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

In ibseq/scs-analysis: Tools for Single Cell Genomics

Description

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

Usage

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

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

Arguments

object	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
group.by	Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class
alpha	Controls opacity of spots. Provide as a vector specifying the min and max
combine	Combine plots into a single patchworked ggplot object. If FALSE, return a list of ggplot objects
feature	Feature to visualize
slot	Which slot to pull expression data from?

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)

ibseq/scs-analysis documentation built on Feb. 27, 2021, 12:35 a.m.