exprCellCellcom#

Last Updated: Jan 29, 2024

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#

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