Plot the variation in normalized values — plot

plot_quantrank() is a GGplot2 implementation for plotting the variability in normalized values, generating two facets. The left facet is a plot of CVs for each normalization method. The right facet is a plot of the 95%CI in abundance, essentially the conservative dynamic range. The goal is to select a normalization method that minimizes CVs while also retaining the dynamic range.

Usage

plot_quantrank(
  data = NULL,
  accounting = NULL,
  type = c("points", "lines"),
  show_error = TRUE,
  show_rank_scale = FALSE,
  limit_rank = NULL,
  display_subset = NULL,
  display_filter = c("none", "log2_foldchange", "p_value", "adj_p_value"),
  display_cutoff = 1,
  palette = "YlGnBu",
  impute_max = 0.5,
  ...
)

Arguments

data: tidyproteomics data object
accounting: character string
type: character string
show_error: a boolean
show_rank_scale: a boolean
limit_rank: a numerical vector of 2
display_subset: a string vector of identifiers to highlight
display_filter: a numeric between 0 and 1
display_cutoff: a numeric between 0 and 1
palette: a string representing the palette for scale_fill_brewer()
impute_max: a numeric representing the largest allowable imputation percentage
...: passthrough for ggsave see plotting

Value

a (tidyproteomics data-object | ggplot-object)

Examples

library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins %>% plot_quantrank()


hela_proteins %>% plot_quantrank(type = "lines")


hela_proteins %>% plot_quantrank(display_filter = "log2_foldchange", display_cutoff = 1)
#> Warning: There were 1868 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `log2_foldchange = max(log2_foldchange, na.rm = TRUE)`.
#> ℹ In group 6: `identifier = "A0A075B6E5"`.
#> Caused by warning in `max()`:
#> ! no non-missing arguments to max; returning -Inf
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 1867 remaining warnings.
#> Warning: ggrepel: 414 unlabeled data points (too many overlaps). Consider increasing max.overlaps


hela_proteins %>% plot_quantrank(limit_rank = c(1,50), show_rank_scale = TRUE)