run UMAP
runUMAP(
gobject,
expression_values = c("normalized", "scaled", "custom"),
reduction = c("cells", "genes"),
dim_reduction_to_use = "pca",
dim_reduction_name = "pca",
dimensions_to_use = 1:10,
name = "umap",
genes_to_use = NULL,
return_gobject = TRUE,
n_neighbors = 40,
n_components = 2,
n_epochs = 400,
min_dist = 0.01,
n_threads = NA,
spread = 5,
set_seed = TRUE,
seed_number = 1234,
verbose = T,
...
)
giotto object
expression values to use
cells or genes
use another dimension reduction set as input
name of dimension reduction set to use
number of dimensions to use as input
arbitrary name for UMAP run
if dim_reduction_to_use = NULL, which genes to use
boolean: return giotto object (default = TRUE)
UMAP param: number of neighbors
UMAP param: number of components
UMAP param: number of epochs
UMAP param: minimum distance
UMAP param: threads/cores to use
UMAP param: spread
use of seed
seed number to use
verbosity of function
additional UMAP parameters
giotto object with updated UMAP dimension recuction
See umap
for more information about these and other parameters.
Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')
To use gene expression as input set dim_reduction_to_use = NULL
If dim_reduction_to_use = NULL, genes_to_use can be used to select a column name of
highly variable genes (see calculateHVG
) or simply provide a vector of genes
multiple UMAP results can be stored by changing the name of the analysis