image.DNAbin: Plot of DNA Sequence Alignement

Description

This function plots an image of an alignment of nucleotide sequences.

Usage

# S3 method for DNAbin
image(x, what, col, bg = "white", xlab = "", ylab = "",
      show.labels = TRUE, cex.lab = 1, legend = TRUE, ...)

Arguments

a matrix of DNA sequences (class "DNAbin").

what

a vector of characters specifying the bases to visualize. If missing, this is set to ``a'', ``g'', ``c'', ``t'', ``n'', and ``-'' (in this order).

col

a vector of colours. If missing, this is set to ``red'', ``yellow'', ``green'', ``blue'', ``grey'', and ``black''. If it is shorter (or longer) than what, it is recycled (or shortened).

the colour used for nucleotides whose base is not among what.

xlab

the label for the x-axis; none by default.

ylab

Idem for the y-axis. Note that by default, the labels of the sequences are printed on the y-axis (see next option).

show.labels

a logical controlling whether the sequence labels are printed (TRUE by default).

cex.lab

a single numeric controlling the size of the sequence labels. Use cex.axis to control the size of the annotations on the x-axis.

legend

a logical controlling whether the legend is plotted (TRUE by default).

…

further arguments passed to image.default (e.g., cex.axis).

Details

The idea of this function is to allow flexible plotting and colouring of a nucleotide alignment. By default, the most common bases (a, g, c, t, and n) and alignment gap are plotted using a standard colour scheme.

It is possible to plot only one base specified as what with a chosen colour: this might be useful to check, for instance, the distribution of alignment gaps (image(x, "-")) or missing data (see examples).

Examples

Run this code

# NOT RUN {
data(woodmouse)
image(woodmouse)
rug(seg.sites(woodmouse), -0.02, 3, 1)
image(woodmouse, "n", "blue") # show missing data
image(woodmouse, c("g", "c"), "green") # G+C
par(mfcol = c(2, 2))
### barcoding style:
for (x in c("a", "g", "c", "t"))
    image(woodmouse, x, "black", cex.lab = 0.5, cex.axis = 0.7)
par(mfcol = c(1, 1))
### zoom on a portion of the data:
image(woodmouse[11:15, 1:50], c("a", "n"), c("blue", "grey"))
grid(50, 5, col = "black")
### see the guanines on a black background:
image(woodmouse, "g", "yellow", "black")
# }

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