MultiAssayExperiment classMultiAssayExperiment object: a rich exampleRangedRaggedAssay classA built html version of this vignette is available.
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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 "Elist" class object of length 0:
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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] "Elist" "pData" "sampleMap" "metadata" "drops"We explain the role of each of these components of MultiAssayExperiment below.
The Elist slot and class is the driver for the MultiAssayExperiment class, as it contains the experimental data. It is a SimpleList (similar to the base list), with one element per data type.
class(Elist(empty)) # Elist## [1] "Elist"
## attr(,"package")
## [1] "MultiAssayExperiment"The elements of Elist can contain ID-based and range-based data class that support a minimum of methods, see API() for details on using data classes not discussed here. The following classes are known to work as Elist elements, to support a good variety of data types:
matrix: the base data class, can be used for ID-based datasets such as gene expression summarized per-gene, microRNA, metabolomics, or microbiome data.ExpressionSet: A richer representation for ID-based datasets, could be used for the same types of data as matrix, but storing additional assay-level metadata.SummarizedExperiment: Another rich representation for ID-based matrix-like datasetsRangedSummarizedExperiment: For rectangular range-based datasets, meaning that one set of genomic ranges are assayed for multiple samples. Could be used for gene expression, methylation, or other data types referring to genomic positions.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 Elist must have column names and row names. The column names correspond to samples, and are used to match assay data to specimen metadata stored in pData, as explained in the following two sections.
[: 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 samplesrownames(): corresponding to features such as genes, proteins, etcdim(): returns a vector of the number of rows and number of columnsThe MultiAssayExperiment keeps one set of “primary” metadata that describes the biological specimens experimental subject, patients, etc. For the sake of simplicity, we will refer to each experimental subject as a patient.
class(pData(empty)) # DataFrame## [1] "DataFrame"
## attr(,"package")
## [1] "S4Vectors"pData contains one row per patient or specimen, and one column per variable of clinical or experimental metadata. rownames(pData) must provide the identifiers used for the patients or specimens.
In the following examples we will have assays for 4 patients, with the following bit of metadata relating to the patients. Although pData is stored internally as a DataFrame, the MultiAssayExperiment constructor will automatically convert a data.frame.
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.
sampleMap is a DataFrame that provides a map between the “primary” data (pData) and 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. 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 of the Elist, where the experimental assays are stored, must be contained in this column.
This design is motivated by the following situations:
Can be any class, for storing study-wide metadata, such as citation information. It can also be NULL, as with the empty MultiAssayExperiment:
class(metadata(empty)) # NULL (class "ANY")## [1] "NULL"Maintains provenance of rows and columns dropped during subsetting operations.
If each assay uses the same colnames (ie, 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(Elist=doubleExp)## Warning in MultiAssayExperiment(Elist = doubleExp): neither sampleMap nor
## pData provided, sampleMap will be generatedsimpleMultiAssay## A "MultiAssayExperiment" object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 2:
## [1] methyl 2k: "matrix" - 4 rows, 4 columns
## [2] methyl 3k: "matrix" - 4 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 a trivial DataFrame:
pData(simpleMultiAssay)## DataFrame with 5 rows and 1 column
## pheno1
## <logical>
## Jack NA
## Jill NA
## Bob NA
## Bobby NA
## Jane NABut 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(Elist=doubleExp, pData=patient.data)## Warning in MultiAssayExperiment(Elist = doubleExp, pData = patient.data):
## sampleMap not provided, trying to generate sampleMap...## Warning in .generateMap(pData, newElist): Data from rows:
## Bobby - methyl 2k
## Jane - methyl 3k
## dropped due to missing phenotype datasimpleMultiAssay2## A "MultiAssayExperiment" object of 2 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 2:
## [1] methyl 2k: "matrix" - 4 rows, 3 columns
## [2] methyl 3k: "matrix" - 4 rows, 2 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 4 rows and 2 columns
## sex age
## <factor> <integer>
## Jack M 38
## Jill F 39
## Bob M 40
## Barbara F 41MultiAssayExperiment 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 currently supported classes of datasets are matrix, ExpressionSet, SummarizedExperiment, RangedSummarizedExperiment, and RangedRaggedAssay.
We have three matrix-like datasets. First let’s say expression data, which in this example we represent 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. 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"))))## 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. 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"))))## 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 provide 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 sample 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)(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 mysnparray4The 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 “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## $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
## primary assay assayname
## <character> <character> <character>
## 1 Jack array1 Affy
## 2 Jill array2 Affy
## 3 Barbara array3 Affy
## 4 Bob array4 Affy
## 5 Jack methyl1 Methyl 450k
## ... ... ... ...
## 15 Jill snparray3 CNV gistic
## 16 Jack mysnparray1 CNV gistic2
## 17 Jill mysnparray2 CNV gistic2
## 18 Bob mysnparray3 CNV gistic2
## 19 Barbara mysnparray4 CNV gistic2Note, dfmap can be reverted to a list with another provided function:
mapToList(dfmap, "assayname")list()Create an named list of experiments for the MultiAssay 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] Methyl 450k: "matrix" - 2 rows, 5 columns
## [3] Mirna: "matrix" - 4 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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:
Elist(myMultiAssay)## "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] Methyl 450k: "matrix" - 2 rows, 5 columns
## [3] Mirna: "matrix" - 4 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 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
## primary assay assayname
## <character> <character> <character>
## 1 Jack array1 Affy
## 2 Jill array2 Affy
## 3 Barbara array3 Affy
## 4 Bob array4 Affy
## 5 Jack methyl1 Methyl 450k
## ... ... ... ...
## 15 Jill snparray3 CNV gistic
## 16 Jack mysnparray1 CNV gistic2
## 17 Jill mysnparray2 CNV gistic2
## 18 Bob mysnparray3 CNV gistic2
## 19 Barbara mysnparray4 CNV gistic2metadata(myMultiAssay)## NULLNote that the Elist 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 Elist element names) are inconsistent with those found in the sampleMap. Input arguments are the same as those in the MultiAssayExperiment (i.e., Elist, 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 Elist is created without names will prompt an error from PrepMultiAssay. Named Elists are essential for checks in MultiAssayExperiment.
objlist3 <- objlist
(names(objlist3) <- NULL)## NULLtry(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)## [1] "AFFY" "METHYL 450K" "MIRNA" "CNV GISTIC" "CNV GISTIC2"unique(dfmap[, "assayname"])## [1] "Affy" "Methyl 450k" "Mirna" "CNV gistic" "CNV gistic2"PrepMultiAssay(objlist3, patient.data, dfmap)$Elist## Names in the Elist do not match sampleMap assaynames
## standardizing will be attempted...## - names set to lowercase## "Elist" class object of length 5:
## [1] affy: "ExpressionSet" - 2 rows, 4 columns
## [2] methyl 450k: "matrix" - 2 rows, 5 columns
## [3] mirna: "matrix" - 4 rows, 3 columns
## [4] cnv gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] cnv gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columnsWhen 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)## $`methyl 2k`
## [1] "Jack" "Jill" "Bob" "Bobby"
##
## $`methyl 3k`
## [1] "Jack" "Jane" "Bob"exampleMap## DataFrame with 5 rows and 3 columns
## primary assay assayname
## <character> <character> <character>
## 1 Jack Jack methyl 2k
## 2 Jill Jill methyl 2k
## 3 Bob Bob methyl 2k
## 4 Jack Jack methyl 3k
## 5 Bob Bob methyl 3kPrepMultiAssay(doubleExp, patient.data, exampleMap)$drops## Not all colnames in the Elist are found in the sampleMap,
## dropping samples from Elist...## methyl 2k methyl 3k
## "Bobby" "Jane"## $`columns.methyl 2k`
## [1] "Bobby"
##
## $`columns.methyl 3k`
## [1] "Jane"A similar operation is performed for checking “primary” sampleMap names and pData rownames. In this example, we add a row corresponding to “Joe” that does not have a match in the experiment data.
exMap <- rbind(dfmap,
DataFrame(primary = "Joe",
assay = "Joe",
assayname = "New methyl"))
PrepMultiAssay(objlist, patient.data, exMap)$drops## Warning in PrepMultiAssay(objlist, patient.data, exMap): Lengths of names
## in the Elist 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
## primary assay assayname
## <character> <character> <character>
## 1 Joe Joe New methyl## $sampleMap_rows
## DataFrame with 1 row and 3 columns
## primary assay assayname
## <character> <character> <character>
## 1 Joe Joe New methylTo 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 Elist and sampleMap are not equal## Not all names in the primary column of the sampleMap
## could be matched to the pData rownames; see $drops## DataFrame with 1 row and 3 columns
## primary assay assayname
## <character> <character> <character>
## 1 Joe Joe New methylpreppedMulti <- MultiAssayExperiment(prepped$Elist, prepped$pData, prepped$sampleMap)
preppedMulti## A "MultiAssayExperiment" object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] Methyl 450k: "matrix" - 2 rows, 5 columns
## [3] Mirna: "matrix" - 4 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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()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 classNameForDisplay coerce
## [37] coerce<- colnames colnames<-
## [40] countOverlaps coverage dim
## [43] disjoin do.call droplevels
## [46] duplicated elementMetadata elementMetadata<-
## [49] elementNROWS elementType end
## [52] end<- endoapply eval
## [55] expand expand.grid extractROWS
## [58] findOverlaps flank getHits
## [61] getListElement head high2low
## [64] ifelse intersect is.na
## [67] is.unsorted isDisjoint isEmpty
## [70] lapply length lengths
## [73] match mcols mcols<-
## [76] mendoapply metadata metadata<-
## [79] mstack names names<-
## [82] narrow ncol nrow
## [85] order overlapsAny parallelSlotNames
## [88] pcompare pcompareRecursively pintersect
## [91] promoters psetdiff punion
## [94] range ranges ranges<-
## [97] rank reduce relist
## [100] rename rep rep.int
## [103] replaceROWS resize restrict
## [106] rev revElements rowRanges<-
## [109] rownames sapply score
## [112] score<- seqinfo seqinfo<-
## [115] seqlevelsInUse seqnames seqnames<-
## [118] setdiff shift shiftApply
## [121] show showAsCell sort
## [124] split split<- splitAsList
## [127] stack start start<-
## [130] strand strand<- subset
## [133] subsetByColumn subsetByOverlaps subsetByRow
## [136] table tail tapply
## [139] union unique unlist
## [142] unsplit updateObject values
## [145] values<- width width<-
## [148] window window<- with
## [151] within xtabs xtfrm
## [154] zipdown
## see '?methods' for accessing help and source codegetMethod("colnames", "RangedRaggedAssay")## Method Definition:
##
## function (x, do.NULL = TRUE, prefix = "col")
## {
## .local <- function (x)
## base::names(x)
## .local(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(Elist(myMultiAssay)[[4]])## [1] "RangedRaggedAssay"
## attr(,"package")
## [1] "MultiAssayExperiment"rownames(Elist(myMultiAssay)[[4]])## [1] "1" "2" "3" "4" "5"colnames(Elist(myMultiAssay)[[4]])## [1] "snparray1" "snparray2" "snparray3"One of the requirements for the assay method (specifically for this RangedRaggedAssay Elist element) is that the metadata have a score column from which to obtain values for the resulting assay matrix. Here we add ficticious values to such column contained within list elements. See assay,RangedRaggedAssay,ANY-method documentation.
metadata(Elist(myMultiAssay)[[4]]) <- list(snparray1 = DataFrame(score = 1),
snparray2 = DataFrame(score = 1),
snparray3 = DataFrame(score = 3))
assay(Elist(myMultiAssay)[[4]], background = 2)## snparray1 snparray2 snparray3
## chr3:58000000-59502360:+ 1 2 2
## chr3:58493000-58501999:+ 2 1 2
## chr3:3-9002:- 2 1 2
## chr1:1-3:- 2 2 3
## chr2:4-9:- 2 2 3The 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:
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 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:
subsetByColumn(myMultiAssay, 1:2)## A "MultiAssayExperiment" object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 2 columns
## [2] Methyl 450k: "matrix" - 2 rows, 3 columns
## [3] Mirna: "matrix" - 4 rows, 1 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 2 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 2 columns
## [2] Methyl 450k: "matrix" - 2 rows, 3 columns
## [3] Mirna: "matrix" - 4 rows, 1 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 2 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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:
malesMultiAssay <- subsetByColumn(myMultiAssay, pData(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[, pData(myMultiAssay)$sex=="M", ]## A "MultiAssayExperiment" object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 2 columns
## [2] Methyl 450k: "matrix" - 2 rows, 3 columns
## [3] Mirna: "matrix" - 4 rows, 2 columns
## [4] CNV gistic: "RangedRaggedAssay" - 1 rows, 1 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 2 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 3-5, 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] Methyl 450k: "matrix" - 2 rows, 2 columns
## [3] Mirna: "matrix" - 4 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 2:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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(Elist(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 "Elist" class object of length 2:
## [1] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [2] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 2:
## [1] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [2] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 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 Affy and Methyl 450k 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)## [1] "MultiAssayExperiment"
## attr(,"package")
## [1] "MultiAssayExperiment"class(Elist(featSubsetted0))## [1] "Elist"
## attr(,"package")
## [1] "MultiAssayExperiment"Elist(featSubsetted0)## "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 1 rows, 4 columns
## [2] Methyl 450k: "matrix" - 1 rows, 5 columns
## [3] Mirna: "matrix" - 0 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 0 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 0 rows, 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(Elist(myMultiAssay)[[1]])## array1 array2 array3 array4
## ENST00000294241 101 103 105 107
## ENST00000355076 102 104 106 108exprs(Elist(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")
Elist(subsetted)## "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 0 rows, 4 columns
## [2] Methyl 450k: "matrix" - 0 rows, 5 columns
## [3] Mirna: "matrix" - 0 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 1 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 0 rows, 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 "Elist" class object of length 2:
## [1] Affy: "ExpressionSet" - 1 rows, 4 columns
## [2] Methyl 450k: "matrix" - 1 rows, 5 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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).
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 "Elist" class object of length 2:
## [1] Affy: "ExpressionSet" - 1 rows, 2 columns
## [2] Methyl 450k: "matrix" - 1 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 1 columns
## [2] Methyl 450k: "matrix" - 2 rows, 2 columns
## [3] Mirna: "matrix" - 4 rows, 1 columns
## [4] CNV gistic: "RangedRaggedAssay" - 1 rows, 1 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 1 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 1 columns
## [2] Methyl 450k: "matrix" - 2 rows, 2 columns
## [3] Mirna: "matrix" - 4 rows, 1 columns
## [4] CNV gistic: "RangedRaggedAssay" - 1 rows, 1 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 1 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 1 columns
## [2] Methyl 450k: "matrix" - 2 rows, 2 columns
## [3] Mirna: "matrix" - 4 rows, 1 columns
## [4] CNV gistic: "RangedRaggedAssay" - 1 rows, 1 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 1 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 1:
## [1] Mirna: "matrix" - 4 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 1:
## [1] Mirna: "matrix" - 4 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 1:
## [1] Mirna: "matrix" - 4 rows, 3 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 1 rows, 4 columns
## [2] Methyl 450k: "matrix" - 1 rows, 5 columns
## [3] Mirna: "matrix" - 0 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 0 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 0 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 "Elist" class object of length 2:
## [1] Affy: "ExpressionSet" - 1 rows, 4 columns
## [2] Methyl 450k: "matrix" - 1 rows, 5 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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"]] 1 2 3 4 5
## [["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 Elist object must support the following methods:
colnames()rownames()[dim()Here is what happens if one of the methods doesn’t:
objlist2 <- objlist
objlist2[[2]] <- data.frame(objlist2[[2]])
invalid.obj <- try(MultiAssayExperiment(objlist2, patient.data, dfmap))
invalid.obj## A "MultiAssayExperiment" object of 5 listed
## experiments with user-defined names and respective classes.
## Containing an "Elist" class object of length 5:
## [1] Affy: "ExpressionSet" - 2 rows, 4 columns
## [2] Methyl 450k: "data.frame" - 2 rows, 5 columns
## [3] Mirna: "matrix" - 4 rows, 3 columns
## [4] CNV gistic: "RangedRaggedAssay" - 5 rows, 3 columns
## [5] CNV gistic2: "RangedSummarizedExperiment" - 5 rows, 4 columns
## To access slots use:
## Elist() - to obtain the "Elist" of experiment instances
## 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 methods are defined for MultiAssayExperiment:
methods(class="MultiAssayExperiment")## [1] Elist Elist<- [ assay
## [5] colnames getHits isEmpty length
## [9] metadata names pData pData<-
## [13] rownames sampleMap sampleMap<- show
## [17] subsetByAssay subsetByColumn subsetByRow
## see '?methods' for accessing help and source codec() function for adding new assays to existing MultiAssayExperiment
sessionInfo()## R version 3.3.0 (2016-05-03)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04 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_1.3.2 GenomicRanges_1.25.0
## [3] GenomeInfoDb_1.9.2 IRanges_2.7.0
## [5] S4Vectors_0.11.0 Biobase_2.33.0
## [7] BiocGenerics_0.19.0 MultiAssayExperiment_0.101.2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.5 knitr_1.13 XVector_0.13.0
## [4] magrittr_1.5 zlibbioc_1.19.0 xtable_1.8-2
## [7] R6_2.1.2 stringr_1.0.0 tools_3.3.0
## [10] shinydashboard_0.5.1 htmltools_0.3.5 yaml_2.1.13
## [13] digest_0.6.9 shiny_0.13.2 formatR_1.4
## [16] evaluate_0.9 mime_0.4 rmarkdown_0.9.6
## [19] stringi_1.0-1 httpuv_1.3.3