pairsplot {PCAtools} | R Documentation

## Draw multiple bi-plots.

### Description

Draw multiple bi-plots.

### Usage

    pairsplot(
      pcaobj,
      components = getComponents(pcaobj, seq_len(5)),
      triangle = TRUE,
      trianglelabSize = 18,
      plotaxes = TRUE,
      margingaps = unit(c(0.1, 0.1, 0.1, 0.1), "cm"),
      ncol = NULL,
      nrow = NULL,
      x = NULL,
      y = NULL,
      colby = NULL,
      colkey = NULL,
      singlecol = NULL,
      shape = NULL,
      shapekey = NULL,
      pointSize = 1,
      legendPosition = "none",
      legendLabSize = 6,
      legendIconSize = 1.5,
      xlim = NULL,
      ylim = NULL,
      lab = NULL,
      labSize = 1.5,
      selectLab = NULL,
      drawConnectors = FALSE,
      widthConnectors = 0.5,
      colConnectors = "grey50",
      xlab = NULL,
      xlabAngle = 0,
      xlabhjust = 0.5,
      xlabvjust = 0.5,
      ylab = NULL,
      ylabAngle = 0,
      ylabhjust = 0.5,
      ylabvjust = 0.5,
      axisLabSize = 10,
      title = NULL,
      titleLabSize = 32,
      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. These will be compared in a pairwise fashion via multiple calls to biplot().
`triangle` | Logical, indicating whether or not to draw the plots in the upper panel in a triangular arrangement? Principal component names will be labeled along the diagonal.
`trianglelabSize` | Size of p rincipal component label (when triangle = TRUE).
`plotaxes` | Logical, indicating whether or not to draw the axis tick, labels, and titles.
`margingaps` | The margins between plots in the plot space. Takes the form of a 'unit()' variable.
`ncol` | If triangle = FALSE, the number of columns in the final merged plot.
`nrow` | If triangle = FALSE, the number of rows in the final merged plot.
`x` | A principal component to plot on x-axis. All principal component names are stored in pcaobj$label.
`y` | A principal component to plot on y-axis. All principal component names are stored in pcaobj$label.
`colby` | If NULL, all points will be coloured differently. If not NULL, value is assumed to be a column name in pcaobj$metadata relating to some grouping/categorical variable.
`colkey` | Vector of name-value pairs relating to value passed to 'col', e.g., c(A='forestgreen', B='gold').
`singlecol` | If specified, all points will be shaded by this colour. Overrides 'col'.
`shape` | If NULL, all points will be have the same shape. If not NULL, value is assumed to be a column name in pcaobj$metadata relating to some grouping/categorical variable.
`shapekey` | Vector of name-value pairs relating to value passed to 'shape', e.g., c(A=10, B=21).
`pointSize` | Size of plotted points.
`legendPosition` | Position of legend ('top', 'bottom', 'left', 'right', 'none').
`legendLabSize` | Size of plot legend text.
`legendIconSize` | Size of plot legend icons / symbols.
`xlim` | Limits of the x-axis.
`ylim` | Limits of the y-axis.
`lab` | A vector containing labels to add to the plot.
`labSize` | Size of labels.
`selectLab` | A vector containing a subset of lab to plot.
`drawConnectors` | Logical, indicating whether or not to connect plot labels to their corresponding points by line connectors.
`widthConnectors` | Line width of connectors.
`colConnectors` | Line colour of connectors.
`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.
`titleLabSize` | Size of plot title.
`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 multiple bi-plots.

### Value

A `cowplot` 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)

      pairsplot(p, triangle = TRUE)

[Package PCAtools version 2.6.0 Index]