MultiAssayExperimentR/MultiAssayExperiment-class.R
MultiAssayExperiment.RdThe constructor function for the MultiAssayExperiment-class combines
multiple data elements from the different hierarchies of data
(study, experiments, and samples). It can create instances where neither
a sampleMap or a colData set is provided. Please see the
MultiAssayExperiment API documentation for more information.
A list or ExperimentList of all
combined experiments
A DataFrame or data.frame of
characteristics for all biological units
A DataFrame or data.frame of assay names,
sample identifiers, and colname samples
An optional argument of "ANY" class (usually list) for content describing the experiments
A list of unmatched information
(included after subsetting)
A MultiAssayExperiment object that can store
experiment and phenotype data
The `colData` input can be either `DataFrame` or `data.frame` with subsequent coercion to DataFrame. The rownames in the `colData` must match the colnames in the experiments if no sampleMap is provided.
The `experiments` input can be of class SimpleList or `list`.
This input becomes the ExperimentList. Each element of the
input `list` or `List` must be named, rectangular with two dimensions, and
have `dimnames`.
The sampleMap can either be input as `DataFrame` or
`data.frame` with eventual coercion to `DataFrame`. The `sampleMap` relates
biological units and biological measurements within each assay. Each row in
the `sampleMap` is a single such link. The standard column names of the
`sampleMap` are "assay", "primary", and "colname". Note that the "assay"
column is a factor corresponding to the names of each experiment in the
`ExperimentList`. In the case where these names do not match between the
`sampleMap` and the experiments, the documented experiments in the
`sampleMap` take precedence and experiments are dropped by the harmonization
procedure. The constructor function will generate a `sampleMap` in the case
where it is not provided and this method may be a 'safer' alternative for
creating the `MultiAssayExperiment` (so long as the rownames are identical
in the `colData`, if provided). An empty `sampleMap` may produce empty
experiments if the levels of the "assay" factor in the `sampleMap` do not
match the names in the `ExperimentList`.
## Run the example ExperimentList
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)
## Create sample maps for each experiment
exprmap <- data.frame(
primary = c("Jack", "Jill", "Barbara", "Bob"),
colname = c("array1", "array2", "array3", "array4"),
stringsAsFactors = FALSE)
methylmap <- data.frame(
primary = c("Jack", "Jack", "Jill", "Barbara", "Bob"),
colname = c("methyl1", "methyl2", "methyl3", "methyl4", "methyl5"),
stringsAsFactors = FALSE)
rnamap <- data.frame(
primary = c("Jack", "Jill", "Bob", "Barbara"),
colname = c("samparray1", "samparray2", "samparray3", "samparray4"),
stringsAsFactors = FALSE)
gistmap <- data.frame(
primary = c("Jack", "Bob", "Jill"),
colname = c("samp0", "samp1", "samp2"),
stringsAsFactors = FALSE)
## Combine as a named list and convert to a DataFrame
maplist <- list(Affy = exprmap, Methyl450k = methylmap,
RNASeqGene = rnamap, GISTIC = gistmap)
## Create a sampleMap
sampMap <- listToMap(maplist)
## Create an example phenotype data
colDat <- data.frame(sex = c("M", "F", "M", "F"), age = 38:41,
row.names = c("Jack", "Jill", "Bob", "Barbara"))
## Create a MultiAssayExperiment instance
mae <- MultiAssayExperiment(experiments = ExpList, colData = colDat,
sampleMap = sampMap)