Show distribution plots of the cell proportions generated by genMixedCellProp

Show distribution plots of the cell proportions generated by the genMixedCellProp function.

Usage

showProbPlot(object, type.data, set, type.plot = "boxplot")

Arguments

object

SpatialDDLS object with prob.cell.types slot with plot slot.

type.data

Subset of data to show: train or test.

set

Integer determining which of the 6 different subsets to display.

type.plot

Character determining which type of visualization to display. It can be 'boxplot', 'violinplot', 'linesplot' or 'ncelltypes'. See Description for more information.

Value

A ggplot object.

Details

These frequencies will determine the proportion of different cell types used during the simulation of mixed transcriptional profiles. Proportions generated by each method (see ?genMixedCellProp) can be visualized in three ways: box plots, violin plots, and lines plots. You can also plot the probabilities based on the number of different cell types present in the samples by setting type.plot = 'nCellTypes'.

See also

genMixedCellProp

Examples

set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(100, lambda = 5), nrow = 40, ncol = 30,
      dimnames = list(paste0("Gene", seq(40)), paste0("RHC", seq(30)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(30)),
    Cell_Type = sample(x = paste0("CellType", seq(4)), size = 30,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(40))
  )
)

SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  project = "Simul_example",
  sc.filt.genes.cluster = FALSE
)
#> === Spatial transcriptomics data not provided
#> === Processing single-cell data
#>       - Filtering features:
#>          - Selected features: 40
#>          - Discarded features: 0
#> 
#> === No mitochondrial genes were found by using ^mt- as regrex
#> 
#> === Final number of dimensions for further analyses: 40
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 10, 
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
#> 
#> === The number of mixed profiles that will be generated is equal to 10
#> 
#> === Training set cells by type:
#>     - CellType1: 5
#>     - CellType2: 5
#>     - CellType3: 5
#>     - CellType4: 5
#> === Test set cells by type:
#>     - CellType1: 2
#>     - CellType2: 3
#>     - CellType3: 3
#>     - CellType4: 2
#> === Probability matrix for training data:
#>     - Mixed spots: 7
#>     - Cell types: 4
#> === Probability matrix for test data:
#>     - Mixed spots: 3
#>     - Cell types: 4
#> DONE
showProbPlot(
   SDDLS,
   type.data = "train",
   set = 1,
   type.plot = "boxplot"
 )