distErrorPlot: Generate box or violin plots showing error distribution

In SpatialDDLS: Deconvolution of Spatial Transcriptomics Data Based on Neural Networks

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.

See Also

calculateEvalMetrics corrExpPredPlot blandAltmanLehPlot barErrorPlot

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
)

SpatialDDLS documentation built on Oct. 31, 2024, 5:07 p.m.