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,
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.
- 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
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
if (FALSE) { # \dontrun{
data("pancreas_sub")
pancreas_sub <- RunPalantir( # bug
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"
)
)
} # }