Skip to contents

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

Source: R/cyt_mint_splsda.R
cyt_mint_splsda.Rd

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,
  cim = FALSE,
  scale = NULL,
  roc = FALSE,
  progress = NULL
)

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 batch column name that contains batch information.

group_col2

A string specifying the second grouping column name. Default is NULL.

colors

A vector of colors for the groups or treatments. If NULL, a random palette (using rainbow) is generated based on the number of groups.

output_file

A string specifying the file name for saving the PDF output. If set to NULL, the function runs in interactive mode.

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.

cim

Logical. Whether to compute and plot the Clustered Image Map (CIM) heatmap. Default is FALSE.

scale

Character. Option for data transformation; if set to "log2", a log2 transformation is applied to the continuous variables. Default is NULL.

roc

Logical. Whether to compute and plot the ROC curve for the model. Default is FALSE.

Value

In Download mode, a PDF file is written. In Interactive mode, a named list (results_list) of plots and results is returned. If group_col2 is used, a nested list is returned, with each element corresponding to a level of group_col2.

Examples

# 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")

#> $global_indiv_plot
#> 
#> $partial_indiv_plot
#> 
#> $correlation_circle_plot
#> 
#> $cim_obj
#> NULL
#> 
#> $partial_loadings_plots
#> $partial_loadings_plots$`Comp 1 — batch_col: 1`
#> 
#> $partial_loadings_plots$`Comp 1 — batch_col: 2`
#> 
#> $partial_loadings_plots$`Comp 1 — batch_col: 3`
#> 
#> $partial_loadings_plots$`Comp 1 — batch_col: 4`
#> 
#> $partial_loadings_plots$`Comp 2 — batch_col: 1`
#> 
#> $partial_loadings_plots$`Comp 2 — batch_col: 2`
#> 
#> $partial_loadings_plots$`Comp 2 — batch_col: 3`
#> 
#> $partial_loadings_plots$`Comp 2 — batch_col: 4`
#> 
#> 
#> $roc_plot
#> NULL
#>