LinkedPlots: Visualize spatial and clustering (dimensional reduction) data...

In satijalab/seurat: Tools for Single Cell Genomics

Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework

Description

Visualize spatial and clustering (dimensional reduction) data in a linked, interactive framework

Usage

LinkedDimPlot(
  object,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  group.by = NULL,
  alpha = c(0.1, 1),
  combine = TRUE
)

LinkedFeaturePlot(
  object,
  feature,
  dims = 1:2,
  reduction = NULL,
  image = NULL,
  image.scale = "lowres",
  slot = "data",
  alpha = c(0.1, 1),
  combine = TRUE
)

Arguments

object	A Seurat object
dims	Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
reduction	Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca
image	Name of the image to use in the plot
image.scale	Choose the scale factor ("lowres"/"hires") to apply in order to matchthe plot with the specified 'image' - defaults to "lowres"
group.by	Name of meta.data column to group the data by
alpha	Controls opacity of spots. Provide as a vector specifying the min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single alpha value for each plot.
combine	Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple features/groupings
feature	Feature to visualize
slot	If plotting a feature, which data slot to pull from (counts, data, or scale.data)

Value

Returns final plots. If combine, plots are stiched together using CombinePlots; otherwise, returns a list of ggplot objects

Examples

## Not run: 
LinkedDimPlot(seurat.object)
LinkedFeaturePlot(seurat.object, feature = 'Hpca')

## End(Not run)

satijalab/seurat documentation built on May 11, 2024, 4:04 a.m.