SingleCellExperiment 1.29.0
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.
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 CRISPRAssume 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] 112 93 101 112 102 116 90 93 88 91
##
## $Spike
## [1] 14 7 7 7 7 8 10 12 13 11
##
## $Protein
## [1] 509 477 489 531 476 520 479 483 492 502
##
## $CRISPR
## [1] 2 1 1 0 2 3 1 0 0 0The 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] 1120 930 1010 1120 1020 1160 900 930 880 910
##
## $Spike
## [1] 100 40 60 40 40 40 60 50 80 60
##
## $Protein
## [1] 3020 2860 2910 3280 2870 3020 2870 2750 3040 2870
##
## $CRISPR
## [1] 20 10 10 0 20 30 10 0 0 0In 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.
SingleCellExperimentThe 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 CRISPRRather than returning a list of SingleCellExperiments, 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 CRISPROf 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.
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellExperiment_1.29.0 SummarizedExperiment_1.37.0
## [3] Biobase_2.67.0 GenomicRanges_1.59.0
## [5] GenomeInfoDb_1.43.0 IRanges_2.41.0
## [7] S4Vectors_0.45.0 BiocGenerics_0.53.0
## [9] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [11] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] Matrix_1.7-1 jsonlite_1.8.9 compiler_4.5.0
## [4] BiocManager_1.30.25 crayon_1.5.3 jquerylib_0.1.4
## [7] yaml_2.3.10 fastmap_1.2.0 lattice_0.22-6
## [10] R6_2.5.1 XVector_0.47.0 S4Arrays_1.7.0
## [13] knitr_1.48 DelayedArray_0.33.0 bookdown_0.41
## [16] GenomeInfoDbData_1.2.13 bslib_0.8.0 rlang_1.1.4
## [19] cachem_1.1.0 xfun_0.48 sass_0.4.9
## [22] SparseArray_1.7.0 cli_3.6.3 zlibbioc_1.53.0
## [25] digest_0.6.37 grid_4.5.0 lifecycle_1.0.4
## [28] evaluate_1.0.1 abind_1.4-8 rmarkdown_2.28
## [31] httr_1.4.7 tools_4.5.0 htmltools_0.5.8.1
## [34] UCSC.utils_1.3.0