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

Source: R/cyt_splsda.R
cyt_splsda.Rd

This function conducts Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) on the provided data. It uses the specified group_col (and optionally group_col2) to define class labels while assuming the remaining columns contain continuous variables. The function supports a log2 transformation via the scale parameter and generates a series of plots, including classification plots, scree plots, loadings plots, and VIP score plots. Optionally, ROC curves are produced when roc is TRUE. Additionally, cross-validation is supported via LOOCV or Mfold methods. When both group_col and group_col2 are provided and differ, the function analyzes each treatment level separately.

Usage

cyt_splsda(
  data,
  group_col = NULL,
  group_col2 = NULL,
  multilevel_col = NULL,
  batch_col = NULL,
  ind_names = FALSE,
  colors = NULL,
  pdf_title = 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,
  verbose = FALSE,
  seed = 123
)

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 column name that contains the first group information. If group_col2 is not provided, an overall analysis will be performed.

group_col2

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

multilevel_col

A string specifying the column name that identifies repeated measurements (e.g., patient or sample IDs). If provided, a multilevel analysis will be performed. Default is NULL.

batch_col

A string specifying the column that identifies the batch or study for each sample.

ind_names

If TRUE, the row names of the first (or second) data matrix is used as names. Default is FALSE. If a character vector is provided, these values will be used as names. If 'pch' is set this will overwrite the names as shapes. See ?mixOmics::plotIndiv for details.

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.

pdf_title

A string specifying the file name for saving the PDF output. Default is NULL which generates figures in the current graphics device.

ellipse

Logical. Whether to draw a 95\ figures. 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.

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.

seed

An integer specifying the seed for reproducibility (default is 123).

Value

Plots consisting of the classification figures, component figures with Variable of Importance in Projection (VIP) scores, and classifications based on VIP scores greater than 1. ROC curves and confusion matrices are also produced if requested.

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

Lê Cao, K.-A., Boitard, S. and Besse, P. (2011). Sparse PLS Discriminant Analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12:253.

Author

Xiaohua Douglas Zhang and Shubh Saraswat

Examples

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

cyt_splsda(data_df, pdf_title = NULL,
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, verbose = FALSE)
#> Warning: the standard deviation is zero








#> Warning: the standard deviation is zero