distErrorPlot {digitalDLSorteR} | R Documentation |
Generate box plots or violin plots to show how the errors are distributed
Description
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.
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 |
|
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 |
Value
A ggplot object with the representation of the desired errors.
See Also
calculateEvalMetrics
corrExpPredPlot
blandAltmanLehPlot
barErrorPlot
Examples
## 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 <- createDDLSobject(
sc.data = sce,
sc.cell.ID.column = "Cell_ID",
sc.gene.ID.column = "Gene_ID",
sc.filt.genes.cluster = FALSE,
sc.log.FC = FALSE
)
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 <- trainDDLSModel(
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)