screeplot: Draw a SCREE plot, showing the distribution of explained...

In PCAtools: PCAtools: Everything Principal Components Analysis

Description

Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.

Usage

screeplot(
  pcaobj,
  components = getComponents(pcaobj),
  xlim = NULL,
  ylim = c(0, 100),
  xlab = "Principal component",
  xlabAngle = 90,
  xlabhjust = 0.5,
  xlabvjust = 0.5,
  ylab = "Explained variation (%)",
  ylabAngle = 0,
  ylabhjust = 0.5,
  ylabvjust = 0.5,
  axisLabSize = 16,
  title = "SCREE plot",
  subtitle = "",
  caption = "",
  titleLabSize = 16,
  subtitleLabSize = 12,
  captionLabSize = 12,
  colBar = "dodgerblue",
  drawCumulativeSumLine = TRUE,
  colCumulativeSumLine = "red2",
  sizeCumulativeSumLine = 1.5,
  drawCumulativeSumPoints = TRUE,
  colCumulativeSumPoints = "red2",
  sizeCumulativeSumPoints = 2,
  hline = NULL,
  hlineType = "longdash",
  hlineCol = "black",
  hlineWidth = 0.4,
  vline = NULL,
  vlineType = "longdash",
  vlineCol = "black",
  vlineWidth = 0.4,
  gridlines.major = TRUE,
  gridlines.minor = TRUE,
  borderWidth = 0.8,
  borderColour = "black",
  returnPlot = TRUE
)

Arguments

pcaobj	Object of class 'pca' created by pca().
components	The principal components to be included in the plot.
xlim	Limits of the x-axis.
ylim	Limits of the y-axis.
xlab	Label for x-axis.
xlabAngle	Rotation angle of x-axis labels.
xlabhjust	Horizontal adjustment of x-axis labels.
xlabvjust	Vertical adjustment of x-axis labels.
ylab	Label for y-axis.
ylabAngle	Rotation angle of y-axis labels.
ylabhjust	Horizontal adjustment of y-axis labels.
ylabvjust	Vertical adjustment of y-axis labels.
axisLabSize	Size of x- and y-axis labels.
title	Plot title.
subtitle	Plot subtitle.
caption	Plot caption.
titleLabSize	Size of plot title.
subtitleLabSize	Size of plot subtitle.
captionLabSize	Size of plot caption.
colBar	Colour of the vertical bars.
drawCumulativeSumLine	Logical, indicating whether or not to overlay plot with a cumulative explained variance line.
colCumulativeSumLine	Colour of cumulative explained variance line.
sizeCumulativeSumLine	Size of cumulative explained variance line.
drawCumulativeSumPoints	Logical, indicating whether or not to draw the cumulative explained variance points.
colCumulativeSumPoints	Colour of cumulative explained variance points.
sizeCumulativeSumPoints	Size of cumulative explained variance points.
hline	Draw one or more horizontal lines passing through this/these values on y-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
hlineType	Line type for hline ('blank', 'solid', 'dashed', 'dotted', 'dotdash', 'longdash', 'twodash').
hlineCol	Colour of hline.
hlineWidth	Width of hline.
vline	Draw one or more vertical lines passing through this/these values on x-axis. For single values, only a single numerical value is necessary. For multiple lines, pass these as a vector, e.g., c(60,90).
vlineType	Line type for vline ('blank', 'solid', 'dashed', 'dotted', 'dotdash', 'longdash', 'twodash').
vlineCol	Colour of vline.
vlineWidth	Width of vline.
gridlines.major	Logical, indicating whether or not to draw major gridlines.
gridlines.minor	Logical, indicating whether or not to draw minor gridlines.
borderWidth	Width of the border on the x and y axes.
borderColour	Colour of the border on the x and y axes.
returnPlot	Logical, indicating whether or not to return the plot object.

Details

Draw a SCREE plot, showing the distribution of explained variance across all or select principal components / eigenvectors.

Value

A ggplot2 object.

Author(s)

Kevin Blighe <[e-mail]>

Examples

  options(scipen=10)
  options(digits=6)

  col <- 20
  row <- 20000
  mat1 <- matrix(
    rexp(col*row, rate = 0.1),
    ncol = col)
  rownames(mat1) <- paste0('gene', 1:nrow(mat1))
  colnames(mat1) <- paste0('sample', 1:ncol(mat1))

  mat2 <- matrix(
    rexp(col*row, rate = 0.1),
    ncol = col)
  rownames(mat2) <- paste0('gene', 1:nrow(mat2))
  colnames(mat2) <- paste0('sample', (ncol(mat1)+1):(ncol(mat1)+ncol(mat2)))

  mat <- cbind(mat1, mat2)

  metadata <- data.frame(row.names = colnames(mat))
  metadata$Group <- rep(NA, ncol(mat))
  metadata$Group[seq(1,40,2)] <- 'A'
  metadata$Group[seq(2,40,2)] <- 'B'
  metadata$CRP <- sample.int(100, size=ncol(mat), replace=TRUE)
  metadata$ESR <- sample.int(100, size=ncol(mat), replace=TRUE)

  p <- pca(mat, metadata = metadata, removeVar = 0.1)

  screeplot(p)

  screeplot(p, hline = 80)

PCAtools documentation built on Nov. 8, 2020, 8:17 p.m.