phyloseq: DPCoA – R documentation

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DPCoA

Calculate Double Principle Coordinate Analysis (DPCoA) using phylogenetic distance

Description

Function uses abundance (otu_table-class) and phylogenetic (phylo) components of a phyloseq-class experiment-level object to perform a Double Principle Coordinate Analysis (DPCoA), relying heavily on the underlying (and more general) function, dpcoa. The distance object ultimately provided is the square root of the cophenetic/patristic (cophenetic.phylo) distance between the species, which is always Euclidean.

Although this distance is Euclidean, for numerical reasons it will sometimes look non-Euclidean, and a correction will be performed. See correction argument.

Usage

DPCoA(physeq, correction = cailliez, scannf = FALSE, ...)

Arguments

`physeq`	(Required). A `phyloseq-class` object containing, at a minimum, abundance (`otu_table-class`) and phylogenetic (`phylo`) components. As a test, the accessors `otu_table` and `phy_tree` should return an object without error.
`correction`	(Optional). A function. The function must be able to take a non-Euclidean `dist`ance object, and return a new `dist`ance object that is Euclidean. If testing a distance object, try `is.euclid`. Although the distance matrix should always be Euclidean, for numerical reasons it will sometimes appear non-Euclidean and a correction method must be applied. Two recommended correction methods are `cailliez` and `lingoes`. The default is `cailliez`, but not for any particularly special reason. If the distance matrix is Euclidian, no correction will be performed, regardless of the value of the `correction` argument.
`scannf`	(Optional). Logical. Default is `FALSE`. This is passed directly to `dpcoa`, and causes a barplot of eigenvalues to be created if `TRUE`. This is not included in `...` because the default for `dpcoa` is `TRUE`, although in many expected situations we would want to suppress creating the barplot.
`...`	Additional arguments passed to `dpcoa`.

Value

A dpcoa-class object (see dpcoa).

Author(s)

Julia Fukuyama julia.fukuyama@gmail.com. Adapted for phyloseq by Paul J. McMurdie.

References

Pavoine, S., Dufour, A.B. and Chessel, D. (2004) From dissimilarities among species to dissimilarities among communities: a double principal coordinate analysis. Journal of Theoretical Biology, 228, 523-537.

Examples

# # # # # # Esophagus
data(esophagus)
eso.dpcoa <- DPCoA(esophagus)
eso.dpcoa
plot_ordination(esophagus, eso.dpcoa, "samples")
plot_ordination(esophagus, eso.dpcoa, "species")
plot_ordination(esophagus, eso.dpcoa, "biplot")
#
#
# # # # # # GlobalPatterns
data(GlobalPatterns)
# subset GP to top-150 taxa (to save computation time in example)
keepTaxa <- names(sort(taxa_sums(GlobalPatterns), TRUE)[1:150])
GP       <- prune_taxa(keepTaxa, GlobalPatterns)
# Perform DPCoA
GP.dpcoa <- DPCoA(GP)
plot_ordination(GP, GP.dpcoa, color="SampleType")

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