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

Usage

cyt_splsda(
  data,
  group_col = NULL,
  group_col2 = NULL,
  splsda_colors = NULL,
  output_file = NULL,
  ellipse = FALSE,
  bg = FALSE,
  conf_mat = FALSE,
  var_num,
  cv_opt = NULL,
  fold_num = 5,
  scale = NULL,
  comp_num = 2,
  pch_values,
  style = NULL,
  roc = FALSE,
  progress = NULL
)

Arguments

data: A matrix or data frame containing the variables. Columns not specified by group_col or group_col2 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.
group_col2: A string specifying the second grouping column name. Default is NULL.
splsda_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: 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.
conf_mat: Logical. Whether to print the confusion matrix for the classifications. Default is FALSE.
var_num: Numeric. The number of variables to be used in the PLS-DA model.
cv_opt: Character. Option for cross-validation method: either "loocv" or "Mfold". Default is NULL.
fold_num: Numeric. The number of folds to use if cv_opt is "Mfold". Default is 5.
scale: Character. Option for data transformation; if set to "log2", a log2 transformation is applied to the continuous variables. Default is NULL.
comp_num: Numeric. The number of components to calculate in the sPLS-DA model. Default is 2.
pch_values: A vector of integers specifying the plotting characters (pch values) to be used in the plots.
style: Character. If set to "3D" or "3d" and comp_num equals 3, a 3D plot is generated using the plot3D package. Default is NULL.
roc: Logical. Whether to compute and plot the ROC curve for the model. Default is FALSE.

Value

In Download mode (output_file not NULL), a PDF file is written and the function returns NULL invisibly. In Interactive mode (output_file = NULL), a named list is returned with the following elements (in this order): 1. overall_indiv_plot: Main individual classification plot 2. overall_3D: Main 3D plot (if generated) 3. overall_ROC: ROC curve plot for the overall model 4. overall_CV: Cross-validation error plot for the overall model 5. loadings: A list of loadings plots (one per component) 6. vip_scores: A list of VIP score bar plots for each component 7. vip_indiv_plot: Main individual plot for the VIP>1 model 8. vip_3D: 3D plot for the VIP>1 model (if generated) 9. vip_ROC: ROC curve plot for the VIP>1 model 10. vip_CV: Cross-validation error plot for the VIP>1 model 11. conf_matrix: Confusion matrix text output

Examples

data_df <- ExampleData1[,-c(3)]
data_df <- dplyr::filter(data_df, Group != "ND", Treatment != "Unstimulated")

cyt_splsda(data_df, output_file = NULL,
splsda_colors = c("black", "purple"), bg = FALSE, scale = "log2",
conf_mat = FALSE, var_num = 25, cv_opt = NULL, comp_num = 2,
pch_values = c(16, 4), style = NULL, ellipse = TRUE,
group_col = "Group", group_col2 = "Treatment", roc = FALSE)
#> Results based on log2 transformation:
#> Warning: the standard deviation is zero
#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types
#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types
#> Warning: the standard deviation is zero
#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types
#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types

#> $`CD3/CD28`
#> $`CD3/CD28`$overall_indiv_plot
#> 
#> $`CD3/CD28`$overall_3D
#> NULL
#> 
#> $`CD3/CD28`$overall_ROC
#> NULL
#> 
#> $`CD3/CD28`$overall_CV
#> NULL
#> 
#> $`CD3/CD28`$loadings
#> $`CD3/CD28`$loadings[[1]]
#> 
#> $`CD3/CD28`$loadings[[2]]
#> 
#> 
#> $`CD3/CD28`$vip_scores
#> $`CD3/CD28`$vip_scores[[1]]

#> 
#> $`CD3/CD28`$vip_scores[[2]]

#> 
#> 
#> $`CD3/CD28`$vip_indiv_plot
#> 
#> $`CD3/CD28`$vip_loadings
#> $`CD3/CD28`$vip_loadings[[1]]
#> 
#> $`CD3/CD28`$vip_loadings[[2]]
#> 
#> 
#> $`CD3/CD28`$vip_3D
#> NULL
#> 
#> $`CD3/CD28`$vip_ROC
#> NULL
#> 
#> $`CD3/CD28`$vip_CV
#> NULL
#> 
#> $`CD3/CD28`$conf_matrix
#> NULL
#> 
#> 
#> $LPS
#> $LPS$overall_indiv_plot
#> 
#> $LPS$overall_3D
#> NULL
#> 
#> $LPS$overall_ROC
#> NULL
#> 
#> $LPS$overall_CV
#> NULL
#> 
#> $LPS$loadings
#> $LPS$loadings[[1]]
#> 
#> $LPS$loadings[[2]]
#> 
#> 
#> $LPS$vip_scores
#> $LPS$vip_scores[[1]]

#> 
#> $LPS$vip_scores[[2]]

#> 
#> 
#> $LPS$vip_indiv_plot
#> 
#> $LPS$vip_loadings
#> $LPS$vip_loadings[[1]]
#> 
#> $LPS$vip_loadings[[2]]
#> 
#> 
#> $LPS$vip_3D
#> NULL
#> 
#> $LPS$vip_ROC
#> NULL
#> 
#> $LPS$vip_CV
#> NULL
#> 
#> $LPS$conf_matrix
#> NULL
#> 
#>