exprCellCellcom | R Documentation |
Cell-Cell communication scores based on expression only
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
)
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 |
gene_set_2 |
deprecated. see |
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 |
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.
Cell-Cell communication scores for feature pairs based on expression only
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