RunSCVELO: Run scVelo workflow
In zh542370159/SCP: Single Cell Pipeline

RunSCVELO

R Documentation

Run scVelo workflow

Description

scVelo is a scalable toolkit for RNA velocity analysis in single cells. This function runs scVelo workflow on a Seurat object.

Usage

RunSCVELO(
  srt = NULL,
  assay_X = "RNA",
  slot_X = "counts",
  assay_layers = c("spliced", "unspliced"),
  slot_layers = "counts",
  adata = NULL,
  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,
  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,
  top_n = 6,
  n_jobs = 1,
  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.
`slot_X`	Slot name for assay_X in the Seurat object.
`assay_layers`	Assays to convert as layers in the anndata object.
`slot_layers`	Slot names for the assay_layers 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".
`mode`	Velocity estimation model to use, e.g., "stochastic".
`fitting_by`	Method used to fit gene velocities, 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 (PCs) used for velocity estimation.
`n_neighbors`	Number of nearest neighbors used for velocity estimation.
`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.
`top_n`	The number of top features to plot.
`n_jobs`	The number of parallel jobs to run.
`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

data("pancreas_sub")
pancreas_sub <- RunSCVELO(srt = pancreas_sub, assay_X = "RNA", group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP")
head(pancreas_sub[[]])
names(pancreas_sub@assays)

FeatureDimPlot(pancreas_sub, c("stochastic_length", "stochastic_confidence"))
FeatureDimPlot(pancreas_sub, "stochastic_pseudotime")
VelocityPlot(pancreas_sub, reduction = "UMAP", plot_type = "stream")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", pt.size = NA, velocity = "stochastic")

pancreas_sub <- Standard_SCP(pancreas_sub, normalization_method = "SCT", nonlinear_reduction = "tsne")
pancreas_sub <- RunSCVELO(srt = pancreas_sub, assay_X = "SCT", group_by = "SubCellType", linear_reduction = "Standardpca", nonlinear_reduction = "StandardTSNE2D")

zh542370159/SCP documentation built on Nov. 22, 2023, 2:34 a.m.