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Analyze Data with Principal Component Analysis (PCA) for Cytokines.

Source: R/cyt_pca.R
cyt_pca.Rd

This function performs Principal Component Analysis (PCA) on cytokine data and generates several types of plots, including:

  • 2D PCA plots using mixOmics' plotIndiv function,

  • 3D scatter plots (if style is "3d" or "3D" and comp_num is 3) via the plot3D package,

  • Scree plots showing both individual and cumulative explained variance,

  • Loadings plots, and

  • Biplots and correlation circle plots.

The function optionally applies a log2 transformation to the numeric data and handles analyses based treatment groups.

Usage

cyt_pca(
  data,
  group_col = NULL,
  group_col2 = NULL,
  colors = NULL,
  pdf_title,
  ellipse = FALSE,
  comp_num = 2,
  scale = NULL,
  pch_values = NULL,
  style = NULL
)

Arguments

data

A data frame containing cytokine data. It should include at least one column representing grouping information and optionally a second column representing treatment or stimulation.

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.

colors

A vector of colors corresponding to the groups. If set to NULL, a palette is generated using rainbow() based on the number of unique groups.

pdf_title

A string specifying the file name of the PDF where the PCA plots will be saved. If NULL, the plots are generated on the current graphics device. Default is NULL.

ellipse

Logical. If TRUE, a 95% confidence ellipse is drawn on the PCA individuals plot. Default is FALSE.

comp_num

Numeric. The number of principal components to compute and display. Default is 2.

scale

Character. If set to "log2", a log2 transformation is applied to the numeric cytokine measurements (excluding the grouping columns). Default is NULL.

pch_values

A vector of plotting symbols (pch values) to be used in the PCA plots. Default is NULL.

style

Character. If set to "3d" or "3D" and comp_num equals 3, a 3D scatter plot is generated using the plot3D package. Default is NULL.

Value

A PDF file containing the PCA plots is generated and saved.

Examples

# Load sample data
data <- ExampleData1[, -c(3,23)]
data_df <- dplyr::filter(data, Group != "ND" & Treatment != "Unstimulated")
# Run PCA analysis and save plots to a PDF file
cyt_pca(
  data = data_df,
  pdf_title = NULL,
  colors = c("black", "red2"),
  scale = "log2",
  comp_num = 3,
  pch_values = c(16, 4),
  style = "3D",
  group_col = "Group",
  group_col2 = "Treatment",
  ellipse = FALSE
)
#> [1] "Results based on log2 transformation:"
#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types






#> Warning: 'col.per.group' is deprecated, please use 'col' to specify colours for each group


#> Warning: 'pch.levels' is deprecated, please use 'pch' to specify point types






#> Warning: 'col.per.group' is deprecated, please use 'col' to specify colours for each group