Skip to contents

Plot the variation in normalized values

Source: R/plot_quantrank.R
plot_quantrank.Rd

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


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


hela_proteins %>% plot_quantrank(display_filter = "log2_foldchange", display_cutoff = 1)


hela_proteins %>% plot_quantrank(limit_rank = c(1,10), show_rank_scale = TRUE)