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Run Palantir analysis

Source: R/RunPalantir.R
RunPalantir.Rd

Run Palantir analysis

Usage

RunPalantir(
  srt = NULL,
  assay_x = "RNA",
  layer_x = "counts",
  assay_y = c("spliced", "unspliced"),
  layer_y = "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,
  cores = 1,
  point_size = 20,
  palette = "Paired",
  palcolor = NULL,
  legend.position = "on data",
  show_plot = FALSE,
  save_plot = FALSE,
  plot_format = c("pdf", "png", "svg"),
  plot_dpi = 300,
  plot_prefix = "palantir",
  dirpath = "./",
  return_seurat = !is.null(srt),
  verbose = TRUE
)

Arguments

srt

A Seurat object. Default is NULL. If provided, adata will be ignored.

assay_x

Assay to convert in the anndata object.

layer_x

Layer name for assay_x in the Seurat object.

assay_y

Assay to convert in the anndata object.

layer_y

Layer names for the assay_y in the Seurat object.

adata

An anndata object. Default is NULL.

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

Whether 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.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

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.

legend.position

Position of legend in plots. Can be "on data", "right margin", "bottom right", etc. Default is "on data".

show_plot

Whether to show the plot. Default is FALSE.

save_plot

Whether to save plots to files. Default is FALSE.

plot_format

Format for saved plots: "png" (default), "pdf", or "svg".

plot_dpi

Resolution (DPI) for saved plots. Default is 300.

plot_prefix

Prefix for saved plot filenames. Default is "cellrank".

dirpath

The directory to save the plots. Default is "./cellrank".

return_seurat

Whether to return a Seurat object instead of an anndata object. Default is TRUE.

verbose

Whether to print the message. Default is TRUE.

See also

srt_to_adata

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunPalantir(
  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")
)

FeatureDimPlot(
  pancreas_sub,
  c("palantir_pseudotime", "palantir_diff_potential")
)

FeatureDimPlot(
  pancreas_sub,
  paste0(
    c("Alpha", "Beta", "Delta", "Epsilon"),
    "_diff_potential"
  )
)
} # }