Combine the main and alternative experiments back into one SingleCellExperiment object.
This is effectively the reverse operation to
A SingleCellExperiment containing alternative experiments in the
Logical scalar indicating whether the (non-
Logical scalar indicating whether the names of column-related fields should be prefixed with the name of the alternative experiment.
Logical scalar indicating whether the combining of the assays should be delayed.
This function is intended for downstream applications that accept a SingleCellExperiment but are not aware of the
By consolidating all data together, applications like iSEE can use the same machinery to visualize any feature of interest across all modalities.
However, for quantitative analyses, it is usually preferable to keep different modalities separate.
Assays with the same name are
rbinded together in the output object.
If a particular name is not present for any experiment, its values are filled in with the appropriately typed
By default, this is done efficiently via ConstantMatrix abstractions to avoid actually creating a dense matrix of
delayed=FALSE, the combining of matrices is done without any DelayedArray wrappers,
yielding a simpler matrix representation at the cost of increasing memory usage.
reducedDims in the alternative experiments are added to those of the main experiment.
The names of these migrated fields are prefixed by the name of the alternative experiment if
delayed=FALSE will reverse the effects of
A SingleCellExperiment where all features in the alternative experiments of
sce are now features in the main experiment.
The output object has no alternative experiments of its own.
splitAltExps, which does the reverse operation of this function.
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counts <- matrix(rpois(10000, 5), ncol=100) sce <- SingleCellExperiment(assays=list(counts=counts)) feat.type <- sample(c("endog", "ERCC", "adt"), nrow(sce), replace=TRUE, p=c(0.8, 0.1, 0.1)) sce <- splitAltExps(sce, feat.type) # Making life a little more complicated. logcounts(sce) <- log2(counts(sce) + 1) sce$cluster <- sample(5, ncol(sce), replace=TRUE) reducedDim(sce, "PCA") <- matrix(rnorm(ncol(sce)*2), ncol=2) # Now, putting Humpty Dumpty back together again. restored <- unsplitAltExps(sce) restored
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