runUMAP {scater} | R Documentation |
Perform uniform manifold approximation and projection (UMAP) for the cells, based on the data in a SingleCellExperiment object.
runUMAP(object, ncomponents = 2, ntop = 500, feature_set = NULL, exprs_values = "logcounts", scale_features = TRUE, use_dimred = NULL, n_dimred = NULL, ...)
object |
A SingleCellExperiment object. |
ncomponents |
Numeric scalar indicating the number of UMAP dimensions to obtain. |
ntop |
Numeric scalar specifying the number of most variable features to use for UMAP. |
feature_set |
Character vector of row names, a logical vector or a numeric vector of indices indicating a set of features to use for UMAP.
This will override any |
exprs_values |
Integer scalar or string indicating which assay of |
scale_features |
Logical scalar, should the expression values be standardised so that each feature has unit variance? |
use_dimred |
String or integer scalar specifying the entry of |
n_dimred |
Integer scalar, number of dimensions of the reduced dimension slot to use when |
... |
Additional arguments to pass to |
The function umap
is used internally to compute the UMAP.
Note that the algorithm is not deterministic, so different runs of the function will produce differing results.
Users are advised to test multiple random seeds, and then use set.seed
to set a random seed for replicable results.
Setting use_dimred
allows users to easily perform UMAP on low-rank approximations of the original expression matrix (e.g., after PCA).
In such cases, arguments such as ntop
, feature_set
, exprs_values
and scale_features
will be ignored.
A SingleCellExperiment object containing the coordinates of the first ncomponent
UMAP dimensions for each cell.
This is stored in the "UMAP"
entry of the reducedDims
slot.
Aaron Lun
McInnes L, Healy J (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv.
## Set up an example SingleCellExperiment data("sc_example_counts") data("sc_example_cell_info") example_sce <- SingleCellExperiment( assays = list(counts = sc_example_counts), colData = sc_example_cell_info ) example_sce <- normalize(example_sce) example_sce <- runUMAP(example_sce) reducedDimNames(example_sce) head(reducedDim(example_sce))