Run PAGA analysis

Graph-based inference of cellular trajectories.

Usage

RunPAGA(
  srt = NULL,
  adata = NULL,
  assay_x = "RNA",
  layer_x = "counts",
  assay_y = c("spliced", "unspliced"),
  layer_y = "counts",
  group.by = NULL,
  linear_reduction = NULL,
  nonlinear_reduction = NULL,
  basis = NULL,
  n_pcs = 30,
  n_neighbors = 30,
  use_rna_velocity = FALSE,
  vkey = "stochastic",
  embedded_with_PAGA = FALSE,
  paga_layout = "fr",
  threshold = 0.1,
  point_size = 20,
  infer_pseudotime = FALSE,
  root_group = NULL,
  root_cell = NULL,
  n_dcs = 10,
  n_branchings = 0,
  min_group_size = 0.01,
  palette = "Chinese",
  palcolor = NULL,
  legend.position = "on data",
  cores = 1,
  show_plot = FALSE,
  save_plot = FALSE,
  plot_format = c("pdf", "png", "svg"),
  plot_dpi = 300,
  plot_prefix = "paga",
  dirpath = "./paga",
  backend = c("python", "cpp"),
  return_seurat = !is.null(srt),
  verbose = TRUE
)

Arguments

srt: A Seurat object. If provided, adata will be ignored.
adata: An anndata object.
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.
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.
use_rna_velocity: Whether to use RNA velocity for PAGA analysis.
vkey: RNA velocity data to use if use_rna_velocity is TRUE. Default is "stochastic". If the corresponding velocity embedding is not found, falls back to the scVelo convention (e.g. velocity_umap), which is what an object converted from an AnnData (via adata_to_srt) contains.
embedded_with_PAGA: Whether to embed data using PAGA layout.
paga_layout: The layout for plotting PAGA graph. See layout param in scanpy.pl.paga function.
threshold: The threshold for plotting PAGA graph. Edges for weights below this threshold will not be drawn.
point_size: The point size for plotting.
infer_pseudotime: Whether to infer pseudotime. When backend = "python", scanpy DPT stores per-cell values in meta.data$dpt_pseudotime. When backend = "cpp", group-level pseudotime is stored in srt@tools[["PAGA"]]$pseudotime and per-cell values are also written to meta.data$dpt_pseudotime.
root_group: The group to use as the root for pseudotime inference.
root_cell: The cell to use as the root for pseudotime inference.
n_dcs: The number of diffusion components to use for pseudotime inference.
n_branchings: Number of branchings to detect.
min_group_size: The minimum size of a group (as a fraction of the total number of cells) to consider it as a potential branching point.
palette, palcolor: Palette name (thisplot::show_palettes) or custom colors.
legend.position: Position of legend in plots. Can be "on data", "right margin", "bottom right", etc.
cores: The number of cores to use for cellrank.
show_plot: Whether to show the plot.
save_plot: Whether to save plots to files.
plot_format: Format for saved plots: "png" (default), "pdf", or "svg".
plot_dpi: Resolution (DPI) for saved plots.
plot_prefix: Prefix for saved plot filenames.
dirpath: The directory to save the plots.
backend: Backend used to compute PAGA. "python" keeps the original scanpy workflow and remains the default. "cpp" uses the package C++ implementation for the standard connectivity graph and tree, plus an approximate R igraph layout stored in paga$pos.
return_seurat: Whether to return a Seurat object instead of an anndata object.
verbose: Whether to print the message. Default is TRUE.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 05:30:21] Start standard processing workflow...
#> ℹ [2026-08-30 05:30:21] Checking a list of <Seurat>...
#> ! [2026-08-30 05:30:21] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:30:21] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:30:21] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:30:21] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:30:21] Number of available HVF: 2000
#> ℹ [2026-08-30 05:30:21] Finished check
#> ℹ [2026-08-30 05:30:21] Perform `ScaleData()`
#> ℹ [2026-08-30 05:30:21] Perform pca linear dimension reduction
#> ℹ [2026-08-30 05:30:22] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 05:30:22] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 05:30:22] Reorder clusters...
#> ℹ [2026-08-30 05:30:22] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:30:22] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 05:30:31] Standard processing workflow completed
pancreas_sub <- RunPAGA(
  pancreas_sub,
  assay_x = "RNA",
  group.by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  backend = "cpp"
)
#> ℹ [2026-08-30 05:30:31] Running PAGA with BiocNeighbors using 29 neighbors
#> ✔ [2026-08-30 05:30:31] PAGA cpp backend completed
PAGAPlot(pancreas_sub, reduction = "UMAP")


CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  paga = pancreas_sub@tools[["PAGA"]]
)

Usage

Arguments

See also

Examples