# Applying a function over a SingleCellExperiment’s contents

# Motivation

The `SingleCellExperiment` is quite a complex class that can hold multiple aspects of the same dataset. It is possible to have multiple assays, multiple dimensionality reduction results, and multiple alternative Experiments - each of which can further have multiple assays and `reducedDims`! In some scenarios, it may be desirable to loop over these pieces and apply the same function to each of them. This is made conveniently possible via the `applySCE()` framework.

# Quick start

Let’s say we have a moderately complicated `SingleCellExperiment` object, containing multiple alternative Experiments for different data modalities.

    library(SingleCellExperiment)
    counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
    sce <- SingleCellExperiment(counts)

    altExp(sce, "Spike") <- SingleCellExperiment(matrix(rpois(20, lambda = 5), ncol=10, nrow=2))
    altExp(sce, "Protein") <- SingleCellExperiment(matrix(rpois(50, lambda = 100), ncol=10, nrow=5))
    altExp(sce, "CRISPR") <- SingleCellExperiment(matrix(rbinom(80, p=0.1, 1), ncol=10, nrow=8))

    sce

    ## class: SingleCellExperiment
    ## dim: 10 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(3): Spike Protein CRISPR

Assume that we want to compute the total count for each modality, using the first assay. We might define a function that looks like the below. (We will come back to the purpose of `multiplier=` and `subset.row=` later.)

    totalCount <- function(x, i=1, multiplier=1, subset.row=NULL) {
        mat <- assay(x, i)
        if (!is.null(subset.row)) {
            mat <- mat[subset.row,,drop=FALSE]
        }
        colSums(mat) * multiplier
    }

We can then easily apply this function across the main and alternative Experiments with:

    totals <- applySCE(sce, FUN=totalCount)
    totals

    ## [[1]]
    ##  [1] 100  88  91 109 111 105 100  88  88  89
    ##
    ## $Spike
    ##  [1] 10 13  9 14 16 10  9 15  7  8
    ##
    ## $Protein
    ##  [1] 533 494 515 497 514 484 525 497 467 493
    ##
    ## $CRISPR
    ##  [1] 0 3 2 1 1 2 1 1 2 2

# Design explanation

The `applySCE()` call above is functionally equivalent to:

    totals.manual <- list(
        totalCount(sce),
        Spike=totalCount(altExp(sce, "Spike")),
        Protein=totalCount(altExp(sce, "Protein")),
        CRISPR=totalCount(altExp(sce, "CRISPR"))
    )
    stopifnot(identical(totals, totals.manual))

Besides being more verbose than `applySCE()`, this approach does not deal well with common arguments. Say we wanted to set `multiplier=10` for all calls. With the manual approach above, this would involve specifying the argument multiple times:

    totals10.manual <- list(
        totalCount(sce, multiplier=10),
        Spike=totalCount(altExp(sce, "Spike"), multiplier=10),
        Protein=totalCount(altExp(sce, "Protein"), multiplier=10),
        CRISPR=totalCount(altExp(sce, "CRISPR"), multiplier=10)
    )

Whereas with the `applySCE()` approach, we can just set it once. This makes it easier to change and reduces the possibility of errors when copy-pasting parameter lists across calls.

    totals10.apply <- applySCE(sce, FUN=totalCount, multiplier=10)
    stopifnot(identical(totals10.apply, totals10.manual))

Now, one might consider just using `lapply()` in this case, which also avoids the need for repeated specification:

    totals10.lapply <- lapply(c(List(sce), altExps(sce)),
        FUN=totalCount, multiplier=10)
    stopifnot(identical(totals10.apply, totals10.lapply))

However, this runs into the opposite problem - it is no longer possible to specify custom arguments for each call. For example, say we wanted to subset to a different set of features for each main and alternative Experiment. With `applySCE()`, this is still possible:

    totals.custom <- applySCE(sce, FUN=totalCount, multiplier=10,
        ALT.ARGS=list(Spike=list(subset.row=2), Protein=list(subset.row=3:5)))
    totals.custom

    ## [[1]]
    ##  [1] 1000  880  910 1090 1110 1050 1000  880  880  890
    ##
    ## $Spike
    ##  [1] 70 60 40 90 90 60 50 80 40 40
    ##
    ## $Protein
    ##  [1] 3330 3120 2990 3150 3260 2910 3110 2880 3000 3050
    ##
    ## $CRISPR
    ##  [1]  0 30 20 10 10 20 10 10 20 20

In cases where we have a mix between custom and common arguments, `applySCE()` provides a more convenient and flexible interface than manual calls or `lapply()`ing.

# Simplifying to a `SingleCellExperiment`

The other convenient aspect of `applySCE()` is that, if the specified `FUN=` returns a `SingleCellExperiment`, `applySCE()` will try to format the output as a `SingleCellExperiment`. To demonstrate, let’s use the `head()` function to take the first few features for each main and alternative Experiment:

    head.sce <- applySCE(sce, FUN=head, n=5)
    head.sce

    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(3): Spike Protein CRISPR

Rather than returning a list of `SingleCellExperiment`s, we can see that the output is neatly organized as a `SingleCellExperiment` with the specified `n=5` features. Moreover, each of the alternative Experiments is also truncated to its first 5 features (or fewer, if there weren’t that many to begin with). This output mirrors, as much as possible, the format of the input `sce`, and is much more convenient to work with than a list of objects.

    altExp(head.sce)

    ## class: SingleCellExperiment
    ## dim: 2 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

    altExp(head.sce, "Protein")

    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

    altExp(head.sce, "CRISPR")

    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

To look under the hood, we can turn off simplification and see what happens. We see that the function indeed returns a list of `SingleCellExperiment` objects corresponding to the `head()` of each Experiment. When `SIMPLIFY=TRUE`, this list is passed through `simplifyToSCE()` to attempt the reorganization into a single object.

    head.sce.list <- applySCE(sce, FUN=head, n=5, SIMPLIFY=FALSE)
    head.sce.list

    ## [[1]]
    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(3): Spike Protein CRISPR
    ##
    ## $Spike
    ## class: SingleCellExperiment
    ## dim: 2 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ##
    ## $Protein
    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):
    ##
    ## $CRISPR
    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(0):

For comparison, if we had to do this manually, it would be rather tedious and error-prone, e.g., if we forgot to set `n=` or if we re-assigned the output of `head()` to the wrong alternative Experiment.

    manual.head <- head(sce, n=5)
    altExp(manual.head, "Spike") <- head(altExp(sce, "Spike"), n=5)
    altExp(manual.head, "Protein") <- head(altExp(sce, "Protein"), n=5)
    altExp(manual.head, "CRISPR") <- head(altExp(sce, "CRISPR"), n=5)
    manual.head

    ## class: SingleCellExperiment
    ## dim: 5 10
    ## metadata(0):
    ## assays(1): ''
    ## rownames: NULL
    ## rowData names(0):
    ## colnames: NULL
    ## colData names(0):
    ## reducedDimNames(0):
    ## mainExpName: NULL
    ## altExpNames(3): Spike Protein CRISPR

Of course, this simplification is only possible when circumstances permit. It requires that `FUN=` returns a `SingleCellExperiment` at each call, and that no more than one result is generated for each alternative Experiment. Failure to meet these conditions will result in a warning and a non-simplified output.

Developers may prefer to set `SIMPLIFY=FALSE` and manually call `simplifyToSCE()`, possibly with `warn.level=3` to trigger an explicit error when simplification fails.

# Session information

    sessionInfo()

    ## R version 4.5.1 (2025-06-13)
    ## 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] SingleCellExperiment_1.33.0 SummarizedExperiment_1.39.2
    ##  [3] Biobase_2.69.1              GenomicRanges_1.63.0
    ##  [5] Seqinfo_0.99.4              IRanges_2.45.0
    ##  [7] S4Vectors_0.49.0            BiocGenerics_0.55.4
    ##  [9] generics_0.1.4              MatrixGenerics_1.21.0
    ## [11] matrixStats_1.5.0           BiocStyle_2.37.1
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] Matrix_1.7-4        jsonlite_2.0.0      compiler_4.5.1
    ##  [4] BiocManager_1.30.26 jquerylib_0.1.4     yaml_2.3.10
    ##  [7] fastmap_1.2.0       lattice_0.22-7      R6_2.6.1
    ## [10] XVector_0.49.3      S4Arrays_1.11.0     knitr_1.50
    ## [13] DelayedArray_0.37.0 maketools_1.3.2     bslib_0.9.0
    ## [16] rlang_1.1.6         cachem_1.1.0        xfun_0.53
    ## [19] sass_0.4.10         sys_3.4.3           SparseArray_1.9.2
    ## [22] cli_3.6.5           digest_0.6.37       grid_4.5.1
    ## [25] lifecycle_1.0.4     evaluate_1.0.5      buildtools_1.0.0
    ## [28] abind_1.4-8         rmarkdown_2.30      tools_4.5.1
    ## [31] htmltools_0.5.8.1