Show distribution plots of the cell proportions generated by generateBulkCellMatrix

Source: R/utils.R

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.

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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