distErrorPlot | R Documentation |
Generate violin plots or box plots to show how the errors are distributed by
proportion bins of 0.1. Errors can be displayed all mixed or split by cell
type (CellType
) or number of cell types present in the samples
(nCellTypes
). See the facet.by
argument and examples for more
details.
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, ... )
object |
|
error |
The error to be represented. Available errors are absolute error
( |
colors |
Vector of colors to be used. Only vectors with a number of
colors equal to or greater than the levels of |
x.by |
Variable used for the X-axis. When |
facet.by |
Variable used to display data in different panels. If
|
color.by |
Variable used to color the data. Options are
|
filter.sc |
Boolean indicating whether single-cell profiles are filtered
out and only errors associated with pseudo-bulk samples are displayed
( |
error.label |
Boolean indicating whether to display the average error as
a plot annotation ( |
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: |
ylimit |
Upper limit in Y-axis if it is required ( |
nrow |
Number of rows if |
ncol |
Number of columns if |
title |
Title of the plot. |
theme |
ggplot2 theme. |
... |
Additional arguments for the facet_wrap function
from ggplot2 if |
A ggplot object with the representation of the desired errors.
calculateEvalMetrics
corrExpPredPlot
blandAltmanLehPlot
barErrorPlot
## Not run: 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)) ) ) DDLS <- loadSCProfiles( single.cell.data = sce, cell.ID.column = "Cell_ID", gene.ID.column = "Gene_ID" ) probMatrixValid <- data.frame( Cell_Type = paste0("CellType", seq(6)), from = c(1, 1, 1, 15, 15, 30), to = c(15, 15, 30, 50, 50, 70) ) DDLS <- generateBulkCellMatrix( object = DDLS, cell.ID.column = "Cell_ID", cell.type.column = "Cell_Type", prob.design = probMatrixValid, num.bulk.samples = 50, verbose = TRUE ) # training of DDLS model tensorflow::tf$compat$v1$disable_eager_execution() DDLS <- trainDigitalDLSorterModel( object = DDLS, on.the.fly = TRUE, batch.size = 15, num.epochs = 5 ) # evaluation using test data DDLS <- calculateEvalMetrics( object = DDLS ) # representation, for more examples, see the vignettes distErrorPlot( object = DDLS, error = "AbsErr", facet.by = "CellType", color.by = "nCellTypes", error.label = TRUE ) distErrorPlot( object = DDLS, error = "AbsErr", x.by = "CellType", facet.by = NULL, filter.sc = FALSE, color.by = "CellType", error.label = TRUE ) ## End(Not run)
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