`exprCellCellcom`¶

exprCellCellcom

Description¶

Cell-Cell communication scores based on expression only

Usage¶

exprCellCellcom(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  cluster_column = "cell_types",
  random_iter = 1000,
  feat_set_1,
  feat_set_2,
  gene_set_1 = NULL,
  gene_set_2 = NULL,
  log2FC_addendum = 0.1,
  detailed = FALSE,
  adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
    "none"),
  adjust_target = c("feats", "cells"),
  set_seed = TRUE,
  seed_number = 1234,
  verbose = T
)

Arguments¶

Argument	Description
`gobject`	giotto object to use
`feat_type`	feature type
`spat_unit`	spatial unit
`cluster_column`	cluster column with cell type information
`random_iter`	number of iterations
`feat_set_1`	first specific feature set from feature pairs
`feat_set_2`	second specific feature set from feature pairs
`gene_set_1`	deprecated. see `feat_set_1`
`gene_set_2`	deprecated. see `feat_set_2`
`log2FC_addendum`	addendum to add when calculating log2FC
`detailed`	provide more detailed information (random variance and z-score)
`adjust_method`	which method to adjust p-values
`adjust_target`	adjust multiple hypotheses at the cell or feature level
`set_seed`	set seed for random simulations (default = TRUE)
`seed_number`	seed number
`verbose`	verbose

Details¶

Statistical framework to identify if pairs of features (such as ligand-receptor combinations): are expressed at higher levels than expected based on a reshuffled null distribution of feature expression values, without considering the spatial position of cells. More details will follow soon.

Value¶

Cell-Cell communication scores for feature pairs based on expression only