Plot dimension reduction components in space

Such as plotting the value of projection of gene expression of each cell to a principal component in space. At present, this function does not work for the 3D array of geographically weighted PCA (GWPCA), but a future version will deal with GWPCA results.

Usage

spatialReducedDim(
  sfe,
  dimred,
  ncomponents = NULL,
  components = ncomponents,
  colGeometryName = 1L,
  sample_id = "all",
  ncol = NULL,
  ncol_sample = NULL,
  annotGeometryName = NULL,
  annot_aes = list(),
  annot_fixed = list(),
  exprs_values = "logcounts",
  bbox = NULL,
  image_id = NULL,
  maxcell = 5e+05,
  aes_use = c("fill", "color", "shape", "linetype"),
  divergent = FALSE,
  diverge_center = NULL,
  annot_divergent = FALSE,
  annot_diverge_center = NULL,
  size = 0,
  shape = 16,
  linewidth = 0,
  linetype = 1,
  alpha = 1,
  color = NA,
  fill = "gray80",
  scattermore = FALSE,
  pointsize = 0,
  bins = NULL,
  summary_fun = sum,
  hex = FALSE,
  dark = FALSE,
  ...
)

Arguments

sfe: A SpatialFeatureExperiment object.
dimred: A string or integer scalar indicating the reduced dimension result in reducedDims(sfe) to plot.
ncomponents: A numeric scalar indicating the number of dimensions to plot, starting from the first dimension. Alternatively, a numeric vector specifying the dimensions to be plotted.
components: A numeric scalar or vector specifying which dimensions to be plotted. Use this instead of ncomponents when plotting only one dimension.
colGeometryName: Name of a colGeometry sf data frame whose numeric columns of interest are to be used to compute the metric. Use colGeometryNames to look up names of the sf data frames associated with cells/spots.
sample_id: Sample(s) in the SFE object whose cells/spots to use. Can be "all" to compute metric for all samples; the metric is computed separately for each sample.
ncol: Number of columns if plotting multiple features. Defaults to NULL, which means using the same logic as facet_wrap, which is used by patchwork's wrap_plots by default.
ncol_sample: If plotting multiple samples as facets, how many columns of such facets. This is distinct from ncols, which is for multiple features. When plotting multiple features for multiple samples, then the result is a multi-panel plot each panel of which is a plot for each feature facetted by samples.
annotGeometryName: Name of a annotGeometry of the SFE object, to annotate the gene expression plot.
annot_aes: A named list of plotting parameters for the annotation sf data frame. The names are which geom (as in ggplot2, such as color and fill), and the values are column names in the annotation sf data frame. Tidyeval is NOT supported.
annot_fixed: Similar to annot_aes, but for fixed aesthetic settings, such as color = "gray". The defaults are the same as the relevant defaults for this function.
exprs_values: Integer scalar or string indicating which assay of x contains the expression values.
bbox: A bounding box to specify a smaller region to plot, useful when the dataset is large. Can be a named numeric vector with names "xmin", "xmax", "ymin", and "ymax", in any order. If plotting multiple samples, it should be a matrix with sample IDs as column names and "xmin", "ymin", "xmax", and "ymax" as row names. If multiple samples are plotted but bbox is a vector rather than a matrix, then the same bounding box will be used for all samples. You may see points at the edge of the geometries if the intersection between the bounding box and a geometry happens to be a point there. If NULL, then the entire tissue is plotted.
image_id: ID of the image to plot behind the geometries. If NULL, then not plotting images. Use imgData to see image IDs present.
maxcell: Maximum number of pixels to plot in the image. If the image is larger, it will be resampled so it have less than this number of pixels to save memory and for faster plotting. We recommend reducing this number when plotting multiple facets.
aes_use: Aesthetic to use for discrete variables. For continuous variables, it's always "fill" for polygons and point shapes 21-25. For discrete variables, it can be fill, color, shape, or linetype, whenever applicable. The specified value will be changed to the applicable equivalent. For example, if the geometry is point but "linetype" is specified, then "shaped" will be used instead.
divergent: Logical, whether a divergent palette should be used.
diverge_center: If divergent = TRUE, the center from which the palette should diverge. If NULL, then not centering.
annot_divergent: Just as divergent, but for the annotGeometry in case it's different.
annot_diverge_center: Just as diverge_center, but for the annotGeometry in case it's different.
size: Fixed size of points. For points defaults to 0.5. Ignored if size_by is specified.
shape: Fixed shape of points, ignored if shape_by is specified and applicable.
linewidth: Width of lines, including outlines of polygons. For polygons, this defaults to 0, meaning no outlines.
linetype: Fixed line type, ignored if linetype_by is specified and applicable.
alpha: Transparency.
color: Fixed color for colGeometry if color_by is not specified or not applicable, or for annotGeometry if annot_color_by is not specified or not applicable.
fill: Similar to color, but for fill.
scattermore: Logical, whether to use the scattermore package to greatly speed up plotting numerous points. Only used for POINT colGeometries. If the geometry is not POINT, then the centroids are used. Recommended for plotting hundreds of thousands or more cells where the cell polygons can't be seen when plotted due to the large number of cells and small plot size such as when plotting multiple panels for multiple features.
pointsize: Radius of rasterized point in scattermore. Default to 0 for single pixels (fastest).
bins: If binning the colGeometry in space due to large number of cells or spots, the number of bins, passed to geom_bin2d or geom_hex. If NULL (default), then the colGeometry is plotted without binning. If binning, a point geometry is recommended. If the geometry is not point, then the centroids will be used.
summary_fun: Function to summarize the feature value when the colGeometry is binned.
hex: Logical, whether to use geom_hex. Note that geom_hex is broken in ggplot2 version 3.4.0. Please update ggplot2 if you are getting horizontal stripes when hex = TRUE.
dark: Logical, whether to use dark theme. When using dark theme, the palette will have lighter color represent higher values as if glowing in the dark. This is intended for plotting gene expression on top of fluorescent images.
...: Other arguments passed to wrap_plots.

Value

Same as in plotSpatialFeature. A ggplot2 object if plotting one component. A patchwork object if plotting multiple components.

Examples

library(SFEData)
library(scater)
sfe <- McKellarMuscleData("small")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
sfe <- logNormCounts(sfe)
sfe <- runPCA(sfe, ncomponents = 2)
spatialReducedDim(sfe, "PCA", ncomponents = 2, "spotPoly",
    annotGeometryName = "tissueBoundary",
    divergent = TRUE, diverge_center = 0
)

# Basically PC1 separates spots not on tissue from those on tissue.

Usage

Arguments

Value

See also

Examples