Show distribution plots of the cell proportions generated by generateBulkCellMatrix

Show distribution plots of the cell proportions generated by generateBulkCellMatrix. These frequencies will determine the proportion of different cell types used during the simulation of pseudo-bulk RNA-Seq samples. There are 6 subsets of proportions generated by different approaches that 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'.

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

Arguments

object

DigitalDLSorter 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 plots are only for diagnostic purposes. This is the reason because they are generated without any parameter introduced by the user.

See also

generateBulkCellMatrix

Examples

# simulating data
set.seed(123) # reproducibility
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))
  )
)
DDLS <- createDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE, 
  sc.log.FC = FALSE
)
#> === Bulk RNA-seq 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
probMatrix <- data.frame(
  Cell_Type = paste0("CellType", seq(4)),
  from = c(1, 1, 1, 30),
  to = c(15, 15, 50, 70)
)
DDLS <- generateBulkCellMatrix(
  object = DDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  prob.design = probMatrix,
  num.bulk.samples = 60
)
#> 
#> === The number of bulk RNA-Seq samples that will be generated is equal to 60
#> 
#> === Training set cells by type:
#>     - CellType1: 5
#>     - CellType2: 6
#>     - CellType3: 6
#>     - CellType4: 5
#> === Test set cells by type:
#>     - CellType1: 2
#>     - CellType2: 2
#>     - CellType3: 2
#>     - CellType4: 2
#> === Probability matrix for training data:
#>     - Bulk RNA-Seq samples: 45
#>     - Cell types: 4
#> === Probability matrix for test data:
#>     - Bulk RNA-Seq samples: 15
#>     - Cell types: 4
#> DONE
lapply(
  X = 1:6, FUN = function(x) {
      showProbPlot(
        DDLS,
        type.data = "train",
        set = x,
        type.plot = "boxplot"
      )
    }
  )
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