subsetContigs: Select contigs
In SQMtools: Analyze Results Generated by the 'SqueezeMeta' Pipeline

subsetContigs

R Documentation

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
)

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 RecA/RadA coverages in the subset. Otherwise, RecA/RadA 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 stats and taxonomy are recalculated based on the contigs present in the subsetted object (default `TRUE`).
`allow_empty`	(internal use only).

Value

SQM object containing only the selected contigs.

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"])

SQMtools documentation built on April 3, 2025, 6:16 p.m.