
Partial Least Squares Discriminant Analysis (PLS-DA)
gly_plsda.Rd
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,
...
)
gly_plsda_(expr_mat, groups, ncomp = 2, 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".
- 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. Only applicable to
gly_plsda()
.- ...
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. Character vectors will be automatically converted to factors.
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 namegroup
: Group assignmentp1
,p2
, etc.: PLS-DA component scores
variables
: PLS-DA loadings for each variable containing the following columns:variable
: Variable namep1
,p2
, etc.: PLS-DA component loadingspcorr1
,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 componentcumulative_prop_var
: Cumulative proportion of variance explained
vip
: Variable Importance in Projection scores containing the following columns:variable
: Variable namevip
: 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 fromropls::opls()
Details
gly_plsda()
is the top-level API that works with glyexp::experiment()
objects and supports
the add_info
parameter for joining experiment metadata.
gly_plsda_()
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:
ropls
for PLS-DA analysis