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 <- 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)
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