Ordination

library("BiocStyle")
library("knitr")
library("rmarkdown")
library("phyloseq"); packageVersion("phyloseq")

[1] ‘1.32.0’

data(GlobalPatterns)
library("ggplot2"); packageVersion("ggplot2")

[1] ‘3.3.2’

library("plyr"); packageVersion("plyr")

[1] ‘1.8.6’

theme_set(theme_bw())
opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
               cache = FALSE, fig.width = 8, fig.height = 5)
#Remove OTUs that do not show appear more than 5 times in more than half the samples

GP = GlobalPatterns
wh0 = genefilter_sample(GP, filterfun_sample(function(x) x > 5), A=0.5*nsamples(GP))
GP1 = prune_taxa(wh0, GP)

#Transform to even sampling depth.

GP1 = transform_sample_counts(GP1, function(x) 1E6 * x/sum(x))

#Keep only the most abundant five phyla.

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)
# Define a human-associated versus non-human categorical variable:
human = get_variable(GP1, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
sample_data(GP1)$human <- factor(human)

Four main ordination plots

Just OTUs

GP.ord <- ordinate(GP1, "NMDS", "bray")
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468 
## Run 1 stress 0.171623 
## Run 2 stress 0.1871687 
## Run 3 stress 0.1570722 
## Run 4 stress 0.1333468 
## ... Procrustes: rmse 4.578937e-06  max resid 1.330355e-05 
## ... Similar to previous best
## Run 5 stress 0.1702603 
## Run 6 stress 0.3181951 
## Run 7 stress 0.1448829 
## Run 8 stress 0.3589772 
## Run 9 stress 0.1333468 
## ... Procrustes: rmse 7.038866e-06  max resid 2.098435e-05 
## ... Similar to previous best
## Run 10 stress 0.1610141 
## Run 11 stress 0.1450956 
## Run 12 stress 0.1333468 
## ... New best solution
## ... Procrustes: rmse 1.595709e-06  max resid 4.013971e-06 
## ... Similar to previous best
## Run 13 stress 0.144883 
## Run 14 stress 0.1815173 
## Run 15 stress 0.168397 
## Run 16 stress 0.2839613 
## Run 17 stress 0.1510167 
## Run 18 stress 0.1622613 
## Run 19 stress 0.1535666 
## Run 20 stress 0.1675647 
## *** Solution reached
p1 = plot_ordination(GP1, GP.ord, type="taxa", color="Phylum", title="taxa")
print(p1)

p1 + facet_wrap(~Phylum, 3)

Just samples

p2 = plot_ordination(GP1, GP.ord, type="samples", color="SampleType", shape="human") 
p2 + geom_polygon(aes(fill=SampleType)) + geom_point(size=5) + ggtitle("samples")

biplot graphic

p3 = plot_ordination(GP1, GP.ord, type="biplot", color="SampleType", shape="Phylum", title="biplot")
# Some stuff to modify the automatic shape scale
GP1.shape.names = get_taxa_unique(GP1, "Phylum")
GP1.shape <- 15:(15 + length(GP1.shape.names) - 1)
names(GP1.shape) <- GP1.shape.names
GP1.shape["samples"] <- 16
p3 + scale_shape_manual(values=GP1.shape)

split graphic

p4 = plot_ordination(GP1, GP.ord, type="split", color="Phylum", shape="human", label="SampleType", title="split") 
p4

gg_color_hue <- function(n){
    hues = seq(15, 375, length=n+1)
    hcl(h=hues, l=65, c=100)[1:n]
}
color.names <- levels(p4$data$Phylum)
p4cols <- gg_color_hue(length(color.names))
names(p4cols) <- color.names
p4cols["samples"] <- "black"
p4 + scale_color_manual(values=p4cols)

@# Supported Ordination Methods

dist = "bray"
ord_meths = c("DCA", "CCA", "RDA", "DPCoA", "NMDS", "MDS", "PCoA")
plist = llply(as.list(ord_meths), function(i, physeq, dist){
        ordi = ordinate(physeq, method=i, distance=dist)
        plot_ordination(physeq, ordi, "samples", color="SampleType")
}, GP1, dist)
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1333468 
## Run 1 stress 0.1385323 
## Run 2 stress 0.1630808 
## Run 3 stress 0.1666795 
## Run 4 stress 0.1530178 
## Run 5 stress 0.3819611 
## Run 6 stress 0.1576439 
## Run 7 stress 0.1333468 
## ... Procrustes: rmse 7.349806e-06  max resid 2.180866e-05 
## ... Similar to previous best
## Run 8 stress 0.1385323 
## Run 9 stress 0.1469145 
## Run 10 stress 0.159001 
## Run 11 stress 0.1385323 
## Run 12 stress 0.1333468 
## ... New best solution
## ... Procrustes: rmse 2.403081e-06  max resid 6.886338e-06 
## ... Similar to previous best
## Run 13 stress 0.1333468 
## ... Procrustes: rmse 4.618725e-06  max resid 1.381285e-05 
## ... Similar to previous best
## Run 14 stress 0.254662 
## Run 15 stress 0.1333468 
## ... Procrustes: rmse 4.785134e-06  max resid 1.436663e-05 
## ... Similar to previous best
## Run 16 stress 0.1333468 
## ... Procrustes: rmse 5.228518e-06  max resid 1.556762e-05 
## ... Similar to previous best
## Run 17 stress 0.1502708 
## Run 18 stress 0.167948 
## Run 19 stress 0.189785 
## Run 20 stress 0.1333468 
## ... Procrustes: rmse 2.606267e-06  max resid 7.795527e-06 
## ... Similar to previous best
## *** Solution reached
names(plist) <- ord_meths
pdataframe = ldply(plist, function(x){
    df = x$data[, 1:2]
    colnames(df) = c("Axis_1", "Axis_2")
    return(cbind(df, x$data))
})
names(pdataframe)[1] = "method"
p = ggplot(pdataframe, aes(Axis_1, Axis_2, color=SampleType, shape=human, fill=SampleType))
p = p + geom_point(size=4) + geom_polygon()
p = p + facet_wrap(~method, scales="free")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + scale_colour_brewer(type="qual", palette="Set1")
p

plist[[2]] 

p = plist[[2]] + scale_colour_brewer(type="qual", palette="Set1")
p = p + scale_fill_brewer(type="qual", palette="Set1")
p = p + geom_point(size=5) + geom_polygon(aes(fill=SampleType))
p

MDS (“PCoA”) on Unifrac Distances

ordu = ordinate(GP1, "PCoA", "unifrac", weighted=TRUE)
plot_ordination(GP1, ordu, color="SampleType", shape="human")

p = plot_ordination(GP1, ordu, color="SampleType", shape="human")
p = p + geom_point(size=7, alpha=0.75)
p = p + scale_colour_brewer(type="qual", palette="Set1")
p + ggtitle("MDS/PCoA on weighted-UniFrac distance, GlobalPatterns")

rm(list=ls())

Alpha diversity graphics

data("GlobalPatterns")
theme_set(theme_bw())
pal = "Set1"
scale_colour_discrete <-  function(palname=pal, ...){
  scale_colour_brewer(palette=palname, ...)
}
scale_fill_discrete <-  function(palname=pal, ...){
  scale_fill_brewer(palette=palname, ...)
}

Prepare data

GP <- prune_species(speciesSums(GlobalPatterns) > 0, GlobalPatterns)

Plot Examples

plot_richness(GP)

plot_richness(GP, measures=c("Chao1", "Shannon"))

plot_richness(GP, x="SampleType", measures=c("Chao1", "Shannon"))

sampleData(GP)$human <- getVariable(GP, "SampleType") %in% c("Feces", "Mock", "Skin", "Tongue")
plot_richness(GP, x="human", color="SampleType", measures=c("Chao1", "Shannon"))

GPst = merge_samples(GP, "SampleType")
# repair variables that were damaged during merge (coerced to numeric)
sample_data(GPst)$SampleType <- factor(sample_names(GPst))
sample_data(GPst)$human <- as.logical(sample_data(GPst)$human)
p = plot_richness(GPst, x="human", color="SampleType", measures=c("Chao1", "Shannon"))
p + geom_point(size=5, alpha=0.7)

rm(list=ls())

Heatmap Plots

theme_set(theme_bw())
data("GlobalPatterns")
gpt <- subset_taxa(GlobalPatterns, Kingdom=="Bacteria")
gpt <- prune_taxa(names(sort(taxa_sums(gpt),TRUE)[1:300]), gpt)
plot_heatmap(gpt, sample.label="SampleType")

Subset a smaller dataset based on an Archaeal phylum

gpac <- subset_taxa(GlobalPatterns, Phylum=="Crenarchaeota")
plot_heatmap(gpac)

Re-label by a sample variable and taxonomic family

(p <- plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family"))

Re-label axis titles

p$scales$scales[[1]]$name <- "My X-Axis"
p$scales$scales[[2]]$name <- "My Y-Axis"
print(p)

Now repeat the plot, but change the color scheme.

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#CCFF66")

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#FF3300")

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#000033", high="#66CCFF")

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#66CCFF", high="#000033", na.value="white")

plot_heatmap(gpac, "NMDS", "bray", "SampleType", "Family", low="#FFFFCC", high="#000033", na.value="white")

Now try different ordination methods, distances

plot_heatmap(gpac, "NMDS", "jaccard")

plot_heatmap(gpac, "DCA", "none", "SampleType", "Family")

Unconstrained redundancy analysis (Principle Components Analysis, PCA)

plot_heatmap(gpac, "RDA", "none", "SampleType", "Family")

PCoA/MDS ordination on the (default) bray-curtis distance.

plot_heatmap(gpac, "PCoA", "bray", "SampleType", "Family")

MDS/PCoA ordination on the Unweighted-UniFrac distance.

plot_heatmap(gpac, "PCoA", "unifrac", "SampleType", "Family")

Now try weighted-UniFrac distance and MDS/PCoA ordination.

plot_heatmap(gpac, "MDS", "unifrac", "SampleType", "Family", weighted=TRUE)

heatmap(otu_table(gpac))

rm(list=ls())

Plot Microbiome Network

data(enterotype)
set.seed(711L)
plot_net(enterotype, maxdist = 0.4, point_label = "Sample_ID")

plot_net(enterotype, maxdist = 0.3, color = "SeqTech", shape="Enterotype")

# Create an igraph-based network based on the default distance method, “Jaccard”, and a maximum distance between connected nodes of 0.3.

ig <- make_network(enterotype, max.dist=0.3)

# Now plot this network representation with the default settings.

plot_network(ig, enterotype)

plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)

ig <- make_network(enterotype, max.dist=0.2)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)

ig <- make_network(enterotype, dist.fun="bray", max.dist=0.3)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.4, label=NULL)