eigencorplot {PCAtools} | R Documentation

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

### Description

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

### Usage

    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
    )

### Arguments

`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.

### Details

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

### Value

A `lattice` 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)

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

[Package PCAtools version 2.6.0 Index]