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Evaluates where ligands are most strongly driving trajectory dynamics.

cellwise_influences.Rd

Performs a sliding window analysis along the trajectory to identify windows where environmental influence is most apparent.

Usage

cellwise_influences(
  obj,
  ligand_target_matrix,
  ligands_list = NULL,
  step_size = 0.1,
  window_pct = 0.3,
  n_top_ligands = 5,
  n_top_genes = 500,
  slot = "data",
  metric = "Covariances"
)

Arguments

obj

A seurat object that has been analyzed with get_path_ligands or get_traj_ligands_monocle.

ligand_target_matrix

NicheNet ligand-target data file.

ligands_list

Optional, a character vector denoting ligands. If supplied, cellwise_influences will calculate influences for the ligands in ligands_list instead of the top ranked ligands.

step_size

Size of the step, or interval, as a percentage of the number of cells in a path.

window_pct

Size of the window, as a percentage of the number of cells in a path.

n_top_ligands

Number of top ligands to consider in the cellwise analysis.

n_top_genes

Number of top dynamical genes to consider in the cellwise analysis. Because we have already evaluated the ligand importances for a branch, we restrict our more granular analysis to the top genes and ligands.

slot

Slot of Seurat object from which to retrieve expression data.

metric

One of "Covariances" or "Correlations". Default "Covariances". Determines whether to use pseudotime covariance or pseudotime correlation in the calculation of TRAINing genes.

Value

a Seurat object updated with per-cell ligand influence score in obj@misc$entrain$path$path_name$cellwise_influences, as well as a column in obj@meta.data