pca: pca: A principal component analysis function

In M3C: Monte Carlo Reference-based Consensus Clustering

Description

This is a flexible PCA function that can be run on a standard data frame. It is a wrapper for prcomp/ggplot2 code and can be customised with different colours and font sizes and more.

Usage

pca(mydata, printres = FALSE, labels = FALSE, text = FALSE,
  axistextsize = 18, legendtextsize = 18, dotsize = 5,
  textlabelsize = 4, legendtitle = "Group", controlscale = FALSE,
  scale = 1, low = "grey", high = "red", colvec = c("skyblue",
  "gold", "violet", "darkorchid", "slateblue", "forestgreen", "violetred",
  "orange", "midnightblue", "grey31", "black"), printheight = 20,
  printwidth = 22, pcx = 1, pcy = 2, scaler = FALSE)

Arguments

mydata	Data frame or matrix: if dataframe/matrix should have samples as columns and rows as features
printres	Logical flag: whether to print the PCA into current directory
labels	Character vector: if we want to just label with gender for example
text	Character vector: if we wanted to label the samples with text IDs to look for outliers
axistextsize	Numerical value: axis text size
legendtextsize	Numerical value: legend text size
dotsize	Numerical value: dot size
textlabelsize	Numerical value: text inside plot label size
legendtitle	Character vector: text legend title
controlscale	Logical flag: whether to control the colour scale
scale	Numerical value: 1=spectral palette, 2=manual low and high palette, 3=categorical labels
low	Character vector: continuous scale low colour
high	Character vector: continuous scale high colour
colvec	Character vector: a series of colours in vector for categorical labels, e.g. c("sky blue", "gold")
printheight	Numerical value: png height (default=20)
printwidth	Numerical value: png width (default=22)
pcx	Numerical value: which PC to plot on X axis (default=1)
pcy	Numerical value: which PC to plot on Y axis (default=2)
scaler	Logical flag: whether to scale the features of the input data (rows) (default=FALSE)

Value

A PCA plot object

Examples

PCA <- pca(mydata)

Example output

***PCA wrapper function***
running...
done.

M3C documentation built on Nov. 8, 2020, 6:50 p.m.