In krisrs1128/mvarVis: Visualize multivariate analysis results.

The main inspiration for the mvarVis package comes from the plot_ordination command in package phyloseq. Given the result of an ordination, we should be able to plot various features onto the projected points easily. plot_ordination implements this idea in the case that the ordination has features from a phyloseq object, but the idea can be applied more generally.

`plot_ordination` analogues

To get an idea for how this package works, here are some examples re-implementing the methods in plot_ordination from the phyloseq documentation.

Loading / reshaping data

First we load the data from the linked page.

library("ape")
library("ade4")
library("phyloseq")
library("vegan")
library("mvarVis")

data("GlobalPatterns")
GP = GlobalPatterns
wh0 = genefilter_sample(GP, filterfun_sample(function(x) x > 5), A = 0.5 * nsamples(GP))
GP1 = prune_taxa(wh0, GP)
GP1 = transform_sample_counts(GP1, function(x) 1e+06 * x/sum(x))
phylum.sum = tapply(taxa_sums(GP1), tax_table(GP1)[, "Phylum"], sum, na.rm = TRUE)
top5phyla = names(sort(phylum.sum, TRUE))[1:5]
GP1 = prune_taxa((tax_table(GP1)[, "Phylum"] %in% top5phyla), GP1)
human = get_variable(GP1, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
sample_data(GP1)$human <- factor(human)

`vegan` methods

gp1_nmds <- ordi(GP1@otu_table, GP1@tax_table, GP1@sam_data, method = "metaMDS")
plot_mvar(gp1_nmds, col = "Phylum", shape = "human", layers_list = "point-text")

# DCA
gp1_dca <- ordi(GP1@otu_table, GP1@tax_table, GP1@sam_data, method = "decorana")
plot_mvar(gp1_dca, col = "Phylum", shape = "human")

`ade4` methods

Note that we can pass in ade4 arguments through ordi (as in the scannf = F below).

# MDS
gp1_mds <- ordi(GP1@otu_table, GP1@tax_table, GP1@sam_data, method = "pco",
                dist_method = "euclidean", scannf = F)
plot_mvar(gp1_mds, col = "Phylum", shape = "human")