edge (version 2.4.2)

maPlot: Plots Log-Fold Change versus Log-Concentration (or, M versus A) for Count Data

Description

To represent counts that were low (e.g. zero in 1 library and non-zero in the other) in one of the two conditions, a 'smear' of points at low A value is presented.

Usage

maPlot(x, y, logAbundance=NULL, logFC=NULL, normalize=FALSE, plot.it=TRUE,
     smearWidth=1, col=NULL, allCol="black", lowCol="orange", deCol="red",
     de.tags=NULL, smooth.scatter=FALSE, lowess=FALSE, ...)

Arguments

x

vector of counts or concentrations (group 1)

y

vector of counts or concentrations (group 2)

logAbundance

vector providing the abundance of each gene on the log2 scale. Purely optional (default is NULL), but in combination with logFC provides a more direct way to create an MA-plot if the log-abundance and log-fold change are available.

logFC

vector providing the log-fold change for each gene for a given experimental contrast. Default is NULL, only to be used together with logAbundance as both need to be non-null for their values to be used.

normalize

logical, whether to divide x and y vectors by their sum

plot.it

logical, whether to produce a plot

smearWidth

scalar, width of the smear

col

vector of colours for the points (if NULL, uses allCol and lowCol)

allCol

colour of the non-smeared points

lowCol

colour of the smeared points

deCol

colour of the DE (differentially expressed) points

de.tags

indices for genes identified as being differentially expressed; use exactTest or glmLRT to identify DE genes. Note that `tag' and `gene' are synonymous here.

smooth.scatter

logical, whether to produce a 'smooth scatter' plot using the KernSmooth::smoothScatter function or just a regular scatter plot; default is FALSE, i.e. produce a regular scatter plot

lowess

logical, indicating whether or not to add a lowess curve to the MA-plot to give an indication of any trend in the log-fold change with log-concentration

...

further arguments passed on to plot

Value

• a plot to the current device (if plot.it=TRUE), and invisibly returns the M (logFC) and A (logConc) values used for the plot, plus identifiers w and v of genes for which M and {A} values, or just M values, respectively, were adjusted to make a nicer looking plot.

Details

The points to be smeared are identified as being equal to the minimum in one of the two groups. The smear is created by using random uniform numbers of width smearWidth to the left of the minimum A value.

See Also

plotSmear

Examples

y <- matrix(rnbinom(10000,mu=5,size=2),ncol=4)
maPlot(y[,1], y[,2])