DimOverlay: Overlay dimensionality reduction vectors on HE images
In jbergenstrahle/STUtility: Analysis and visualization of Spatial Transcriptomics data

DimOverlay

R Documentation

Overlay dimensionality reduction vectors on HE images

Description

Graphs the selected vectors of a dimensional reduction technique on a 2D grid of spots overlaid on top of an HE images. Draws sample 1 as default, but can take multiple samples as well.

Usage

DimOverlay(
  object,
  dims = c(1:2),
  reduction = NULL,
  sampleids = 1,
  spots = NULL,
  ncols = NULL,
  layout.by.dims = TRUE,
  type = NULL,
  min.cutoff = NA,
  max.cutoff = NA,
  blend = FALSE,
  pt.size = 1,
  pt.alpha = 1,
  pt.border = TRUE,
  add.alpha = FALSE,
  palette = "MaYl",
  cols = NULL,
  center.zero = TRUE,
  channels.use = NULL,
  dark.theme = FALSE,
  show.sb = TRUE,
  value.scale = c("samplewise", "all"),
  label.by = NULL,
  verbose = FALSE,
  ...
)

Arguments

`object`	Seurat object
`dims`	List of dimensions for original images. This list has to contain one element for each sample and each element should be a vector of length 2 specifying the dimensions of the original HE image.
`reduction`	Which dimensionality reduction to use. If not specified, first searches for "umap", then "tsne", then "pca"
`sampleids`	Integer vector specifying sample indices to include in the plot [default: 1]
`spots`	Character vector with spot IDs to plot [default: all spots]
`ncols`	Number of columns in the subplot layout grid. (see Arrange plots for a detailed description)
`layout.by.dims`	Flip the arrangement of the subplot layout grid.
`type`	Image type to plot on. Here you can specify any of the images available in your Seurat object. To get this list you can run the `rasterlists` function on your Seurat object. If the type is not specified, the images will be prioritized in the following order if they are available; "processed", "masked" and "raw".
`min.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'). This can be useful if you have outlier values that skew the colorscale in the plot. For example, if you specify 'q1', you will trim of values below the 1st percentile. [default: no cuttoffs]
`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'). This can be useful if you have outlier values that skew the colorscale in the plot. For example, if you specify 'q1', you will trim of values below the 1st percentile. [default: no cuttoffs]
`blend`	Scale and blend expression values to visualize coexpression of two features (this options will override other coloring parameters). See 'Blending values' below for a more thourough description.
`pt.size`	Point size of each ST spot [default: 1]
`pt.alpha`	Opacity of each ST spot [default: 1]
`pt.border`	Should a border be drawn around the spots? [default: TRUE]
`add.alpha`	Scale spot opacity by selected feature. Higher values get more opaque while lower values make spots transparent.
`palette`	Color palette used for spatial heatmap (see `palette.select(info = T)` for available options). Disabled if a color vector is provided (see `cols` below).
`cols`	A vector of colors to use for colorscale, e.g. `cols = c("blue", "white", "red")` will create a gradient color scale going from blue to white to red. This options will deactivate the `palette` option.
`center.zero`	Specifies whther or not the colorscale should be centered around 0. For some values, such as Principal Component vectors, the distribution of values is centered at 0 and in that case it can be appropriate to use a divergent colorscale with a predefined value for 0. If this parameter is set to TRUE, the ggplot2 function `scale_color_gradient2` will be used to control the coloring instead of `scale_color_gradientn`. If center.zero is set to FALSE, the colorscale will simply map the values in equally spaced intervals which could skew the interpretaion of the output plot.
`channels.use`	Color channels to use for blending. Has to be a character vector of length 2 or 3 with "red", "green" and "blue" color names specified [default: c("red", "green", "blue)]
`dark.theme`	Switches color of scalebar to 'white'
`show.sb`	Should the size bar be displayed? [default: TRUE]
`value.scale`	Defines how the feature values should be mapped to the colorbar. If 'value.scale = "samplewise"', each feature will be scaled independently and if 'value.scale = "all"' the features will all have the same value reange.
`label.by`	Feature to relabel facets by. By default, facets are given a unique section number ranging grom 1 to the number of sections available in the 'Staffli' object. If you want to relabel these facets you can pass the name of a column that keeps the labels that you want to use. For example, if you wish to rename the facets to use labels defined by a charcater vector in column "section_id" in your meta.data slot, you can pass 'label.by = "section_id"' to relabel the facets. Only works for categorical group variables with where the number of groups is less than or equal to the number of tissue sections in your Staffli object.
`verbose`	Print messages
`...`	Parameters passed to other methods

Details

It is typically difficult to explore details in the HE image when diplaying multiple samples side by side, so we recommend to draw the plots for one sample at the time. If you have higher resolution images, it could also take significant time to draw the plots.

Blending values

The blend option can be useful if you wish to visualize multiple dimensionality reduction simultaneuosly and works for two or three value vectors. Each of the selected vectors are rescaled from 0 to 1 and are used as RGB color channels to produce mixed color for each spot. This can be particularly useful when looking at overlapping value vectors. For example, if you are looking at two overlapping value vectors "A" and "B" and use the blend option, the "A" values will be encoded in the "red" channel and the "B" values in the "green" channel. If a spot is purely "A" the color will be red and if it is purely "B" it will green. Any mixture of "A" and "B" will produce a color between red and green where a 50/50 mixture gives yellow color.

Arrange plots

The 'ncols' argument will determine the number of columns in the grid of subplots layout output by DimOverlay. By default, the subplots are arranged with dimensions in columns and sections in rows where 'ncols = length(dims)'. The 'layout.by.dims' can be used to flip the arrangement so that the sections are put in columns and dimensions along rows.

Examples

## Not run: 
# Load images and run PCA
se <- LoadImages(se) %>%
   RunPCA()

# Plot the first 2 dimensions on the first two tissue sections
DimOverlay(se, dims = 1:2, reduction = "pca", sampleids = 1:2)

# Blend values for dimensions 1 and 2 on the first two tissue sections
DimOverlay(se, dims = 1:2, reduction = "pca", sampleids = 1:2, blend = T)

# Plot the first 2 dimensions and trim off 1st percentile values on the first two tissue sections
DimOverlay(se, dims = 1:2, reduction = "pca", sampleids = 1:2, min.cutoff = 'q1')

# Mask images and plot the first 2 dimensions on the masked images for samples 1 and 2
se <- MaskImages(se)
DimOverlay(se, dims = 1:2, reduction = "pca", sampleids = 1:2, type = "masked")

## End(Not run)

jbergenstrahle/STUtility documentation built on March 14, 2023, 7:15 a.m.