Calculate Cell-Cell Communication Scores

exprCellCellcom()

Cell-Cell communication scores based only on expression.

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

A cell-Cell communication score 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.