Run MultiNicheNet analysis

Usage

RunMultiNichenetr(
  srt,
  group.by,
  sample.by,
  condition.by,
  condition_oi,
  condition_reference,
  receiver_celltypes,
  sender_celltypes = NULL,
  assay = NULL,
  sample_agnostic = FALSE,
  contrast_tbl = NULL,
  batches = NULL,
  covariates = NULL,
  species = c("Homo_sapiens", "Mus_musculus"),
  lr_network = NULL,
  ligand_target_matrix = NULL,
  fraction_cutoff = 0.05,
  min_cells = 10,
  empirical_pval = TRUE,
  top_n_interactions = 250,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
group.by: Metadata column defining cell types.
sample.by: Metadata column defining biological samples.
condition.by: Metadata column defining conditions.
condition_oi: Condition of interest.
condition_reference: Reference condition.
receiver_celltypes: Receiver cell types of interest.
sender_celltypes: Sender cell types of interest. Default is all available cell types.
assay: Assay to use.
sample_agnostic: Whether to use the sample-agnostic MultiNicheNet wrapper.
contrast_tbl: Optional contrast table passed to MultiNicheNet. If NULL, a simple contrast table will be created from condition_oi and condition_reference.
batches: Optional metadata column(s) used as batches.
covariates: Optional metadata column(s) used as covariates.
species: Species for default NicheNet prior model loading.
lr_network: Optional ligand-receptor prior model or path to an .rds file.
ligand_target_matrix: Optional ligand-target prior model or path to an .rds file.
fraction_cutoff: Minimum expression fraction cutoff used by MultiNicheNet.
min_cells: Minimum number of cells per sample-celltype combination.
empirical_pval: Whether to use empirical p-values.
top_n_interactions: Number of top prioritized interactions kept in the standardized long table.
verbose: Whether to print the message. Default is TRUE.

Value

A Seurat object with standardized MultiNicheNet results stored in srt@tools[["MultiNichenetr"]].