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rdapply() function are
deprecated! The rdapply function applies a user function over
the spaces of a RangedData. The parameters to
rdapply are collected into an instance of RDApplyParams,
which is passed as the sole parameter to rdapply.
rdapply(x, ...)RDApplyParams instance, see below for how to
make one.list holding the result of each invocation of the
user function, but see details.
RDApplyParams(rangedData, applyFun, applyParams,
filterRules, simplify, reducerFun, reducerParams):
Constructs a RDApplyParams object with each setting
specified by the argument of the same name. See the Details section
for more information.
x is an RDApplyParams
object. rangedData(x), rangedData(x) <- value: Get or set the
RangedData instance over which applyFun is applied.
applyFun(x), applyFun(x) <- value: Get or set the user
function to be applied to each space in the RangedData.
applyParams(x), applyParams(x) <- value: Get
or set the list of additional parameters to pass to
applyFun.
filterRules(x), filterRules(x) <- value: Get
or set the instance of FilterRules that is used to
filter each subset of the RangedData passed to the user
function.
simplify(x), simplify(x) <- value: Get or set a
a scalar logical (TRUE or FALSE) indicating whether
the list to be returned from rdapply should be
simplified as by sapply.
reducerFun(x), reducerFun(x) <- value: Get or set the
function that is used to convert the list that would
otherwise be returned from rdapply to something more convenient.
reducerParams(x), reducerParams(x) <- value:
Get or set a list of additional parameters to pass to
reducerFun.
iteratorFun(x), iteratorFun(x) <- value:
Get or set the function used for applying over the RangedData.
rdapply function is an attempt to facilitate the common
operation of performing the same operation over each space
(e.g. chromosome) in a RangedData. To facilitate a wide array
of such tasks, the function takes a large number of options. The
RDApplyParams class is meant to help manage this
complexity. In particular, it facilitates experimentation through its
support for incremental changes to parameter settings.
There are two RangedData settings that are required: the
user function object and the RangedData over which it is
applied. The rest of the settings determine what is actually passed to
the user function and how the return value is processed before
relaying it to the user. The following is the description and
rationale for each setting.
rangedDataRangedData instance over which applyFun is
applied.
applyFunfunction to be applied to each space in the
RangedData. The function must expect the RangedData as its
first parameter and also accept the parameters specified in
applyParams.
applyParamslist of additional parameters to pass to
applyFun. Usually empty.
filterRulesFilterRules that is used to
filter each subset of the RangedData passed to the user
function. This is an efficient and convenient means for performing
the same operation over different subsets of the data on a
space-by-space basis. In particular, this avoids the need to store
subsets of the entire RangedData. A common workflow is to
invoke rdapply with one set of active filters, enable
different filters, reinvoke rdapply, and compare the
results.
simplifyTRUE or FALSE) indicating whether
the list to be returned from rdapply should be
simplified as by sapply. Defaults to FALSE.
reducerFunfunction that is used to convert the list that
would otherwise be returned from rdapply to something more
convenient. The function should take the list as its first
parameter and also accept the parameters specified in
reducerParams. This is an alternative to the
primitive behavior of the simplify option (so
simplify must be FALSE if this option is set). The
aim is to orthogonalize the applyFun operation (i.e. the
statistics) from the data structure of the result.
reducerParamslist of additional parameters to pass to reducerFun.
Can only be set if reducerFun is set. Usually empty.
iteratorFunRangedData. By
default, this is lapply, but it could also be a specialized
function, like mclapply.
RangedData, FilterRules
## WARNING: RDApplyParams objects and the rdapply() function are
## deprecated!
## Not run:
# ranges <- IRanges(c(1,2,3),c(4,5,6))
# score <- c(2L, 0L, 1L)
# rd <- RangedData(ranges, score, space = c("chr1","chr2","chr1"))
#
# ## a single function
# countrows <- function(rd) nrow(rd)
# params <- RDApplyParams(rd, countrows)
# rdapply(params) # list(chr1 = 2L, chr2 = 1L)
#
# ## with a parameter
# params <- RDApplyParams(rd, function(rd, x) nrow(rd)*x, list(x = 2))
# rdapply(params) # list(chr1 = 4L, chr2 = 2L)
#
# ## add a filter
# cutoff <- 0
# rules <- FilterRules(filter = score > cutoff)
# params <- RDApplyParams(rd, countrows, filterRules = rules)
# rdapply(params) # list(chr1 = 2L, chr2 = 0L)
# rules <- FilterRules(list(fun = function(rd) rd[["score"]] < 2),
# filter = score > cutoff)
# params <- RDApplyParams(rd, countrows, filterRules = rules)
# rdapply(params) # list(chr1 = 1L, chr2 = 0L)
# active(filterRules(params))["filter"] <- FALSE
# rdapply(params) # list(chr1 = 1L, chr2 = 1L)
#
# ## simplify
# params <- RDApplyParams(rd, countrows, simplify = TRUE)
# rdapply(params) # c(chr1 = 2L, chr2 = 1L)
#
# ## reducing
# params <- RDApplyParams(rd, countrows, reducerFun = unlist,
# reducerParams = list(use.names = FALSE))
# rdapply(params) ## c(2L, 1L)
# ## End(Not run)
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