Determine if TRFLP profiles may match those in a database of knowns. The resulting object can be used to produce a presence/absence matrix of known profiles in environmental samples.
The TRAMPR package contains a vignette, which includes a worked
example; type vignette("TRAMPRdemo") to view it.
TRAMP(samples, knowns, accept.error=1.5, min.comb=4, method="maximum")A TRAMPsamples object, containing
unidentified samples.
A TRAMPknowns object, containing
identified TRFLP patterns.
The largest acceptable difference (in base pairs)
between any peak in the sample data and the knowns database (see
Details; interpretation will depend on the value of method).
Minimum number of enzyme/primer combinations required
before presence will be tested. The default (4) should be
reasonable in most cases. Setting min.comb to NA will
require that all enzyme/primer combinations in the knowns database
are present in the samples.
Method used in calculating the difference between
samples and knowns; may be one of "maximum",
"euclidian" or "manhattan" (or any unambiguous
abbreviation).
A TRAMP object, with elements:
Presence/absence matrix. Rows are different samples
(with rownames from labels(samples)) and columns are
different knowns (with colnames from labels(knowns)). Do not
access the presence/absence matrix directly, but use
summary.TRAMP, which provides options for labelling
knowns, grouping knowns, and excluding “ignored” matches.
Matrix of distances between the samples and known,
calculated by one of the methods described above. Rows correspond
to different samples, and columns correspond to different knowns.
The matrix dimension names are set to the values sample.pk
and knowns.pk for the samples and knowns, respectively.
A two-dimensional matrix (same dimensions as error),
recording the number of enzyme/primer combinations present for each
combination of samples and knowns.
Three-dimensional array of output from
create.diffsmatrix.
Different enzyme/primer combinations present in
the data, in the order of the third dimension of diffsmatrix
(see create.diffsmatrix for details).
The input data objects and arguments, unmodified.
In addition, an element presence.ign is included to allow matches to be ignored. However, this interface is experimental and its current format should not be relied on - use remove.TRAMP.match rather than interacting directly with presence.ign.
Matching is based only on peak size (in base pairs), and does not consider peak heights.
TRAMP attempts to determine which species in the
‘knowns’ database may be present in a collection of
samples.
A sample matches a known if it has a peak that is “close
enough” to every peak in the known for every enzyme/primer
combination that they share. The default is to accept matches where
the largest distance between a peak in the knowns database and the
sample is less than accept.error base pairs (default 2), and
where at least min.comb enzyme/primer combinations are shared
between a sample and a known (default 4).
The three-dimensional matrix of match errors is generated by
create.diffsmatrix. In the resulting array,
m[i,j,k] is the difference (in base pairs) between the
ith sample and the jth known for the kth
enzyme/primer combination.
If \(p_k\) and \(q_k\) are the sizes of peaks for the \(k\)th enzyme/primer combination for a sample and known (respectively), then maximum distance is defined as
$$\max(|p_k - q_k|)$$
Euclidian distance is defined as $$\frac{1}{n}\sqrt{\sum (p_k - q_k)^2}$$
and Manhattan distance is defined as $$\frac{1}{n}\sum{|p_k - q_k|}$$
where \(n\) is the number of shared enzyme/primer combinations,
since this may vary across sample/known combinations. For Euclidian
and Manhattan distances, accept.error then becomes the
mean distance, rather than the total distance.
See create.diffsmatrix for discussion of how differences
between sample and known profiles are generated.
plot.TRAMP, which displays TRAMP fits graphically.
summary.TRAMP, which creates a presence/absence matrix.
remove.TRAMP.match, which marks TRAMP matches as
ignored.
# NOT RUN {
data(demo.knowns)
data(demo.samples)
res <- TRAMP(demo.samples, demo.knowns)
## The resulting object can be interrogated with methods:
## The goodness of fit of the sample with sample.pk=101 (see
## ?\link{plot.TRAMP}).
plot(res, 101)
# }
# NOT RUN {
## To see all plots (this produces many figures), one after another.
op <- par(ask=TRUE)
plot(res)
par(op)
# }
# NOT RUN {
## Produce a presence/absence matrix (see ?\link{summary.TRAMP}).
m <- summary(res)
head(m)
# }
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