Step: Principal Component Analysis (PCA)

Run PCA using glystats::gly_pca() and plot it with glyvis::plot_pca(). Loading plot for glycoproteomics data can be crowded with too many variables. Ignore the resulting plot if it is not informative.

Usage

step_pca(
  on = "exp",
  center = TRUE,
  scale = TRUE,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

on: Name of the experiment to run PCA on. Can be "exp", "sig_exp", "trait_exp", "sig_trait_exp", "motif_exp", "sig_motif_exp".
center: A logical indicating whether to center the data. Default is TRUE.
scale: A logical indicating whether to scale the data. Default is TRUE.
plot_width: Width of plots in inches. Default is 5.
plot_height: Height of plots in inches. Default is 5.
...: Additional arguments passed to prcomp().

Value

A glysmith_step object.

Details

Data required:

exp (if on = "exp"): The experiment to run PCA on
trait_exp (if on = "trait_exp"): The trait experiment to run PCA on
motif_exp (if on = "motif_exp"): The motif experiment to run PCA on

Tables generated (with suffixes):

pca_samples: A table containing the PCA scores for each sample
pca_variables: A table containing the PCA loadings for each variable
pca_eigenvalues: A table containing the PCA eigenvalues

Plots generated (with suffixes):

pca_scores: A PCA score plot colored by group
pca_loadings: A PCA loading plot
pca_screeplot: A PCA screeplot

AI Prompt

This section is for AI in inquire_blueprint() only.

Include this step if needed.

Examples

step_pca()
#> <step "step_pca()"> Principal component analysis