View source: R/phyloseq_validate.R
phyloseq_validate | R Documentation |
It 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
)
ps |
phyloseq object |
remove_undetected |
if TRUE, removes taxa that sum to zero across all samples |
min_tax_length |
minimum number of characters to not consider a tax_table entry suspiciously short |
verbose |
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)
# 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)
# 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)
# 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_validate(soilrep)
# If no messages or warnings are emitted,
# this means no problems were detected, and nothing was changed
# (but only if verbose = TRUE)
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