Run scVelo workflow

scVelo is a scalable toolkit for RNA velocity analysis in single cells. This function runs an enhanced scVelo workflow on a Seurat object with improved error handling, version compatibility, and modular design.

Usage

RunSCVELO(
  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,
  mode = "stochastic",
  fitting_by = "stochastic",
  magic_impute = FALSE,
  knn = 5,
  t = 2,
  min_shared_counts = 30,
  n_pcs = 30,
  n_neighbors = 30,
  filter_genes = TRUE,
  min_counts = 3,
  min_counts_u = 3,
  normalize_per_cell = TRUE,
  log_transform = TRUE,
  use_raw = FALSE,
  diff_kinetics = FALSE,
  stream_smooth = NULL,
  stream_density = 2,
  arrow_length = 5,
  arrow_size = 5,
  arrow_density = 0.5,
  denoise = FALSE,
  denoise_topn = 3,
  kinetics = FALSE,
  kinetics_topn = 100,
  calculate_velocity_genes = FALSE,
  compute_velocity_confidence = TRUE,
  compute_terminal_states = TRUE,
  compute_pseudotime = TRUE,
  compute_paga = TRUE,
  top_n = 6,
  cores = 1,
  palette = "Paired",
  palcolor = NULL,
  legend.position = "on data",
  show_plot = TRUE,
  save_plot = FALSE,
  plot_format = c("pdf", "png", "svg"),
  plot_dpi = 300,
  plot_prefix = "scvelo",
  dirpath = "./scvelo",
  return_seurat = !is.null(srt),
  verbose = TRUE
)

Arguments

srt: A Seurat object. Default is NULL. If provided, adata will be ignored.
adata: An anndata object. Default is NULL.
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.
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".
mode: Velocity estimation models to use. Can be a vector containing "deterministic", "stochastic", and/or "dynamical".
fitting_by: Method used to fit gene velocities for dynamical modeling, e.g., "stochastic".
magic_impute: Flag indicating whether to perform magic imputation.
knn: The number of nearest neighbors for magic.MAGIC.
t: power to which the diffusion operator is powered for magic.MAGIC.
min_shared_counts: Minimum number of counts (both unspliced and spliced) required for a gene.
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.
filter_genes: Whether to filter genes based on minimum counts.
min_counts: Minimum counts for gene filtering.
min_counts_u: Minimum unspliced counts for gene filtering.
normalize_per_cell: Whether to normalize counts per cell.
log_transform: Whether to apply log transformation.
use_raw: Whether to use raw data for dynamical modeling.
diff_kinetics: Whether to use differential kinetics.
stream_smooth: Multiplication factor for scale in Gaussian kernel around grid point.
stream_density: Controls the closeness of streamlines. When density = 2 (default), the domain is divided into a 60x60 grid, whereas density linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline.
arrow_length: Length of arrows.
arrow_size: Size of arrows.
arrow_density: Amount of velocities to show.
denoise: Boolean flag indicating whether to denoise.
denoise_topn: Number of genes with highest likelihood selected to infer velocity directions.
kinetics: Boolean flag indicating whether to estimate RNA kinetics.
kinetics_topn: Number of genes with highest likelihood selected to infer velocity directions.
calculate_velocity_genes: Boolean flag indicating whether to calculate velocity genes.
compute_velocity_confidence: Whether to compute velocity confidence metrics.
compute_terminal_states: Whether to compute terminal states (root and end points).
compute_pseudotime: Whether to compute velocity pseudotime.
compute_paga: Whether to compute PAGA (Partition-based graph abstraction).
top_n: The number of top features to plot.
cores: The number of cores to use for parallelization with foreach::foreach. Default is 1.
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.

Examples

if (FALSE) { # \dontrun{
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunSCVELO(
  pancreas_sub,
  assay_x = "RNA",
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP"
)

FeatureDimPlot(
  pancreas_sub,
  c(
    "stochastic_length",
    "stochastic_confidence",
    "stochastic_pseudotime"
  )
)

VelocityPlot(
  pancreas_sub,
  reduction = "UMAP",
  plot_type = "stream"
)

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  pt.size = NA,
  velocity = "stochastic"
)

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
pancreas_sub <- RunSCVELO(
  pancreas_sub,
  assay_x = "RNA",
  group_by = "SubCellType",
  linear_reduction = "PCA",
  nonlinear_reduction = "UMAP",
  mode = c("deterministic", "stochastic"),
  filter_genes = TRUE,
  min_counts = 5,
  compute_velocity_confidence = TRUE,
  compute_terminal_states = TRUE,
  compute_pseudotime = TRUE,
  compute_paga = TRUE
)
} # }

Usage

Arguments

See also

Examples