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Plot PCA values

Source: R/plot_pca.R
plot_pca.Rd

plot_pca() is a GGplot2 implementation for plotting two principal components from a PCA analysis, visualized as a scatter.

Usage

plot_pca(
  data = NULL,
  variables = c("PC1", "PC2"),
  labels = TRUE,
  label_size = 3,
  ...
)

Arguments

data

tidyproteomics data object

variables

a character vector of the 2 PCs to plot. Acceptable values include (PC1, PC2, PC3 ... PC9). Default c('PC1','PC2').

labels

a boolean

label_size

a numeric

...

passthrough for ggsave see plotting

Value

a (tidyproteomics data-object | ggplot-object)

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 <- hela_proteins %>%
  normalize(.method = c("scaled", "median", "linear", "limma", "loess")) %>%
  select_normalization()
#> ℹ Normalizing quantitative data
#> ℹ ... using scaled shift
#> ✔ ... using scaled shift [15ms]
#> 
#> ℹ ... using median shift
#> ✔ ... using median shift [15ms]
#> 
#> ℹ ... using linear regression
#> ✔ ... using linear regression [25ms]
#> 
#> ℹ ... using limma regression
#> ✔ ... using limma regression [19ms]
#> 
#> ℹ ... using loess regression
#> ✔ ... using loess regression [25ms]
#> 
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected loess
#> ℹ Selecting best normalization method
#> ✔ Selecting best normalization method ... done
#> 
#> ℹ  ... selected loess

hela_proteins %>% plot_pca()


# a different PC set
hela_proteins %>% plot_pca(variables = c("PC2", "PC3"))


# a PC scree plot
hela_proteins %>% plot_pca("scree")