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Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

gly_oplsda.Rd

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,
  ...
)

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".

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.

...

Additional arguments passed to ropls::opls().

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()

  • meta_data: A list containing metadata from the input experiment

Required packages

This function requires the following packages to be installed:

  • ropls for OPLS-DA analysis

See also

ropls::opls()