blandAltmanLehPlot | R Documentation |
Generate Bland-Altman agreement plots between predicted and expected cell
type proportions from test data results. The Bland-Altman agreement plots can
be displayed all mixed or split by cell type (CellType
) or the number
of cell types present in samples (nCellTypes
). See the facet.by
argument and examples for more information.
blandAltmanLehPlot( object, colors, color.by = "CellType", facet.by = NULL, log.2 = FALSE, filter.sc = TRUE, density = TRUE, color.density = "darkblue", size.point = 0.05, 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 |
color.by |
Variable used to color data. Options are |
facet.by |
Variable used to display the data in different panels. If
|
log.2 |
Whether to display the Bland-Altman agreement plot in log2 space
( |
filter.sc |
Boolean indicating whether single-cell profiles are filtered
out and only correlations of results associated with bulk samples are
displayed ( |
density |
Boolean indicating whether density lines must be displayed
( |
color.density |
Color of density lines if the |
size.point |
Size of the points (0.1 by default). |
alpha.point |
Alpha of the points (0.1 by default). |
ncol |
Number of columns if |
nrow |
Number of rows if |
title |
Title of the plot. |
theme |
ggplot2 theme. |
... |
Additional argument for the |
A ggplot object with Bland-Altman agreement plots between expected and actual proportions.
calculateEvalMetrics
corrExpPredPlot
distErrorPlot
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 ) # Bland-Altman plot by cell type blandAltmanLehPlot( object = DDLS, facet.by = "CellType", color.by = "CellType" ) # Bland-Altman plot of all samples mixed blandAltmanLehPlot( object = DDLS, facet.by = NULL, color.by = "CellType", alpha.point = 0.3, log2 = TRUE ) ## End(Not run)
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