mjn: Median-Joining Network

Description

This function computes the median-joining network (MJN) as described by Bandelt et al. (1999).

Usage

mjn(x, epsilon = 0, max.n.cost = 10000, prefix = "median.vector_",
    quiet = FALSE)
# S3 method for mjn
plot(x, shape = c("circles", "diamonds"),
      bg = c("green", "slategrey"), labels = FALSE, ...)

Arguments

a matrix (or data frame) of DNA sequences or binary 0/1 data; an object of class "mjn" for plot.

epsilon

tolerance parameter.

max.n.cost

the maximum number of costs to be computed.

prefix

the prefix used to label the median vectors.

quiet

a logical value; by default, the progress of the calculatins is printed.

shape, bg

the default shapes and colours for observed haplotypes and median vectors.

labels

by default, the labels of the haplotypes are printed.

…

other arguments passed to plot.haploNet.

Value

an object of class c("mjn", "haploNet") with an extra attribute (data) containing the original data together with the median vectors.

Details

MJN is a network method where unobserved sequences (the median vectors) are reconstructed and included in the final network. Unlike mst, rmst, and msn, mjn works with the original sequences, the distances being calculated internally using a Hamming distance method (with dist(x, "manhattan") for binary data or dist.dna(x, "N") for DNA sequences).

The parameter epsilon controls how the search for new median vectors is performed: the larger this parameter, the wider the search (see the example with binary data).

If the sequences are very divergent, the search for new median vectors can take a very long time. The argument max.n.cost controls how many such vectors are added to the network (the default value should avoid the function to run endlessly).

References

Bandelt, H. J., Forster, P. and Rohl, A. (1999) Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution, 16, 37--48.

Examples

Run this code

# NOT RUN {
## data in Table 1 of Bandelt et al. (1999):
x <- c(0, 0, 0, 0, 0, 0, 0, 0, 0,
       1, 1, 1, 1, 0, 0, 0, 0, 0,
       1, 0, 0, 0, 1, 1, 1, 0, 0,
       0, 1, 1, 1, 1, 1, 0, 1, 1)
x <- matrix(x, 4, 9, byrow = TRUE)
rownames(x) <- LETTERS[1:4]
(nt0 <- mjn(x))
(nt1 <- mjn(x, 1))
(nt2 <- mjn(x, 2))
plot(nt0)

# }
# NOT RUN {
## same like in Fig. 4 of Bandelt et al. (1999):
plotNetMDS(nt2, dist(attr(nt2, "data"), "manhattan"), 3)
# }
# NOT RUN {
## data in Table 2 of Bandelt et al. (1999):
z <- list(c("g", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a"),
          c("a", "g", "g", "a", "a", "a", "a", "a", "a", "a", "a", "a"),
          c("a", "a", "a", "g", "a", "a", "a", "a", "a", "a", "g", "g"),
          c("a", "a", "a", "a", "g", "g", "a", "a", "a", "a", "g", "g"),
          c("a", "a", "a", "a", "a", "a", "a", "a", "g", "g", "c", "c"),
          c("a", "a", "a", "a", "a", "a", "g", "g", "g", "g", "a", "a"))
names(z) <- c("A1", "A2", "B1", "B2", "C", "D")
z <- as.matrix(as.DNAbin(z))
(ntz <- mjn(z, 2))

# }
# NOT RUN {
## same like in Fig. 5 of Bandelt et al. (1999):
plotNetMDS(ntz, dist.dna(attr(ntz, "data"), "N"), 3)
# }

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