Partial Least Squares Discriminant Analysis (PLS-DA)

Perform partial least squares discriminant analysis on the expression data. The function uses ropls::opls() to perform PLS-DA and returns tidy results.

Usage

gly_plsda(
  exp,
  group_col = "group",
  ncomp = 2,
  scale = TRUE,
  add_info = TRUE,
  ...,
  log_transform = TRUE
)

Arguments

exp: A glyexp::GlycomicSE() or glyexp::GlycoproteomicSE() object, or another SummarizedExperiment containing an expression matrix and sample information.
group_col: A character string specifying the column name in sample information that contains group labels. Default is "group".
ncomp: An integer indicating the number of components to include. Default is 2.
scale: A logical indicating whether to scale the data. Default is TRUE.
add_info: A logical value. If TRUE (default), sample and variable information from the experiment will be added to the result tibbles. If FALSE, only the PLS-DA results are returned.
...: Additional arguments passed to ropls::opls().
log_transform: A single logical value indicating whether to transform expression values using log(x + 1) before analysis. Default is TRUE. Set to FALSE for data that are already log-transformed.

Value

A list containing:

tidy_result: A list of tibbles with PLS-DA results:
- samples: PLS-DA scores for each sample containing the following columns:
  - sample: Sample name
  - group: Group assignment
  - p1, p2, etc.: PLS-DA component scores
- variables: PLS-DA loadings for each variable containing the following columns:
  - variable: Variable name
  - p1, p2, etc.: PLS-DA component loadings
  - pcorr1, pcorr2, etc.: Correlation between each variable and component
- variance: PLS-DA explained variance containing the following columns:
  - component: Component name (p1, p2, etc.)
  - prop_var_explained: Proportion of variance explained by each component
  - cumulative_prop_var: Cumulative proportion of variance explained
- vip: Variable Importance in Projection scores containing the following columns:
  - variable: Variable name
  - vip: VIP score
- perm_test: Permutation test results containing the following columns:
  - model: Model type ("Original" for the original model, "Permutation" for permuted models)
  - perm_id: Permutation ID (0 for original model, 1+ for permutations)
  - Additional columns from the permutation test matrix (e.g., R2X, R2Y, Q2, etc.)
raw_result: The raw ropls opls object from ropls::opls()
meta_data: A list containing metadata from the input experiment

Details

By default, expression values are transformed using log(x + 1) before analysis. Set log_transform = FALSE to use the expression values as supplied, for example when they are already log-transformed.

Required packages

This function requires the following packages to be installed: