eigencorplot: Correlate principal components to continuous variable...
In PCAtools: PCAtools: Everything Principal Components Analysis

Description Usage Arguments Details Value Author(s) Examples

Correlate principal components to continuous variable metadata and test significancies of these.

eigencorplot(
  pcaobj,
  components = getComponents(pcaobj, seq_len(10)),
  metavars,
  titleX = "",
  cexTitleX = 1,
  rotTitleX = 0,
  colTitleX = "black",
  fontTitleX = 2,
  titleY = "",
  cexTitleY = 1,
  rotTitleY = 0,
  colTitleY = "black",
  fontTitleY = 2,
  cexLabX = 1,
  rotLabX = 0,
  colLabX = "black",
  fontLabX = 2,
  cexLabY = 1,
  rotLabY = 0,
  colLabY = "black",
  fontLabY = 2,
  posLab = "bottomleft",
  col = c("blue4", "blue3", "blue2", "blue1", "white", "red1", "red2", "red3", "red4"),
  posColKey = "right",
  cexLabColKey = 1,
  cexCorval = 1,
  colCorval = "black",
  fontCorval = 1,
  scale = TRUE,
  main = "",
  cexMain = 2,
  rotMain = 0,
  colMain = "black",
  fontMain = 2,
  corFUN = "pearson",
  corUSE = "pairwise.complete.obs",
  corMultipleTestCorrection = "none",
  signifSymbols = c("***", "**", "*", ""),
  signifCutpoints = c(0, 0.001, 0.01, 0.05, 1),
  colFrame = "white",
  plotRsquared = FALSE,
  returnPlot = TRUE
)

`pcaobj`	Object of class 'pca' created by pca().
`components`	The principal components to be included in the plot.
`metavars`	A vector of column names in metadata representing continuos variables.
`titleX`	X-axis title.
`cexTitleX`	X-axis title cex.
`rotTitleX`	X-axis title rotation in degrees.
`colTitleX`	X-axis title colour.
`fontTitleX`	X-axis title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`titleY`	Y-axis title.
`cexTitleY`	Y-axis title cex.
`rotTitleY`	Y-axis title rotation in degrees.
`colTitleY`	Y-axis title colour.
`fontTitleY`	Y-axis title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`cexLabX`	X-axis labels cex.
`rotLabX`	X-axis labels rotation in degrees.
`colLabX`	X-axis labels colour.
`fontLabX`	X-axis labels font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`cexLabY`	Y-axis labels cex.
`rotLabY`	Y-axis labels rotation in degrees.
`colLabY`	Y-axis labels colour.
`fontLabY`	Y-axis labels font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`posLab`	Positioning of the X- and Y-axis labels. 'bottomleft', bottom and left; 'topright', top and right; 'all', bottom / top and left /right; 'none', no labels.
`col`	Colour shade gradient for RColorBrewer.
`posColKey`	Position of colour key. 'bottom', 'left', 'top', 'right'.
`cexLabColKey`	Colour key labels cex.
`cexCorval`	Correlation values cex.
`colCorval`	Correlation values colour.
`fontCorval`	Correlation values font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`scale`	Logical, indicating whether or not to scale the colour range to max and min cor values.
`main`	Plot title.
`cexMain`	Plot title cex.
`rotMain`	Plot title rotation in degrees.
`colMain`	Plot title colour.
`fontMain`	Plot title font style. 1, plain; 2, bold; 3, italic; 4, bold-italic.
`corFUN`	Correlation method: 'pearson', 'spearman', or 'kendall'.
`corUSE`	Method for handling missing values (see documentation for cor function via ?cor). 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'.
`corMultipleTestCorrection`	Multiple testing p-value adjustment method. Any method from stats::p.adjust() can be used. Activating this function means that signifSymbols and signifCutpoints then relate to adjusted (not nominal) p-values.
`signifSymbols`	Statistical significance symbols to display beside correlation values.
`signifCutpoints`	Cut-points for statistical significance.
`colFrame`	Frame colour.
`plotRsquared`	Logical, indicating whether or not to plot R-squared values.
`returnPlot`	Logical, indicating whether or not to return the plot object.

Correlate principal components to continuous variable metadata and test significancies of these.

A lattice object.

Kevin Blighe <kevin@clinicalbioinformatics.co.uk>

  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)

  eigencorplot(p, components = getComponents(p, 1:10),
    metavars = c('ESR', 'CRP'))