exprCellCellcom

Cell-Cell communication scores based on expression only

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

Arguments

gobject

giotto object to use

cluster_column

cluster column with cell type information

random_iter

number of iterations

gene_set_1

first specific gene set from gene pairs

gene_set_2

second specific gene set from gene pairs

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 gene level

set_seed

set seed for random simulations (default = TRUE)

seed_number

seed number

verbose

verbose

Value

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

Details

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