A large number of Bioconductor packages contain extensions of the
standard SummarizedExperiment class from the
SummarizedExperiment package. This allows developers to take
advantage of the power of the SummarizedExperiment representation
for synchronising data and metadata, while still accommodating
specialized data structures for particular scientific applications.
This document is intended to provide a developer-level “best
practices” reference for the creation of these derived classes.
To introduce various concepts, we will start off with a simple derived
class that does not add any new slots. This is occasionally useful
when additional constraints need to be placed on the derived class.
In this example, we will assume that we want our class to minimally
hold a "counts" assay that contains non-negative
values1 For simplicity’s sake, we won’t worry about enforcing integer type, as fractional values are possible, e.g., when dealing with expected counts..
We name our new class CountSE and define it using the setClass
function from the methods package, as is conventionally done for all
S4 classes. We use Roxygen’s #' tags to trigger the generation of
import/export statements in the NAMESPACE of our package.
#' @export
#' @import methods
#' @importClassesFrom SummarizedExperiment SummarizedExperiment
.CountSE <- setClass("CountSE", contains="SummarizedExperiment")We define a constructor that accepts a count matrix to create a
CountSE object. We use ... to pass further arguments to the
SummarizedExperiment constructor, which allows us to avoid
re-specifying all its arguments.
#' @export
#' @importFrom SummarizedExperiment SummarizedExperiment
CountSE <- function(counts, ...) {
se <- SummarizedExperiment(list(counts=counts), ...)
.CountSE(se)
}We define a validity method that enforces the constraints that we
described earlier. This is done by defining a validity function using
setValidity2 from the S4Vectors
package2 This allows us to turn off the validity checks in internal functions where intermediate objects may not be valid within the scope of the function..
Returning a string indicates that there is a problem and triggers an
error in the R session.
setValidity2("CountSE", function(object) {
msg <- NULL
if (assayNames(object)[1] != "counts") {
msg <- c(msg, "'counts' must be first assay")
}
if (min(assay(object)) < 0) {
msg <- c(msg, "negative values in 'counts'")
}
if (is.null(msg)) {
TRUE
} else msg
})## Class "CountSE" [in ".GlobalEnv"]
##
## Slots:
##
## Name: colData assays NAMES
## Class: DataFrame Assays character_OR_NULL
##
## Name: elementMetadata metadata
## Class: DataFrame list
##
## Extends:
## Class "SummarizedExperiment", directly
## Class "Vector", by class "SummarizedExperiment", distance 2
## Class "Annotated", by class "SummarizedExperiment", distance 3The constructor yields the expected output when counts are provided:
CountSE(matrix(rpois(100, lambda=1), ncol=5))## class: CountSE
## dim: 20 5
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):… and an (expected) error otherwise:
CountSE(matrix(rnorm(100), ncol=5))## Error in validObject(.Object): invalid class "CountSE" object:
## negative values in 'counts'A generic is a group of functions with the same name that operate on different classes. Upon calling the generic on an object, the S4 dispatch system will choose the most appropriate function to use based on the object class. This allows users and developers to write code that is agnostic to the type of input class.
Let’s say that it is of particular scientific interest to obtain the
counts with a flipped sign. We observe that there are no existing
generics that do this task, e.g., in BiocGenerics or
S4Vectors3 If you have an idea for a generally applicable generic that is not yet available, please contact the Bioconductor core team..
Instead, we define a new generic negcounts:
#' @export
setGeneric("negcounts", function(x, ...) standardGeneric("negcounts"))## [1] "negcounts"We then define a specific method for our CountSE
class4 The ... in the generic function definition means that custom arguments like withDimnames= can be provided for specific methods, if necessary.:
#' @export
#' @importFrom SummarizedExperiment assay
setMethod("negcounts", "CountSE", function(x, withDimnames=TRUE) {
-assay(x, withDimnames=withDimnames)
})If any other developers need to compute negative counts for their own
classes, they can simply use the negcounts generic defined in our
package.
It is convention to put all class definitions (i.e., the setClass
statement) in a file named AllClasses.R, all new generic definitions
in a file named AllGenerics.R, and all method definitions in files
that are alphanumerically ordered below the first two. This is
because R collates files by alphanumeric order when building a
package. It is critical that the collation (and definition) of the
classes and generics occurs before that of the corresponding
methods, otherwise errors will occur. If alphanumeric ordering is
inappropriate, developers can manually specify the collation order
using Collate: in the DESCRIPTION file - see
Writing R Extensions for more details.
In practice, most derived classes will need to store application-specific data structures. For the rest of this document, we will be considering the derivation of a class with custom slots to hold such structures. First, we consider 1D data structures:
rowVec: 1:1 mapping from each value to a row of the
SummarizedExperiment.colVec: 1:1 mapping from each value to a column of the
SummarizedExperiment.Any 1D structure can be used if it supports length, c,
[ and [<-. For simplicity, we will use integer vectors for the *.vec
slots.
We also consider some 2D data structures:
rowToRowMat: 1:1 mapping from each row to a row of the
SummarizedExperiment.colToColMat: 1:1 mapping from each column to a column of the
SummarizedExperiment.rowToColMat: 1:1 mapping from each row to a column of the
SummarizedExperiment.colToRowMat: 1:1 mapping from each column to a row of the
SummarizedExperiment.Any 2D structure can be used if it supports nrow, ncol, cbind, rbind,
[ and [<-. For simplicity, we will use (numeric) matrices for the *.mat
slots.
Definition of the class is achieved using setClass, using the slots=
argument to specify the new custom slots5 It does no harm to repeat the Roxygen tags, which explicitly specifies the imports required for each class and function..
#' @export
#' @import methods
#' @importClassesFrom SummarizedExperiment SummarizedExperiment
.ExampleClass <- setClass("ExampleClass",
slots= representation(
rowVec="integer",
colVec="integer",
rowToRowMat="matrix",
colToColMat="matrix",
rowToColMat="matrix",
colToRowMat="matrix"
),
contains="SummarizedExperiment"
)The constructor should provide some arguments for setting the new
slots in the derived class definition. The default values should be
set such that calling the constructor without any arguments returns a
valid ExampleClass object.
#' @export
#' @importFrom SummarizedExperiment SummarizedExperiment
ExampleClass <- function(
rowVec=integer(0),
colVec=integer(0),
rowToRowMat=matrix(0,0,0),
colToColMat=matrix(0,0,0),
rowToColMat=matrix(0,0,0),
colToRowMat=matrix(0,0,0),
...)
{
se <- SummarizedExperiment(...)
.ExampleClass(se, rowVec=rowVec, colVec=colVec,
rowToRowMat=rowToRowMat, colToColMat=colToColMat,
rowToColMat=rowToColMat, colToRowMat=colToRowMat)
}We define some getter generics for the custom slots containing the 1D structures.
#' @export
setGeneric("rowVec", function(x, ...) standardGeneric("rowVec"))## [1] "rowVec"#' @export
setGeneric("colVec", function(x, ...) standardGeneric("colVec"))## [1] "colVec"We then define the class-specific methods for these generics. Note
the withDimnames=TRUE argument, which enforces consistency between
the names of the extracted object and the original
SummarizedExperiment. It is possible to turn this off for greater
efficiency, e.g., for internal usage where names are not necessary.
#' @export
setMethod("rowVec", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@rowVec
if (withDimnames)
names(out) <- rownames(x)
out
})
#' @export
setMethod("colVec", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@colVec
if (withDimnames)
names(out) <- colnames(x)
out
})We repeat this process for the 2D structures.
#' @export
setGeneric("rowToRowMat", function(x, ...) standardGeneric("rowToRowMat"))## [1] "rowToRowMat"#' @export
setGeneric("colToColMat", function(x, ...) standardGeneric("colToColMat"))## [1] "colToColMat"#' @export
setGeneric("rowToColMat", function(x, ...) standardGeneric("rowToColMat"))## [1] "rowToColMat"#' @export
setGeneric("colToRowMat", function(x, ...) standardGeneric("colToRowMat"))## [1] "colToRowMat"Again, we define class-specific methods for these generics.
#' @export
setMethod("rowToRowMat", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@rowToRowMat
if (withDimnames)
rownames(out) <- rownames(x)
out
})
#' @export
setMethod("colToColMat", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@colToColMat
if (withDimnames)
colnames(out) <- colnames(x)
out
})
#' @export
setMethod("rowToColMat", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@rowToColMat
if (withDimnames)
rownames(out) <- colnames(x)
out
})
#' @export
setMethod("colToRowMat", "ExampleClass", function(x, withDimnames=TRUE) {
out <- x@colToRowMat
if (withDimnames)
colnames(out) <- rownames(x)
out
})SummarizedExperiment slotsThe getter methods defined in SummarizedExperiment can be directly
used to retrieve data from slots in the base class. These should
generally not require any re-defining for a derived class. However,
if it is necessary, the methods should use callNextMethod
internally. This will call the method for the base
SummarizedExperiment class, the output of which can be modified as
required.
#' @export
#' @importMethodsFrom SummarizedExperiment rowData
setMethod("rowData", "ExampleClass", function(x, ...) {
out <- callNextMethod()
# Do something extra here.
out$extra <- runif(nrow(out))
# Returning the rowData object.
out
})We use setValidity2 to define a validity function for
ExampleClass. We use the previously defined getter functions to
retrieve the slot values rather than using @. This is generally a
good idea to keep the interface separate from the
implementation6 This protects against changes to the slot names, and simplifies development when the storage mode differs from the conceptual meaning of the data, e.g., for efficiency purposes..
We also set withDimnames=FALSE in our getter calls, as consistent
naming is not necessary for internal functions.
#' @importFrom BiocGenerics NCOL NROW
setValidity2("ExampleClass", function(object) {
NR <- NROW(object)
NC <- NCOL(object)
msg <- NULL
# 1D
if (length(rowVec(object, withDimnames=FALSE)) != NR) {
msg <- c(msg, "'rowVec' should have length equal to the number of rows")
}
if (length(colVec(object, withDimnames=FALSE)) != NC) {
msg <- c(
msg, "'colVec' should have length equal to the number of columns"
)
}
# 2D
if (NROW(rowToRowMat(object, withDimnames=FALSE)) != NR) {
msg <- c(
msg, "'nrow(rowToRowMat)' should be equal to the number of rows"
)
}
if (NCOL(colToColMat(object, withDimnames=FALSE)) != NC) {
msg <- c(
msg, "'ncol(colToColMat)' should be equal to the number of columns"
)
}
if (NROW(rowToColMat(object, withDimnames=FALSE)) != NC) {
msg <- c(
msg, "'nrow(rowToColMat)' should be equal to the number of columns"
)
}
if (NCOL(colToRowMat(object, withDimnames=FALSE)) != NR) {
msg <- c(
msg, "'ncol(colToRowMat)' should be equal to the number of rows"
)
}
if (length(msg)) {
msg
} else TRUE
})## Class "ExampleClass" [in ".GlobalEnv"]
##
## Slots:
##
## Name: rowVec colVec rowToRowMat
## Class: integer integer matrix
##
## Name: colToColMat rowToColMat colToRowMat
## Class: matrix matrix matrix
##
## Name: colData assays NAMES
## Class: DataFrame Assays character_OR_NULL
##
## Name: elementMetadata metadata
## Class: DataFrame list
##
## Extends:
## Class "SummarizedExperiment", directly
## Class "Vector", by class "SummarizedExperiment", distance 2
## Class "Annotated", by class "SummarizedExperiment", distance 3We use the NCOL and NROW methods from BiocGenerics as these
support various Bioconductor objects, whereas the base methods do not.
show methodThe default show method will only display information about the
SummarizedExperiment slots. We can augment it to display some
relevant aspects of the custom slots. This is done by calling the
base show method before printing additional fields as necessary.
#' @export
#' @importMethodsFrom SummarizedExperiment show
setMethod("show", "ExampleClass", function(object) {
callNextMethod()
cat(
"rowToRowMat has ", ncol(rowToRowMat(object)), " columns\n",
"colToColMat has ", nrow(colToColMat(object)), " rows\n",
"rowToColMat has ", ncol(rowToRowMat(object)), " columns\n",
"colToRowMat has ", ncol(rowToRowMat(object)), " rows\n",
sep=""
)
})We define some setter methods for the custom slots containing the 1D structures. Again, this usually requires the creation of new generics.
#' @export
setGeneric("rowVec<-", function(x, ..., value) standardGeneric("rowVec<-"))## [1] "rowVec<-"#' @export
setGeneric("colVec<-", function(x, ..., value) standardGeneric("colVec<-"))## [1] "colVec<-"We define the class-specific methods for these generics. Note that
use of validObject to ensure that the assigned input is still valid.
#' @export
setReplaceMethod("rowVec", "ExampleClass", function(x, value) {
x@rowVec <- value
validObject(x)
x
})
#' @export
setReplaceMethod("colVec", "ExampleClass", function(x, value) {
x@colVec <- value
validObject(x)
x
})We repeat this process for the 2D structures.
#' @export
setGeneric("rowToRowMat<-", function(x, ..., value)
standardGeneric("rowToRowMat<-")
)## [1] "rowToRowMat<-"#' @export
setGeneric("colToColMat<-", function(x, ..., value)
standardGeneric("colToColMat<-")
)## [1] "colToColMat<-"#' @export
setGeneric("rowToColMat<-", function(x, ..., value)
standardGeneric("rowToColMat<-")
)## [1] "rowToColMat<-"#' @export
setGeneric("colToRowMat<-", function(x, ..., value)
standardGeneric("colToRowMat<-")
)## [1] "colToRowMat<-"Again, we define class-specific methods for these generics.
#' @export
setReplaceMethod("rowToRowMat", "ExampleClass", function(x, value) {
x@rowToRowMat <- value
validObject(x)
x
})
#' @export
setReplaceMethod("colToColMat", "ExampleClass", function(x, value) {
x@colToColMat <- value
validObject(x)
x
})
#' @export
setReplaceMethod("rowToColMat", "ExampleClass", function(x, value) {
x@rowToColMat <- value
validObject(x)
x
})
#' @export
setReplaceMethod("colToRowMat", "ExampleClass", function(x, value) {
x@colToRowMat <- value
validObject(x)
x
})SummarizedExperiment slotsAgain, we can use the setter methods defined in
SummarizedExperiment to modify slots in the base class. These
should generally not require any re-defining. However, if it is
necessary, the methods should use callNextMethod internally:
#' @export
#' @importMethodsFrom SummarizedExperiment "rowData<-"
setReplaceMethod("rowData", "ExampleClass", function(x, ..., value) {
y <- callNextMethod() # returns a modified ExampleClass
# Do something extra here.
message("hi!\n")
y
})Imagine that we want to write a function that returns a modified
ExampleClass, e.g., where the signs of the *.vec fields are
reversed. For example, we will pretend that we want to write a
normalize function, using the generic from BiocGenerics.
#' @export
#' @importFrom BiocGenerics normalize
setMethod("normalize", "ExampleClass", function(object) {
# do something exciting, i.e., flip the signs
new.row <- -rowVec(object, withDimnames=FALSE)
new.col <- -colVec(object, withDimnames=FALSE)
BiocGenerics:::replaceSlots(object, rowVec=new.row,
colVec=new.col, check=FALSE)
})We use BiocGenerics:::replaceSlots instead of the setter methods
that we defined above. This is because our setters perform validity
checks that are unnecessary if we know that the modification cannot
alter the validity of the object. The replaceSlots function allows
us to skip these validity checks (check=FALSE) for greater
efficiency.
A key strength of the SummarizedExperiment class is that subsetting
is synchronized across the various (meta)data fields. This avoids
book-keeping errors and guarantees consistency throughout an
interactive analysis session. We need to ensure that the values in
our custom slots are also subsetted.
#' @export
setMethod("[", "ExampleClass", function(x, i, j, drop=TRUE) {
rv <- rowVec(x, withDimnames=FALSE)
cv <- colVec(x, withDimnames=FALSE)
rrm <- rowToRowMat(x, withDimnames=FALSE)
ccm <- colToColMat(x, withDimnames=FALSE)
rcm <- rowToColMat(x, withDimnames=FALSE)
crm <- colToRowMat(x, withDimnames=FALSE)
if (!missing(i)) {
if (is.character(i)) {
fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s")
i <- SummarizedExperiment:::.SummarizedExperiment.charbound(
i, rownames(x), fmt
)
}
i <- as.vector(i)
rv <- rv[i]
rrm <- rrm[i,,drop=FALSE]
crm <- crm[,i,drop=FALSE]
}
if (!missing(j)) {
if (is.character(j)) {
fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s")
j <- SummarizedExperiment:::.SummarizedExperiment.charbound(
j, colnames(x), fmt
)
}
j <- as.vector(j)
cv <- cv[j]
ccm <- ccm[,j,drop=FALSE]
rcm <- rcm[j,,drop=FALSE]
}
out <- callNextMethod()
BiocGenerics:::replaceSlots(out, rowVec=rv, colVec=cv,
rowToRowMat=rrm, colToColMat=ccm,
rowToColMat=rcm, colToRowMat=crm, check=FALSE)
})Note the special code for handling character indices, and the use of
callNextMethod to subset the base SummarizedExperiment slots.
Subset assignment can be similarly performed, though the signature needs to be specified so that the replacement value is of the same class. This is generally necessary for sensible replacement of the custom slots.
#' @export
setReplaceMethod("[", c("ExampleClass", "ANY", "ANY", "ExampleClass"),
function(x, i, j, ..., value) {
rv <- rowVec(x, withDimnames=FALSE)
cv <- colVec(x, withDimnames=FALSE)
rrm <- rowToRowMat(x, withDimnames=FALSE)
ccm <- colToColMat(x, withDimnames=FALSE)
rcm <- rowToColMat(x, withDimnames=FALSE)
crm <- colToRowMat(x, withDimnames=FALSE)
if (!missing(i)) {
if (is.character(i)) {
fmt <- paste0("<", class(x), ">[i,] index out of bounds: %s")
i <- SummarizedExperiment:::.SummarizedExperiment.charbound(
i, rownames(x), fmt
)
}
i <- as.vector(i)
rv[i] <- rowVec(value, withDimnames=FALSE)
rrm[i,] <- rowToRowMat(value, withDimnames=FALSE)
crm[,i] <- colToRowMat(value, withDimnames=FALSE)
}
if (!missing(j)) {
if (is.character(j)) {
fmt <- paste0("<", class(x), ">[,j] index out of bounds: %s")
j <- SummarizedExperiment:::.SummarizedExperiment.charbound(
j, colnames(x), fmt
)
}
j <- as.vector(j)
cv[j] <- colVec(value, withDimnames=FALSE)
ccm[,j] <- colToColMat(value, withDimnames=FALSE)
rcm[j,] <- rowToColMat(value, withDimnames=FALSE)
}
out <- callNextMethod()
BiocGenerics:::replaceSlots(out, rowVec=rv, colVec=cv,
rowToRowMat=rrm, colToColMat=ccm,
rowToColMat=rcm, colToRowMat=crm, check=FALSE)
})We need to define a rbind method for our custom class. This is done
by combining the custom per-row slots across class instances.
#' @export
setMethod("rbind", "ExampleClass", function(..., deparse.level=1) {
args <- list(...)
all.rv <- lapply(args, rowVec, withDimnames=FALSE)
all.rrm <- lapply(args, rowToRowMat, withDimnames=FALSE)
all.crm <- lapply(args, colToRowMat, withDimnames=FALSE)
all.rv <- do.call(c, all.rv)
all.rrm <- do.call(rbind, all.rrm)
all.crm <- do.call(cbind, all.crm)
# Checks for identical column state.
ref <- args[[1]]
ref.cv <- colVec(ref, withDimnames=FALSE)
ref.ccm <- colToColMat(ref, withDimnames=FALSE)
ref.rcm <- rowToColMat(ref, withDimnames=FALSE)
for (x in args[-1]) {
if (!identical(ref.cv, colVec(x, withDimnames=FALSE))
|| !identical(ref.ccm, colToColMat(x, withDimnames=FALSE))
|| !identical(ref.rcm, rowToColMat(x, withDimnames=FALSE)))
{
stop("per-column values are not compatible")
}
}
old.validity <- S4Vectors:::disableValidity()
S4Vectors:::disableValidity(TRUE)
on.exit(S4Vectors:::disableValidity(old.validity))
out <- callNextMethod()
BiocGenerics:::replaceSlots(out, rowVec=all.rv,
rowToRowMat=all.rrm, colToRowMat=all.crm,
check=FALSE)
})We check the other per-column slots across all elements to ensure that they are the same. This protects the user against combining incompatible objects. However, depending on the application, this may not be necessary (or too costly) for all slots, in which case it can be limited to critical slots.
We also use the disableValidity method to avoid the validity check
in the base cbind method. This is because the object is technically
invalid when the base slots are combined but before it is updated with
the new combined values for the custom slots. The on.exit call
ensures that the original validity setting is restored upon exit of
the function.
We similarly define a cbind method to handle the custom slots.
#' @export
setMethod("cbind", "ExampleClass", function(..., deparse.level=1) {
args <- list(...)
all.cv <- lapply(args, colVec, withDimnames=FALSE)
all.ccm <- lapply(args, colToColMat, withDimnames=FALSE)
all.rcm <- lapply(args, rowToColMat, withDimnames=FALSE)
all.cv <- do.call(c, all.cv)
all.ccm <- do.call(cbind, all.ccm)
all.rcm <- do.call(rbind, all.rcm)
# Checks for identical column state.
ref <- args[[1]]
ref.rv <- rowVec(ref, withDimnames=FALSE)
ref.rrm <- rowToRowMat(ref, withDimnames=FALSE)
ref.crm <- colToRowMat(ref, withDimnames=FALSE)
for (x in args[-1]) {
if (!identical(ref.rv, rowVec(x, withDimnames=FALSE))
|| !identical(ref.rrm, rowToRowMat(x, withDimnames=FALSE))
|| !identical(ref.crm, colToRowMat(x, withDimnames=FALSE)))
{
stop("per-row values are not compatible")
}
}
old.validity <- S4Vectors:::disableValidity()
S4Vectors:::disableValidity(TRUE)
on.exit(S4Vectors:::disableValidity(old.validity))
out <- callNextMethod()
BiocGenerics:::replaceSlots(out, colVec=all.cv,
colToColMat=all.ccm, rowToColMat=all.rcm,
check=FALSE)
})SummarizedExperimentWe define a method to coerce SummarizedExperiment objects into our new ExampleClass class.
#' @exportMethods coerce
setAs("SummarizedExperiment", "ExampleClass", function(from) {
new("ExampleClass", from,
rowVec=integer(nrow(from)),
colVec=integer(ncol(from)),
rowToRowMat=matrix(0,nrow(from),0),
colToColMat=matrix(0,0,ncol(from)),
rowToColMat=matrix(0,ncol(from),0),
colToRowMat=matrix(0,0,nrow(from)))
})… which works as expected:
se <- SummarizedExperiment(matrix(rpois(100, lambda=1), ncol=5))
as(se, "CountSE")## class: CountSE
## dim: 20 5
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):This was not strictly necessary for our previous CountSE class as no new slots were added.
Of course, developers can still explicitly write a conversion method perform additional work to achieve a “sensible” conversion -
for example, one might take the absolute values of all entries of the first matrix to ensure that the CountSE is valid for all input SummarizedExperiment objects.
RangedSummarizedExperimentNote that, if we were deriving from a RangedSummarizedExperiment (e.g., for some ExampleClassRanged),
it would be necessary to define explicit conversions from both RangedSummarizedExperiment and SummarizedExperment to ExampleClassRanged.
In theory, we should only have to define a conversion from RangedSummarizedExperiment to ExampleClassRanged -
then, any attempt to convert from a SummarizedExperiment to ExampleClassRanged would:
SummarizedExperiment to RangedSummarizedExperiment converter defined in SummarizedExperiment, and thenRangedSummarizedExperiment to ExampleClassRanged converter that we just defined.Unfortunately, in cases involving conversion to non-direct subclasses, the S4 system automatically creates methods for any conversions that are not explicitly defined.
This means that the correct “chain” of methods listed above is not used when converting from a SummarizedExperiment to a ExampleClassRanged object.
The automatically generated method is used instead, which may not yield a valid object when the specifics of the conversion are ignored.
We avoid this scenario by explicitly defining converters for both SummarizedExperiment and RangedSummarizedExperiment to ExampleClassRanged.
We test our new methods using the expect_* functions from the
testthat package. Each function will test an expression and will
raise an error if the output is not as expected. This can be used to
construct unit tests for the tests/ subdirectory of the package.
Unit testing ensures that the methods behave as expected, especially
after any refactoring that may be performed in the future.
For testing, we will construct an instance of ExampleClass that has
10 rows and 7 columns:
RV <- 1:10
CV <- sample(50, 7)
RRM <- matrix(runif(30), nrow=10)
CCM <- matrix(rnorm(14), ncol=7)
RCM <- matrix(runif(21), nrow=7)
CRM <- matrix(rnorm(20), ncol=10)
thing <- ExampleClass(rowVec=RV, colVec=CV,
rowToRowMat=RRM, colToColMat=CCM,
rowToColMat=RCM, colToRowMat=CRM,
assays=list(counts=matrix(rnorm(70), nrow=10)),
colData=DataFrame(whee=LETTERS[1:7]),
rowData=DataFrame(yay=letters[1:10])
)We will also add some row and column names, which will come in handy later.
rownames(thing) <- paste0("FEATURE_", seq_len(nrow(thing)))
colnames(thing) <- paste0("SAMPLE_", seq_len(ncol(thing)))
thing## class: ExampleClass
## dim: 10 7
## metadata(0):
## assays(1): counts
## rownames(10): FEATURE_1 FEATURE_2 ... FEATURE_9 FEATURE_10
## rowData names(2): yay extra
## colnames(7): SAMPLE_1 SAMPLE_2 ... SAMPLE_6 SAMPLE_7
## colData names(1): whee
## rowToRowMat has 3 columns
## colToColMat has 2 rows
## rowToColMat has 3 columns
## colToRowMat has 3 rowsWe test that the thing object we constructed is valid:
expect_true(validObject(thing))Another useful set of unit tests involves checking that the default constructors (internal and exported) yield valid objects:
expect_true(validObject(.ExampleClass())) # internal
expect_true(validObject(ExampleClass())) # exportedWe can also verify that the validity method fails on invalid objects:
expect_error(ExampleClass(rowVec=1), "rowVec")
expect_error(ExampleClass(colVec=1), "colVec")
expect_error(ExampleClass(rowToRowMat=rbind(1)), "rowToRowMat")
expect_error(ExampleClass(colToColMat=rbind(1)), "colToColMat")
expect_error(ExampleClass(rowToColMat=rbind(1)), "rowToColMat")
expect_error(ExampleClass(colToRowMat=rbind(1)), "colToRowMat")Finally, we check that the coercion method yields a valid object.
se <- as(thing, "SummarizedExperiment")
conv <- as(se, "ExampleClass")
expect_true(validObject(conv))Testing the 1D getter methods:
expect_identical(names(rowVec(thing)), rownames(thing))
expect_identical(rowVec(thing, withDimnames=FALSE), RV)
expect_identical(names(colVec(thing)), colnames(thing))
expect_identical(colVec(thing, withDimnames=FALSE), CV)Testing the 2D getter methods:
expect_identical(rowToRowMat(thing, withDimnames=FALSE), RRM)
expect_identical(rownames(rowToRowMat(thing)), rownames(thing))
expect_identical(colToColMat(thing, withDimnames=FALSE), CCM)
expect_identical(colnames(colToColMat(thing)), colnames(thing))
expect_identical(rowToColMat(thing, withDimnames=FALSE), RCM)
expect_identical(rownames(rowToColMat(thing)), colnames(thing))
expect_identical(colToRowMat(thing, withDimnames=FALSE), CRM)
expect_identical(colnames(colToRowMat(thing)), rownames(thing))Testing the custom rowData method:
expect_true("extra" %in% colnames(rowData(thing)))Testing the 1D setter methods:
rowVec(thing) <- 0:9
expect_equivalent(rowVec(thing), 0:9)
colVec(thing) <- 7:1
expect_equivalent(colVec(thing), 7:1)Testing the 2D setter methods:
old <- rowToRowMat(thing)
rowToRowMat(thing) <- -old
expect_equivalent(rowToRowMat(thing), -old)
old <- colToColMat(thing)
colToColMat(thing) <- 2 * old
expect_equivalent(colToColMat(thing), 2 * old)
old <- rowToColMat(thing)
rowToColMat(thing) <- old + 1
expect_equivalent(rowToColMat(thing), old + 1)
old <- colToRowMat(thing)
colToRowMat(thing) <- old / 10
expect_equivalent(colToRowMat(thing), old / 10)Testing our custom rowData<- method:
expect_message(rowData(thing) <- 1, "hi")We ensure that we can successfully trigger errors on the validity method:
expect_error(rowVec(thing) <- 0, "rowVec")
expect_error(colVec(thing) <- 0, "colVec")
expect_error(rowToRowMat(thing) <- rbind(0), "rowToRowMat")
expect_error(colToColMat(thing) <- rbind(0), "colToColMat")
expect_error(rowToColMat(thing) <- rbind(0), "rowToColMat")
expect_error(colToRowMat(thing) <- rbind(0), "colToRowMat")We test our new normalize method:
modified <- normalize(thing)
expect_equal(rowVec(modified), -rowVec(thing))
expect_equal(colVec(modified), -colVec(thing))Subsetting by row:
subbyrow <- thing[1:5,]
expect_identical(rowVec(subbyrow), rowVec(thing)[1:5])
expect_identical(rowToRowMat(subbyrow), rowToRowMat(thing)[1:5,])
expect_identical(colToRowMat(subbyrow), colToRowMat(thing)[,1:5])
# columns unaffected...
expect_identical(colVec(subbyrow), colVec(thing))
expect_identical(colToColMat(subbyrow), colToColMat(thing))
expect_identical(rowToColMat(subbyrow), rowToColMat(thing))Subsetting by column:
subbycol <- thing[,1:2]
expect_identical(colVec(subbycol), colVec(thing)[1:2])
expect_identical(colToColMat(subbycol), colToColMat(thing)[,1:2])
expect_identical(rowToColMat(subbycol), rowToColMat(thing)[1:2,])
# rows unaffected...
expect_identical(rowVec(subbycol), rowVec(thing))
expect_identical(rowToRowMat(subbycol), rowToRowMat(thing))
expect_identical(colToRowMat(subbycol), colToRowMat(thing))Checking that subsetting to create an empty object is possible:
norow <- thing[0,]
expect_true(validObject(norow))
expect_identical(nrow(norow), 0L)
nocol <- thing[,0]
expect_true(validObject(nocol))
expect_identical(ncol(nocol), 0L)Subset assignment:
modified <- thing
modified[1:5,1:2] <- thing[5:1,2:1]
rperm <- c(5:1, 6:nrow(thing))
expect_identical(rowVec(modified), rowVec(thing)[rperm])
expect_identical(rowToRowMat(modified), rowToRowMat(thing)[rperm,])
expect_identical(colToRowMat(modified), colToRowMat(thing)[,rperm])
cperm <- c(2:1, 3:ncol(thing))
expect_identical(colVec(modified), colVec(thing)[cperm])
expect_identical(colToColMat(modified), colToColMat(thing)[,cperm])
expect_identical(rowToColMat(modified), rowToColMat(thing)[cperm,])Checking that we obtain the same object after trivial assignment operations:
modified <- thing
modified[0,] <- thing[0,]
expect_equal(modified, thing)
modified[1,] <- thing[1,]
expect_equal(modified, thing)
modified[,0] <- thing[,0]
expect_equal(modified, thing)
modified[,1] <- thing[,1]
expect_equal(modified, thing)We double-check that we can get an error upon invalid assignment:
expect_error(modified[1,1] <- thing[0,0], "replacement has length zero")Combining by row:
combined <- rbind(thing, thing)
rtwice <- rep(seq_len(nrow(thing)), 2)
expect_identical(rowVec(combined), rowVec(thing)[rtwice])
expect_identical(rowToRowMat(combined), rowToRowMat(thing)[rtwice,])
expect_identical(colToRowMat(combined), colToRowMat(thing)[,rtwice])
# Columns are unaffected:
expect_identical(colVec(combined), colVec(thing))
expect_identical(colToColMat(combined), colToColMat(thing))
expect_identical(rowToColMat(combined), rowToColMat(thing))And combining by column. We use test_equivalent here for
simplicity, as column names are altered to preserve uniqueness.
combined <- cbind(thing, thing)
ctwice <- rep(seq_len(ncol(thing)), 2)
expect_equivalent(colVec(combined), colVec(thing)[ctwice])
expect_equivalent(colToColMat(combined), colToColMat(thing)[,ctwice])
expect_equivalent(rowToColMat(combined), rowToColMat(thing)[ctwice,])
# Rows are unaffected:
expect_equivalent(rowVec(combined), rowVec(thing))
expect_equivalent(rowToRowMat(combined), rowToRowMat(thing))
expect_equivalent(colToRowMat(combined), colToRowMat(thing))Checking that we get the same object if we combine a single object or an empty object:
expect_equal(thing, rbind(thing))
expect_equal(thing, rbind(thing, thing[0,]))
expect_equal(thing, cbind(thing))
expect_equal(thing, cbind(thing, thing[,0]))And checking that the compatibility errors are properly thrown:
expect_error(rbind(thing, thing[,ncol(thing):1]), "not compatible")
expect_error(cbind(thing, thing[nrow(thing):1,]), "not compatible")We suggest creating at least two separate documentation (i.e. *.Rd)
files. The first file would document the class and the constructor:
\name{ExampleClass class}
\alias{ExampleClass-class}
\alias{ExampleClass}
\title{The ExampleClass class}
\description{An overview of the ExampleClass class and constructor.}
\usage{
ExampleClass(rowVec=integer(0), colVec=integer(0),
# etc., etc., I won't write it all out here.
)
}
\arguments{
\item{rowVec}{An integer vector mapping to the rows, representing
something important.}
\item{colVec}{An integer vector mapping to the columns, representing
something else that's important.}
% And so on...
}
\details{
% Some context on why this class and its slots are necessary.
The ExampleClass provides an example of how to derive from the
SummarizedExperiment class. Its slots have no scientific meaning and
are purely for demonstration purposes.
}The second file would document all of the individual methods:
\name{ExampleClass methods}
% New generics:
\alias{rowVec}
\alias{rowVec,ExampleClass-method}
\alias{rowVec<-}
\alias{rowVec<-,ExampleClass-method}
%% And so on...
% Already have a generic:
\alias{[,ExampleClass-method}
\alias{[,ExampleClass,ANY-method}
\alias{[,ExampleClass,ANY,ANY-method}
\alias{rbind,ExampleClass-method}
%% And so on...
\title{ExampleClass methods}
\description{Methods for the ExampleClass class.}
\usage{
\S4method{rowVec}{ExampleClass}(x, withDimnames=FALSE)
\S4method{rowVec}{ExampleClass}(x) <- value
\S4method{[}{ExampleClass}(x, i, j, drop=TRUE)
\S4method{rbind}{ExampleClass}(..., , i, j, drop=TRUE)
%% And so on...
}
\arguments{
\item{x}{An ExampleClass object.}
\item{withDimnames}{A logical scalar indicating whether dimension names
from \code{x} should be returned.}
\item{value}{
For \code{rowVec}, an integer vector of length equal to the number of
rows.
For \code{colVec}, an integer vector of length equal to the number of
columns.
}
%% And so on...
}
\section{Accessors}{
% Add some details about accessor behaviour here.
}
\section{Subsetting}{
% Add some details about subsetting behaviour here.
}
\section{Combining}{
% Add some details about combining behaviour here.
}sessionInfo()## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] testthat_2.2.1 SummarizedExperiment_1.14.1
## [3] DelayedArray_0.10.0 BiocParallel_1.18.0
## [5] matrixStats_0.54.0 Biobase_2.44.0
## [7] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
## [9] IRanges_2.18.1 S4Vectors_0.22.0
## [11] BiocGenerics_0.30.0 BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.2 knitr_1.23 XVector_0.24.0
## [4] magrittr_1.5 zlibbioc_1.30.0 lattice_0.20-38
## [7] R6_2.4.0 rlang_0.4.0 stringr_1.4.0
## [10] tools_3.6.1 grid_3.6.1 xfun_0.8
## [13] htmltools_0.3.6 yaml_2.2.0 digest_0.6.20
## [16] bookdown_0.12 Matrix_1.2-17 GenomeInfoDbData_1.2.1
## [19] BiocManager_1.30.4 bitops_1.0-6 RCurl_1.95-4.12
## [22] evaluate_0.14 rmarkdown_1.14 stringi_1.4.3
## [25] compiler_3.6.1