Principle Component Analysis plot — plotPCA • metabolyseR

Plot Principle Component Analysis results.

Usage

plotPCA(
  analysis,
  cls = "class",
  label = NULL,
  scale = TRUE,
  center = TRUE,
  xAxis = "PC1",
  yAxis = "PC2",
  shape = FALSE,
  ellipses = TRUE,
  title = "PCA",
  legendPosition = "bottom",
  labelSize = 2,
  ...
)

# S4 method for AnalysisData
plotPCA(
  analysis,
  cls = "class",
  label = NULL,
  scale = TRUE,
  center = TRUE,
  xAxis = "PC1",
  yAxis = "PC2",
  shape = FALSE,
  ellipses = TRUE,
  title = "Principle Component Analysis (PCA)",
  legendPosition = "bottom",
  labelSize = 2
)

# S4 method for Analysis
plotPCA(
  analysis,
  cls = "class",
  label = NULL,
  scale = TRUE,
  center = TRUE,
  xAxis = "PC1",
  yAxis = "PC2",
  shape = FALSE,
  ellipses = TRUE,
  title = "PCA",
  legendPosition = "bottom",
  labelSize = 2,
  type = c("pre-treated", "raw")
)

Arguments

analysis: object of class AnalysisData or Analysis
cls: name of class information column to use for sample labelling
label: name of class information column to use for sample labels. Set to NULL for no labels.
scale: scale the data
center: center the data
xAxis: principle component to plot on the x-axis
yAxis: principle component to plot on the y-axis
shape: TRUE/FALSE use shape aesthetic for plot points. Defaults to TRUE when the number of classes is greater than 12
ellipses: TRUE/FALSE, plot multivariate normal distribution 95\ confidence ellipses for each class
title: plot title
legendPosition: legend position to pass to legend.position argument of ggplot2::theme. Set to "none" to remove legend.
labelSize: label size. Ignored if label is NULL
...: arguments to pass to the appropriate method
type: raw or pre-treated data to plot

Examples

library(metaboData)

d <- analysisData(abr1$neg,abr1$fact) %>% 
 occupancyMaximum(cls = 'day')

## PCA plot
plotPCA(d,cls = 'day')