runUMAP#

Date

2022-10-06

https://github.com/drieslab/Giotto/tree/suite/R/dimension_reduction.R#L1242

Description#

run UMAP

Usage#

runUMAP(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  dim_reduction_to_use = "pca",
  dim_reduction_name = NULL,
  dimensions_to_use = 1:10,
  name = NULL,
  feats_to_use = NULL,
  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,
  toplevel_params = 2,
  ...
)

Arguments#

Argument

Description

gobject

giotto object

feat_type

feature type

spat_unit

spatial unit

expression_values

expression values to use

reduction

cells or genes

dim_reduction_to_use

use another dimension reduction set as input

dim_reduction_name

name of dimension reduction set to use

dimensions_to_use

number of dimensions to use as input

name

arbitrary name for UMAP run

feats_to_use

if dim_reduction_to_use = NULL, which genes to use

genes_to_use

deprecated, use feats_to_use

return_gobject

boolean: return giotto object (default = TRUE)

n_neighbors

UMAP param: number of neighbors

n_components

UMAP param: number of components

n_epochs

UMAP param: number of epochs

min_dist

UMAP param: minimum distance

n_threads

UMAP param: threads/cores to use

spread

UMAP param: spread

set_seed

use of seed

seed_number

seed number to use

verbose

verbosity of function

toplevel_params

parameters to extract

...

additional UMAP parameters

Details#

See `umap <#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 `calculateHVF <#calculatehvf>`__ ) or simply provide a vector of genes

  • multiple UMAP results can be stored by changing the name of the analysis

Value#

giotto object with updated UMAP dimension reduction