Plot Imputation Diagnostic Across Conditions

plot_imputation_diagnostic() provides a targeted quality diagnostic visual for missing-value imputation. It visualizes individual replicate abundances across two conditions for candidate proteins, distinguishing observed values from those imputed (e.g. at the limit of detection). By default, it prioritizes top candidates that possess imputed values, allowing rapid assessment of whether fold changes are continuous or detection-limited on/off artifacts.

Usage

plot_imputation_diagnostic(
  data = NULL,
  ...,
  candidates = NULL,
  proteins = NULL,
  labels_column = "gene_name",
  top_n = 10,
  log2fc_min = 1,
  log2fc_column = "log2_foldchange",
  significance_max = 0.05,
  significance_column = "adj_p_value",
  regulation = c("both", "up", "down"),
  show_title = TRUE,
  tag = NULL,
  destination = "plot",
  height = 6,
  width = 8,
  path = NULL
)

Arguments

data

a tidyproteomics data object

...

passthrough arguments.

candidates

a character vector of protein identifiers or gene names to plot. If NULL, defaults to proteins or automatically selects the top candidates with imputed values.

proteins

alias for candidates for backward compatibility.

labels_column

a character defining the column name for facet titles (default is "gene_name").

top_n

integer defining the number of proteins to plot when candidates is NULL. Default is 10.

log2fc_column

a character defining the column name of log2 foldchange values. Default is "log2_foldchange".

significance_column

a character defining the column name of significance values. Default is "adj_p_value".

show_title

boolean or character string for plot title. Default is TRUE.

tag

optional character string to append to the plot file name.

destination

character string indicating output type: "plot", "save", "png", "svg", "tiff", "jpeg". Default is "plot".

height

numeric plot height in inches. Default is 6.

width

numeric plot width in inches. Default is 8.

path

optional path to save destination plot.

Value

a ggplot2 object or tidyproteomics data object depending on destination.

Examples

library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)

# subset for fast execution
hela_proteins <- hela_proteins %>% subset(description %like% "Ribosome")
#> 
#> ℹ Subsetting data: description %like% Ribosome
#> ✔ Subsetting data: description %like% Ribosome ... done
#> 

hela_proteins %>%
  expression(knockdown/control) %>%
  plot_imputation_diagnostic(knockdown/control, top_n = 6)
#> ℹ .. expression::t_test testing knockdown / control
#> ✔ .. expression::t_test testing knockdown / control [65ms]
#> 
#> ℹ no values significant at current settings
#> ℹ ... log2_foldchange (1) and adj_p_value (0.05)
#> ℹ ... falling back to top candidates ranked by effect and significance