Calculate the ligand activities and infer ligand-target links based on a list of niche-specific genes per receiver cell type

get_ligand_activities_targets Calculate the ligand activities and infer ligand-target links based on a list of niche-specific genes per receiver cell type.

Usage

get_ligand_activities_targets(niche_geneset_list, ligand_target_matrix, top_n_target)

Arguments

niche_geneset_list

List of lists/niches giving the geneset of interest for the receiver cell type in each niche.

ligand_target_matrix

The NicheNet ligand-target matrix of the organism of interest denoting regulatory potential scores between ligands and targets (ligands in columns).

top_n_target

To predict active, affected targets of the prioritized ligands, consider only DE genes if they also belong to the a priori top n ("top_n_targets") targets of a ligand. Default = 200.

Value

A tibble of ligands, their activities and targets in each receiver cell type

Examples

if (FALSE) { # \dontrun{
seurat_obj <- readRDS(url("https://zenodo.org/record/5840787/files/seurat_obj_subset_integrated_zonation.rds"))
niches <- list(
  "KC_niche" = list(
    "sender" = c("LSECs_portal", "Hepatocytes_portal", "Stellate cells_portal"),
    "receiver" = c("KCs")
  ),
  "MoMac2_niche" = list(
    "sender" = c("Cholangiocytes", "Fibroblast 2"),
    "receiver" = c("MoMac2")
  ),
  "MoMac1_niche" = list(
    "sender" = c("Capsule fibroblasts", "Mesothelial cells"),
    "receiver" = c("MoMac1")
  )
)
DE_receiver <- calculate_niche_de(seurat_obj, niches, "receiver")
expression_pct <- 0.10
lfc_cutoff <- 0.15 # recommended for 10x as min_lfc cutoff.
specificity_score_targets <- "min_lfc"
DE_receiver_processed_targets <- process_receiver_target_de(DE_receiver_targets = DE_receiver, niches = niches, expression_pct = expression_pct, specificity_score = specificity_score_targets)
background <- DE_receiver_processed_targets %>%
  pull(target) %>%
  unique()
geneset_KC <- DE_receiver_processed_targets %>%
  filter(receiver == niches$KC_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>%
  pull(target) %>%
  unique()
geneset_MoMac2 <- DE_receiver_processed_targets %>%
  filter(receiver == niches$MoMac2_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>%
  pull(target) %>%
  unique()
geneset_MoMac1 <- DE_receiver_processed_targets %>%
  filter(receiver == niches$MoMac1_niche$receiver & target_score >= lfc_cutoff & target_significant == 1 & target_present == 1) %>%
  pull(target) %>%
  unique()
top_n_target <- 250
niche_geneset_list <- list(
  "KC_niche" = list(
    "receiver" = "KCs",
    "geneset" = geneset_KC,
    "background" = background
  ),
  "MoMac1_niche" = list(
    "receiver" = "MoMac1",
    "geneset" = geneset_MoMac1,
    "background" = background
  ),
  "MoMac2_niche" = list(
    "receiver" = "MoMac2",
    "geneset" = geneset_MoMac2,
    "background" = background
  )
)
ligand_activities_targets <- get_ligand_activities_targets(niche_geneset_list = niche_geneset_list, ligand_target_matrix = ligand_target_matrix, top_n_target = top_n_target)
} # }