Plot cell types per cluster

plotCellTypesPerCluster(object, markers, ...)

# S4 method for SingleCellExperiment,KnownMarkers
plotCellTypesPerCluster(
  object,
  markers,
  min = 1L,
  max = Inf,
  reduction = "UMAP",
  expression = c("mean", "sum"),
  headerLevel = 2L,
  ...,
  BPPARAM = BiocParallel::bpparam()
)

# S4 method for Seurat,KnownMarkers
plotCellTypesPerCluster(
  object,
  markers,
  min = 1L,
  max = Inf,
  reduction = "UMAP",
  expression = c("mean", "sum"),
  headerLevel = 2L,
  ...,
  BPPARAM = BiocParallel::bpparam()
)

Arguments

object

Object.

markers

Object containing gene markers.

min

numeric(1). Recommended minimum value cutoff.

max

numeric(1). Recommended maximum value cutoff.

reduction

vector(1). Dimension reduction name or index position.

expression

character(1). Calculation to apply. Uses match.arg() internally and defaults to the first argument in the character vector.

headerLevel

integer(1) (1-7). Markdown header level.

...

Passthrough arguments to plotMarker().

BPPARAM

bpparamClass. BiocParallel parameter to specify the desired processor configuration.
We recommend using one of the following:

Value

Show graphical output. Invisibly return list.

Details

Plot the geometric mean of the significant marker genes for every known cell type (per unbiased cluster). Cell types with too few (min cutoff) or too many (max cutoff) marker genes will be skipped.

Note

Updated 2020-02-21.

Examples

data(Seurat, package = "acidtest") data(seurat_known_markers) ## Seurat ==== object <- Seurat markers <- seurat_known_markers plotCellTypesPerCluster( object = object, markers = markers, reduction = "UMAP" )
#> #> #> ## Cluster 1 {.tabset} #> #> #> #> ### Macrophage #>
#> #> #> ## Cluster 2 {.tabset} #> #> #> #> ### Dendritic Cell #>