Step: Partial Least Squares Discriminant Analysis (PLS-DA)

Perform PLS-DA using glystats::gly_plsda() and plot it with glyvis::plot_plsda(). PLS-DA is a supervised method that finds components maximizing covariance between predictors and the response variable (group membership).

Usage

step_plsda(
  on = "exp",
  ncomp = 2,
  scale = TRUE,
  plot_width = 5,
  plot_height = 5,
  ...
)

Arguments

on: Name of the experiment to run PLS-DA on. Can be "exp", "sig_exp", "trait_exp", "sig_trait_exp", "motif_exp", "sig_motif_exp".
ncomp: Number of components to include. Default is 2.
scale: 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 glystats::gly_plsda().

Value

A glysmith_step object.

Details

Data required:

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

Tables generated (with suffixes):

plsda_samples: A table containing the PLS-DA scores for each sample
plsda_variables: A table containing the PLS-DA loadings for each variable
plsda_variance: A table containing the explained variance for each component
plsda_vip: A table containing the Variable Importance in Projection (VIP) scores
plsda_perm_test: A table containing permutation test results

Plots generated (with suffixes):

plsda_scores: A PLS-DA score plot colored by group
plsda_loadings: A PLS-DA loading plot
plsda_variance: A PLS-DA variance (scree) plot
plsda_vip: A PLS-DA VIP score plot

AI Prompt

This section is for AI in inquire_blueprint() only.

Include this step when users explicitly asks for PLS-DA.

Examples

step_plsda()
#> <step "step_plsda()"> Partial least squares discriminant analysis
step_plsda(ncomp = 3)
#> <step "step_plsda(ncomp = 3)"> Partial least squares discriminant analysis