Trying to generate ASV table from phyloseq - r

Question

I recognize most people have the opposite problem. But I'm trying to create an ASV table, with column names as "identified OTUs" (aka the column name is drawn from the taxonomy information from GlobalPatterns#tax.table, rather than just being the assigned OTU code that's encoded in GlobalPatterns#otu.table), and row names as sample name.
I also want to append the metadata to the end of the ASV table, to allow for analysis based on said metadata.
I managed to generate a table without the taxonomic information with this code, using GlobalPatterns for reproducibility:
data(GlobalPatterns)
asv.matrix <- as.matrix(GlobalPatterns#otu_table#.Data)
asv <- data.frame(t(asv.matrix)) #transposing to make sample name the row name
meta.df <- as.data.frame(GlobalPatterns#sam_data)
asv.full <- data.frame(asv,meta.df)
write.csv(asv.full, file = "full_asv.csv",quote = FALSE,sep = ",")
However, I can't figure out how to force taxonomy information into the column names, which makes the ASV table functionally useless for analysis.
EDIT:
My preferred format is (abbreviated with faked metadata appended) as below. Tried to make a table, failed, have a fake code chunk.
Sample-ID / Species1 / Species2 / ...etc... / Metadata1 / Metadata2 /...etc... /
--------- / -------- / -------- / --------- / --------- / --------- /--------- /
Sample1 / 1 / 5 / ...etc... / lake / summer /...etc... /
Sample2 / 4 / 0 / ...etc... / bog / spring /...etc... /

Answer 1

I think you're looking for the phyloseq::psmelt function, which combines the otu_table, tax_table and sample_data tables into a single, long format table that is suitable for analysis.
One way of dealing with unresolved taxonomy is to assign the highest known taxonomy to any unresolved level. You can use the name_na_taxa function from the fantaxtic package for this, prior to using psmelt.
EDIT
After seeing your updated post, I understand a bit better what you want. You can take the output from psmelt and pivot this into a semi-wide format; see the code chunk below.
require("phyloseq")
require("fantaxtic")
require("tidyverse")
# Load data
data(GlobalPatterns)
# Generate (unique) species names using fantaxtic
ps <- name_na_taxa(GlobalPatterns)
ps <- label_duplicate_taxa(ps, tax_level = "Species", asv_as_id = T)
# Convert to long data format
ps_long <- psmelt(ps)
# Convert to semi-wide data format where each column has a taxon name
# and contains the abundance in each sample
meta_vars <- sample_variables(ps)
ps_wide <- ps_long %>%
select(all_of(meta_vars), Species, Abundance) %>%
pivot_wider(names_from = Species,
values_from = Abundance)
# Inspect the final table
head(ps_wide)
#> # A tibble: 6 x 19,223
#> X.SampleID Primer Final_Barcode Barcode_truncate~ Barcode_full_le~ SampleType
#> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AQC4cm ILBC_17 ACAGCT AGCTGT CAAGCTAGCTG Freshwate~
#> 2 LMEpi24M ILBC_13 ACACTG CAGTGT CATGAACAGTG Freshwater
#> 3 AQC7cm ILBC_18 ACAGTG CACTGT ATGAAGCACTG Freshwate~
#> 4 AQC1cm ILBC_16 ACAGCA TGCTGT GACCACTGCTG Freshwate~
#> 5 M31Tong ILBC_10 ACACGA TCGTGT TGTGGCTCGTG Tongue
#> 6 M11Fcsw ILBC_05 AAGCTG CAGCTT CGACTGCAGCT Feces
#> # ... with 19,217 more variables: Description <fct>,
#> # `Unknown Stramenopiles (Order) 549656` <dbl>,
#> # `Unknown Dolichospermum (Genus) 279599` <dbl>,
#> # `Unknown Neisseria (Genus) 360229` <dbl>,
#> # `Unknown Bacteroides (Genus) 331820` <dbl>,
#> # `Haemophilusparainfluenzae 94166` <dbl>,
#> # `Unknown ACK-M1 (Family) 329744` <dbl>, ...
Created on 2022-09-26 by the reprex package (v2.0.1)
Note that this will potentially lead to a table with thousands of columns (about 20k in the case of GlobalPatterns), which might be hard to work with.