| runMultiUMAP {scater} | R Documentation |
Perform UMAP with multiple input matrices by intersecting their simplicial sets. Typically used to combine results from multiple data modalities into a single embedding.
runMultiUMAP(inputs, ..., metric = "euclidean")
inputs |
A list of numeric matrices where each row is a cell and each column is some dimension/variable. For gene expression data, this is usually the matrix of PC coordinates. |
... |
Further arguments to pass to |
metric |
String specifying the type of distance to use. |
This is simply a convenience wrapper around umap for multi-modal analysis.
All modes use the distance metric of metric to construct the simplicial sets within each mode.
Comparisons across modes are then performed after intersecting the sets to obtain a single graph.
A numeric matrix containing the low-dimensional UMAP embedding.
Aaron Lun
runUMAP, for the more straightforward application of UMAP.
# Mocking up a gene expression + ADT dataset:
exprs_sce <- mockSCE()
exprs_sce <- logNormCounts(exprs_sce)
exprs_sce <- runPCA(exprs_sce)
adt_sce <- mockSCE(ngenes=20)
adt_sce <- logNormCounts(adt_sce)
altExp(exprs_sce, "ADT") <- adt_sce
# Running a multimodal analysis using PCs for expression
# and log-counts for the ADTs:
output <- runMultiUMAP(
list(
reducedDim(exprs_sce, "PCA"),
t(logcounts(altExp(exprs_sce, "ADT")))
)
)
reducedDim(exprs_sce, "combinedUMAP") <- output
plotReducedDim(exprs_sce, "combinedUMAP")