ImageFeaturePlot: Spatial Feature Plots

In satijalab/seurat: Tools for Single Cell Genomics

Spatial Feature Plots

Description

Visualize expression in a spatial context

Usage

ImageFeaturePlot(
  object,
  features,
  fov = NULL,
  boundaries = NULL,
  cols = if (isTRUE(x = blend)) {
     c("lightgrey", "#ff0000", "#00ff00")
 } else {
   
     c("lightgrey", "firebrick1")
 },
  size = 0.5,
  min.cutoff = NA,
  max.cutoff = NA,
  split.by = NULL,
  molecules = NULL,
  mols.size = 0.1,
  mols.cols = NULL,
  nmols = 1000,
  alpha = 1,
  border.color = "white",
  border.size = NULL,
  dark.background = TRUE,
  blend = FALSE,
  blend.threshold = 0.5,
  crop = FALSE,
  cells = NULL,
  scale = c("feature", "all", "none"),
  overlap = FALSE,
  axes = FALSE,
  combine = TRUE,
  coord.fixed = TRUE
)

Arguments

object	Seurat object
features	Vector of features to plot. Features can come from: An Assay feature (e.g. a gene name - "MS4A1") A column name from meta.data (e.g. mitochondrial percentage - "percent.mito") A column name from a DimReduc object corresponding to the cell embedding values (e.g. the PC 1 scores - "PC_1")
fov	Name of FOV to plot
boundaries	A vector of segmentation boundaries per image to plot; can be a character vector, a named character vector, or a named list. Names should be the names of FOVs and values should be the names of segmentation boundaries
cols	The two colors to form the gradient over. Provide as string vector with the first color corresponding to low values, the second to high. Also accepts a Brewer color scale or vector of colors. Note: this will bin the data into number of colors provided. When blend is TRUE, takes anywhere from 1-3 colors: 1 color: Treated as color for double-negatives, will use default colors 2 and 3 for per-feature expression 2 colors: Treated as colors for per-feature expression, will use default color 1 for double-negatives 3+ colors: First color used for double-negatives, colors 2 and 3 used for per-feature expression, all others ignored
size	Point size for cells when plotting centroids
min.cutoff, max.cutoff	Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')
split.by	A factor in object metadata to split the plot by, pass 'ident' to split by cell identity'
molecules	A vector of molecules to plot
mols.size	Point size for molecules
mols.cols	A vector of color for molecules. The "Set1" palette from RColorBrewer is used by default.
nmols	Max number of each molecule specified in 'molecules' to plot
alpha	Alpha value for plotting (default is 1)
border.color	Color of cell segmentation border; pass NA to suppress borders for segmentation-based plots
border.size	Thickness of cell segmentation borders; pass NA to suppress borders for centroid-based plots
dark.background	Set plot background to black
blend	Scale and blend expression values to visualize coexpression of two features
blend.threshold	The color cutoff from weak signal to strong signal; ranges from 0 to 1.
crop	Crop the plots to area with cells only
cells	Vector of cells to plot (default is all cells)
scale	Set color scaling across multiple plots; choose from: “feature”: Plots per-feature are scaled across splits “all”: Plots per-feature are scaled across all features “none”: Plots are not scaled; note: setting scale to “none” will result in color scales that are not comparable between plots Ignored if blend = TRUE
overlap	Overlay boundaries from a single image to create a single plot; if TRUE, then boundaries are stacked in the order they're given (first is lowest)
axes	Keep axes and panel background
combine	Combine plots into a single patchworked ggplot object. If FALSE, return a list of ggplot objects
coord.fixed	Plot cartesian coordinates with fixed aspect ratio

Value

If combine = TRUE, a patchwork ggplot object; otherwise, a list of ggplot objects

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