# Using DelayedMatrix with MultiAssayExperiment

# Integrating an HDF5 backend for MultiAssayExperiment

## Dependencies

    library(MultiAssayExperiment)
    library(HDF5Array)
    library(SummarizedExperiment)

## HDF5Array and DelayedArray Constructor

The `HDF5Array` package provides an on-disk representation of large datasets without the need to load them into memory. Convenient lazy evaluation operations allow the user to manipulate such large data files based on metadata. The `DelayedMatrix` class in the `DelayedArray` package provides a way to connect to a large matrix that is stored on disk.

First, we create a small matrix for constructing the `DelayedMatrix` class.

    smallMatrix <- matrix(rnorm(10e5), ncol = 20)

We add rownames and column names to the matrix object for compatibility with the `MultiAssayExperiment` representation.

    rownames(smallMatrix) <- paste0("GENE", seq_len(nrow(smallMatrix)))
    colnames(smallMatrix) <- paste0("SampleID", seq_len(ncol(smallMatrix)))

Here we use the `DelayedArray` constructor function to create a `DelayedMatrix` object.

    smallMatrix <- DelayedArray(smallMatrix)
    class(smallMatrix)

    ## [1] "DelayedMatrix"
    ## attr(,"package")
    ## [1] "DelayedArray"

    # show method
    smallMatrix

    ## <50000 x 20> DelayedMatrix object of type "double":
    ##             SampleID1   SampleID2   SampleID3 ...  SampleID19  SampleID20
    ##     GENE1  0.05570534  1.54711842 -0.05580923   .   0.5942913   1.1763103
    ##     GENE2  1.11302720  0.32449466 -0.27091715   .   0.1482763  -2.6199750
    ##     GENE3  0.79656518  1.55215496  0.49295775   .   0.1492782  -0.0750453
    ##     GENE4  2.33746223  0.46114804 -0.29468889   .  -1.0632865   0.7166360
    ##     GENE5 -0.07467271  0.56719098  0.27596848   .  -0.5444277   0.6801094
    ##       ...           .           .           .   .           .           .
    ## GENE49996 -0.94763339 -0.97810497  0.12218385   . -0.35316983  0.46887585
    ## GENE49997  0.61244629  1.61263919 -1.00542607   .  0.57052596  1.68672049
    ## GENE49998  0.09365115 -0.78353921  0.28665612   .  0.25024470 -0.17998895
    ## GENE49999 -1.32514013 -0.05554379 -1.21511526   .  0.96372691 -0.09791808
    ## GENE50000 -1.65332561  1.61698029  2.86722230   .  0.26615607 -0.74046415

    dim(smallMatrix)

    ## [1] 50000    20

## Writing to a file with dimnames

Finally, the `rhdf5` package stores `dimnames` in a standard location.

In order to make use of this functionality, we would use `writeHDF5Array` with the `with.dimnames` argument:

    testh5 <- tempfile(fileext = ".h5")
    writeHDF5Array(smallMatrix, filepath = testh5, name = "smallMatrix",
        with.dimnames = TRUE)

    ## <50000 x 20> HDF5Matrix object of type "double":
    ##             SampleID1   SampleID2   SampleID3 ...  SampleID19  SampleID20
    ##     GENE1  0.05570534  1.54711842 -0.05580923   .   0.5942913   1.1763103
    ##     GENE2  1.11302720  0.32449466 -0.27091715   .   0.1482763  -2.6199750
    ##     GENE3  0.79656518  1.55215496  0.49295775   .   0.1492782  -0.0750453
    ##     GENE4  2.33746223  0.46114804 -0.29468889   .  -1.0632865   0.7166360
    ##     GENE5 -0.07467271  0.56719098  0.27596848   .  -0.5444277   0.6801094
    ##       ...           .           .           .   .           .           .
    ## GENE49996 -0.94763339 -0.97810497  0.12218385   . -0.35316983  0.46887585
    ## GENE49997  0.61244629  1.61263919 -1.00542607   .  0.57052596  1.68672049
    ## GENE49998  0.09365115 -0.78353921  0.28665612   .  0.25024470 -0.17998895
    ## GENE49999 -1.32514013 -0.05554379 -1.21511526   .  0.96372691 -0.09791808
    ## GENE50000 -1.65332561  1.61698029  2.86722230   .  0.26615607 -0.74046415

To see the file structure we use `h5ls`:

    h5ls(testh5)

    ##                    group                  name       otype dclass        dim
    ## 0                      / .smallMatrix_dimnames   H5I_GROUP
    ## 1 /.smallMatrix_dimnames                     1 H5I_DATASET STRING      50000
    ## 2 /.smallMatrix_dimnames                     2 H5I_DATASET STRING         20
    ## 3                      /           smallMatrix H5I_DATASET  FLOAT 50000 x 20

## Importing HDF5 files

Note that a large matrix from an HDF5 file can also be loaded using the `HDF5ArraySeed` and `DelayedArray` functions.

    hdf5Data <- HDF5ArraySeed(file = testh5, name = "smallMatrix")
    newDelayedMatrix <- DelayedArray(hdf5Data)
    class(newDelayedMatrix)

    ## [1] "HDF5Matrix"
    ## attr(,"package")
    ## [1] "HDF5Array"

    newDelayedMatrix

    ## <50000 x 20> HDF5Matrix object of type "double":
    ##             SampleID1   SampleID2   SampleID3 ...  SampleID19  SampleID20
    ##     GENE1  0.05570534  1.54711842 -0.05580923   .   0.5942913   1.1763103
    ##     GENE2  1.11302720  0.32449466 -0.27091715   .   0.1482763  -2.6199750
    ##     GENE3  0.79656518  1.55215496  0.49295775   .   0.1492782  -0.0750453
    ##     GENE4  2.33746223  0.46114804 -0.29468889   .  -1.0632865   0.7166360
    ##     GENE5 -0.07467271  0.56719098  0.27596848   .  -0.5444277   0.6801094
    ##       ...           .           .           .   .           .           .
    ## GENE49996 -0.94763339 -0.97810497  0.12218385   . -0.35316983  0.46887585
    ## GENE49997  0.61244629  1.61263919 -1.00542607   .  0.57052596  1.68672049
    ## GENE49998  0.09365115 -0.78353921  0.28665612   .  0.25024470 -0.17998895
    ## GENE49999 -1.32514013 -0.05554379 -1.21511526   .  0.96372691 -0.09791808
    ## GENE50000 -1.65332561  1.61698029  2.86722230   .  0.26615607 -0.74046415

## Using a `DelayedMatrix` with `MultiAssayExperiment`

A `DelayedMatrix` alone conforms to the `MultiAssayExperiment` API requirements. Shown below, the `DelayedMatrix` can be put into a named `list` and passed into the `MultiAssayExperiment` constructor function.

    HDF5MAE <- MultiAssayExperiment(experiments = list(smallMatrix = smallMatrix))
    sampleMap(HDF5MAE)

    ## DataFrame with 20 rows and 3 columns
    ##           assay     primary     colname
    ##        <factor> <character> <character>
    ## 1   smallMatrix   SampleID1   SampleID1
    ## 2   smallMatrix   SampleID2   SampleID2
    ## 3   smallMatrix   SampleID3   SampleID3
    ## 4   smallMatrix   SampleID4   SampleID4
    ## 5   smallMatrix   SampleID5   SampleID5
    ## ...         ...         ...         ...
    ## 16  smallMatrix  SampleID16  SampleID16
    ## 17  smallMatrix  SampleID17  SampleID17
    ## 18  smallMatrix  SampleID18  SampleID18
    ## 19  smallMatrix  SampleID19  SampleID19
    ## 20  smallMatrix  SampleID20  SampleID20

    colData(HDF5MAE)

    ## DataFrame with 20 rows and 0 columns

### `SummarizedExperiment` with `DelayedMatrix` backend

A more information rich `DelayedMatrix` can be created when used in conjunction with the `SummarizedExperiment` class and it can even include `rowRanges`. The flexibility of the `MultiAssayExperiment` API supports classes with minimal requirements. Additionally, this `SummarizedExperiment` with the `DelayedMatrix` backend can be part of a bigger `MultiAssayExperiment` object. Below is a minimal example of how this would work:

    HDF5SE <- SummarizedExperiment(assays = smallMatrix)
    assay(HDF5SE)

    ## <50000 x 20> DelayedMatrix object of type "double":
    ##             SampleID1   SampleID2   SampleID3 ...  SampleID19  SampleID20
    ##     GENE1  0.05570534  1.54711842 -0.05580923   .   0.5942913   1.1763103
    ##     GENE2  1.11302720  0.32449466 -0.27091715   .   0.1482763  -2.6199750
    ##     GENE3  0.79656518  1.55215496  0.49295775   .   0.1492782  -0.0750453
    ##     GENE4  2.33746223  0.46114804 -0.29468889   .  -1.0632865   0.7166360
    ##     GENE5 -0.07467271  0.56719098  0.27596848   .  -0.5444277   0.6801094
    ##       ...           .           .           .   .           .           .
    ## GENE49996 -0.94763339 -0.97810497  0.12218385   . -0.35316983  0.46887585
    ## GENE49997  0.61244629  1.61263919 -1.00542607   .  0.57052596  1.68672049
    ## GENE49998  0.09365115 -0.78353921  0.28665612   .  0.25024470 -0.17998895
    ## GENE49999 -1.32514013 -0.05554379 -1.21511526   .  0.96372691 -0.09791808
    ## GENE50000 -1.65332561  1.61698029  2.86722230   .  0.26615607 -0.74046415

    MultiAssayExperiment(list(HDF5SE = HDF5SE))

    ## A MultiAssayExperiment object of 1 listed
    ##  experiment with a user-defined name and respective class.
    ##  Containing an ExperimentList class object of length 1:
    ##  [1] HDF5SE: SummarizedExperiment with 50000 rows and 20 columns
    ## Functionality:
    ##  experiments() - obtain the ExperimentList instance
    ##  colData() - the primary/phenotype DataFrame
    ##  sampleMap() - the sample coordination DataFrame
    ##  `$`, `[`, `[[` - extract colData columns, subset, or experiment
    ##  *Format() - convert into a long or wide DataFrame
    ##  assays() - convert ExperimentList to a SimpleList of matrices
    ##  exportClass() - save data to flat files

Additional scenarios are currently in development where an `HDF5Matrix` is hosted remotely. Many opportunities exist when considering on-disk and off-disk representations of data with `MultiAssayExperiment`.

# Session info

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## 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
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] HDF5Array_1.38.0            h5mread_1.2.1
    ##  [3] rhdf5_2.54.1                DelayedArray_0.36.0
    ##  [5] SparseArray_1.10.8          S4Arrays_1.10.1
    ##  [7] abind_1.4-8                 Matrix_1.7-4
    ##  [9] survminer_0.5.1             ggpubr_0.6.2
    ## [11] ggplot2_4.0.1               survival_3.8-6
    ## [13] UpSetR_1.4.0                RaggedExperiment_1.34.0
    ## [15] MultiAssayExperiment_1.36.1 SummarizedExperiment_1.40.0
    ## [17] Biobase_2.70.0              GenomicRanges_1.62.1
    ## [19] Seqinfo_1.0.0               IRanges_2.44.0
    ## [21] S4Vectors_0.48.0            BiocGenerics_0.56.0
    ## [23] generics_0.1.4              MatrixGenerics_1.22.0
    ## [25] matrixStats_1.5.0           BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tidyselect_1.2.1     dplyr_1.1.4          farver_2.1.2
    ##  [4] S7_0.2.1             fastmap_1.2.0        digest_0.6.39
    ##  [7] lifecycle_1.0.5      magrittr_2.0.4       compiler_4.5.2
    ## [10] rlang_1.1.7          sass_0.4.10          tools_4.5.2
    ## [13] yaml_2.3.12          data.table_1.18.2.1  knitr_1.51
    ## [16] ggsignif_0.6.4       labeling_0.4.3       plyr_1.8.9
    ## [19] xml2_1.5.2           RColorBrewer_1.1-3   withr_3.0.2
    ## [22] purrr_1.2.1          sys_3.4.3            grid_4.5.2
    ## [25] xtable_1.8-4         Rhdf5lib_1.32.0      scales_1.4.0
    ## [28] cli_3.6.5            rmarkdown_2.30       km.ci_0.5-6
    ## [31] reshape2_1.4.5       commonmark_2.0.0     BiocBaseUtils_1.12.0
    ## [34] cachem_1.1.0         stringr_1.6.0        splines_4.5.2
    ## [37] BiocManager_1.30.27  XVector_0.50.0       survMisc_0.5.6
    ## [40] vctrs_0.7.1          jsonlite_2.0.0       carData_3.0-6
    ## [43] litedown_0.9         car_3.1-3            rstatix_0.7.3
    ## [46] Formula_1.2-5        maketools_1.3.2      tidyr_1.3.2
    ## [49] jquerylib_0.1.4      glue_1.8.0           ggtext_0.1.2
    ## [52] stringi_1.8.7        gtable_0.3.6         tibble_3.3.1
    ## [55] pillar_1.11.1        rhdf5filters_1.22.0  htmltools_0.5.9
    ## [58] R6_2.6.1             KMsurv_0.1-6         evaluate_1.0.5
    ## [61] lattice_0.22-7       markdown_2.0         backports_1.5.0
    ## [64] gridtext_0.1.5       broom_1.0.12         bslib_0.10.0
    ## [67] Rcpp_1.1.1           gridExtra_2.3        xfun_0.56
    ## [70] zoo_1.8-15           buildtools_1.0.0     pkgconfig_2.0.3