Alpha diversity
Subset to the relevant datasets
V13r <- subset_samples(V13,Exp.beadbeating == "YES" & Beadbeating == "160s6ms") %>%
rarefy_even_depth(sample.size = 85000, rngseed = 712)
V34r <- rarefy_even_depth(V34, sample.size = 85000, rngseed = 712)
V4r <- subset_samples(V4,Exp.beadbeating == "YES" & Beadbeating == "160s6ms") %>%
rarefy_even_depth(sample.size = 85000, rngseed = 712)
Richness estimates
V13_richness <- estimate_richness(V13r)
V34_richness <- estimate_richness(V34r)
V4_richness <- estimate_richness(V4r)
richness <- rbind.data.frame(V13_richness, V34_richness, V4_richness)
richness$Primer <- c(rep("V1-3", 3), rep("V3-4", 3),rep("V4", 3))
ri <- group_by(richness, Primer) %>%
summarise(Chao1 = round(mean(Chao1),0),
ACE = round(mean(ACE),0),
Shannon = round(mean(Shannon),1))
ri_table <- data.frame(ri[,-1], row.names = ri$Primer)
print(ri_table, rownames = F)
## Chao1 ACE Shannon
## V1-3 3944 3988 6.3
## V3-4 2824 2843 6.0
## V4 3037 3023 6.1
Rankabundance curves
V13_curve <- amp_rabund(data = V13r, scale.seq = 85000, tax.aggregate = "OTU", plot.type = "curve", output = "complete")
V34_curve <- amp_rabund(data = V34r, scale.seq = 85000, tax.aggregate = "OTU", plot.type = "curve", output = "complete")
V4_curve <- amp_rabund(data = V4r, scale.seq = 85000, tax.aggregate = "OTU", plot.type = "curve", output = "complete")
curve <- rbind.data.frame(V13_curve$data, V34_curve$data, V4_curve$data)
group <- data.frame(Group = levels(curve$Group), Primer = c(rep("V1-3", 3),rep("V3-4", 3),rep("V4", 3)))
curve <- merge(curve, group, by = "Group")
Figure 5B: Richness
cols <- hcl(h=seq(15, 375, length=4), l=65, c=100)[1:3]
ggplot(data = curve, aes(x=Rank, y = Cumsum, color = Primer, group = Group)) +
geom_line() +
scale_x_continuous(limits=c(1,1500), breaks = c(1, 100, 500, 1000, 1500)) +
ylim(0, 100) +
xlab("OTU rank abundance") +
ylab("Cumulative read abundance (%)") +
annotation_custom(tableGrob(ri_table,
gpar.coretext = gpar(fontsize = 8),
gpar.rowtext = gpar(fontsize = 8),
gpar.coltext = gpar(fontsize = 8)
),
xmin=500, xmax=1500, ymin=0, ymax=50) +
annotate("text", x = c(50,50,50), y = c(100,95,90), label = c("V1-3","V3-4","V4"), size = 3, color = cols, hjust = 0) +
theme(legend.position = "none",
axis.text.x = element_text(hjust = 0.5, size = 8, color = "black"),
axis.text.y = element_text(size = 8, color = "black"),
axis.title.y = element_text(vjust = 1, size = 8),
axis.title.x = element_text(size = 8),
panel.grid = element_blank(),
panel.background = element_blank(),
axis.line = element_line(color = "black"),
title = element_text(size = 8),
plot.margin = unit(c(0,0,0,0), "mm"),
axis.ticks.length = unit(1, "mm"),
axis.ticks = element_line(color = "black")
)
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)
Save the plot
ggsave("plots/Fig5B.eps", width = 80, height = 70, units = "mm")