R: Generate box or violin plots showing error distribution

distErrorPlot {SpatialDDLS}

R Documentation

Generate box or violin plots showing error distribution

Description

Generate box or violin plots to show how errors are distributed. Errors can be shown all mixed or either split by cell type (CellType) or number of cell types present in the spots (nCellTypes). See the facet.by argument and examples for more details.

Usage

distErrorPlot(
  object,
  error,
  colors,
  x.by = "pBin",
  facet.by = NULL,
  color.by = "nCellTypes",
  filter.sc = TRUE,
  error.label = FALSE,
  pos.x.label = 4.6,
  pos.y.label = NULL,
  size.point = 0.1,
  alpha.point = 1,
  type = "violinplot",
  ylimit = NULL,
  nrow = NULL,
  ncol = NULL,
  title = NULL,
  theme = NULL,
  ...
)

Arguments

`object`	`SpatialDDLS` object with `trained.model` slot containing metrics in the `test.deconv.metrics` slot of a `DeconvDLModel` object.
`error`	Error to be represented. Available metric errors are: absolute error (`'AbsErr'`), proportional absolute error (`'ppAbsErr'`), squared error (`'SqrErr'`), and proportional squared error (`'ppSqrErr'`).
`colors`	Vector of colors to be used.
`x.by`	Variable used for the X-axis. When `facet.by` is not `NULL`, the best choice is `pBin` (probability bins). Options: `nCellTypes` (number of different cell types), `CellType` (cell type), and `pBin`.
`facet.by`	Show data in different panels. Options are `nCellTypes` (number of different cell types) and `CellType` (cell type) (`NULL` by default).
`color.by`	Variable used to color data. Options are `nCellTypes` and `CellType`.
`filter.sc`	Boolean indicating whether single-cell profiles are filtered out and only mixed transcriptional profile errors are shown (`TRUE` by default).
`error.label`	Boolean indicating whether to show the average error as a plot annotation (`FALSE` by default).
`pos.x.label`	X-axis position of error annotations.
`pos.y.label`	Y-axis position of error annotations.
`size.point`	Size of points (0.1 by default).
`alpha.point`	Alpha of points (0.1 by default).
`type`	Type of plot: `'boxplot'` or `'violinplot'` (the latter by default).
`ylimit`	Upper limit in Y-axis if it is required (`NULL` by default).
`nrow`	Number of rows if `facet.by` is not `NULL`.
`ncol`	Number of columns if `facet.by` is not `NULL`.
`title`	Title of the plot.
`theme`	ggplot2 theme.
`...`	Additional arguments for the facet_wrap function of ggplot2 if `facet.by` is not `NULL`.

Value

A ggplot object.

Examples


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 = 50,
  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 = 15,
  num.epochs = 5
)
# evaluation using test data
SDDLS <- calculateEvalMetrics(object = SDDLS)
# representation, for more examples, see the vignettes
distErrorPlot(
  object = SDDLS,
  error = "AbsErr",
  facet.by = "CellType",
  color.by = "nCellTypes",
  error.label = TRUE
)
distErrorPlot(
  object = SDDLS,
  error = "AbsErr",
  x.by = "CellType",
  facet.by = NULL,
  color.by = "CellType",
  error.label = TRUE
)