RunPalantir: Run Palantir analysis
In zh542370159/SCP: Single Cell Pipeline

RunPalantir

R Documentation

Run Palantir analysis

Description

Run Palantir analysis

Usage

RunPalantir(
  srt = NULL,
  assay_X = "RNA",
  slot_X = "counts",
  assay_layers = c("spliced", "unspliced"),
  slot_layers = "counts",
  adata = NULL,
  group_by = NULL,
  linear_reduction = NULL,
  nonlinear_reduction = NULL,
  basis = NULL,
  n_pcs = 30,
  n_neighbors = 30,
  dm_n_components = 10,
  dm_alpha = 0,
  dm_n_eigs = NULL,
  early_group = NULL,
  early_cell = NULL,
  terminal_cells = NULL,
  terminal_groups = NULL,
  num_waypoints = 1200,
  scale_components = TRUE,
  use_early_cell_as_start = TRUE,
  adjust_early_cell = FALSE,
  adjust_terminal_cells = FALSE,
  max_iterations = 25,
  n_jobs = 8,
  point_size = 20,
  palette = "Paired",
  palcolor = NULL,
  show_plot = TRUE,
  save = FALSE,
  dpi = 300,
  dirpath = "./",
  fileprefix = "",
  return_seurat = !is.null(srt)
)

Arguments

`srt`	A Seurat object.
`assay_X`	Assay to convert as the main data matrix (X) in the anndata object.
`slot_X`	Slot name for assay_X in the Seurat object.
`assay_layers`	Assays to convert as layers in the anndata object.
`slot_layers`	Slot names for the assay_layers in the Seurat object.
`adata`	An anndata object.
`group_by`	Variable to use for grouping cells in the Seurat object.
`linear_reduction`	Linear reduction method to use, e.g., "PCA".
`nonlinear_reduction`	Non-linear reduction method to use, e.g., "UMAP".
`basis`	The basis to use for reduction, e.g., "UMAP".
`n_pcs`	Number of principal components to use for linear reduction. Default is 30.
`n_neighbors`	Number of neighbors to use for constructing the KNN graph. Default is 30.
`dm_n_components`	The number of diffusion components to calculate.
`dm_alpha`	Normalization parameter for the diffusion operator.
`dm_n_eigs`	Number of eigen vectors to use.
`early_group`	Name of the group to start Palantir analysis from.
`early_cell`	Name of the cell to start Palantir analysis from.
`terminal_cells`	Character vector specifying terminal cells for Palantir analysis.
`terminal_groups`	Character vector specifying terminal groups for Palantir analysis.
`num_waypoints`	Number of waypoints to be included.
`scale_components`	Should the cell fate probabilities be scaled for each component independently?
`use_early_cell_as_start`	Should the starting cell for each terminal group be set as early_cell?
`adjust_early_cell`	Whether to adjust the early cell to the cell with the minimum pseudotime value.
`adjust_terminal_cells`	hether to adjust the terminal cells to the cells with the maximum pseudotime value for each terminal group.
`max_iterations`	Maximum number of iterations for pseudotime convergence.
`n_jobs`	The number of parallel jobs to run.
`point_size`	The point size for plotting.
`palette`	The palette to use for coloring cells.
`palcolor`	A vector of colors to use as the palette.
`show_plot`	Whether to show the PAGA plot.
`save`	Whether to save the PAGA plots.
`dpi`	The DPI (dots per inch) for saving the PAGA plot.
`dirpath`	The directory to save the PAGA plots.
`fileprefix`	The file prefix to use for the PAGA plots.
`return_seurat`	Whether to return a Seurat object instead of an anndata object. Default is TRUE.

Examples

data("pancreas_sub")
pancreas_sub <- RunPalantir(
  srt = pancreas_sub, group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP",
  early_group = "Ductal", use_early_cell_as_start = TRUE,
  terminal_groups = c("Alpha", "Beta", "Delta", "Epsilon")
)
head(pancreas_sub[[]])
FeatureDimPlot(pancreas_sub, c("palantir_pseudotime", "palantir_diff_potential"))
FeatureDimPlot(pancreas_sub, paste0(c("Alpha", "Beta", "Delta", "Epsilon"), "_diff_potential"))

zh542370159/SCP documentation built on Nov. 22, 2023, 2:34 a.m.