3
## [["CNV gistic"]] snparray1 snparray2 snparray3
## [["CNV gistic2"]] mysnparray1 mysnparray2 mysnparray3 mysnparray4Any data classes in the ExperimentList object must support the following methods:
colnames()rownames()[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## [1] "Error in if (dim(object)[1] > 0 && is.null(rownames(object))) { : \n missing value where TRUE/FALSE needed\n"
## attr(,"class")
## [1] "try-error"
## attr(,"condition")
## <simpleError in if (dim(object)[1] > 0 && is.null(rownames(object))) { msg <- paste(" rownames in", class(object), "are NULL")} else { NULL}: missing value where TRUE/FALSE needed>The following methods are defined for MultiAssayExperiment:
methods(class="MultiAssayExperiment")## [1] $ $<- [ assay
## [5] complete.cases dimnames experiments experiments<-
## [9] getHits isEmpty length metadata
## [13] metadata<- names pData pData<-
## [17] sampleMap sampleMap<- show subsetByAssay
## [21] subsetByColumn subsetByRow updateObject
## see '?methods' for accessing help and source codec() function for adding new assays to existing MultiAssayExperiment
sessionInfo()## R version 3.3.2 (2016-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.1 LTS
##
## 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
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SummarizedExperiment_1.4.0 GenomicRanges_1.26.2
## [3] GenomeInfoDb_1.10.3 IRanges_2.8.1
## [5] S4Vectors_0.12.1 Biobase_2.34.0
## [7] BiocGenerics_0.20.0 MultiAssayExperiment_1.0.1
## [9] BiocStyle_2.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.9 knitr_1.15.1 XVector_0.14.0
## [4] magrittr_1.5 zlibbioc_1.20.0 xtable_1.8-2
## [7] lattice_0.20-34 R6_2.2.0 stringr_1.2.0
## [10] tools_3.3.2 shinydashboard_0.5.3 grid_3.3.2
## [13] htmltools_0.3.5 yaml_2.1.14 rprojroot_1.2
## [16] digest_0.6.12 Matrix_1.2-8 shiny_1.0.0
## [19] bitops_1.0-6 RCurl_1.95-4.8 mime_0.5
## [22] evaluate_0.10 rmarkdown_1.3 stringi_1.1.2
## [25] backports_1.0.5 httpuv_1.3.3