SpatialDDLS (version 1.0.1)

barErrorPlot: Generate bar error plots

Description

Generate bar error plots by cell type (CellType) or by number of different cell types (nCellTypes) on test mixed transcriptional profiles.

Usage

barErrorPlot(
  object,
  error = "MSE",
  by = "CellType",
  dispersion = "se",
  filter.sc = TRUE,
  title = NULL,
  angle = NULL,
  theme = NULL
)

Value

A ggplot object.

Arguments

object

SpatialDDLS object with trained.model slot containing metrics in the test.deconv.metrics slot of a DeconvDLModel object.

error

'MAE' or 'MSE'.

by

Variable used to show errors. Available options are: 'nCellTypes', 'CellType'.

dispersion

Standard error ('se') or standard deviation ('sd'). The former by default.

filter.sc

Boolean indicating whether single-cell profiles are filtered out and only correlation of results associated with mixed transcriptional profiles are shown (TRUE by default).

title

Title of the plot.

angle

Angle of ticks.

theme

ggplot2 theme.

See Also

calculateEvalMetrics corrExpPredPlot distErrorPlot blandAltmanLehPlot

Examples

# \donttest{
set.seed(123)
sce <- SingleCellExperiment::SingleCellExperiment(
  assays = list(
    counts = matrix(
      rpois(30, lambda = 5), nrow = 15, ncol = 20,
      dimnames = list(paste0("Gene", seq(15)), paste0("RHC", seq(20)))
    )
  ),
  colData = data.frame(
    Cell_ID = paste0("RHC", seq(20)),
    Cell_Type = sample(x = paste0("CellType", seq(6)), size = 20,
                       replace = TRUE)
  ),
  rowData = data.frame(
    Gene_ID = paste0("Gene", seq(15))
  )
)
SDDLS <- createSpatialDDLSobject(
  sc.data = sce,
  sc.cell.ID.column = "Cell_ID",
  sc.gene.ID.column = "Gene_ID",
  sc.filt.genes.cluster = FALSE
)
SDDLS <- genMixedCellProp(
  object = SDDLS,
  cell.ID.column = "Cell_ID",
  cell.type.column = "Cell_Type",
  num.sim.spots = 100,
  train.freq.cells = 2/3,
  train.freq.spots = 2/3,
  verbose = TRUE
)
SDDLS <- simMixedProfiles(SDDLS)
# training of DDLS model
SDDLS <- trainDeconvModel(
  object = SDDLS,
  batch.size = 10,
  num.epochs = 5
)
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# bar error plots
barErrorPlot(
  object = SDDLS,
  error = "MSE",
  by = "CellType"
)
barErrorPlot(
  object = SDDLS,
  error = "MAE",
  by = "nCellTypes"
)
# }