Removes taxa (from all samples) that do not meet a given criterion or combination of criteria. If a value for min_prevalence, min_total_abundance or min_sample_abundance is 1 or greater, then it is treated as an absolute minimum number of samples/reads. If <1, it is treated as proportion of all samples/reads. This function is designed to work with counts. otu_table must contain counts particularly if you want to set a non-zero value for min_total_abundance.
tax_filter(
ps,
min_prevalence = 1,
prev_detection_threshold = 1,
min_total_abundance = 0,
min_sample_abundance = 0,
tax_level = NA,
names_only = FALSE,
use_counts = TRUE,
undetected = NULL,
verbose = TRUE
)phyloseq or psExtra (ideally with count data available)
number or proportion of samples that a taxon must be present in
min required counts (or value) for a taxon to be considered present in that sample (or set undetected arg)
minimum total readcount of a taxon, summed across all samples (can be proportion of all counts)
taxa must have at least this many reads in one or more samples
if given, aggregates data at named taxonomic rank before filtering, but returns phyloseq at the ORIGINAL level of aggregation!
if names_only is true return only names of taxa, not the phyloseq
expect count data in phyloseq otu_table? default is TRUE
e.g. 0, value at (or below) which a taxon is considered not present in that sample. If set, this overrides prev_detection_threshold.
message about proportional prevalence calculations?
filtered phyloseq object AT ORIGINAL LEVEL OF AGGREGATION (not at the level in tax_level)
data("dietswap", package = "microbiome")
# Dropping rare and low abundance taxa #
# Filter at unique taxa level, keeping only those with a prevalence of 10% or more
# and at least 10 thousand reads when summed across all samples.
# Then aggregate to Family level taxonomy.
dietswap %>%
tax_filter(min_prevalence = 0.1, min_total_abundance = 10000) %>%
tax_agg("Family")
#> Proportional min_prevalence given: 0.1 --> min 23/222 samples.
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 9 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 9 taxa by 2 taxonomic ranks ]
#>
#> psExtra info:
#> tax_agg = "Family"
# Keeping ubiquitous families #
# keep only families that have at least 1000 counts present in 90% of samples
# then aggregate the remaining taxa at 'Genus' level
dietswap %>%
tax_filter(
tax_level = "Family", min_prevalence = 0.90,
prev_detection_threshold = 1000
) %>%
tax_agg("Genus")
#> Proportional min_prevalence given: 0.9 --> min 200/222 samples.
#> psExtra object - a phyloseq object with extra slots:
#>
#> phyloseq-class experiment-level object
#> otu_table() OTU Table: [ 28 taxa and 222 samples ]
#> sample_data() Sample Data: [ 222 samples by 8 sample variables ]
#> tax_table() Taxonomy Table: [ 28 taxa by 3 taxonomic ranks ]
#>
#> psExtra info:
#> tax_agg = "Genus"