corrExpPredPlot: Generate correlation plots between predicted and expected...

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

Generate correlation plots between predicted and expected cell type proportions of test data

Description

Generate correlation plots between predicted and expected cell type proportions of test data. Correlation plots can be shown all mixed or either split by cell type (CellType) or the number of different cell types present in the spots (nCellTypes).

Usage

corrExpPredPlot(
  object,
  colors,
  facet.by = NULL,
  color.by = "CellType",
  corr = "both",
  filter.sc = TRUE,
  pos.x.label = 0.01,
  pos.y.label = 0.95,
  sep.labels = 0.15,
  size.point = 0.1,
  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.
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.
corr	Correlation value shown as an annotation on the plot. Available metrics are Pearson's correlation coefficient ('pearson') and concordance correlation coefficient ('ccc'). It can be 'pearson', 'ccc' or 'both' (by default).
filter.sc	Boolean indicating whether single-cell profiles are filtered out and only mixed transcriptional profile errors are shown (TRUE by default).
pos.x.label	X-axis position of correlation annotations (0.95 by default).
pos.y.label	Y-axis position of correlation annotations (0.1 by default).
sep.labels	Space separating annotations if corr is equal to 'both' (0.15 by default).
size.point	Size of points (0.1 by default).
alpha.point	Alpha of points (0.1 by default).
ncol	Number of columns if facet.by is other than NULL.
nrow	Number of rows if facet.by is different from 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 distErrorPlot 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)
# correlations by cell type
corrExpPredPlot(
  object = SDDLS,
  facet.by = "CellType",
  color.by = "CellType",
  corr = "both"
)
# correlations of all samples mixed
corrExpPredPlot(
  object = SDDLS,
  facet.by = NULL,
  color.by = "CellType",
  corr = "ccc",
  pos.x.label = 0.2,
  alpha.point = 0.3
)

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