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Select taxa in a phyloseq object based on names in a given column of the tax_table

Source: R/select_taxa_pq.R
select_taxa_pq.Rd

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Usage

select_taxa_pq(
  physeq,
  taxnames = NULL,
  taxonomic_rank = "currentCanonicalSimple",
  verbose = TRUE,
  clean_pq = FALSE,
  ...
)

Arguments

physeq

A phyloseq object

taxnames

(optional) A character vector of taxonomic names.

taxonomic_rank

(Character, default "currentCanonicalSimple") The column(s) present in the @tax_table slot of the phyloseq object. Can be a vector of two columns (e.g. c("Genus", "Species")).

verbose

(logical, default TRUE) If TRUE, prompt some messages.

clean_pq

(logical, default FALSE) If TRUE, clean the phyloseq object after subsetting (i.e. remove empty taxa and samples). If FALSE, only empty taxa are removed to take all samples.

...

Additional arguments to pass to [subset_taxa_pq()].

Value

A new phyloseq object containing only the selected taxa.

See also

[MiscMetabar::subset_taxa_pq()]

Author

Adrien Taudiere

Examples

if (FALSE) { # \dontrun{
data_fungi_mini_cleanNames <- gna_verifier_pq(data_fungi_mini, data_sources = 210)

select_taxa_pq(data_fungi_mini_cleanNames,
  taxonomic_rank = "currentCanonicalSimple",
  taxnames = c("Xylodon flaviporus", "Basidiodendron eyrei"),
  verbose = FALSE,
  clean_pq = FALSE
)
} # }
select_taxa_pq(data_fungi,
  taxonomic_rank = c("Genus", "Species"),
  taxnames = c("Xylodon flaviporus"), verbose = FALSE, clean_pq = FALSE
)
#> phyloseq-class experiment-level object
#> otu_table()   OTU Table:         [ 1 taxa and 185 samples ]
#> sample_data() Sample Data:       [ 185 samples by 7 sample variables ]
#> tax_table()   Taxonomy Table:    [ 1 taxa by 12 taxonomic ranks ]
#> refseq()      DNAStringSet:      [ 1 reference sequences ]

select_taxa_pq(data_fungi, taxonomic_rank = "Trait", taxnames = c("Soft Rot")) |>
  summary_plot_pq()
#> Warning: At least one of your sample contains less than 500 sequences.
#> Warning: At least one of your taxa is represent by less than 1 sequences.
#> Warning: At least one of your samples metadata columns contains NA.
#> Number of non-matching ASV 0
#> Number of matching ASV 1420
#> Number of filtered-out ASV 1344
#> Number of kept ASV 76
#> Number of kept samples 185
#> Cleaning suppress 0 taxa and 23 samples.