screeplot: Draw a SCREE plot, showing the distribution of explained...
In kevinblighe/PCAtools: PCAtools: Everything Principal Components Analysis

screeplot

R Documentation

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

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 <kevin@clinicalbioinformatics.co.uk>

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)

kevinblighe/PCAtools documentation built on Oct. 22, 2023, 12:01 p.m.