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tip_glom

Agglomerate closely-related taxa using single-linkage clustering.


Description

All tips of the tree separated by a cophenetic distance smaller than h will be agglomerated into one taxa using merge_taxa.

Usage

tip_glom(physeq, h = 0.2, hcfun = agnes, ...)

Arguments

physeq

(Required). A phyloseq-class, containing a phylogenetic tree. Alternatively, a phylogenetic tree phylo will also work.

h

(Optional). Numeric scalar of the height where the tree should be cut. This refers to the tree resulting from hierarchical clustering of cophenetic.phylo(phy_tree(physeq)), not necessarily the original phylogenetic tree, phy_tree(physeq). Default value is 0.2. Note that this argument used to be named speciationMinLength, before this function/method was rewritten.

hcfun

(Optional). A function. The (agglomerative, hierarchical) clustering function to use. Good examples are agnes and hclust. The default is agnes.

...

(Optional). Additional named arguments to pass to hcfun.

Details

Can be used to create a non-trivial OTU Table, if a phylogenetic tree is available.

For now, a simple, “greedy”, single-linkage clustering is used. In future releases it should be possible to specify different clustering approaches available in R, in particular, complete-linkage clustering appears to be used more commonly for OTU clustering applications.

Value

An instance of the phyloseq-class. Or alternatively, a phylo object if the physeq argument was just a tree. In the expected-use case, the number of OTUs will be fewer (see ntaxa), after merging OTUs that are related enough to be called the same OTU.

See Also

Examples

data("esophagus")
# for speed
esophagus = prune_taxa(taxa_names(esophagus)[1:25], esophagus)
plot_tree(esophagus, label.tips="taxa_names", size="abundance", title="Before tip_glom()")
plot_tree(tip_glom(esophagus, h=0.2), label.tips="taxa_names", size="abundance", title="After tip_glom()")

phyloseq

Handling and analysis of high-throughput microbiome census data

v1.34.0
AGPL-3
Authors
Paul J. McMurdie <joey711@gmail.com>, Susan Holmes <susan@stat.stanford.edu>, with contributions from Gregory Jordan and Scott Chamberlain
Initial release
2019-04-23

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