plotPCA: Principle Component Analysis plot

plotPCAR Documentation

Principle Component Analysis plot

Description

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 signature '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 signature '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')

jasenfinch/metabolyseR documentation built on Sept. 18, 2023, 1:25 a.m.