HDF5Array-class: HDF5 datasets as DelayedArray objects
In HDF5Array: HDF5 backend for DelayedArray objects

Description Usage Arguments Value Note See Also Examples

The HDF5Array class is a DelayedArray subclass for representing a conventional (i.e. dense) HDF5 dataset.

All the operations available for DelayedArray objects work on HDF5Array objects.

1 2	## Constructor function: HDF5Array(filepath, name, as.sparse=FALSE, type=NA)

`filepath`	The path (as a single string) to the HDF5 file where the dataset is located.
`name`	The name of the dataset in the HDF5 file.
`as.sparse`	Whether the HDF5 dataset should be flagged as sparse or not, that is, whether it should be considered sparse (and treated as such) or not. Note that HDF5 doesn't natively support sparse storage at the moment so HDF5 datasets cannot be stored in a sparse format, only in a dense one. However a dataset stored in a dense format can still contain a lot of zeroes. Using `as.sparse=TRUE` on such dataset will enable some optimizations that can lead to a lower memory footprint (and possibly better performance) when operating on the HDF5Array. IMPORTANT NOTE: If the dataset is in the 10x Genomics format (i.e. if it uses the HDF5-based sparse matrix representation from 10x Genomics), you should use the `TENxMatrix()` constructor instead of the `HDF5Array()` constructor.
`type`	By default the `type` of the returned object is inferred from the H5 datatype of the HDF5 dataset. This can be overridden by specifying the `type` argument. The specified type must be an R atomic type (e.g. `"integer"`) or `"list"`.

An HDF5Array object.

The 1.3 Million Brain Cell Dataset and other datasets published by 10x Genomics use an HDF5-based sparse matrix representation instead of the conventional (i.e. dense) HDF5 representation.

If your dataset uses the conventional (i.e. dense) HDF5 representation, use the HDF5Array() constructor.

If your dataset uses the HDF5-based sparse matrix representation from 10x Genomics, use the TENxMatrix() constructor.

TENxMatrix objects for representing 10x Genomics datasets as DelayedMatrix objects.
ReshapedHDF5Array objects for representing HDF5 datasets as DelayedArray objects with a user-supplied upfront virtual reshaping.
DelayedArray objects in the DelayedArray package.
writeHDF5Array for writing an array-like object to an HDF5 file.
HDF5-dump-management for controlling the location and physical properties of automatically created HDF5 datasets.
saveHDF5SummarizedExperiment and loadHDF5SummarizedExperiment in this package (the HDF5Array package) for saving/loading an HDF5-based SummarizedExperiment object to/from disk.
The HDF5ArraySeed helper class.
h5ls in the rhdf5 package.

## ---------------------------------------------------------------------
## CONSTRUCTION
## ---------------------------------------------------------------------

toy_h5 <- system.file("extdata", "toy.h5", package="HDF5Array")
library(rhdf5)  # for h5ls()
h5ls(toy_h5)

HDF5Array(toy_h5, "M2")
HDF5Array(toy_h5, "M2", type="integer")
HDF5Array(toy_h5, "M2", type="complex")

library(h5vcData)
tally_file <- system.file("extdata", "example.tally.hfs5",
                          package="h5vcData")
h5ls(tally_file)

## Pick up "Coverages" dataset for Human chromosome 16:
name <- "/ExampleStudy/16/Coverages"
cvg <- HDF5Array(tally_file, name)
cvg

is(cvg, "DelayedArray")  # TRUE
seed(cvg)
path(cvg)
chunkdim(cvg)

## The data in the dataset looks sparse. In this case it is recommended
## to set 'as.sparse' to TRUE when constructing the HDF5Array object.
## This will make block processing (used in operations like sum()) more
## memory efficient and likely faster:
cvg0 <- HDF5Array(tally_file, name, as.sparse=TRUE)
is_sparse(cvg0)  # TRUE

## Note that we can also flag the HDF5Array object as sparse after
## creation:
is_sparse(cvg) <- TRUE
cvg  # same as 'cvg0'

## ---------------------------------------------------------------------
## dim/dimnames
## ---------------------------------------------------------------------
dim(cvg0)

dimnames(cvg0)
dimnames(cvg0) <- list(paste0("s", 1:6), c("+", "-"), NULL)
dimnames(cvg0)

## ---------------------------------------------------------------------
## SLICING (A.K.A. SUBSETTING)
## ---------------------------------------------------------------------
cvg1 <- cvg0[ , , 29000001:29000007]
cvg1

dim(cvg1)
as.array(cvg1)
stopifnot(identical(dim(as.array(cvg1)), dim(cvg1)))
stopifnot(identical(dimnames(as.array(cvg1)), dimnames(cvg1)))

cvg2 <- cvg0[ , "+", 29000001:29000007]
cvg2
as.matrix(cvg2)

## ---------------------------------------------------------------------
## SummarizedExperiment OBJECTS WITH DELAYED ASSAYS
## ---------------------------------------------------------------------

## DelayedArray objects can be used inside a SummarizedExperiment object
## to hold the assay data and to delay operations on them.
 
library(SummarizedExperiment)

pcvg <- cvg0[ , 1, ]  # coverage on plus strand
mcvg <- cvg0[ , 2, ]  # coverage on minus strand

nrow(pcvg)  # nb of samples
ncol(pcvg)  # length of Human chromosome 16

## The convention for a SummarizedExperiment object is to have 1 column
## per sample so first we need to transpose 'pcvg' and 'mcvg':
pcvg <- t(pcvg)
mcvg <- t(mcvg)
se <- SummarizedExperiment(list(pcvg=pcvg, mcvg=mcvg))
se
stopifnot(validObject(se, complete=TRUE))

## A GPos object can be used to represent the genomic positions along
## the dataset:
gpos <- GPos(GRanges("16", IRanges(1, nrow(se))))
gpos
rowRanges(se) <- gpos
se
stopifnot(validObject(se))
assays(se)$pcvg
assays(se)$mcvg