# The h5mread package

Hervé Pagès1

1Fred Hutch Cancer Center, Seattle, WA

#### Compiled 28 April 2026; Modified 14 January 2025

#### Package

h5mread 1.4.0

# Contents

# 1 Introduction

h5mread is an R/Bioconductor package that allows fast and memory-efficient loading of HDF5 data into R. The main function in the package is `h5mread()` which allows reading arbitrary data from an HDF5 dataset into R, similarly to what the `h5read()` function from the rhdf5 package does. In the case of `h5mread()`, the implementation has been optimized to make it as fast and memory-efficient as possible.

In addition to the `h5mread()` function, the package also provides the following low-level functionality:

- `h5dim()` and `h5chunkdim()`;

- Utility functions to manipulate the dimnames of an HDF5 dataset;

- H5File objects to facilitate access to remote HDF5 files like files stored in an Amazon S3 bucket.

Note that the primary use case for the h5mread package is to support higher-level functionality implemented in the HDF5Array package.

# 2 Install and load the package

Like any other Bioconductor package, h5mread should always be installed with `BiocManager::install()`:

    if (!require("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("h5mread")

Load the package:

    library(h5mread)

# 3 The h5mread() function

`h5mread()` is an efficient and flexible alternative to `rhdf5::h5read()`.

Note that we’ll use `writeHDF5Array()` from the HDF5Array package to conveniently create the HDF5 datasets used in the examples below.

## 3.1 Basic example

Create a 70,000 x 1,500 random dataset:

    set.seed(2009)
    m0 <- matrix(runif(105e6), ncol=1500)  # 70,000 x 1,500 matrix
    temp0_h5 <- tempfile(fileext=".h5")
    HDF5Array::writeHDF5Array(m0, temp0_h5, "m0", chunkdim=c(100, 100))

Load 1,000 random rows from the HDF5 dataset:

    h5ls(temp0_h5)

    ##   group name       otype dclass          dim
    ## 0     /   m0 H5I_DATASET  FLOAT 70000 x 1500

    nrow0 <- h5dim(temp0_h5, "m0")[[1L]]
    starts <- list(sample(nrow0, 1000), NULL)
    m <- h5mread(temp0_h5, "m0", starts=starts)

See `?h5mread` for more information and additional examples.

Sanity check:

    stopifnot(identical(m, m0[starts[[1L]], ]))

## 3.2 An example involving sparse data

The HDF5 format doesn’t natively support sparse data representation. However, because it stores the data in compressed chunks, sparse data gets efficiently compressed in the HDF5 file.

The more sparse the data, the smaller the resulting HDF5 file:

    a1 <- poissonSparseArray(c(6100, 960, 75), density=0.5)
    a2 <- poissonSparseArray(c(6100, 960, 75), density=0.05)

    temp1_h5 <- tempfile(fileext=".h5")
    HDF5Array::writeHDF5Array(a1, temp1_h5, "a1", chunkdim=c(50, 40, 5), level=5)
    temp2_h5 <- tempfile(fileext=".h5")
    HDF5Array::writeHDF5Array(a2, temp2_h5, "a2", chunkdim=c(50, 40, 5), level=5)

    file.size(temp1_h5)

    ## [1] 128949150

    file.size(temp2_h5)

    ## [1] 38533754

However, the small size on disk won’t translate into a small size in memory if the data gets loaded back as an ordinary array:

    a21 <- h5mread(temp2_h5, "a2")  # not memory-efficient
    object.size(a21)

    ## 1756800224 bytes

To keep memory usage as low as possible, `h5mread()` can load the data in a SparseArray derivative from the SparseArray package. This is achieved by setting its `as.sparse` argument to `TRUE`:

    a22 <- h5mread(temp2_h5, "a2", as.sparse=TRUE)  # memory-efficient
    object.size(a22)

    ## 351486208 bytes

Note that the data is loaded as a COO_SparseArray object:

    class(a22)

    ## [1] "COO_SparseArray"
    ## attr(,"package")
    ## [1] "SparseArray"

See `?h5mread` for more information and additional examples.

Sanity checks:

    stopifnot(
      identical(a21, as.array(a2)),
      is(a22, "COO_SparseArray"),
      identical(as.array(a22), as.array(a2))
    )

## 3.3 A note about COO_SparseArray objects

The COO_SparseArray class is one of the two SparseArray concrete subclasses defined in the SparseArray package, the other one being SVT_SparseArray. Note that the latter tends to be even more memory-efficient than the former and to achieve better performance overall.

Use coercion to switch back and forth between the two representations:

    a22 <- as(a22, "SVT_SparseArray")
    class(a22)

    ## [1] "SVT_SparseArray"
    ## attr(,"package")
    ## [1] "SparseArray"

About half the memory footprint of the COO_SparseArray representation:

    object.size(a22)

    ## 188106768 bytes

See `?SparseArray` in the SparseArray package for more information.

# 4 Other functionality provided by the h5mread package

## 4.1 h5dim() and h5chunkdim()

Two convenience functions to obtain the dimensions of an HDF5 dataset as well as the dimensions of its chunks.

See `?h5dim` for more information and some examples.

## 4.2 Utility functions to manipulate the dimnames of an HDF5 dataset

A small set of low-level utilities is provided to manipulate the dimnames of an HDF5 dataset.

See `?h5writeDimnames` for more information and some examples.

## 4.3 H5File objects

Use an H5File object to access an HDF5 file stored in an Amazon S3 bucket.

See `?H5File` for more information and some examples.

# 5 Session information

    sessionInfo()

    ## R version 4.6.0 Patched (2026-04-24 r89963)
    ## Platform: aarch64-apple-darwin23
    ## Running under: macOS Tahoe 26.3.1
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] h5mread_1.4.0         SparseArray_1.12.0    S4Arrays_1.12.0
    ##  [4] IRanges_2.46.0        abind_1.4-8           S4Vectors_0.50.0
    ##  [7] MatrixGenerics_1.24.0 matrixStats_1.5.0     Matrix_1.7-5
    ## [10] BiocGenerics_0.58.0   generics_0.1.4        rhdf5_2.56.0
    ## [13] BiocStyle_2.40.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] cli_3.6.6           knitr_1.51          rlang_1.2.0
    ##  [4] xfun_0.57           otel_0.2.0          DelayedArray_0.38.0
    ##  [7] jsonlite_2.0.0      htmltools_0.5.9     sass_0.4.10
    ## [10] rmarkdown_2.31      grid_4.6.0          evaluate_1.0.5
    ## [13] jquerylib_0.1.4     fastmap_1.2.0       yaml_2.3.12
    ## [16] lifecycle_1.0.5     Rhdf5lib_2.0.0      bookdown_0.46
    ## [19] BiocManager_1.30.27 compiler_4.6.0      rhdf5filters_1.24.0
    ## [22] XVector_0.52.0      lattice_0.22-9      digest_0.6.39
    ## [25] R6_2.6.1            HDF5Array_1.40.0    bslib_0.10.0
    ## [28] tools_4.6.0         cachem_1.1.0