A group of helper functions for manipulating and cleaning a MultiAssayExperiment
Source:R/MultiAssayExperiment-helpers.R
MultiAssayExperiment-helpers.Rd
A set of helper functions were created to help clean and
manipulate a MultiAssayExperiment object. intersectRows
also works
for ExperimentList
objects.
complete.cases: Returns a logical vector corresponding to 'colData' rows that have data across all experiments
isEmpty: Returns a logical
TRUE
value for zero lengthMultiAssayExperiment
objectsintersectRows: Takes all common rows across experiments, excludes experiments with empty rownames
intersectColumns: A wrapper for
complete.cases
to return aMultiAssayExperiment
with only those biological units that have measurements across all experimentsreplicated: Identifies, via logical vectors,
colname
s that originate from a single biological unit within each assayreplicates: Provides the replicate
colname
s found with thereplicated
function by their name, empty list if noneanyReplicated: Whether the assay has replicate measurements
showReplicated: Displays the actual columns that are replicated per assay and biological unit, i.e.,
primary
value (colData
rowname) in thesampleMap
mergeReplicates: A function that combines replicated / repeated measurements across all experiments and is guided by the replicated return value
longFormat: A
MultiAssayExperiment
method that returns a small and skinnyDataFrame
. ThecolDataCols
arguments allows the user to appendcolData
columns to the data.wideFormat: A function to reshape the data in a
MultiAssayExperiment
to a "wide" formatDataFrame
. Each row in theDataFrame
represents an observation (corresponding to an entry in thecolData
). If replicates are present, their data will be appended at the end of the corresponding row and will generate additionalNA
data. It is recommended to remove or consolidate technical replicates withmergeReplicates
. OptionalcolDataCols
can be added when the original object is aMultiAssayExperiment
.hasRowRanges: A function that identifies ExperimentList elements that have a
rowRanges
methodgetWithColData: A convenience function for extracting an assay and associated colData
renamePrimary: A convenience function to rename the primary biological units as represented in the
rownames(colData)
renameColname: A convenience function to rename the colnames of a particular assay
Usage
# S4 method for class 'MultiAssayExperiment'
complete.cases(...)
# S4 method for class 'MultiAssayExperiment'
isEmpty(x)
intersectRows(x)
intersectColumns(x)
replicated(x)
# S4 method for class 'MultiAssayExperiment'
replicated(x)
anyReplicated(x)
# S4 method for class 'MultiAssayExperiment'
anyReplicated(x)
showReplicated(x)
# S4 method for class 'MultiAssayExperiment'
showReplicated(x)
replicates(x, ...)
# S4 method for class 'MultiAssayExperiment'
replicates(x, ...)
mergeReplicates(x, replicates = list(), simplify = BiocGenerics::mean, ...)
# S4 method for class 'MultiAssayExperiment'
mergeReplicates(
x,
replicates = replicated(x),
simplify = BiocGenerics::mean,
...
)
# S4 method for class 'ANY'
mergeReplicates(x, replicates = list(), simplify = BiocGenerics::mean, ...)
longFormat(object, colDataCols = NULL, i = 1L)
wideFormat(
object,
colDataCols = NULL,
check.names = TRUE,
collapse = "_",
i = 1L
)
hasRowRanges(x)
# S4 method for class 'MultiAssayExperiment'
hasRowRanges(x)
# S4 method for class 'ExperimentList'
hasRowRanges(x)
getWithColData(x, i, mode = c("append", "replace"), verbose = FALSE)
renamePrimary(x, value)
renameColname(x, i, value)
splitAssays(x, hitList)
# S4 method for class 'MultiAssayExperiment'
splitAssays(x, hitList)
makeHitList(x, patternList)
Arguments
- ...
Additional arguments. See details for more information.
- x
A MultiAssayExperiment or ExperimentList
- replicates
A list of
LogicalList
s indicating multiple / duplicate entries for each biological unit per assay, seereplicated
(defaultreplicated(x)
).- simplify
A function for merging repeat measurements in experiments as indicated by the
replicated
method forMultiAssayExperiment
- object
Any supported class object
- colDataCols
A
character
,logical
, ornumeric
index forcolData
columns to be included- i
longFormat: The i-th assay in
SummarizedExperiment
-like objects. A vector input is supported in the case that theSummarizedExperiment
object(s) has more than one assay (default 1L), renameColname: Either anumeric
orcharacter
index indicating the assay whose colnames are to be renamed- check.names
(logical default TRUE) Column names of the output
DataFrame
will be checked for syntactic validity and made unique, if necessary- collapse
(character default "_") A single string delimiter for output column names. In
wideFormat
, experiments and rownames (and when replicate samples are present, colnames) are seperated by this delimiter- mode
String indicating how
MultiAssayExperiment
column-level metadata should be added to theSummarizedExperiment
colData
.- verbose
logical(1)
Whether tosuppressMessages
on subsetting operations ingetWithColData
(default FALSE)- value
renamePrimary: A
character
vector of the same length as the existingrownames(colData)
to use for replacement, renameColname: ACharacterList
orlist
with matchinglengths
to replacecolnames(x)
- hitList
a named
list
orList
of logical vectors that indicate groupings in the assays- patternList
a named
list
orList
of atomic character vectors that are the input togrepl
for identifying groupings in the assays
Details
The replicated
function finds replicate measurements in each
assay and returns a list of LogicalList
s.
Each element in a single LogicalList
corresponds to a
biological or primary unit as in the sampleMap
. Below is a
small graphic for one particular biological unit in one assay, where the
logical vector corresponds to the number of measurements/samples in the
assay:
> replicated(MultiAssayExperiment)
(list str) '-- $ AssayName
(LogicalList str) '-- [[ "Biological Unit" ]]
Replicated if sum(...) > 1 '-- TRUE TRUE FALSE FALSE
anyReplicated
determines if any of the assays have at least one
replicate. Note. These methods are not available for the
ExperimentList
class due to a missing sampleMap
structure
(by design).
showReplicated
returns a list of CharacterList
s where
each element corresponds to the the biological or primary units that
are replicated in that assay element. The values in the inner list are
the colnames
in the assay that are technical replicates.
The replicates
function (noun) returns the colname
s
from the sampleMap
that were identified as replicates. It returns a
list of CharacterList
s for each assay present in the
MultiAssayExperiment
and an inner entry for each biological unit
that has replicate observations in that assay.
The mergeReplicates
function is a house-keeping method
for a MultiAssayExperiment
where only complete.cases
are
returned. This by-assay operation averages replicate measurements
(by default) and columns are aligned by the row order in colData
.
Users can provide their own function for merging replicates with the
simplify
functional argument. Additional inputs ...
are
sent to the 'simplify' function.
The mergeReplicates
"ANY" method consolidates duplicate
measurements for rectangular data structures, returns object of the same
class (endomorphic). The ellipsis or ...
argument allows the
user to provide additional arguments to the simplify
functional
argument.
The longFormat
"ANY" class method, works with classes such as
ExpressionSet
and SummarizedExperiment
as well as matrix
to provide a consistent long and skinny DataFrame
.
The hasRowRanges
method identifies assays that support
a rowRanges
method and
return a GRanges
object.
mergeReplicates
The mergeReplicates
function makes use of the output from
replicated
which will point out the duplicate measurements by
biological unit in the MultiAssayExperiment
. This function will
return a MultiAssayExperiment
with merged replicates. Additional
arguments can be provided to the simplify argument via the ellipsis
(...
). For example, when replicates "TCGA-B" and "TCGA-A" are found in
the assay, the name of the first appearing replicate is taken (i.e., "B").
Note that a typical use case of merging replicates occurs when there are
multiple measurements on the same sample (within the same assay)
and can therefore be averaged.
longFormat
The 'longFormat' method takes data from the ExperimentList
in a MultiAssayExperiment
and returns a uniform
DataFrame
. The resulting DataFrame has columns indicating
primary, rowname, colname and value. This method can optionally include
columns of the MultiAssayExperiment colData named by colDataCols
character
vector argument. (MultiAssayExperiment
method only). The i
argument
allows the user to specify the assay value for the
SummarizedExperiment
assay function's i
argument.
wideFormat
The wideFormat
function returns standardized wide DataFrame
where each row represents a biological unit as in the colData
.
Depending on the data and setup, biological units can be patients, tumors,
specimens, etc. Metadata columns are
generated based on the names produced in the wide format
DataFrame
. These can be accessed via the
mcols()
function.
See the wideFormat
section for description of the colDataCols
and
i
arguments.
hasRowRanges
The hasRowRanges
method identifies assays with associated ranged
row data by directly testing the method on the object. The result from the
test must be a GRanges
class object to satisfy the test.
getWithColData
The getWithColData
function allows the user to conveniently extract
a particular assay as indicated by the i
index argument. It
will also attempt to provide the
colData
along with the extracted object using the colData<-
replacement
method when possible. Typically, this method is available for
SummarizedExperiment
and RaggedExperiment
classes.
The setting of mode
determines how the colData
is added. If mode="append"
, the MultiAssayExperiment
metadata is appended onto that of the SummarizedExperiment
.
If any fields are duplicated by name, the values in the
SummarizedExperiment
are retained, with a warning emitted if
the values are different. For mode="replace"
, the
MultiAssayExperiment
metadata replaces that of the
SummarizedExperiment
, while for mode="none"
,
no replacement or appending is performed.
rename*
The renamePrimary
function allows the user to conveniently change the
actual names of the primary biological units as seen in
rownames(colData)
. renameColname
allows the user to change the
names of a particular assay based on index i
. i
can either be
a single numeric or character value. See colnames<-
method for
renaming multiple colnames in a MultiAssayExperiment
.
splitAssays
The splitAssays
method separates columns in each of the assays based
on the hitList
input. The hitList
can be generated using
the makeHitList
helper function. To use the makeHitList
helper, the user should input a list of patterns that will match on the
column names of each assay. These matches should be mutually exclusive as
to avoid repetition of columns across assays. See the examples section.
Examples
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)
complete.cases(mae)
#> [1] TRUE TRUE TRUE FALSE
isEmpty(MultiAssayExperiment())
#> [1] TRUE
## renaming biological units (primary)
mae2 <- renamePrimary(mae, paste0("pt", 1:4))
colData(mae2)
#> DataFrame with 4 rows and 2 columns
#> sex age
#> <character> <integer>
#> pt1 M 38
#> pt2 F 39
#> pt3 M 40
#> pt4 F 41
sampleMap(mae2)
#> DataFrame with 16 rows and 3 columns
#> assay primary colname
#> <factor> <character> <character>
#> 1 Affy pt1 array1
#> 2 Affy pt2 array2
#> 3 Affy pt4 array3
#> 4 Affy pt3 array4
#> 5 Methyl450k pt1 methyl1
#> ... ... ... ...
#> 12 RNASeqGene pt3 samparray3
#> 13 RNASeqGene pt4 samparray4
#> 14 GISTIC pt1 samp0
#> 15 GISTIC pt3 samp1
#> 16 GISTIC pt2 samp2
## renaming observational units (colname)
mae2 <- renameColname(mae, i = "Affy", paste0("ARRAY", 1:4))
colnames(mae2)
#> CharacterList of length 4
#> [["Affy"]] ARRAY1 ARRAY2 ARRAY3 ARRAY4
#> [["Methyl450k"]] methyl1 methyl2 methyl3 methyl4 methyl5
#> [["RNASeqGene"]] samparray1 samparray2 samparray3 samparray4
#> [["GISTIC"]] samp0 samp1 samp2
sampleMap(mae2)
#> DataFrame with 16 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 Methyl450k Jack methyl1
#> ... ... ... ...
#> 12 RNASeqGene Bob samparray3
#> 13 RNASeqGene Barbara samparray4
#> 14 GISTIC Jack samp0
#> 15 GISTIC Bob samp1
#> 16 GISTIC Jill samp2
patts <- list(
normals = "TCGA-[A-Z0-9]{2}-[A-Z0-9]{4}-11",
tumors = "TCGA-[A-Z0-9]{2}-[A-Z0-9]{4}-01"
)
data("miniACC")
hits <- makeHitList(miniACC, patts)
## only turmors present
splitAssays(miniACC, hits)
#> A MultiAssayExperiment object of 5 listed
#> experiments with user-defined names and respective classes.
#> Containing an ExperimentList class object of length 5:
#> [1] tumors_RNASeq2GeneNorm: SummarizedExperiment with 198 rows and 79 columns
#> [2] tumors_gistict: SummarizedExperiment with 198 rows and 90 columns
#> [3] tumors_RPPAArray: SummarizedExperiment with 33 rows and 46 columns
#> [4] tumors_Mutations: matrix with 97 rows and 90 columns
#> [5] tumors_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