Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

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

Usage

gly_oplsda(
  exp,
  group_col = "group",
  pred_i = 1,
  ortho_i = NA,
  scale = TRUE,
  add_info = TRUE,
  ...
)

gly_oplsda_(expr_mat, groups, pred_i = 1, ortho_i = NA, scale = TRUE, ...)

Arguments

exp: A glyexp::experiment() object containing expression matrix and sample information.
group_col: A character string specifying the column name in sample information that contains group labels. Default is "group".
pred_i: An integer indicating the number of predictive components to include. Default is 1.
ortho_i: An integer indicating the number of orthogonal components to include. Default is NA (automatic).
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 OPLS-DA results are returned. Only applicable to gly_oplsda().
...: Additional arguments passed to ropls::opls().
expr_mat: A numeric matrix with variables as rows and samples as columns.
groups: A factor or character vector specifying group membership for each sample. Must have exactly 2 levels. Character vectors will be automatically converted to factors.

Value

A list containing:

tidy_result: A list of tibbles with OPLS-DA results:
- samples: OPLS-DA scores for each sample containing the following columns:
  - sample: Sample name
  - group: Group assignment
  - p1, p2, etc.: Predictive component scores
  - o1, o2, etc.: Orthogonal component scores
- variables: OPLS-DA loadings for each variable containing the following columns:
  - variable: Variable name
  - p1, p2, etc.: Predictive component loadings
  - o1, o2, etc.: Orthogonal component loadings
  - pcorr1, pcorr2, etc.: Correlation between each variable and the corresponding predictive component
- variance: OPLS-DA explained variance containing the following columns:
  - component: Component name (p1, o1, 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()

Details

gly_oplsda() is the top-level API that works with glyexp::experiment() objects and supports the add_info parameter for joining experiment metadata.

gly_oplsda_() is the underlying API that works with matrices and factor vectors directly, providing more flexibility for users who don't use the glyexp package.

Required packages

This function requires the following packages to be installed: