| Title: | Fast K-Mer Counting and Clustering for Biological Sequence Analysis |
|---|---|
| Description: | Contains tools for rapidly computing distance matrices and clustering large sequence datasets using fast alignment-free k-mer counting and recursive k-means partitioning. See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> for a review of k-mer counting methods and applications for biological sequence analysis. |
| Authors: | Shaun Wilkinson [aut, cre] |
| Maintainer: | Shaun Wilkinson <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.2.9000 |
| Built: | 2024-11-07 04:12:45 UTC |
| Source: | https://github.com/shaunpwilkinson/kmer |
This function recursively splits a sequence set into smaller and smaller subsets, returning a "dendrogram" object.
cluster(x, k = 5, residues = NULL, gap = "-", ...)cluster(x, k = 5, residues = NULL, gap = "-", ...)
x |
a list or matrix of sequences, possibly an object of class
|
k |
integer. The k-mer size required. |
residues |
either NULL (default; emitted residues are automatically detected from the sequences), a case sensitive character vector specifying the residue alphabet, or one of the character strings "RNA", "DNA", "AA", "AMINO". Note that the default option can be slow for large lists of character vectors. Specifying the residue alphabet is therefore recommended unless the sequence list is a "DNAbin" or "AAbin" object. |
gap |
the character used to represent gaps in the alignment matrix
(if applicable). Ignored for |
... |
further arguments to be passed to |
This function creates a tree by successively splitting the dataset into smaller and smaller subsets (recursive partitioning). This is a divisive, or "top-down" approach to tree-building, as opposed to agglomerative "bottom-up" methods such as neighbor joining and UPGMA. It is particularly useful for large large datasets with many sequences (n > 10,000) since the need to compute a large n * n distance matrix is circumvented. Instead, a matrix of k-mer counts is computed, and split recursively row-wise using a k-means clustering algorithm (k = 2). This effectively reduces the time and memory complexity from quadratic to linear, while generally maintaining comparable accuracy.
If a more accurate tree is required, users can increase the value
of nstart passed to kmeans via the ... argument.
While this can increase computation time, it can improve tree accuracy
considerably.
DNA and amino acid sequences can be passed to the function either as
a list of non-aligned sequences or a matrix of aligned sequences,
preferably in the "DNAbin" or "AAbin" raw-byte format
(Paradis et al 2004, 2012; see the ape package
documentation for more information on these S3 classes).
Character sequences are supported; however ambiguity codes may
not be recognized or treated appropriately, since raw ambiguity
codes are counted according to their underlying residue frequencies
(e.g. the 5-mer "ACRGT" would contribute 0.5 to the tally for "ACAGT"
and 0.5 to that of "ACGGT").
To minimize computation time when counting longer k-mers (k > 3), amino acid sequences in the raw "AAbin" format are automatically compressed using the Dayhoff-6 alphabet as detailed in Edgar (2004). Note that amino acid sequences will not be compressed if they are supplied as a list of character vectors rather than an "AAbin" object, in which case the k-mer length should be reduced (k < 4) to avoid excessive memory use and computation time.
Returns an object of class "dendrogram".
Shaun Wilkinson
Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Research, 32, 380-385.
Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290.
Paradis E (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). Springer, New York.
## Not run: ## Cluster the woodmouse dataset (ape package) library(ape) data(woodmouse) ## trim gappy ends to subset global alignment woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ## build tree divisively suppressWarnings(RNGversion("3.5.0")) set.seed(999) woodmouse.tree <- cluster(woodmouse, nstart = 5) ## plot tree op <- par(no.readonly = TRUE) par(mar = c(5, 2, 4, 8) + 0.1) plot(woodmouse.tree, main = "Woodmouse phylogeny", horiz = TRUE) par(op) ## End(Not run)## Not run: ## Cluster the woodmouse dataset (ape package) library(ape) data(woodmouse) ## trim gappy ends to subset global alignment woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ## build tree divisively suppressWarnings(RNGversion("3.5.0")) set.seed(999) woodmouse.tree <- cluster(woodmouse, nstart = 5) ## plot tree op <- par(no.readonly = TRUE) par(mar = c(5, 2, 4, 8) + 0.1) plot(woodmouse.tree, main = "Woodmouse phylogeny", horiz = TRUE) par(op) ## End(Not run)
Count all k-letter words in a sequence or set of sequences with a sliding window of length k.
kcount(x, k = 5, residues = NULL, gap = "-", named = TRUE, compress = TRUE, encode = FALSE)kcount(x, k = 5, residues = NULL, gap = "-", named = TRUE, compress = TRUE, encode = FALSE)
x |
a matrix of aligned sequences, a list of unaligned sequences, or a vector representing a single sequence. Accepted modes are "character" and "raw" (the latter being applicable for "DNAbin" and "AAbin" objects). |
k |
integer representing the k-mer size. Defaults to 5. Note that high values of k may be slow to compute and use a lot of memory due to the large numbers of calculations required, particularly when the residue alphabet is also large. |
residues |
either NULL (default; the residue alphabet is automatically detected from the sequences), a case sensitive character vector specifying the residue alphabet, or one of the character strings "RNA", "DNA", "AA", "AMINO". Note that the default option can be slow for large lists of character vectors. Specifying the residue alphabet is therefore recommended unless x is a "DNAbin" or "AAbin" object. |
gap |
the character used to represent gaps in the alignment matrix
(if applicable). Ignored for |
named |
logical. Should the k-mers be returned as column names in the returned matrix? Defaults to TRUE. |
compress |
logical indicating whether to compress AAbin sequences using the Dayhoff(6) alphabet for k-mer sizes exceeding 4. Defaults to TRUE to avoid memory overflow and excessive computation time. |
encode |
logical indicating if the resulting matrix should be encoded
in raw bytes (output matrix can be decoded with |
This function computes a vector or matrix of k-mer counts
from a sequence or set of sequences using a sliding a window of length k.
DNA and amino acid sequences can be passed to the function either as
a list of non-aligned sequences or a matrix of aligned sequences,
preferably in the "DNAbin" or "AAbin" raw-byte format
(Paradis et al 2004, 2012; see the ape package
documentation for more information on these S3 classes).
Character sequences are supported; however ambiguity codes may
not be recognized or treated appropriately, since raw ambiguity
codes are counted according to their underlying residue frequencies
(e.g. the 5-mer "ACRGT" would contribute 0.5 to the tally for "ACAGT"
and 0.5 to that of "ACGGT").
To minimize computation time when counting longer k-mers (k > 3), amino acid sequences in the raw "AAbin" format are automatically compressed using the Dayhoff-6 alphabet as detailed in Edgar (2004). Note that amino acid sequences will not be compressed if they are supplied as a list of character vectors rather than an "AAbin" object, in which case the k-mer length should be reduced (k < 4) to avoid excessive memory use and computation time.
Returns a matrix of k-mer counts with one row for each sequence and n^k columns (where n is the size of the residue alphabet and k is the k-mer size)
Shaun Wilkinson
Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Research, 32, 380-385.
Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290.
Paradis E (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). Springer, New York.
kdistance for k-mer distance matrix computation.
## compute a matrix of k-mer counts for the woodmouse ## data (ape package) using a k-mer size of 3 library(ape) data(woodmouse) x <- kcount(woodmouse, k = 3) x ## 64 columns for nucleotide 3-mers AAA, AAC, ... TTT ## convert to AAbin object and repeat the operation y <- kcount(ape::trans(woodmouse, 2), k = 2) y ## 400 columns for amino acid 2-mers AA, AB, ... , YY## compute a matrix of k-mer counts for the woodmouse ## data (ape package) using a k-mer size of 3 library(ape) data(woodmouse) x <- kcount(woodmouse, k = 3) x ## 64 columns for nucleotide 3-mers AAA, AAC, ... TTT ## convert to AAbin object and repeat the operation y <- kcount(ape::trans(woodmouse, 2), k = 2) y ## 400 columns for amino acid 2-mers AA, AB, ... , YY
Computes the matrix of k-mer distances between all pairwise comparisons of a set of sequences.
kdistance(x, k = 5, method = "edgar", residues = NULL, gap = "-", compress = TRUE, ...)kdistance(x, k = 5, method = "edgar", residues = NULL, gap = "-", compress = TRUE, ...)
x |
a matrix of aligned sequences or a list of unaligned sequences. Accepted modes are "character" and "raw" (the latter being applicable for "DNAbin" and "AAbin" objects). |
k |
integer representing the k-mer size to be used for calculating the distance matrix. Defaults to 5. Note that high values of k may be slow to compute and use a lot of memory due to the large numbers of calculations required, particularly when the residue alphabet is also large. |
method |
a character string giving the k-mer distance measure
to be used. Currently the available options are |
residues |
either NULL (default; the residue alphabet is automatically detected from the sequences), a case sensitive character vector specifying the residue alphabet, or one of the character strings "RNA", "DNA", "AA", "AMINO". Note that the default option can be slow for large lists of character vectors. Specifying the residue alphabet is therefore recommended unless x is a "DNAbin" or "AAbin" object. |
gap |
the character used to represent gaps in the alignment matrix
(if applicable). Ignored for |
compress |
logical indicating whether to compress AAbin sequences using the Dayhoff(6) alphabet for k-mer sizes exceeding 4. Defaults to TRUE to avoid memory overflow and excessive computation time. |
... |
further arguments to be passed to |
This function computes the n * n k-mer distance matrix
(where n is the number of sequences), returning an object of class
"dist". DNA and amino acid sequences can be passed to the function
either as a list of non-aligned sequences or as a matrix of aligned sequences,
preferably in the "DNAbin" or "AAbin" raw-byte format
(Paradis et al 2004, 2012; see the ape package
documentation for more information on these S3 classes).
Character sequences are supported; however ambiguity codes may
not be recognized or treated appropriately, since raw ambiguity
codes are counted according to their underlying residue frequencies
(e.g. the 5-mer "ACRGT" would contribute 0.5 to the tally for "ACAGT"
and 0.5 to that of "ACGGT").
To minimize computation time when counting longer k-mers (k > 3), amino acid sequences in the raw "AAbin" format are automatically compressed using the Dayhoff-6 alphabet as detailed in Edgar (2004). Note that amino acid sequences will not be compressed if they are supplied as a list of character vectors rather than an "AAbin" object, in which case the k-mer length should be reduced (k < 4) to avoid excessive memory use and computation time.
an object of class "dist".
Shaun Wilkinson
Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Research, 32, 380-385.
Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290.
Paradis E (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). Springer, New York.
kcount for k-mer counting, and
mbed for leaner distance matrices
## compute a k-mer distance matrix for the woodmouse ## dataset (ape package) using a k-mer size of 5 library(ape) data(woodmouse) ### subset global alignment by removing gappy ends woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ### compute the distance matrix woodmouse.dist <- kdistance(woodmouse, k = 5) ### cluster and plot UPGMA tree woodmouse.tree <- as.dendrogram(hclust(woodmouse.dist, "average")) plot(woodmouse.tree)## compute a k-mer distance matrix for the woodmouse ## dataset (ape package) using a k-mer size of 5 library(ape) data(woodmouse) ### subset global alignment by removing gappy ends woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ### compute the distance matrix woodmouse.dist <- kdistance(woodmouse, k = 5) ### cluster and plot UPGMA tree woodmouse.tree <- as.dendrogram(hclust(woodmouse.dist, "average")) plot(woodmouse.tree)
The kmer package contains tools for rapidly computing distance matrices, building large trees, and clustering operational taxonomic units using fast alignment-free k-mer counting and divisive clustering techniques.
A breif description of the primary kmer functions are provided with links to their help pages below.
kcount counts all k-letter words in a
sequence or set of sequences using a sliding window of
length k
kdistance calculates pairwise
distances between sequences based on k-mer counts
mbed embeds sequences as vectors of
k-mer distances to a set of seed' sequences
cluster builds a phylogenetic tree by
successively splitting a set of sequences
(recursive partitioning) based on k-mer counts
otu heirarchically clusters a set of sequences
until a predefined furthest neighbor dissimilarity threshold is reached.
This function computes a matrix of distances from each sequence to a subset of 'seed' sequences using the method outlined in Blacksheilds et al (2010).
mbed(x, seeds = NULL, k = 5, residues = NULL, gap = "-", counts = FALSE)mbed(x, seeds = NULL, k = 5, residues = NULL, gap = "-", counts = FALSE)
x |
a matrix of aligned sequences or a list of unaligned sequences. Accepted modes are "character" and "raw" (the latter is for "DNAbin" and "AAbin" objects). |
seeds |
optional integer vector indicating which sequences should
be used as the seed sequences. If |
k |
integer representing the k-mer size to be used for calculating the distance matrix. Defaults to 5. Note that high values of k may be slow to compute and use a lot of memory due to the large numbers of calculations required, particularly when the residue alphabet is also large. |
residues |
either NULL (default; emitted residues are automatically detected from the sequences), a case sensitive character vector specifying the residue alphabet, or one of the character strings "RNA", "DNA", "AA", "AMINO". Note that the default option can be slow for large lists of character vectors. Specifying the residue alphabet is therefore recommended unless x is a "DNAbin" or "AAbin" object. |
gap |
the character used to represent gaps in the alignment matrix
(if applicable). Ignored for |
counts |
logical indicating whether the (usually large) matrix of k-mer counts should be returned as an attribute of the returned object. Defaults to FALSE. |
This function computes a n * log(n, 2)^2 k-mer distance matrix
(where n is the number of sequences), returning an object of class
"mbed". If the number of sequences is less than or equal to 19, the full
n * n distance matrix is produced (since the rounded up value of
log(19, 2)^2 is 19). Currently the only distance measure supported is
that of Edgar (2004).
For maximum information retention following the embedding process it is generally desirable to select the seed sequences based on their uniqueness, rather than simply selecting a random subset (Blackshields et al. 2010). Hence if 'seeds' is set to NULL (the default setting) the the 'mbed' function selects the subset by clustering the sequence set into t groups using the k-means algorithm (k = t), and choosing one representative from each group. Users can alternatively pass an integer vector (as in the above example) to specify the seeds manually. See Blackshields et al (2010) for other seed selection options.
DNA and amino acid sequences can be passed to the function
either as a list of non-aligned sequences or as a matrix of aligned sequences,
preferably in the "DNAbin" or "AAbin" raw-byte format
(Paradis et al 2004, 2012; see the ape package
documentation for more information on these S3 classes).
Character sequences are supported; however ambiguity codes may
not be recognized or treated appropriately, since raw ambiguity
codes are counted according to their underlying residue frequencies
(e.g. the 5-mer "ACRGT" would contribute 0.5 to the tally for "ACAGT"
and 0.5 to that of "ACGGT").
To minimize computation time when counting longer k-mers (k > 3), amino acid sequences in the raw "AAbin" format are automatically compressed using the Dayhoff-6 alphabet as detailed in Edgar (2004). Note that amino acid sequences will not be compressed if they are supplied as a list of character vectors rather than an "AAbin" object, in which case the k-mer length should be reduced (k < 4) to avoid excessive memory use and computation time.
Note that agglomerative (bottom-up) tree-building methods
such as neighbor-joining and UPGMA depend on a full
n * n distance matrix.
See the kdistance function for details on computing
symmetrical distance matrices.
Returns an object of class "mbed", whose primary object is
an n * log(n, 2)^2 matrix
(where n is the number of sequences). The returned
object contains additional attributes including an
integer vector of seed sequence indices ("seeds"), a logical vector
identifying the duplicated sequences ("duplicates"), an integer vector
giving the matching indices of the non-duplicated sequences ("pointers"),
a character vector of MD5 digests of the sequences ("hashes"),
an integer vector of sequence lengths ("seqlengths"), and if
counts = TRUE, the matrix of k-mer counts ("kcounts";
see kcount for details).
Shaun Wilkinson
Blackshields G, Sievers F, Shi W, Wilm A, Higgins DG (2010) Sequence embedding for fast construction of guide trees for multiple sequence alignment. Algorithms for Molecular Biology, 5, 21.
Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Research, 32, 380-385.
Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290.
Paradis E (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). Springer, New York.
kdistance for full n * n distance
matrix computation.
## compute an embedded k-mer distance matrix for the woodmouse ## dataset (ape package) using a k-mer size of 5 library(ape) data(woodmouse) ## randomly select three sequences as seeds suppressWarnings(RNGversion("3.5.0")) set.seed(999) seeds <- sample(1:15, size = 3) ## embed the woodmouse dataset in three dimensions woodmouse.mbed <- mbed(woodmouse, seeds = seeds, k = 5) ## print the distance matrix (without attributes) print(woodmouse.mbed[,], digits = 2)## compute an embedded k-mer distance matrix for the woodmouse ## dataset (ape package) using a k-mer size of 5 library(ape) data(woodmouse) ## randomly select three sequences as seeds suppressWarnings(RNGversion("3.5.0")) set.seed(999) seeds <- sample(1:15, size = 3) ## embed the woodmouse dataset in three dimensions woodmouse.mbed <- mbed(woodmouse, seeds = seeds, k = 5) ## print the distance matrix (without attributes) print(woodmouse.mbed[,], digits = 2)
This function performs divisive heirarchical clustering on a set of DNA sequences using sequential k-means partitioning, returning an integer vector of OTU membership.
otu(x, k = 5, threshold = 0.97, method = "central", residues = NULL, gap = "-", ...)otu(x, k = 5, threshold = 0.97, method = "central", residues = NULL, gap = "-", ...)
x |
a "DNAbin" object. |
k |
integer giving the k-mer size used to generate the input matrix for k-means clustering. |
threshold |
numeric between 0 and 1 giving the OTU identity cutoff. Defaults to 0.97. |
method |
the maximum distance criterion to use for terminating the recursive partitioning procedure. Accepted options are "central" (splitting stops if the similarity between the central sequence and its farthest neighbor within the cluster is greater than the threshold), "centroid" (splitting stops if the similarity between the centroid and its farthest neighbor within the cluster is greater than the threshold), and "farthest" (splitting stops if the similarity between the two farthest sequences within the cluster is greater than the threshold). Defaults to "central". |
residues |
either NULL (default; emitted residues are automatically detected from the sequences), a case sensitive character vector specifying the residue alphabet, or one of the character strings "RNA", "DNA", "AA", "AMINO". Note that the default option can be slow for large lists of character vectors. Specifying the residue alphabet is therefore recommended unless the sequence list is a "DNAbin" or "AAbin" object. |
gap |
the character used to represent gaps in the alignment matrix
(if applicable). Ignored for |
... |
further arguments to be passed to |
This function clusters sequences into OTUs by first generating a matrix of k-mer counts, and then splitting the matrix into two subsets (row-wise) using the k-means algorithm (k = 2). The splitting continues recursively until the farthest k-mer distance in every cluster is below the threshold value.
This is a divisive, or "top-down" approach to OTU clustering, as opposed to agglomerative "bottom-up" methods. It is particularly useful for large large datasets with many sequences (n > 10, 000) since the need to compute a large n * n distance matrix is circumvented. This effectively reduces the time and memory complexity from quadratic to linear, while generally maintaining comparable accuracy.
It is recommended to increase the value
of nstart passed to kmeans via the ... argument
to at least 20.
While this can increase computation time, it can improve clustering accuracy
considerably.
DNA and amino acid sequences can be passed to the function either as
a list of non-aligned sequences or a matrix of aligned sequences,
preferably in the "DNAbin" or "AAbin" raw-byte format
(Paradis et al 2004, 2012; see the ape package
documentation for more information on these S3 classes).
Character sequences are supported; however ambiguity codes may
not be recognized or treated appropriately, since raw ambiguity
codes are counted according to their underlying residue frequencies
(e.g. the 5-mer "ACRGT" would contribute 0.5 to the tally for "ACAGT"
and 0.5 to that of "ACGGT").
To minimize computation time when counting longer k-mers (k > 3), amino acid sequences in the raw "AAbin" format are automatically compressed using the Dayhoff-6 alphabet as detailed in Edgar (2004). Note that amino acid sequences will not be compressed if they are supplied as a list of character vectors rather than an "AAbin" object, in which case the k-mer length should be reduced (k < 4) to avoid excessive memory use and computation time.
a named integer vector of cluster membership with values ranging from 1 to the total number of OTUs. Asterisks indicate the representative sequence within each cluster.
Shaun Wilkinson
Edgar RC (2004) Local homology recognition and distance measures in linear time using compressed amino acid alphabets. Nucleic Acids Research, 32, 380-385.
Paradis E, Claude J, Strimmer K, (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289-290.
Paradis E (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). Springer, New York.
## Not run: ## Cluster the woodmouse dataset (from the ape package) into OTUs library(ape) data(woodmouse) ## trim gappy ends to subset global alignment woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ## cluster sequences into OTUs at 0.97 threshold with kmer size = 5 suppressWarnings(RNGversion("3.5.0")) set.seed(999) woodmouse.OTUs <- otu(woodmouse, k = 5, threshold = 0.97, nstart = 20) woodmouse.OTUs ## End(Not run)## Not run: ## Cluster the woodmouse dataset (from the ape package) into OTUs library(ape) data(woodmouse) ## trim gappy ends to subset global alignment woodmouse <- woodmouse[, apply(woodmouse, 2, function(v) !any(v == 0xf0))] ## cluster sequences into OTUs at 0.97 threshold with kmer size = 5 suppressWarnings(RNGversion("3.5.0")) set.seed(999) woodmouse.OTUs <- otu(woodmouse, k = 5, threshold = 0.97, nstart = 20) woodmouse.OTUs ## End(Not run)