Plot row standard deviations versus row means

Methods for objects of classes matrix, ExpressionSet, vsn and MAList to plot row standard deviations versus row means.

Usage

meanSdPlot(x, 
           ranks = TRUE,
           xlab  = ifelse(ranks, "rank(mean)", "mean"),
           ylab  = "sd",
           pch,
           plot  = TRUE,
     bins  = 50,
           ...)

Arguments

x: An object of class matrix, ExpressionSet, vsn or MAList.
ranks: Logical, indicating whether the x-axis (means) should be plotted on the original scale (FALSE) or on the rank scale (TRUE). The latter distributes the data more evenly along the x-axis and allows a better visual assessment of the standard deviation as a function of the mean.
xlab: Character, label for the x-axis.
ylab: Character, label for the y-axis.
pch: Ignored - exists for backward compatibility.
plot: Logical. If TRUE (default), a plot is produced. Calling the function with plot=FALSE can be useful if only its return value is of interest.
bins: Gets passed on to geom_hex.
...: Further arguments that get passed on to geom_hex.

Details

Standard deviation and mean are calculated row-wise from the expression matrix (in) x. The scatterplot of these versus each other allows you to visually verify whether there is a dependence of the standard deviation (or variance) on the mean. The red line depicts the running median estimator (window-width 10%). If there is no variance-mean dependence, then the line should be approximately horizontal.

Value

A named list with five components: its elements px and py are the x- and y-coordinates of the individual data points in the plot; its first and second element are the x-coordinates and values of the running median estimator (the red line in the plot). Its element gg is the plot object (see examples). Depending on the value of plot, the method can (and by default does) have a side effect, which is to print gg on the active graphics device.

Author

Wolfgang Huber

Examples

  data("kidney")
  log.na <- function(x) log(ifelse(x>0, x, NA))

  exprs(kidney) <- log.na(exprs(kidney))
  msd <- meanSdPlot(kidney)
#> Warning: Removed 898 rows containing non-finite outside the scale range
#> (`stat_binhex()`).


  ## The `ggplot` object is returned in list element `gg`, here is an example of how to modify the plot
  library("ggplot2")
  msd$gg +
    ggtitle("Hello world") +
    scale_fill_gradient(low = "yellow", high = "darkred") +
    scale_y_continuous(limits = c(0, 7))
#> Scale for fill is already present.
#> Adding another scale for fill, which will replace the existing scale.
#> Warning: Removed 898 rows containing non-finite outside the scale range
#> (`stat_binhex()`).
#> Warning: Removed 49 rows containing missing values or values outside the scale range
#> (`geom_hex()`).


  ## Try this out with not log-transformed data, vsn2-transformed data, the lymphoma data, your data ...