Plot STdeconvolve topic proportions

Plot stored topic proportions without rerunning the optional backend. Multi-topic point maps use one shared proportion scale and reserve title space above the automatic layout.

Usage

STdeconvolvePlot(
  srt,
  tool_name = "STdeconvolve",
  topics = NULL,
  prefix = NULL,
  plot_type = c("point", "pie"),
  combine = TRUE,
  nrow = NULL,
  ncol = NULL,
  byrow = TRUE,
  ...,
  image.scale = c("lowres", "hires")
)

Arguments

srt

A Seurat object.

tool_name

Result key written to srt@tools by RunSTdeconvolve().

topics

Topic names, topic numbers, or metadata columns to plot. If NULL, all topics in the stored result are used.

prefix

Metadata prefix used by RunSTdeconvolve(). If NULL, the prefix is read from the stored result.

plot_type

"point" or "pie". Pie uses numeric group.by columns, values, or "<prefix>_prop_*"/"<prefix>_frac_*" when group.by is "<prefix>_dominant_type".

combine

Whether to combine point plots. If FALSE, a named list of plots is returned.

nrow, ncol, byrow

Point-plot layout controls. When both nrow and ncol are NULL, a near-square layout with at most three columns is used.

image.scale

Image scale factor matching the raster stored in the selected image. Use "hires" for a hires raster; do not modify Seurat scale-factor slots.

...

Additional arguments passed to SpatialSpotPlot().

Value

A ggplot, patchwork, or list of ggplot objects.

Examples

thisutils::check_r("JEFworks-Lab/STdeconvolve", verbose = FALSE)
data(visium_human_pancreas_sub)
spatial <- RunSTdeconvolve(
  visium_human_pancreas_sub,
  assay = "Spatial",
  features = rownames(visium_human_pancreas_sub)[1:300],
  k = 3,
  prefix = "STFull",
  tool_name = "STdeconvolveFull",
  verbose = FALSE
)
#> Removing 0 genes present in 100% or more of pixels...
#> 300 genes remaining...
#> Removing 0 genes present in 5% or less of pixels...
#> 300 genes remaining...
#> Restricting to overdispersed genes with alpha = 0.05...
#> Calculating variance fit ...
#> Using gam with k=5...
#> 68 overdispersed genes ... 
#>  Using top 1000 overdispersed genes.
#>  number of top overdispersed genes available: 68
#> Time to fit LDA models was 0.07 mins
#> Computing perplexity for each fitted model...
#> Time to compute perplexities was 0.04 mins
#> Getting predicted cell-types at low proportions...
#> Time to compute cell-types at low proportions was 0 mins
#> Plotting...
#> Warning: Ignoring unknown parameters: `linewidth`
#> Warning: Ignoring unknown parameters: `linewidth`
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?
#> `geom_line()`: Each group consists of only one observation.
#> ℹ Do you need to adjust the group aesthetic?

#> Filtering out cell-types in pixels that contribute less than 0.05 of the pixel proportion.
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to max; returning -Inf
STdeconvolvePlot(
  spatial,
  tool_name = "STdeconvolveFull",
  topics = 1:2,
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)