subClusterCells

Source: R/clustering.R

subcluster cells

subClusterCells(
  gobject,
  name = "sub_clus",
  cluster_method = c("leiden", "louvain_community", "louvain_multinet"),
  cluster_column = NULL,
  selected_clusters = NULL,
  hvg_param = list(reverse_log_scale = T, difference_in_cov = 1, expression_values =
    "normalized"),
  hvg_min_perc_cells = 5,
  hvg_mean_expr_det = 1,
  use_all_genes_as_hvg = FALSE,
  min_nr_of_hvg = 5,
  pca_param = list(expression_values = "normalized", scale_unit = T),
  nn_param = list(dimensions_to_use = 1:20),
  k_neighbors = 10,
  resolution = 1,
  n_iterations = 1000,
  gamma = 1,
  omega = 1,
  python_path = NULL,
  nn_network_to_use = "sNN",
  network_name = "sNN.pca",
  return_gobject = TRUE,
  verbose = T
)

Arguments

gobject

giotto object

name

name for new clustering result

cluster_method

clustering method to use

cluster_column

cluster column to subcluster

selected_clusters

only do subclustering on these clusters

hvg_param

parameters for calculateHVG

hvg_min_perc_cells

threshold for detection in min percentage of cells

hvg_mean_expr_det

threshold for mean expression level in cells with detection

use_all_genes_as_hvg

forces all genes to be HVG and to be used as input for PCA

min_nr_of_hvg

minimum number of HVG, or all genes will be used as input for PCA

pca_param

parameters for runPCA

nn_param

parameters for parameters for createNearestNetwork

k_neighbors

number of k for createNearestNetwork

resolution

resolution

n_iterations

number of interations to run the Leiden algorithm.

gamma

gamma

omega

omega

python_path

specify specific path to python if required

nn_network_to_use

type of NN network to use (kNN vs sNN)

network_name

name of NN network to use

return_gobject

boolean: return giotto object (default = TRUE)

verbose

verbose

Value

giotto object with new subclusters appended to cell metadata

Details

This function performs subclustering on selected clusters. The systematic steps are:

  • 1. subset Giotto object

  • 2. identify highly variable genes

  • 3. run PCA

  • 4. create nearest neighbouring network

  • 5. do clustering