Multivariate analysis (PCA, PLS, OPLS)

Performs PCA, PLS, or OPLS using ropls and generates a formatted scores plot based on the first two components.

Usage

mva(
  X,
  sample_data,
  group_colour = NULL,
  group_shape = NULL,
  plot_title = NULL,
  verbose = TRUE,
  ...
)

Arguments

X: A numeric matrix or data frame of features (e.g., metabolites, genes), with samples as rows and features as columns.
sample_data: A data.frame containing sample-level data. Row names must match the sample identifiers in X and must be also in a column named "sample_id".
group_colour: Optional. Character colname in sample_data used for point color mapping.
group_shape: Optional. Character colname in sample_data used for point shape mapping.
plot_title: Optional. Character string specifying the plot title.
verbose: Logical. If TRUE, displays progress messages.
...: Additional arguments passed to ropls::opls() (e.g.predI =, orthoI =).

Value

A named list with two elements:

ropls_obj: The ropls::opls() object.
scores_plot: A ggplot2::ggplot() object showing the scores plot.

Details

The analysis type depends on the ... arguments passed to ropls::opls().

Examples

# PCA
if (requireNamespace("ropls", quietly = TRUE)) {
set.seed(123)
mock_X <- matrix(rnorm(40),
                 nrow = 10,
                 dimnames = list(paste0("sample", 1:10),
                                 paste0("feat", 1:4))
                 )

sample_data <- data.frame(
  sample_id = rownames(mock_X),
  group = factor(rep(c("A", "B"), each = 5)),
  batch = factor(rep(1:2, times = 5)),
  row.names = rownames(mock_X),
  stringsAsFactors = FALSE
)

result <- mva(
  X = mock_X,
  sample_data = sample_data,
  group_colour = "group",
  group_shape = "batch",
  plot_title = "Test PCA Plot",
  predI = 2,  # PCA: set components
  verbose = FALSE
)

# PCA plot
result$scores_plot
}
#> PCA
#> 10 samples x 4 variables
#> standard scaling of predictors
#>       R2X(cum) pre ort
#> Total    0.817   2   0