RunPAGA: Run PAGA analysis
In zh542370159/SCP: Single Cell Pipeline

RunPAGA

R Documentation

Run PAGA analysis

Description

PAGA is a graph-based method used to infer cellular trajectories. This function runs the PAGA analysis on a Seurat object.

Usage

RunPAGA(
  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,
  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 = "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".
`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.
`use_rna_velocity`	Whether to use RNA velocity for PAGA analysis. Default is FALSE.
`vkey`	The name of the RNA velocity data to use if `use_rna_velocity` is TRUE. Default is "stochastic".
`embedded_with_PAGA`	Whether to embed data using PAGA layout. Default is FALSE.
`paga_layout`	The layout for plotting PAGA graph. See See layout param in scanpy.pl.paga function.
`threshold`	The threshold for plotting PAGA graph. Edges for weights below this threshold will not draw.
`point_size`	The point size for plotting.
`infer_pseudotime`	Whether to infer 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`	TThe 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`	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 <- RunPAGA(srt = pancreas_sub, assay_X = "RNA", group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "draw_graph_fr")
PAGAPlot(pancreas_sub, reduction = "UMAP")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "UMAP", paga = pancreas_sub@misc$paga)

pancreas_sub <- RunPAGA(
  srt = pancreas_sub, group_by = "SubCellType", linear_reduction = "PCA", nonlinear_reduction = "UMAP",
  embedded_with_PAGA = TRUE, infer_pseudotime = TRUE, root_group = "Ductal"
)
head(pancreas_sub[[]])
names(pancreas_sub@reductions)
FeatureDimPlot(pancreas_sub, features = "dpt_pseudotime", reduction = "PAGAUMAP2D")
PAGAPlot(pancreas_sub, reduction = "PAGAUMAP2D")
CellDimPlot(pancreas_sub, group.by = "SubCellType", reduction = "PAGAUMAP2D", paga = pancreas_sub@misc$paga)

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