Plot the variation in normalized values

plot_variation_cv() 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_variation_cv(data = NULL, ...)

Arguments

data

tidyproteomics data object

...

passthrough for ggsave see plotting

Value

a (tidyproteomics data-object | ggplot-object)

Examples

library(dplyr, warn.conflicts = FALSE)
library(tidyproteomics)
hela_proteins %>%
  normalize(.method = c("scaled", "median", "linear", "limma", "loess")) %>%
  plot_variation_cv()
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [122ms]
#> 
#> ℹ ... using median shift
#> ✔ ... using median shift [100ms]
#> 
#> ℹ ... using linear regression
#> ✔ ... using linear regression [203ms]
#> 
#> ℹ ... using limma regression
#> ✔ ... using limma regression [347ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [1.3s]
#> 
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected loess

#> TableGrob (2 x 2) "arrange": 3 grobs
#>   z     cells    name                 grob
#> 1 1 (2-2,1-1) arrange       gtable[layout]
#> 2 2 (2-2,2-2) arrange       gtable[layout]
#> 3 3 (1-1,1-2) arrange text[GRID.text.5309]