Uniform Manifold Approximation and Projection (UMAP)

Run nonlinear dimensionality reduction using UMAP with a dimensional reduction as input.

Usage

RunUMAP.SingleCellExperiment(
  object,
  dims,
  dimred.type,
  return.model,
  umap.method,
  dimred.name,
  ...
)

# S4 method for class 'SingleCellExperiment'
RunUMAP(
  object,
  dims = NULL,
  dimred.type = "PCA",
  return.model = FALSE,
  umap.method = "umap",
  dimred.name = "UMAP",
  ...
)

Arguments

object: An object of SingleCellExperiment class.
dims: Dimensions to select from dimred.type. By default NULL, i.e., all the dimensions are selected. Provide a numeric vector to select a specific range, e.g., dims = 1:10 to select the first 10 dimensions.
dimred.type: Dimensional reduction type to use. By default "PCA".
return.model: Return UMAP model. By default FALSE.
umap.method: UMAP method to use: "umap" or "uwot". By default "umap".
dimred.name: Dimensional reduction name given to the returned UMAP. By default "UMAP".
...: Parameters to be passed to the umap function. The parameters given should match the parameters accepted by the umap function depending on the umap.method given. Check possible parameters with ?umap::umap or ?uwot::umap depending if umap.method is "umap" or "uwot".

Value

A SingleCellExperiment object.

Examples

# Import package
suppressPackageStartupMessages(library("SingleCellExperiment"))

# Create toy SCE data
batches <- c("b1", "b2")
set.seed(239)
batch <- sample(x = batches, size = nrow(iris), replace = TRUE)
sce <- SingleCellExperiment(assays = list(logcounts = t(iris[,1:4])),  
                            colData = DataFrame("Species" = iris$Species, 
                                               "Batch" = batch))
colnames(sce) <- paste0("samp", 1:ncol(sce))

# Prepare SCE object for analysis
sce <- PrepareData(sce)
#> Converting object of `matrix` class into `dgCMatrix`. Please note that Coralysis has been designed to work with sparse data, i.e. data with a high proportion of zero values! Dense data will likely increase run time and memory usage drastically!
#> 4/4 features remain after filtering features with only zero values.

# Multi-level integration (just for highlighting purposes; use default parameters)
set.seed(123)
sce <- RunParallelDivisiveICP(object = sce, batch.label = "Batch", 
                              k = 2, L = 25, C = 1, train.k.nn = 10, 
                              train.k.nn.prop = NULL, use.cluster.seed = FALSE,
                              build.train.set = FALSE, ari.cutoff = 0.1, 
                             threads = 2)
#> 
#> Initializing divisive ICP clustering...
#> 
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#> 
#> Divisive ICP clustering completed successfully.
#> 
#> Predicting cell cluster probabilities using ICP models...
#> Prediction of cell cluster probabilities completed successfully.
#> 
#> Multi-level integration completed successfully.

# Integrated PCA
set.seed(125) # to ensure reproducibility for the default 'irlba' method
sce <- RunPCA(object = sce, assay.name = "joint.probability", p = 10)
#> Divisive ICP: selecting ICP tables multiple of 1

# Plot result 
cowplot::plot_grid(PlotDimRed(object = sce, color.by = "Batch", 
                              legend.nrow = 1),
                   PlotDimRed(object = sce, color.by = "Species", 
                             legend.nrow = 1), ncol = 2)


# Run UMAP
set.seed(123)
sce <- RunUMAP(sce, dimred.type = "PCA")

# Plot results
# Plot result 
cowplot::plot_grid(PlotDimRed(object = sce, color.by = "Batch", 
                              legend.nrow = 1),
                   PlotDimRed(object = sce, color.by = "Species", 
                             legend.nrow = 1), ncol = 2)