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.

This step depends on the on parameter (default: exp).

When on = "exp", requires exp (usually after step_preprocess()).
When on = "sig_exp", requires sig_exp from one of step_dea_limma(), step_dea_ttest(), step_dea_wilcox(), step_dea_anova(), or step_dea_kruskal().
When on = "trait_exp", requires trait_exp from step_derive_traits().
When on = "sig_trait_exp", requires sig_trait_exp from DEA on traits.
When on = "dynamic_motif_exp", requires dynamic_motif_exp from step_quantify_dynamic_motifs().
When on = "sig_dynamic_motif_exp", requires sig_dynamic_motif_exp from DEA on motifs.
When on = "branch_motif_exp", requires branch_motif_exp from step_quantify_branch_motifs().
When on = "sig_branch_motif_exp", requires sig_branch_motif_exp from DEA on motifs.

Usage

step_pca(
  on = "exp",
  center = TRUE,
  scale = TRUE,
  loadings = FALSE,
  screeplot = 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", "dynamic_motif_exp", "sig_dynamic_motif_exp", "branch_motif_exp", "sig_branch_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.
loadings: Logical indicating whether to generate the loading plot. Default is FALSE since loading plots for glycoproteomics data can be crowded.
screeplot: Logical indicating whether to generate the screeplot. 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
dynamic_motif_exp (if on = "dynamic_motif_exp"): The dynamic motif experiment to run PCA on
branch_motif_exp (if on = "branch_motif_exp"): The branch 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 (always generated)
pca_loadings: A PCA loading plot (if loadings = TRUE)
pca_screeplot: A PCA screeplot (if screeplot = TRUE)

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