Create a SQM object containing only the contigs with a given consensus taxonomy, the ORFs contained in them and the bins that contain them.
subsetTax(
SQM,
rank,
tax,
trusted_functions_only = FALSE,
ignore_unclassified_functions = FALSE,
rescale_tpm = TRUE,
rescale_copy_number = TRUE
)SQM object containing only the requested taxon.
SQM object to be subsetted.
character. The taxonomic rank from which to select the desired taxa (superkingdom, phylum, class, order, family, genus, species)
character. The taxon to select.
logical. If TRUE, only highly trusted functional annotations (best hit + best average) will be considered when generating aggregated function tables. If FALSE, best hit annotations will be used (default FALSE).
logical. If FALSE, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If TRUE, they will be ignored (default FALSE).
logical. If TRUE, TPMs for KEGGs, COGs, and PFAMs will be recalculated (so that the TPMs in the subset actually add up to 1 million). Otherwise, per-function TPMs will be calculated by aggregating the TPMs of the ORFs annotated with that function, and will thus keep the scaling present in the parent object. By default it is set to TRUE, which means that the returned TPMs will be scaled by million of reads of the selected taxon.
logical. If TRUE, copy numbers with be recalculated using the RecA/RadA coverages in the subset. Otherwise, RecA/RadA coverages will be taken from the parent object. By default it is set to TRUE, which means that the returned copy numbers for each function will represent the average copy number of that function per genome of the selected taxon.
subsetFun, subsetContigs, subsetSamples, combineSQM. The most abundant items of a particular table contained in a SQM object can be selected with mostAbundant.
data(Hadza)
Hadza.Prevotella = subsetTax(Hadza, "genus", "Prevotella")
Hadza.Proteobacteria = subsetTax(Hadza, "phylum", "Proteobacteria")
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