Plot the PCA variation in normalized values

plot_variation_pca() is a GGplot2 implementation for plotting the variability in normalized values by PCA analysis, 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_pca(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 %>%
  subset(description %like% "Ribosome") %>%
  normalize(.method = c("linear", "loess", "randomforest")) %>%
  plot_variation_pca()
#> 
#> ℹ Subsetting data: description %like% Ribosome
#> ✔ Subsetting data: description %like% Ribosome ... done
#> 
#> ℹ Normalizing quantitative data
#> ℹ ... using linear regression
#> ✔ ... using linear regression [32ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [25ms]
#> 
#> ℹ ... using randomforest regression
#> ✔ ... using randomforest regression [52ms]
#> 
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected randomforest