corrExpPredPlot | R Documentation |
Generate correlation plot between predicted and expected cell type
proportions from test data. Correlation plots 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 information. Moreover, a user-selected correlation value is
displayed as an annotation on the plots. See the corr
argument for
details.
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, ... )
object |
|
colors |
Vector of colors to be used. Only vectors with a number of
colors equal to or greater than the levels of |
facet.by |
Variable used to display data in different panels. If
|
color.by |
Variable used to color data. Options are |
corr |
Correlation value displayed as an annotation on the plot.
Available metrics are Pearson's correlation coefficient ( |
filter.sc |
Boolean indicating whether single-cell profiles are filtered
out and only errors associated with pseudo-bulk samples are displayed
( |
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 |
size.point |
Size of points (0.1 by default). |
alpha.point |
Alpha of points (0.1 by default). |
ncol |
Number of columns if |
nrow |
Number of rows if |
title |
Title of the plot. |
theme |
ggplot2 theme. |
... |
Additional arguments for the facet_wrap function
from ggplot2 if |
A ggplot object with the correlation plots between expected and actual proportions.
calculateEvalMetrics
distErrorPlot
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 ) # correlations by cell type corrExpPredPlot( object = DDLS, facet.by = "CellType", color.by = "CellType", corr = "both" ) # correlations of all samples mixed corrExpPredPlot( object = DDLS, facet.by = NULL, color.by = "CellType", corr = "ccc", pos.x.label = 0.2, alpha.point = 0.3 ) ## End(Not run)
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