Generate 2D cell embeddings using UMAP
calc_umap(fsce, expt = "rnaseq", method = "PCA", n_dims = NULL, n_neighbors = 30, min_dist = 0.3, metric = "euclidean", seed = NA, ...)
Data to use for calculating variable features
dimenality reduction method to use for UMAP (default is "PCA")
number of dimensions to pass to UMAP, defaults to all present in dr matrix
number of nearest neighbors to use for learning the manifold. Low values will preserve local structure, at the expense missing higher order organization. Higher values will capture more global structure but miss fine grained detail. Defaults to 30.
Numeric between 0 and 0.99. min_dist controls how tightly points can be packed together in 2D space. Lower values will generate more clumpy projections, but more accurately preserve local structure.
distance metric for UMAP, defaults to pearson.
seed to generate reproducible UMAP projection. Defaults to no seed.
additional arguments for
fsce with UMAP values added to reducedDims
See https://umap-learn.readthedocs.io for a detailed description of parameters.