cyt_mint_splsda: Analyze data with Multivariate INTegration (MINT) Sparse Partial Least Squares Discriminant Analysis (sPLS-DA).

Description

This function performs a MINT (Multivariate INTegrative) sPLS-DA to handle batch effects by modeling a global biological signal across different studies or batches. If a second grouping column (group_col2) is provided, the analysis is stratified and performed for each level of that column.

Usage

cyt_mint_splsda(
  data,
  group_col,
  batch_col,
  group_col2 = NULL,
  colors = NULL,
  output_file = NULL,
  ellipse = TRUE,
  bg = FALSE,
  var_num = 20,
  comp_num = 2,
  scale = c("none", "log2", "log10", "zscore", "custom"),
  custom_fn = NULL,
  tune = FALSE,
  cim = FALSE,
  roc = FALSE,
  verbose = FALSE
)

Value

Plots consisting of the classification figures, ROC curves, correlation circle plots, and heatmaps.

Arguments

data: A matrix or data frame containing the variables. Columns not specified by group_col, group_col2, or multilevel_col are assumed to be continuous variables for analysis.
group_col: A string specifying the first grouping column name that contains grouping information. If group_col2 is not provided, it will be used for both grouping and treatment.
batch_col: A string specifying the column that identifies the batch or study for each sample.
group_col2: A string specifying the second grouping column name. Default is NULL.
colors: A vector of splsda_colors for the groups or treatments. If NULL, a random palette (using rainbow) is generated based on the number of groups.
output_file: Optional string specifying the name of the file to be created. When NULL (default), plots are drawn on the current graphics device. Ensure that the file extension matches the desired format (e.g., ".pdf" for PDF output or ".png" for PNG output or .tiff for TIFF output).
ellipse: Logical. Whether to draw a 95\ Default is FALSE.
bg: Logical. Whether to draw the prediction background in the figures. Default is FALSE.
var_num: Numeric. The number of variables to be used in the PLS-DA model.
comp_num: Numeric. The number of components to calculate in the sPLS-DA model. Default is 2.
scale: Character string specifying a transformation to apply to the numeric predictor columns prior to model fitting. Options are "none", "log2", "log10", "zscore", or "custom". When "custom" is selected a user defined function must be supplied via custom_fn. Defaults to "none".
custom_fn: A custom transformation function used when scale = "custom". Ignored otherwise. It should take a numeric vector and return a numeric vector of the same length.
tune: Logical. If TRUE, performs tuning of ncomp and keepX via cross‑validation. Default is FALSE.
cim: Logical. Whether to compute and plot the Clustered Image Map (CIM) heatmap. Default is FALSE.
roc: Logical. Whether to compute and plot the ROC curve for the model. Default is FALSE.
verbose: A logical value indicating whether to print additional informational output to the console. When TRUE, the function will display progress messages, and intermediate results when FALSE (the default), it runs quietly.

Author

Shubh Saraswat

Details

When verbose is set to TRUE, additional information about the analysis and confusion matrices are printed to the console. These can be suppressed by keeping verbose = FALSE.

References

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

Examples

Run this code

# Loading ExampleData5 dataset with batch column
data_df <- ExampleData5[,-c(2,4)]
data_df <- dplyr::filter(data_df, Group != "ND")

cyt_mint_splsda(data_df, group_col = "Group",
 batch_col = "Batch", colors = c("black", "purple"),
 ellipse = TRUE, var_num = 25, comp_num = 2,
 scale = "log2", verbose = FALSE)

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