These functions create and interact with
TRAMPknowns objects (collections of known TRFLP
patterns). Knowns contrast with “samples” (see
TRAMPsamples) in that knowns contain identified
profiles, while samples contain unidentified profiles. Knows must
have at most one peak per enzyme/primer combination (see Details).
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data.frame containing peak information.
data.frame, describing individual samples (see Details for definitions of both data.frames).
Parameters used when clustering the knowns database. See Details.
Optional partial filename in which to store knowns
database after modification. Files
Logical: Should a warning be given if any columns
Logical: Should the output be augmented with the
contents of the
The object has at least two components, which relate to each other (in
the sense of a relational database).
info holds information
about the individual samples, and
data holds information about
individual peaks (many of which may belong to a single sample).
Unique positive integer, used to identify individual knowns (i.e. a “primary key”).
Character, giving species name.
Positive integer, indicating which sample
the peak belongs to (by matching against
(i.e. a “foreign key”).
Character, giving the name of the primer used.
Character, giving the name of the restriction digest enzyme used.
Numeric, giving size (in base pairs) of the peak.
TRAMPknowns will create additional columns holding
clustering information (see
columns are allowed (and retained, but ignored) in both data.frames.
Additional objects are allowed as part of the
object, but these will not be written by
write.TRAMPknowns; any extra objects passed (via
...) will be included in the final
cluster.pars argument controls how knowns will be clustered
(this will happen automatically as needed). Elements of the list
cluster.pars may be any of the three arguments to
group.knowns, and will be used as defaults in
subsequent calls to
group.knowns. If not provided, default
cut.height=2.5 (if only some
cluster.pars are provided, the remaining elements
default to the values above). To change values of clustering
parameters in an existing
TRAMPknowns object, use
A known contains at most one peak per enzyme/primer combination.
Where a species is known to have multiple TRFLP profiles, these should
be treated as separate knowns with different, unique,
values, but with identical
species values. A sample containing
either pattern will then be recorded as having that species present
A sorted vector of the unique samples
A data.frame, with the size of the peak (if
present) for each enzyme/primer combination, with each known
TRAMPknowns object, primer and enzyme names must be
exactly the same (including case and whitespace) to be
considered the same. For example
"ITS 4" and
"ITS4 " would be considered to be four
Factors will not merge correctly (with
TRAMPknowns will attempt to catch factor columns and convert
them into characters for the
Other objects (passed as part of
...) will not be altered.
TRAMPsamples, which constructs an analagous object to
hold “samples” data.
plot.TRAMPknowns, which creates a graphical
representation of the knowns data.
group.knowns, which groups similar knowns (generally
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## This example builds a TRAMPknowns object from completely artificial ## data: ## The info data.frame: knowns.info <- data.frame(knowns.pk=1:8, species=rep(paste("Species", letters[1:5]), length=8)) knowns.info ## The data data.frame: knowns.data <- expand.grid(knowns.fk=1:8, primer=c("ITS1F", "ITS4"), enzyme=c("BsuRI", "HpyCH4IV")) knowns.data$size <- runif(nrow(knowns.data), min=40, max=800) ## Construct the TRAMPknowns object: demo.knowns <- TRAMPknowns(knowns.data, knowns.info, warn.factors=FALSE) ## A plot of the pretend knowns: plot(demo.knowns, cex=1, group.clusters=TRUE)
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