R/phyloseq_validate.R
phyloseq_validate.RdIt checks for, and messages about, common uninformative entries in the tax_table, which often cause unwanted results
If there is no sample_data, it creates a sample_data dataframe with the sample_names (as "SAMPLE" variable)
If there is no tax_table, it creates a 1-column tax_table matrix with the taxa_names, and calls the rank "unique"
If remove_undetected = TRUE, it removes taxa where phyloseq::taxa_sums() is equal to zero, with a warning
phyloseq_validate(
ps,
remove_undetected = FALSE,
min_tax_length = 4,
verbose = TRUE
)phyloseq object
if TRUE, removes taxa that sum to zero across all samples
minimum number of characters to not consider a tax_table entry suspiciously short
print informative messages if true
possibly modified phyloseq object
data(dietswap, package = "microbiome")
# expect warning about taxa summing to zero
phyloseq_validate(dietswap, remove_undetected = TRUE, verbose = TRUE)
#> Warning: Some taxa_sums were zero, removing the following taxa:
#> Aerococcus
#> Aneurinibacillus
#> Asteroleplasma et rel.
#> Clostridium felsineum et rel.
#> Clostridium thermocellum et rel.
#> Methylobacterium
#> Micrococcaceae
#> This may be caused by using `subset_samples()`.
#> Try using `ps_filter()` instead, with .keep_all_taxa = FALSE.
#> Otherwise, to avoid this warning, try filtering out taxa summing to zero with `tax_filter()`.
#> If you have already transformed and/or scaled your taxa, e.g. with a log transformation or scale,
#> seeing this warning is possible, but very unlikely and possibly a bug. Please report this.
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 123 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 123 taxa by 3 taxonomic ranks ]
# verbose = FALSE will suppress messages and warnings but still:
# replace NULL sample_data and remove taxa that sum to 0 across all samples
# (if remove_undetected = TRUE)
phyloseq_validate(dietswap, verbose = FALSE)
#> 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 ]
# Sometimes you might have a phyloseq with no sample_data
# This isn't compatible with some microViz functions, like comp_barplot
# So some functions internally use phyloseq_validate to fix this
dietswap@sam_data <- NULL
phyloseq_validate(dietswap)
#> Note: Replacing missing sample_data with a dataframe of only sample_names.
#> Try `ps <- phyloseq_validate(ps, verbose = FALSE)` to avoid this message
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 130 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 1 sample variables ]
#> tax_table() Taxonomy Table: [ 130 taxa by 3 taxonomic ranks ]
# Sometimes you might have a phyloseq with no tax_table
# This isn't compatible with some microViz functions, like tax_top,
# so this is another reason to start your analyses with phyloseq_validate!
data("soilrep", package = "phyloseq")
soilrep # has NULL tax_table
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 16825 taxa and 56 samples ]
#> sample_data() Sample Data: [ 56 samples by 4 sample variables ]
phyloseq_validate(soilrep)
#> Note: Replacing missing tax_table with a 1-column table of only taxa_names.
#> Try `ps <- phyloseq_validate(ps, verbose = FALSE)` to avoid this message
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 16825 taxa and 56 samples ]
#> sample_data() Sample Data: [ 56 samples by 4 sample variables ]
#> tax_table() Taxonomy Table: [ 16825 taxa by 1 taxonomic ranks ]
# If no messages or warnings are emitted,
# this means no problems were detected, and nothing was changed
# (but only if verbose = TRUE)