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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.

progress

Optional. A Shiny Progress object for reporting progress updates.

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")
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the mixOmics package.
#>   Please report the issue at
#>   <https://github.com/mixOmicsTeam/mixOmics/issues/>.

#> $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
#>