SQMtools (version 1.7.2)

subsetContigs: Select contigs

Description

Create a SQM object containing only the requested contigs, the ORFs contained in them and the bins that contain them.

Usage

subsetContigs(
  SQM,
  contigs,
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  recalculate_bin_stats = TRUE,
  allow_empty = FALSE
)

Value

SQM object containing only the selected contigs.

Arguments

SQM

SQM object to be subsetted.

contigs

character. Vector of contigs to be selected.

trusted_functions_only

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).

ignore_unclassified_functions

logical. If FALSE, ORFs with no functional classification will be aggregated together into an "Unclassified" category. If TRUE, they will be ignored (default FALSE).

rescale_tpm

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 (default FALSE).

rescale_copy_number

logical. If TRUE, copy numbers with be recalculated using the median single-copy gene coverages in the subset. Otherwise, single-copy gene coverages will be taken from the parent object. By default it is set to FALSE, which means that the returned copy numbers for each function will represent the average copy number of that function per genome in the parent object.

recalculate_bin_stats

logical. If TRUE, bin abundance, quality and taxonomy are recalculated based on the contigs present in the subsetted object (default TRUE).

allow_empty

(internal use only).

See Also

subsetORFs

Examples

data(Hadza)
# Which contigs have a GC content below 40?
lowGCcontigNames = rownames(Hadza$contigs$table[Hadza$contigs$table[,"GC perc"]<40,])
lowGCcontigs = subsetContigs(Hadza, lowGCcontigNames)
hist(lowGCcontigs$contigs$table[,"GC perc"])