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 )
| 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 |
Cell-Cell communication scores for gene pairs based on expression only
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.