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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,
  magic_impute = FALSE,
  magic_layer = "MAGIC_imputed_data",
  cores = 1,
  point_size = 20,
  palette = "Chinese",
  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 = "./",
  envname = NULL,
  conda = "auto",
  backend = c("python", "cpp"),
  allow_approximate = FALSE,
  return_seurat = !is.null(srt),
  verbose = TRUE
)

Arguments

srt

A Seurat object. If provided, adata will be ignored.

assay_x

Assay to convert as the main data matrix in the anndata object.

layer_x

Layer name for assay_x in the Seurat object.

assay_y

Assays to convert as layers in the anndata object.

layer_y

Layer names for the assay_y in the Seurat object.

adata

An anndata object.

group.by

Metadata column(s) used to color cells.

linear_reduction

Linear reduction ("pca", "svd", "ica", "nmf", "mds", "glmpca"). linear_reduction_dims_use = NULL uses estimated dimensions, else the first 50.

nonlinear_reduction

Nonlinear reduction ("umap", "umap-naive", "tsne", "dm", "phate", "pacmap", "trimap", "largevis", "fr").

basis

The basis to use for reduction, e.g., "UMAP".

n_pcs

Number of principal components to use for linear reduction.

n_neighbors

Number of neighbors to use for constructing the KNN graph.

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.

magic_impute

Whether to calculate a Palantir MAGIC expression layer. This layer is for visualization and trend fitting, not count-based testing.

magic_layer

Name of the MAGIC layer to store.

cores

The number of cores to use for cellrank.

point_size

The point size for plotting.

palette, palcolor

Palette name (thisplot::show_palettes) or custom colors.

legend.position

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

show_plot

Whether to show the plot.

save_plot

Whether to save plots to files.

plot_format

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

plot_dpi

Resolution (DPI) for saved plots.

plot_prefix

Prefix for saved plot filenames.

dirpath

The directory to save the plots.

envname

Optional Python environment name. NULL uses the current SCOP environment selection.

conda

Conda-compatible executable used by PrepareEnv.

backend

Backend used to compute Palantir. "python" keeps the reference Palantir workflow and remains the default. "cpp" uses an approximate C++ workflow and stores results in srt@tools[["Palantir"]].

allow_approximate

Whether to allow the approximate C++ workflow. This must be TRUE when backend = "cpp".

return_seurat

Whether to return a Seurat object instead of an anndata object.

verbose

Whether to print the message. Default is TRUE.

See also

PalantirTrajectoryPlot

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 05:31:12] Start standard processing workflow...
#> ℹ [2026-08-30 05:31:12] Checking a list of <Seurat>...
#> ! [2026-08-30 05:31:12] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:31:12] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:31:12] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:31:12] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:31:12] Number of available HVF: 2000
#> ℹ [2026-08-30 05:31:12] Finished check
#> ℹ [2026-08-30 05:31:12] Perform `ScaleData()`
#> ℹ [2026-08-30 05:31:12] Perform pca linear dimension reduction
#> ℹ [2026-08-30 05:31:13] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 05:31:13] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 05:31:13] Reorder clusters...
#> ℹ [2026-08-30 05:31:13] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:31:13] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 05:31:22] Standard processing workflow completed
pancreas_sub <- RunPalantir(
  pancreas_sub,
  group.by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  early_group = "Ductal",
  terminal_groups = c("Alpha", "Beta", "Delta", "Epsilon"),
  backend = "cpp",
  allow_approximate = TRUE
)
#> ℹ [2026-08-30 05:31:22] Computing Palantir KNN graph with BiocNeighbors...
#> ✔ [2026-08-30 05:31:24] Palantir cpp backend completed

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


FeatureDimPlot(
  pancreas_sub,
  grep(
    "TerminalState_.*_diff_potential$",
    colnames(pancreas_sub@meta.data),
    value = TRUE
  )
)


PalantirTrajectoryPlot(
  pancreas_sub,
  reduction = "UMAP",
  pseudotime_interval = c(0, 0.9)
)


PalantirTrajectoryPlot(
  pancreas_sub,
  reduction = "UMAP",
  cell_color = "branch_selection",
  pseudotime_interval = c(0, 0.9)
)