Plot number of significant features vs FDR

Plot the number of features considered significant vs observed false discovery rate (FDR), for given adjusted p-value thresholds and/or as curves traced out by considering all threshold values.

Usage

plot_fdrnbrcurve(
  cobraplot,
  title = "",
  stripsize = 15,
  titlecol = "black",
  pointsize = 5,
  xaxisrange = c(0, 1),
  plottype = c("curve", "points"),
  linewidth = 1
)

Arguments

cobraplot

A COBRAPlot object.

title

A character string giving the title of the plot.

stripsize

A numeric value giving the size of the strip text, when the results are stratified by an annotation.

titlecol

A character string giving the color of the title.

pointsize

A numeric value giving the size of the plot characters.

xaxisrange

A numeric vector with two elements, giving the lower and upper boundary of the x-axis, respectively.

plottype

A character vector giving the type of plot to construct. Can be any combination of the two elements "curve" and "points".

linewidth

The line width used for plotting

Value

A ggplot object

Author

Charlotte Soneson

Examples

data(cobradata_example)
cobraperf <- calculate_performance(cobradata_example,
                                   binary_truth = "status",
                                   aspects = c("fdrnbr", "fdrnbrcurve"))
#> Warning: Object doesn't have a slot sval. Please run update_cobradata(). For consistency, I will return an empty data.frame
#> column DESeq2 is being ignored for NBRS calculations
#> column DESeq2 is being ignored for TPR calculations
#> column DESeq2 is being ignored for FDR calculations
cobraplot <- prepare_data_for_plot(cobraperf, colorscheme = "Dark2",
                                   incltruth = TRUE)
plot_fdrnbrcurve(cobraplot, plottype = c("curve", "points"))
#> Warning: Removed 3 rows containing missing values or values outside the scale range
#> (`geom_path()`).