Rarefying

dataset: phyloseq

The diet swap data set represents a study with African and African American groups undergoing a two-week diet swap. For details, see https://www.nature.com/articles/ncomms7342.

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 130 taxa and 222 samples ]
sample_data() Sample Data:       [ 222 samples by 8 sample variables ]
tax_table()   Taxonomy Table:    [ 130 taxa by 3 taxonomic ranks ]

Part 1:

Rarefying the phyloseq object to obtain a rarefied phyloseq.

phyloseq-class experiment-level object
otu_table()   OTU Table:         [ 120 taxa and 222 samples ]
sample_data() Sample Data:       [ 222 samples by 8 sample variables ]
tax_table()   Taxonomy Table:    [ 120 taxa by 3 taxonomic ranks ]

part 2:

Make use of alpha diversity index like shannon. Calculate the shannon index for the original phyloseq and the rarefied one, as follows:

theme_set(theme_bw(15))
shannon <- estimate_richness(phy, measures = "shannon")
rare_shannon <- estimate_richness(phy_rare, measures = "shannon")
compare_shannon <- data.frame(shannon, rare_shannon)
p <- ggplot(data = compare_shannon, aes(x = Shannon, y = Shannon.1)) + labs(title = "Shannon indexes for rarefied data against the original data") +
    xlab("original data") + ylab("rarefied data") + geom_point()

p

As the shannon indexes are correlated, it means our data is expressing the biological variation quite well. Yet this is not common due to the available techniques for obtaining the operational taxonomic units (OTUs).

Part 3:

More plot taxa abundances for the original phyloseq, relative abundance phyloseq and for the rarefying one.

Original phyloseq

Compositional phyloseq

Rarefying phyloseq

More info see 1, 2, 3