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

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

if (FALSE) { # \dontrun{
LinkedDimPlot(seurat.object)
LinkedFeaturePlot(seurat.object, feature = 'Hpca')
} # }