Run tSNE dimensional reduction — runtSNE • Giotto

run tSNE

runtSNE(
  gobject,
  spat_unit = NULL,
  feat_type = 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,
  return_gobject = TRUE,
  dims = 2,
  perplexity = 30,
  theta = 0.5,
  do_PCA_first = FALSE,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = TRUE,
  toplevel = 1L,
  ...
)

Arguments

gobject: giotto object
spat_unit: spatial unit
feat_type: feature type
expression_values: expression values to use
reduction: cells or feats
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 tSNE run
feats_to_use: if dim_reduction_to_use = NULL, which features to use
return_gobject: boolean: return giotto object (default = TRUE)
dims: tSNE param: number of dimensions to return
perplexity: tSNE param: perplexity
theta: tSNE param: theta
do_PCA_first: tSNE param: do PCA before tSNE (default = FALSE)
set_seed: use of seed
seed_number: seed number to use
verbose: verbosity of the function
toplevel: relative stackframe where call was made from
...: additional tSNE parameters

Value

giotto object with updated tSNE dimension reduction

Details

See Rtsne for more information about these and other parameters.

Input for tSNE 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, feats_to_use can be used to select a column name of highly variable genes (see calculateHVF) or simply provide a vector of genes
multiple tSNE results can be stored by changing the name of the analysis

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

runtSNE(g)
#> > tsne already exists and will be replaced with
#>  new dimension reduction object
#> Setting dimension reduction [cell][rna] tsne
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> dimensions    : 634, 624 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used