| DelayedArray-class {DelayedArray} | R Documentation |
Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism.
DelayedArray(seed) # constructor function seed(x) # seed getter type(x)
seed |
An array-like object. |
x |
A DelayedArray object. (Can also be an ordinary array in case of
|
To realize a DelayedArray object (i.e. to trigger execution of the
delayed operations carried by the object and return the result as an
ordinary array), call as.array on it. However this realizes the
full object at once in memory which could require too much memory
if the object is big. A big DelayedArray object is preferrably realized
on disk e.g. by calling writeHDF5Array on
it (this function is defined in the HDF5Array package) or coercing it
to an HDF5Array object with as(x, "HDF5Array").
Other on-disk backends can be supported. This uses a block-processing
strategy so that the full object is not realized at once in memory. Instead
the object is processed block by block i.e. the blocks are realized in
memory and written to disk one at a time.
See ?writeHDF5Array in the HDF5Array package
for more information about this.
DelayedArray objects support the same set of getters as ordinary arrays
i.e. dim(), length(), and dimnames().
In addition, they support type(), which is the DelayedArray
equivalent of typeof() or storage.mode() for ordinary
arrays. Note that, for convenience and consistency, type() also
works on ordinary arrays.
Only dimnames() is supported as a setter.
A DelayedArray object can be subsetted with [ like an ordinary array
but with the following differences:
Multi-dimensional single bracket subsetting (i.e. subsetting
of the form x[i_1, i_2, ..., i_n] with one (possibly missing)
subscript per dimension) returns a DelayedArray object where the
subsetting is actually delayed. So it's a very light operation.
The drop argument of the [ operator is ignored i.e.
subsetting a DelayedArray object always returns a DelayedArray
object with the same number of dimensions as the original object.
You need to call drop() on the subsetted object to actually
drop its ineffective dimensions (i.e. the dimensions equal to 1).
drop() is also a delayed operation so is very light.
Linear single bracket subsetting (a.k.a. 1D-style subsetting,
that is, subsetting of the form x[i]) only works if subscript
i is a numeric vector at the moment. Furthermore, i
cannot contain NAs and all the indices in it must be >= 1 and <=
length(x) for now. It returns an atomic vector of the same
length as i. This is NOT a delayed operation.
Subsetting with [[ is supported but only the linear form
of it at the moment i.e. the x[[i]] form where i is a
single numeric value >= 1 and <= length(x). It is equivalent
to x[i].
DelayedArray objects support only 2 forms of subassignment at the moment:
x[i] <- value and x[] <- value. The former is supported only
when the subscript i is a logical DelayedArray object with the same
dimensions as x and when value is a scalar (i.e. an
atomic vector of length 1). The latter is supported only when value
is an atomic vector and length(value) is a divisor of nrow(x).
Both are delayed operations so are very light.
Single value replacement (x[[...]] <- value) is not supported.
Binding DelayedArray objects along the rows (or columns) is supported
via the rbind and arbind (or cbind and acbind)
methods for DelayedArray objects. All these operations are delayed.
realize for realizing a DelayedArray object in memory
or on disk.
DelayedArray-utils for common operations on DelayedArray objects.
cbind in the base package for
rbind/cbind'ing ordinary arrays.
acbind in this package (DelayedArray) for
arbind/acbind'ing ordinary arrays.
RleArray objects.
HDF5Array objects in the HDF5Array package.
DataFrame objects in the S4Vectors package.
array objects in base R.
## ---------------------------------------------------------------------
## A. WRAP AN ORDINARY ARRAY IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
a <- array(runif(1500000), dim=c(10000, 30, 5))
A <- DelayedArray(a)
A
## The seed of A is treated as a "read-only" object so won't change when
## we start operating on A:
stopifnot(identical(a, seed(A)))
type(A)
## Multi-dimensional single bracket subsetting:
m <- a[11:20 , 5, ] # a matrix
A[11:20 , 5, ] # not a DelayedMatrix (still 3 dimensions)
M <- drop(A[11:20 , 5, ]) # a DelayedMatrix object
stopifnot(identical(m, as.array(M)))
stopifnot(identical(a, seed(M)))
## Linear single bracket subsetting:
A[11:20]
A[which(A <= 1e-5)]
## Subassignment:
A[A < 0.2] <- NA
a[a < 0.2] <- NA
stopifnot(identical(a, as.array(A)))
## Other operations:
toto <- function(x) (5 * x[ , , 1] ^ 3 + 1L) * log(x[, , 2])
b <- toto(a)
head(b)
B <- toto(A) # very fast! (operations are delayed)
B # still 3 dimensions (subsetting a DelayedArray object never drops
# dimensions)
B <- drop(B)
B
cs <- colSums(b)
CS <- colSums(B)
stopifnot(identical(cs, CS))
## ---------------------------------------------------------------------
## B. WRAP A DataFrame OBJECT IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
## Generate random coverage and score along an imaginary chromosome:
cov <- Rle(sample(20, 5000, replace=TRUE), sample(6, 5000, replace=TRUE))
score <- Rle(sample(100, nrun(cov), replace=TRUE), runLength(cov))
DF <- DataFrame(cov, score)
A2 <- DelayedArray(DF)
A2
seed(A2) # 'DF'
## Coercion of a DelayedMatrix object to DataFrame produces a DataFrame
## object with Rle columns:
as(A2, "DataFrame")
stopifnot(identical(DF, as(A2, "DataFrame")))
t(A2) # transposition is delayed so is very fast and very memory
# efficient
stopifnot(identical(DF, seed(t(A2)))) # the "seed" is still the same
colSums(A2)
## ---------------------------------------------------------------------
## C. A HDF5Array OBJECT IS A (PARTICULAR KIND OF) DelayedArray OBJECT
## ---------------------------------------------------------------------
library(HDF5Array)
A3 <- as(a, "HDF5Array") # write 'a' to an HDF5 file
A3
is(A3, "DelayedArray") # TRUE
seed(A3) # a HDF5ArraySeed object
B3 <- toto(A3) # very fast! (operations are delayed)
B3 # not a HDF5Array object because now it
# carries delayed operations
B3 <- drop(B3)
CS3 <- colSums(B3)
stopifnot(identical(cs, CS3))
## ---------------------------------------------------------------------
## D. PERFORM THE DELAYED OPERATIONS
## ---------------------------------------------------------------------
as(B3, "HDF5Array") # "realize" 'B3' on disk
## If this is just an intermediate result, you can either keep going
## with B3 or replace it with its "realized" version:
B3 <- as(B3, "HDF5Array") # no more delayed operations on new 'B3'
seed(B3)
## For convenience, realize() can be used instead of explicit coercion.
## The current "realization backend" controls where realization
## happens e.g. in memory if set to NULL or in an HDF5 file if set
## to "HDF5Array":
D <- cbind(B3, exp(B3))
D
setRealizationBackend("HDF5Array")
D <- realize(D)
D
## See '?realize' for more information about "realization backends".
## ---------------------------------------------------------------------
## E. BIND DelayedArray OBJECTS
## ---------------------------------------------------------------------
## rbind/cbind
library(HDF5Array)
toy_h5 <- system.file("extdata", "toy.h5", package="HDF5Array")
h5ls(toy_h5)
M1 <- HDF5Array(toy_h5, "M1")
M2 <- HDF5Array(toy_h5, "M2")
M12 <- rbind(M1, t(M2))
M12
colMeans(M12)
## arbind/acbind
example(acbind) # to create arrays a1, a2, a3
A1 <- DelayedArray(a1)
A2 <- DelayedArray(a2)
A3 <- DelayedArray(a3)
A <- arbind(A1, A2, A3)
A
## Sanity check:
stopifnot(identical(arbind(a1, a2, a3), as.array(A)))
## ---------------------------------------------------------------------
## F. WRAP A SPARSE MATRIX IN A DelayedArray OBJECT
## ---------------------------------------------------------------------
## Not run:
library(Matrix)
M <- 75000L
N <- 1800L
p <- sparseMatrix(sample(M, 9000000, replace=TRUE),
sample(N, 9000000, replace=TRUE),
x=runif(9000000), dims=c(M, N))
P <- DelayedArray(p)
P
p2 <- as(P, "sparseMatrix")
stopifnot(identical(p, p2))
## The following is based on the following post by Murat Tasan on the
## R-help mailing list:
## https://stat.ethz.ch/pipermail/r-help/2017-May/446702.html
## As pointed out by Murat, the straight-forward row normalization
## directly on sparse matrix 'p' would consume too much memory:
row_normalized_p <- p / rowSums(p^2) # consumes too much memory
## because the rowSums() result is being recycled (appropriately) into a
## *dense* matrix with dimensions equal to dim(p).
## Murat came up with the following solution that is very fast and memory
## efficient:
row_normalized_p1 <- Diagonal(x=1/sqrt(Matrix::rowSums(p^2)))
## With a DelayedArray object, the straight-forward approach uses a
## block processing strategy behind the scene so it doesn't consume
## too much memory.
## First, let's see the block processing in action:
DelayedArray:::set_verbose_block_processing(TRUE)
## and set block size to a bigger value than the default:
getOption("DelayedArray.block.size")
options(DelayedArray.block.size=80e6)
row_normalized_P <- P / sqrt(DelayedArray::rowSums(P^2))
## Increasing the block size increases the speed but also memory usage:
options(DelayedArray.block.size=200e6)
row_normalized_P2 <- P / sqrt(DelayedArray::rowSums(P^2))
stopifnot(all.equal(row_normalized_P, row_normalized_P2))
## Back to sparse representation:
DelayedArray:::set_verbose_block_processing(FALSE)
row_normalized_p2 <- as(row_normalized_P, "sparseMatrix")
stopifnot(all.equal(row_normalized_p1, row_normalized_p2))
options(DelayedArray.block.size=10e6)
## End(Not run)