Phyloseq is a
popular package for working with microbiome data. Here we show how to
use the phy_to_floral_data
helper function to convert
phyloseq data into a format accepted by FLORAL.
The following code downloads data described in this paper
and turns it into a phyloseq object. The tax_glom step here takes some
time, and can be replaced with speedyseq::tax_glom
for better performace.
samples <- get0("samples", envir = asNamespace("FLORAL"))
counts <- get0("counts", envir = asNamespace("FLORAL"))
taxonomy <- get0("taxonomy", envir = asNamespace("FLORAL"))
phy <- phyloseq(
sample_data(samples %>% column_to_rownames("SampleID")),
tax_table(taxonomy %>% select(ASV, Kingdom:Genus) %>% column_to_rownames("ASV") %>% as.matrix()),
otu_table(counts %>% pivot_wider(names_from = "SampleID", values_from = "Count", values_fill = 0) %>% column_to_rownames("ASV") %>% as.matrix(), taxa_are_rows = TRUE)
) %>% subset_samples(DayRelativeToNearestHCT > -30 & DayRelativeToNearestHCT < 0) %>%
tax_glom("Genus")
Next, we convert that phyloseq object into a list of results to be
used by FLORAL; we have to specify the main outcome of interest as
y
, and any metadata columns (from
sample_data(phy)
) to use as covariates. Note that the
analysis described here is just an example for using the function;
this
The resulting list has named entities for the main arguments to FLORAL: