Visualize Spatial Landscape of Cells / Spots

Visualize Spatial Landscape of Cells / Spots

Usage

SpatialView(
  obj,
  by,
  X = "X",
  Y = "Y",
  pt.shape = 20,
  pt.size = 0.5,
  pt.alpha = 1,
  jitter = FALSE,
  slot = "data",
  coord.fix = FALSE,
  highlight.cells = NULL,
  control.cells = NULL,
  ncol = 3,
  bg.downsample = 2000,
  bg.color = "gray80",
  bg.size = 0.5,
  bg.alpha = 0.7
)

Arguments

obj

A Seurat object or a data frame (with cell names as row names).

by

A feature name for plotting, e.g. cell type, region, gene expression.

X

A character specifying the spatial coordinate of x-axis.

Y

A character specifying the the spatial coordinate of y-axis.

pt.shape

Point shape for plotting

pt.size

A numeric specifying the point size of non-control cells.

pt.alpha

A numeric specifying the point transparency of non-control cells.

jitter

A boolean specifying whether add jitters to the cells.

slot

The slot in Seurat object to pull feature from.

highlight.cells

A vector specifying the cells for highlighting.

control.cells

A vector specifying the control cells as background. If not specified, all the non-highlighting cells will be considered as control.cells.

bg.downsample

An integer specifying the aim for downsampling the control.cells.

bg.color

Color of control cells.

bg.size

A numeric specifying the point size of control cells.

bg.alpha

A numeric specifying the point transparency of control cells.

Value

A ggplot object.

Examples

library(data.table)
library(Seurat)
library(SpatialEcoTyper)
library(ggplot2)
library(googledrive)
drive_deauth() # no Google sign-in is required
drive_download(as_id("1CgUOQKrWY_TG61o5aw7J9LZzE20D6NuI"),
                    "HumanMelanomaPatient1_subset_scmeta.tsv", overwrite = TRUE)
#> File downloaded:
#> • HumanMelanomaPatient1_subset_scmeta.tsv
#>   <id: 1CgUOQKrWY_TG61o5aw7J9LZzE20D6NuI>
#> Saved locally as:
#> • HumanMelanomaPatient1_subset_scmeta.tsv
scmeta <- fread("HumanMelanomaPatient1_subset_scmeta.tsv",
                sep = "\t",header = TRUE, data.table = FALSE)

# Visualize the cell type annotations in the tissue
SpatialView(scmeta, by = "CellType", X = "X", Y = "Y") +
            scale_color_manual(values = pals::kelly()[-1])
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.

SpatialView(scmeta, by = "Region", X = "X", Y = "Y") +
            scale_color_brewer(type = "qual", palette = "Set1")
#> Scale for colour is already present.
#> Adding another scale for colour, which will replace the existing scale.