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 as the cophenetic/patristic (cophenetic.phylo) distance between the species.

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 distance object, and return a new distance object that is Euclidean. If testing a distance object, try is.euclid.

In most real-life, real-data applications, the phylogenetic tree will not provide a Euclidean distance matrix, and so a correction will be needed. Two recommended correction methods are cailliez and lingoes. The default is cailliez, but not for any particularly special reason. If the patristic distance matrix turns out to be 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).

Details

In most real-life, real-data applications, the phylogenetic tree will not provide a Euclidean distance matrix, and so a correction will be performed, if needed. See correction argument.

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

Run this code

# # # # # # 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")

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