Cluster Sequences By Distance or Sequence
Groups the sequences represented by a distance matrix into clusters of similarity.
1 2 3 4 5 6 7 8 9 10
A symmetric N x N distance matrix with the values of dissimilarity between N sequences, or
An agglomeration method to be used. This should be (an abbreviation of) one of
A vector with the maximum edge length separating the sequences in the same cluster. Multiple cutoffs may be provided in ascending or descending order. (See details section below.)
Logical specifying whether or not to plot the resulting dendrogram. Not applicable if
Character string indicating the type of output desired. This should be (an abbreviation of) one of
Numeric controlling which edges of the tree are removed by collapsing their nodes. If
One or more of the available
The number of processors to use, or
Logical indicating whether to display progress.
IdClusters groups the input sequences into clusters using a set dissimilarities representing the distance between N sequences. Initially a phylogenetic tree is formed using the specified
method. Then each leaf (sequence) of the tree is assigned to a cluster based on its edge lengths to the other sequences. The available clustering methods are described as follows:
Ultrametric methods: The method
complete assigns clusters using complete-linkage so that sequences in the same cluster are no more than
cutoff percent apart. The method
single assigns clusters using single-linkage so that sequences in the same cluster are within
cutoff of at least one other sequence in the same cluster.
UPGMA (the default) or
WPGMA assign clusters using average-linkage which is a compromise between the sensitivity of complete-linkage clustering to outliers and the tendency of single-linkage clustering to connect distant relatives that do not appear to be closely related.
UPGMA produces an unweighted tree, where each leaf contributes equally to the average edge lengths, whereas
WPGMA produces a weighted result.
NJ uses the Neighbor-Joining method proposed by Saitou and Nei that does not assume lineages evolve at the same rate (the molecular clock hypothesis). The
NJ method is typically the most phylogenetically accurate of the above distance-based methods.
ML creates a neighbor-joining tree and then iteratively maximizes the likelihood of the tree given the aligned sequences (
myXStringSet). This is accomplished through a combination of optimizing edge lengths with Brent's method and improving tree topology with nearest-neighbor interchanges (NNIs). When
method="ML", one or more
MODELS of DNA evolution must be specified. Model parameters are iteratively optimized to maximize likelihood, except base frequencies which are empirically determined. If multiple
models are given, the best
model is automatically chosen based on BIC calculated from the likelihood and the sample size (defined as the number of variable sites in the DNA sequence).
inexact uses a heuristic algorithm to directly assign sequences to clusters without a distance matrix. First the sequences are ordered by length and the longest sequence becomes the first cluster seed. If the second sequence is less than
cutoff percent distance then it is added to the cluster, otherwise it becomes a new cluster representative. The remaining sequences are matched to cluster representatives based on their k-mer distribution and then aligned to find the closest sequence. This approach is repeated until all sequences belong to a cluster. In the vast majority of cases, this process results in clusters with members separated by less than
cutoff distance, where distance is defined as the percent dissimilarity between the overlapping region of a “glocal” alignment.
Multiple cutoffs may be provided if they are in increasing or decreasing order. If
cutoffs are provided in descending order then clustering at each new value of
cutoff is continued within the prior
cutoff's clusters. In this way clusters at lower values of
cutoff are completely contained within their umbrella clusters at higher values of
cutoff. This is useful for defining taxonomy, where lower level groups (e.g., genera) are expected not to straddle multiple higher level groups (e.g., families). If multiple cutoffs are provided in ascending order then clustering at each level of
cutoff is independent of the prior level. This may result in fewer high-level clusters for
ML methods, but will have no impact on ultrametric methods. Providing
cutoffs in descending order makes
inexact clustering faster, but has negligible impact on the other
"clusters" (the default), then a data.frame is returned with a column for each cutoff specified. This data.frame has dimensions N*M, where each one of N sequences is assigned to a cluster at the M-level of cutoff. The row.names of the data.frame correspond to the dimnames of
"dendrogram", then an object of class
dendrogram is returned that can be used for plotting. Leaves of the dendrogram are colored by cluster number.
"both" then a list is returned containing both the
Erik Wright DECIPHER@cae.wisc.edu
Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution, 17(6), 368-376.
Ghodsi, M., Liu, B., & Pop, M. (2011) DNACLUST. BMC Bioinformatics, 12(1), 271. doi:10.1186/1471-2105-12-271.
Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4(4), 406-425.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
# using the matrix from the original paper by Saitou and Nei m <- matrix(0,8,8) m[2:8,1] <- c(7, 8, 11, 13, 16, 13, 17) m[3:8,2] <- c(5, 8, 10, 13, 10, 14) m[4:8,3] <- c(5, 7, 10, 7, 11) m[5:8,4] <- c(8, 11, 8, 12) m[6:8,5] <- c(5, 6, 10) m[7:8,6] <- c(9, 13) m[8,7] <- c(8) # returns an object of class "dendrogram" tree <- IdClusters(m, cutoff=10, method="NJ", showPlot=TRUE, type="dendrogram") # example of specifying multiple cutoffs clusters <- IdClusters(m, cutoff=c(2,6,10,20)) # returns a data frame head(clusters) # example of 'inexact' clustering fas <- system.file("extdata", "50S_ribosomal_protein_L2.fas", package="DECIPHER") dna <- readDNAStringSet(fas) IdClusters(myXStringSet=dna, method="inexact", cutoff=0.05)
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.