Description Objects from the Class Slots Extends Methods Warning Author(s) See Also Examples

This is a small extension of a matrix representation of the
sequences. The sequences are represented as integers, where each integer
corresponds to a character type from the alphabet. For example, if the
sequence is ADC, and the alphabet is `['A', 'B', 'C', 'D']`

, the
sequence will be `[1,4,3]`

. The matrix itself has each sequence
as a row, and the alphabet slot contains the key that shows how the
integers correspond to the characters in the sequence.

Objects can be created by calls of the form ```
new("Sequences", data,
nrow, ncol, byrow, dimnames, ...)
```

.

`.Data`

:Object of class

`"matrix"`

The sequences, where each row is a sequence and the sequence is a series of integers`alphabet`

:Object of class

`"character"`

The character representation of each integer`nseqs`

:The number of unique sequences in the class.

Class `"matrix"`

, from data part.
Class `"array"`

, by class "matrix", distance 2.
Class `"structure"`

, by class "matrix", distance 3.
Class `"vector"`

, by class "matrix", distance 4, with explicit coerce.

- dist
`dist(seqs, method="substitution", params=default.MetricParams,...)`

: dist calculates the sequence-sequences distance matrix. Use`method="substitution"`

to use a substitution matrix for weighting sequence mutations and use`method="hamming"`

to use equal weighting of all mutations. Also accepts`params=aMetricParams`

for using a substitution matrix other than the default. Each substitution is given a weight of 1 using the hamming method or the score from the corresponding substitution matrix when using the substitution method. The distance matrix is converted to a dissimilarity distance by making all elements negative and adding the maximum score/weight- plot
`plot(seqs, clusterNumber=4, params=default.MetricParams, distanceMatrix=dist.Sequences(seqs, params=params), clusters=aclust(dmat, clusterNumber))`

: This method plots a summary plot of the sequences. Each point represents a sequence and the points plotted on a projection onto their two principal components as found from the distance matrix. Additionally, they are colored and placed into clusters using the given cluster number and the`kmeans`

algorithm found in the`stats`

package. This method provides a quick way of estimating the number of clusters in the sequences and looking for any simple patterns in the data. It also can be used to test different substitution matrices to see which best segregates data. For example, a BLOSUM90 substitution matrix may work well for very similar sequences, whereas a BLOSUM50 substitution matrix will work better for very different sequences. The distance matrix may be specified and the clusters as well.- rbind
`rbind(seq1, seq2)`

: This method just overwrites the traditional rbind method by passing the alphabet along. Note that most matrix methods editing methods do not return a Sequence class by default, except this rbind method.

The gap character is always assumed to be the last character in the
sequence slot. Do not change this convention, since the distance
method relies on this. Not all data.frame manipulations methods have
been overridden. Thus, you may get a data.frame back instead of a
Sequences object. Only `rbind`

and array access has been overridden.

Andrew White

`read.sequences`

, which allows you to create Sequence objects from
a file, `descriptors`

, which creates a
`Descriptors`

object for a Sequences object.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
##load example data and plot it
data(TULASequences)
plot(TULASequences)
## Access all sequences which have a 4 in position 1
print(TULASequences[TULASequences[,1] == 4,])
## Access all sequences which have an tyrosine residue in position 1 and
## cluster
TULASequences.subset <- TULASequences[TULASequences[,1] == which(TULASequences@alphabet == 'Y'),]
plot(TULASequences.subset)
##Calculate distance matrix on this subset and use agglomerative
## clustering to plotit
TULA.dmatrix <- dist(TULASequences.subset)
TULA.hclusters <- hclust(TULA.dmatrix)
plot(TULA.hclusters)
``` |

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