blandAltmanLehPlot: Generate Bland-Altman agreement plots between predicted and...

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

Generate Bland-Altman agreement plots between predicted and expected cell type proportions of test data

Description

Generate Bland-Altman agreement plots between predicted and expected cell type proportions from test data. The Bland-Altman agreement plots can be shown all mixed or split by either cell type (CellType) or the number of cell types present in spots (nCellTypes). See the facet.by argument and examples for more information.

Usage

blandAltmanLehPlot(
  object,
  colors,
  color.by = "CellType",
  facet.by = NULL,
  log.2 = FALSE,
  filter.sc = TRUE,
  density = TRUE,
  color.density = "darkblue",
  size.point = 0.05,
  alpha.point = 1,
  ncol = NULL,
  nrow = 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.
colors	Vector of colors to be used.
color.by	Variable used to color data. Options are nCellTypes and CellType.
facet.by	Variable used to show the data in different panels. If NULL, the plot is not split into different panels. Options are nCellTypes (by number of different cell types) and CellType (by cell type).
log.2	Whether to show the Bland-Altman agreement plot in log2 space (FALSE by default).
filter.sc	Boolean indicating whether single-cell profiles are filtered out and only correlations of results associated with mixed spot profiles are shown (TRUE by default).
density	Boolean indicating whether density lines should be shown (TRUE by default).
color.density	Color of density lines if the density argument is TRUE.
size.point	Size of the points (0.1 by default).
alpha.point	Alpha of the points (0.1 by default).
ncol	Number of columns if facet.by is used.
nrow	Number of rows if facet.by is used.
title	Title of the plot.
theme	ggplot2 theme.
...	Additional argument for the facet_wrap function of ggplot2 if facet.by is not NULL.

Value

A ggplot object.

See Also

calculateEvalMetrics corrExpPredPlot distErrorPlot 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)
# Bland-Altman plot by cell type
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = "CellType",
  color.by = "CellType"
)
# Bland-Altman plot of all samples mixed
blandAltmanLehPlot(
  object = SDDLS,
  facet.by = NULL,
  color.by = "CellType",
  alpha.point = 0.3,
  log2 = TRUE
)

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