biplot {PCAtools} | R Documentation

## Draw a bi-plot, comparing 2 selected principal components / eigenvectors.

### Description

Draw a bi-plot, comparing 2 selected principal components / eigenvectors.

### Usage

    biplot(
      pcaobj,
      x = "PC1",
      y = "PC2",
      showLoadings = FALSE,
      ntopLoadings = 5,
      showLoadingsNames = if (showLoadings) TRUE else FALSE,
      colLoadingsNames = "black",
      sizeLoadingsNames = 3,
      boxedLoadingsNames = TRUE,
      fillBoxedLoadings = alpha("white", 1/4),
      drawConnectorsLoadings = TRUE,
      widthConnectorsLoadings = 0.5,
      colConnectorsLoadings = "grey50",
      lengthLoadingsArrowsFactor = 1.5,
      colLoadingsArrows = "black",
      widthLoadingsArrows = 0.5,
      alphaLoadingsArrow = 1,
      colby = NULL,
      colkey = NULL,
      colLegendTitle = if (!is.null(colby)) colby else NULL,
      singlecol = NULL,
      shape = NULL,
      shapekey = NULL,
      shapeLegendTitle = if (!is.null(shape)) shape else NULL,
      pointSize = 3,
      legendPosition = "none",
      legendLabSize = 12,
      legendTitleSize = 14,
      legendIconSize = 5,
      encircle = FALSE,
      encircleFill = TRUE,
      encircleFillKey = NULL,
      encircleAlpha = 1/4,
      encircleLineSize = 0.25,
      encircleLineCol = NULL,
      ellipse = FALSE,
      ellipseConf = 0.95,
      ellipseFill = TRUE,
      ellipseFillKey = NULL,
      ellipseAlpha = 1/4,
      ellipseLineSize = 0.25,
      ellipseLineCol = NULL,
      xlim = if (showLoadings) c(min(pcaobj$rotated[, x]) - 5, max(pcaobj$rotated[, x]) +
        5) else c(min(pcaobj$rotated[, x]) - 1, max(pcaobj$rotated[, x]) + 1),
      ylim = if (showLoadings) c(min(pcaobj$rotated[, y]) - 5, max(pcaobj$rotated[, y]) +
        5) else c(min(pcaobj$rotated[, y]) - 1, max(pcaobj$rotated[, y]) + 1),
      lab = rownames(pcaobj$metadata),
      labSize = 3,
      labhjust = 1.5,
      labvjust = 0,
      boxedLabels = FALSE,
      selectLab = NULL,
      drawConnectors = TRUE,
      widthConnectors = 0.5,
      colConnectors = "grey50",
      xlab = paste0(x, ", ", round(pcaobj$variance[x], digits = 2), "% variation"),
      xlabAngle = 0,
      xlabhjust = 0.5,
      xlabvjust = 0.5,
      ylab = paste0(y, ", ", round(pcaobj$variance[y], digits = 2), "% variation"),
      ylabAngle = 0,
      ylabhjust = 0.5,
      ylabvjust = 0.5,
      axisLabSize = 16,
      title = "",
      subtitle = "",
      caption = "",
      titleLabSize = 16,
      subtitleLabSize = 12,
      captionLabSize = 12,
      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().
`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.
`showLoadings` | Logical, indicating whether or not to overlay variable loadings.
`ntopLoadings` | If showLoadings == TRUE, select this many variables based on absolute ordered variable loading for each PC in the biplot. As a result of looking across 2 PCs, it can occur whereby greater than this number are actually displayed.
`showLoadingsNames` | Logical, indicating to show variable loadings names or not.
`colLoadingsNames` | If 'showLoadings == TRUE', colour of text labels.
`sizeLoadingsNames` | If 'showLoadings == TRUE', size of text labels.
`boxedLoadingsNames` | Logical, if 'showLoadings == TRUE', draw text labels in boxes.
`fillBoxedLoadings` | When 'boxedLoadingsNames == TRUE', this controls the background fill of the boxes. To control both the fill and transparency, user can specify a value of the form 'alpha(<colour>, <alpha>)'.
`drawConnectorsLoadings` | If 'showLoadings == TRUE', draw line connectors to the variable loadings arrows in order to fit more labels in the plot space.
`widthConnectorsLoadings` | If 'showLoadings == TRUE', width of the line connectors drawn to the variable loadings arrows.
`colConnectorsLoadings` | If 'showLoadings == TRUE', colour of the line connectors drawn to the variable loadings arrows.
`lengthLoadingsArrowsFactor` | If 'showLoadings == TRUE', multiply the internally-determined length of the variable loadings arrows by this factor.
`colLoadingsArrows` | If showLoadings == TRUE, colour of the variable loadings arrows.
`widthLoadingsArrows` | If showLoadings == TRUE, width of the variable loadings arrows.
`alphaLoadingsArrow` | If showLoadings == TRUE, colour transparency of the variable loadings arrows.
`colby` | If NULL, all points will be coloured differently. If not NULL, value is assumed to be a column name in pcaobj$metadata relating to some grouping/categorical variable.
`colkey` | Vector of name-value pairs relating to value passed to 'col', e.g., c(A='forestgreen', B='gold').
`colLegendTitle` | Title of the legend for the variable specified by 'colby'.
`singlecol` | If specified, all points will be shaded by this colour. Overrides 'col'.
`shape` | If NULL, all points will be have the same shape. If not NULL, value is assumed to be a column name in pcaobj$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).
`shapeLegendTitle` | Title of the legend for the variable specified by 'shape'.
`pointSize` | Size of plotted points.
`legendPosition` | Position of legend ('top', 'bottom', 'left', 'right', 'none').
`legendLabSize` | Size of plot legend text.
`legendTitleSize` | Size of plot legend title text.
`legendIconSize` | Size of plot legend icons / symbols.
`encircle` | Logical, indicating whether to draw a polygon around the groups specified by 'colby'.
`encircleFill` | Logical, if 'encircle == TRUE', this determines whether to fill the encircled region or not.
`encircleFillKey` | Vector of name-value pairs relating to value passed to 'encircleFill', e.g., c(A='forestgreen', B='gold'). If NULL, the fill is controlled by whatever has already been used for 'colby' / 'colkey'.
`encircleAlpha` | Alpha for purposes of controlling colour transparency of the encircled region. Used when 'encircle == TRUE'.
`encircleLineSize` | Line width of the encircled line when 'encircle == TRUE'.
`encircleLineCol` | Colour of the encircled line when 'encircle == TRUE'.
`ellipse` | Logical, indicating whether to draw a stat ellipse around the groups specified by 'colby'.
`ellipseConf` | Confidence intervals of the stat ellipses when ellipse == TRUE.
`ellipseFill` | Logical, if 'ellipse == TRUE', this determines whether to fill the region or not.
`ellipseFillKey` | Vector of name-value pairs relating to value passed to 'ellipseFill', e.g., c(A='forestgreen', B='gold'). If NULL, the fill is controlled by whatever has already been used for 'colby' / 'colkey'.
`ellipseAlpha` | Alpha for purposes of controlling colour transparency of the ellipse region. Used when 'ellipse == TRUE'.
`ellipseLineSize` | Line width of the ellipse line when 'ellipse == TRUE'.
`ellipseLineCol` | Colour of the ellipse line when 'ellipse == TRUE'.
`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.
`labhjust` | Horizontal adjustment of label.
`labvjust` | Vertical adjustment of label.
`boxedLabels` | Logical, draw text labels in boxes.
`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.
`subtitle` | Plot subtitle.
`caption` | Plot caption.
`titleLabSize` | Size of plot title.
`subtitleLabSize` | Size of plot subtitle.
`captionLabSize` | Size of plot caption.
`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 bi-plot, comparing 2 selected 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)

      biplot(p)

      biplot(p, colby = 'Group', shape = 'Group')

      biplot(p, colby = 'Group', colkey = c(A = 'forestgreen', B = 'gold'),
        legendPosition = 'right')

      biplot(p, colby = 'Group', colkey = c(A='forestgreen', B='gold'),
        shape = 'Group', shapekey = c(A=10, B=21), legendPosition = 'bottom')

[Package PCAtools version 2.2.0 Index]