Subsetting a MultiAssayExperiment object — subsetBy • MultiAssayExperiment

A set of functions for extracting and dividing a MultiAssayExperiment

Usage

subsetByRow(x, y, ...)

subsetByRowData(x, y, rowDataCol, ...)

subsetByColData(x, y)

subsetByColumn(x, y)

subsetByAssay(x, y)

# S4 method for class 'ExperimentList,ANY'
subsetByRow(x, y, ...)

# S4 method for class 'ExperimentList,list'
subsetByRow(x, y, ...)

# S4 method for class 'ExperimentList,List'
subsetByRow(x, y, ...)

# S4 method for class 'ExperimentList,logical'
subsetByRow(x, y, ...)

# S4 method for class 'ExperimentList,list'
subsetByColumn(x, y)

# S4 method for class 'ExperimentList,List'
subsetByColumn(x, y)

# S4 method for class 'ExperimentList,logical'
subsetByColumn(x, y)

# S4 method for class 'ExperimentList'
subsetByAssay(x, y)

# S4 method for class 'MultiAssayExperiment,ANY'
subsetByColData(x, y)

# S4 method for class 'MultiAssayExperiment,character'
subsetByColData(x, y)

# S4 method for class 'MultiAssayExperiment,ANY'
subsetByRow(x, y, i = TRUE, ...)

# S4 method for class 'MultiAssayExperiment,list'
subsetByRow(x, y, ...)

# S4 method for class 'MultiAssayExperiment,List'
subsetByRow(x, y, ...)

# S4 method for class 'MultiAssayExperiment,ANY'
subsetByColumn(x, y)

# S4 method for class 'MultiAssayExperiment'
subsetByAssay(x, y)

# S4 method for class 'MultiAssayExperiment,character,character'
subsetByRowData(x, y, rowDataCol, i = TRUE, ...)

intersectByRowData(x, y, rowDataCol, i, ...)

# S4 method for class 'MultiAssayExperiment,character,character'
intersectByRowData(x, y, rowDataCol, i = TRUE, ...)

# S4 method for class 'MultiAssayExperiment,ANY,ANY,ANY'
x[i, j, k, ..., drop = FALSE]

# S4 method for class 'MultiAssayExperiment,ANY,ANY'
x[[i, j, ...]]

# S4 method for class 'MultiAssayExperiment,ANY,ANY'
x[[i, j, ...]] <- value

# S4 method for class 'MultiAssayExperiment,ANY,ANY,ANY'
x[i, j, ...] <- value

Arguments

x: A MultiAssayExperiment or ExperimentList
y: Either a character, integer, logical, list, List, or GRanges object for subsetting by rows within the experiments
...: Additional arguments passed on to lower level functions.
rowDataCol: character(1) The name of the column in the rowData. If the column is not present, the experiment will be skipped. When rowDataCol is "rownames" or "row.names", the values of y will be matched with the row names in the rowData of the experiment.
i: For the subsetByRow and subsetByRowData MultiAssayExperiment methods, either a character, logical, or numeric vector to selectively subset experiments with y (default is TRUE). For bracket ([) methods, see y input.
j: Either a character, logical, or numeric vector for subsetting by colData rows. See details for more information.
k: Either a character, logical, or numeric vector for subsetting by assays
drop: logical (default FALSE) whether to drop all empty assay elements in the ExperimentList
value: An assay compatible with the MultiAssayExperiment API

Value

subsetBy*: operations are endomorphic and return either MultiAssayExperiment or ExperimentList depending on the input.

Details

Subsetting a MultiAssayExperiment by the j index can yield a call to either subsetByColData or subsetByColumn. For vector inputs, the subset will be applied to the colData rows. For List-type inputs, the List will be applied to each of the elements in the ExperimentList. The order of the subsetting elements in the List must match that of the ExperimentList in the MultiAssayExperiment.

subsetBycolData: Select biological units by vector input types
subsetByColumn: Select observations by assay or for each assay
subsetByRow: Select rows by assay or for each assay
subsetByAssay: Select experiments
subsetByRowData: Select rows by values in the rowData
intersectByRowData: Intersect with values in the rowData

rowData

Some assays may have additional metadata associated with the rows. This metadata is stored in the rowData slot of the object, typically a SummarizedExperiment or RangedSummarizedExperiment.

subsetByRowData allows the user to subset the rows of the assays based on the values in the rowData.

intersectByRowData is a special case of subsetByRowData where the rowData values are intersected with the y values. Naturally, the y values are expected to be of type character.

Note that rowDataCol allows the user to specify a particular column from which to extract the values for subsetting. This column name must be consistent across assays. If the column is not present in an assay, the assay will be skipped and considered a no-op. Assays are also skipped when there are no values in the rowData that match the y values.

Note that the use of rownames or row.names as the rowDataCol requires that the assay class have a rownames() method.

Examples

## Load the example MultiAssayExperiment
example("MultiAssayExperiment")
#> 
#> MltAsE> ## Run the example ExperimentList
#> MltAsE> example("ExperimentList")
#> 
#> ExprmL> ## Create an empty ExperimentList instance
#> ExprmL> ExperimentList()
#> ExperimentList class object of length 0:
#>  
#> ExprmL> ## Create array matrix and AnnotatedDataFrame to create an ExpressionSet class
#> ExprmL> arraydat <- matrix(data = seq(101, length.out = 20), ncol = 4,
#> ExprmL+     dimnames = list(
#> ExprmL+         c("ENST00000294241", "ENST00000355076",
#> ExprmL+         "ENST00000383706","ENST00000234812", "ENST00000383323"),
#> ExprmL+         c("array1", "array2", "array3", "array4")
#> ExprmL+     ))
#> 
#> ExprmL> colDat <- data.frame(slope53 = rnorm(4),
#> ExprmL+     row.names = c("array1", "array2", "array3", "array4"))
#> 
#> ExprmL> ## SummarizedExperiment constructor
#> ExprmL> exprdat <- SummarizedExperiment::SummarizedExperiment(arraydat,
#> ExprmL+     colData = colDat)
#> 
#> ExprmL> ## Create a sample methylation dataset
#> ExprmL> methyldat <- matrix(data = seq(1, length.out = 25), ncol = 5,
#> ExprmL+     dimnames = list(
#> ExprmL+         c("ENST00000355076", "ENST00000383706",
#> ExprmL+           "ENST00000383323", "ENST00000234812", "ENST00000294241"),
#> ExprmL+         c("methyl1", "methyl2", "methyl3",
#> ExprmL+           "methyl4", "methyl5")
#> ExprmL+     ))
#> 
#> ExprmL> ## Create a sample RNASeqGene dataset
#> ExprmL> rnadat <- matrix(
#> ExprmL+     data = sample(c(46851, 5, 19, 13, 2197, 507,
#> ExprmL+         84318, 126, 17, 21, 23979, 614), size = 20, replace = TRUE),
#> ExprmL+     ncol = 4,
#> ExprmL+     dimnames = list(
#> ExprmL+         c("XIST", "RPS4Y1", "KDM5D", "ENST00000383323", "ENST00000234812"),
#> ExprmL+         c("samparray1", "samparray2", "samparray3", "samparray4")
#> ExprmL+     ))
#> 
#> ExprmL> ## Create a mock RangedSummarizedExperiment from a data.frame
#> ExprmL> rangedat <- data.frame(chr="chr2", start = 11:15, end = 12:16,
#> ExprmL+     strand = c("+", "-", "+", "*", "."),
#> ExprmL+     samp0 = c(0,0,1,1,1), samp1 = c(1,0,1,0,1), samp2 = c(0,1,0,1,0),
#> ExprmL+     row.names = c(paste0("ENST", "00000", 135411:135414), "ENST00000383323"))
#> 
#> ExprmL> rangeSE <- SummarizedExperiment::makeSummarizedExperimentFromDataFrame(rangedat)
#> 
#> ExprmL> ## Combine to a named list and call the ExperimentList constructor function
#> ExprmL> assayList <- list(Affy = exprdat, Methyl450k = methyldat, RNASeqGene = rnadat,
#> ExprmL+                 GISTIC = rangeSE)
#> 
#> ExprmL> ## Use the ExperimentList constructor
#> ExprmL> ExpList <- ExperimentList(assayList)
#> 
#> MltAsE> ## Create sample maps for each experiment
#> MltAsE> exprmap <- data.frame(
#> MltAsE+     primary = c("Jack", "Jill", "Barbara", "Bob"),
#> MltAsE+     colname = c("array1", "array2", "array3", "array4"),
#> MltAsE+     stringsAsFactors = FALSE)
#> 
#> MltAsE> methylmap <- data.frame(
#> MltAsE+     primary = c("Jack", "Jack", "Jill", "Barbara", "Bob"),
#> MltAsE+     colname = c("methyl1", "methyl2", "methyl3", "methyl4", "methyl5"),
#> MltAsE+     stringsAsFactors = FALSE)
#> 
#> MltAsE> rnamap <- data.frame(
#> MltAsE+     primary = c("Jack", "Jill", "Bob", "Barbara"),
#> MltAsE+     colname = c("samparray1", "samparray2", "samparray3", "samparray4"),
#> MltAsE+     stringsAsFactors = FALSE)
#> 
#> MltAsE> gistmap <- data.frame(
#> MltAsE+     primary = c("Jack", "Bob", "Jill"),
#> MltAsE+     colname = c("samp0", "samp1", "samp2"),
#> MltAsE+     stringsAsFactors = FALSE)
#> 
#> MltAsE> ## Combine as a named list and convert to a DataFrame
#> MltAsE> maplist <- list(Affy = exprmap, Methyl450k = methylmap,
#> MltAsE+     RNASeqGene = rnamap, GISTIC = gistmap)
#> 
#> MltAsE> ## Create a sampleMap
#> MltAsE> sampMap <- listToMap(maplist)
#> 
#> MltAsE> ## Create an example phenotype data
#> MltAsE> colDat <- data.frame(sex = c("M", "F", "M", "F"), age = 38:41,
#> MltAsE+     row.names = c("Jack", "Jill", "Bob", "Barbara"))
#> 
#> MltAsE> ## Create a MultiAssayExperiment instance
#> MltAsE> mae <- MultiAssayExperiment(experiments = ExpList, colData = colDat,
#> MltAsE+     sampleMap = sampMap)

## Using experiment names
subsetByAssay(mae, "Affy")
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 12 sampleMap rows not in names(experiments)
#> A MultiAssayExperiment object of 1 listed
#>  experiment with a user-defined name and respective class.
#>  Containing an ExperimentList class object of length 1:
#>  [1] Affy: SummarizedExperiment with 5 rows and 4 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Using numeric indices
subsetByAssay(mae, 1:2)
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 7 sampleMap rows not in names(experiments)
#> A MultiAssayExperiment object of 2 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 2:
#>  [1] Affy: SummarizedExperiment with 5 rows and 4 columns
#>  [2] Methyl450k: matrix with 5 rows and 5 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Using a logical vector
subsetByAssay(mae, c(TRUE, FALSE, TRUE))
#> Warning: 'experiments' dropped; see 'drops()'
#> harmonizing input:
#>   removing 5 sampleMap rows not in names(experiments)
#> A MultiAssayExperiment object of 3 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 3:
#>  [1] Affy: SummarizedExperiment with 5 rows and 4 columns
#>  [2] RNASeqGene: matrix with 5 rows and 4 columns
#>  [3] GISTIC: RangedSummarizedExperiment with 5 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Subset by character vector (Jack)
subsetByColData(mae, "Jack")
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 1 columns
#>  [2] Methyl450k: matrix with 5 rows and 2 columns
#>  [3] RNASeqGene: matrix with 5 rows and 1 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 1 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Subset by numeric index of colData rows (Jack and Bob)
subsetByColData(mae, c(1, 3))
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 2 columns
#>  [2] Methyl450k: matrix with 5 rows and 3 columns
#>  [3] RNASeqGene: matrix with 5 rows and 2 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 2 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Subset by logical indicator of colData rows (Jack and Jill)
subsetByColData(mae, c(TRUE, TRUE, FALSE, FALSE))
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 2 columns
#>  [2] Methyl450k: matrix with 5 rows and 3 columns
#>  [3] RNASeqGene: matrix with 5 rows and 2 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 2 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

subsetByColumn(mae, list(Affy = 1:2,
    Methyl450k = c(3,5,2), RNASeqGene = 2:4, GISTIC = 1))
#> harmonizing input:
#>   removing 7 sampleMap rows with 'colname' not in colnames of experiments
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 2 columns
#>  [2] Methyl450k: matrix with 5 rows and 3 columns
#>  [3] RNASeqGene: matrix with 5 rows and 3 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 1 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

subsetWith <- S4Vectors::mendoapply(`[`, colnames(mae),
    MoreArgs = list(1:2))
subsetByColumn(mae, subsetWith)
#> harmonizing input:
#>   removing 8 sampleMap rows with 'colname' not in colnames of experiments
#>   removing 1 colData rownames not in sampleMap 'primary'
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 2 columns
#>  [2] Methyl450k: matrix with 5 rows and 2 columns
#>  [3] RNASeqGene: matrix with 5 rows and 2 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 2 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Use a GRanges object to subset rows where ranged data present
egr <- GenomicRanges::GRanges(seqnames = "chr2",
    IRanges::IRanges(start = 11, end = 13), strand = "-")
subsetByRow(mae, egr)
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 0 rows and 4 columns
#>  [2] Methyl450k: matrix with 0 rows and 5 columns
#>  [3] RNASeqGene: matrix with 0 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 1 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Use a logical vector (recycling used)
subsetByRow(mae, c(TRUE, FALSE))
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 3 rows and 4 columns
#>  [2] Methyl450k: matrix with 3 rows and 5 columns
#>  [3] RNASeqGene: matrix with 3 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 3 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Use a character vector
subsetByRow(mae, "ENST00000355076")
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 1 rows and 4 columns
#>  [2] Methyl450k: matrix with 1 rows and 5 columns
#>  [3] RNASeqGene: matrix with 0 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 0 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Use i index to selectively subsetByRow
subsetByRow(mae, "ENST00000355076", i = c(TRUE, TRUE, FALSE, FALSE))
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 1 rows and 4 columns
#>  [2] Methyl450k: matrix with 1 rows and 5 columns
#>  [3] RNASeqGene: matrix with 5 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## only subset assays with rowRanges with GRanges input
subsetByRow(mae, egr, i = hasRowRanges(mae))
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 5 rows and 4 columns
#>  [2] Methyl450k: matrix with 5 rows and 5 columns
#>  [3] RNASeqGene: matrix with 5 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 1 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## Use i index to selectively subsetByRowData
subsetByRowData(
    mae, "ENST00000355076", "rownames", i = "Affy"
)
#> A MultiAssayExperiment object of 4 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 4:
#>  [1] Affy: SummarizedExperiment with 1 rows and 4 columns
#>  [2] Methyl450k: matrix with 5 rows and 5 columns
#>  [3] RNASeqGene: matrix with 5 rows and 4 columns
#>  [4] GISTIC: RangedSummarizedExperiment with 5 rows and 3 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## use miniACC as example MAE
data("miniACC")

## intersect values of y with rownames in rowData
intersectByRowData(
    x = miniACC,
    y = c("G6PD", "PETN"),
    rowDataCol = "rownames",
    i = c("RNASeq2GeneNorm", "gistict")
)
#> A MultiAssayExperiment object of 5 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 5:
#>  [1] RNASeq2GeneNorm: SummarizedExperiment with 1 rows and 79 columns
#>  [2] gistict: SummarizedExperiment with 1 rows and 90 columns
#>  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
#>  [4] Mutations: matrix with 97 rows and 90 columns
#>  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files

## no-op when rowDataCol is not present or there is no data
intersectByRowData(
    x = miniACC, y = c("G6PD", "PETN"), rowDataCol = "Genes",
    i = c("RNASeq2GeneNorm", "gistict")
)
#> Warning: No 'rowData' intersected for assays:
#>   'RNASeq2GeneNorm', 'gistict'
#> A MultiAssayExperiment object of 5 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 5:
#>  [1] RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
#>  [2] gistict: SummarizedExperiment with 198 rows and 90 columns
#>  [3] RPPAArray: SummarizedExperiment with 33 rows and 46 columns
#>  [4] Mutations: matrix with 97 rows and 90 columns
#>  [5] miRNASeqGene: SummarizedExperiment with 471 rows and 80 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files