Calculate Niche Gene Regulatory Networks using NicheNet

Predicts ligand-target gene regulatory networks within niches using the NicheNet framework. Identifies potential signaling from sender to receiver cells.

Usage

CalSampGRN(
  STID_obj = NULL,
  loop_id = "LoopAllSamp",
  niche_key = NULL,
  group_by = NULL,
  ref_data = NULL,
  sender_celltypes = NULL,
  receiver_celltypes = NULL,
  target_features = NULL,
  expression_pct = 0.05,
  top_ligand_num = 10,
  top_target_num = 5,
  remove_genes = NULL,
  return_data = TRUE,
  grp_nm = NULL,
  dir_nm = "M3_CalSampGRN"
)

Arguments

STID_obj

An STID object containing niche analysis results

loop_id

Character, sample grouping identifier (default: "LoopAllSamp")

niche_key

Character, niche key to analyze

group_by

Character, column name for cell type grouping

ref_data

List, NicheNet reference data (default: NULL, downloads automatically)

sender_celltypes

Character vector, sender cell types

receiver_celltypes

Character vector, receiver cell types

target_features

Character vector, target genes of interest

expression_pct

Numeric, expression percentage threshold (default: 0.05)

top_ligand_num

Integer, number of top ligands to prioritize (default: 10)

top_target_num

Integer, number of top targets per ligand (default: 5)

remove_genes

Character vector, genes to exclude

return_data

Logical, whether to return results list (default: TRUE)

grp_nm

Character, group name for output organization (default: NULL)

dir_nm

Character, directory name for output (default: "M3_CalSampGRN")

Value

If return_data = TRUE, returns a list of NicheNet results per sample

Examples

if (FALSE) { # \dontrun{
# Predict ligand-target networks
results <- CalSampGRN(
  STID_obj = STID_obj,
  niche_key = "niche_virulence",
  sender_celltypes = "Epithelial",
  receiver_celltypes = "Immune",
  target_features = c("gene1", "gene2", "gene3")
)
} # }