subsetORFs

subsetORFs

R Documentation

Select ORFs

Description

Create a SQM object containing only the requested ORFs, and the contigs and bins that contain them. Internally, all the other subset functions in this package end up calling subsetORFs to do the work for them.

Usage

subsetORFs(
  SQM,
  orfs,
  tax_source = "orfs",
  trusted_functions_only = FALSE,
  ignore_unclassified_functions = FALSE,
  rescale_tpm = FALSE,
  rescale_copy_number = FALSE,
  recalculate_bin_stats = TRUE,
  contigs_override = NULL,
  allow_empty = FALSE
)

Arguments

`SQM`	SQM object to be subsetted.
`orfs`	character. Vector of ORFs to be selected.
`tax_source`	character, source data used for the taxonomy tables present in `SQM$taxa`, either `"orfs"`, `"contigs"`, `"bins"` (GTDB bin taxonomy if available, SQM bin taxonomy otherwise), `"bins_gtdb"` (GTDB bin taxonomy) or `"bins_sqm"` (SQM bin taxonomy). Default `"orfs"`.
`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`).
`contigs_override`	character. Optional vector of contigs to be included in the subsetted object.
`allow_empty`	(internal use only).

Value

SQM object containing the requested ORFs.

A note on contig/bins subsetting

While this function selects the contigs and bins that contain the desired orfs, it DOES NOT recalculate contig abundance and statistics based on the selected ORFs only. This means that the abundances presented in tables such as SQM$contig$abund will still refer to the complete contigs, regardless of whether only a fraction of their ORFs are actually present in the returned SQM object. This is also true for the statistics presented in SQM$contigs$table. Bin statistics may be recalculated if rescale_copy_number is set to TRUE, but recalculation will be based on contigs, not ORFs.

Examples

data(Hadza)
# Select the 100 most abundant ORFs in our dataset.
mostAbundantORFnames = names(sort(rowSums(Hadza$orfs$tpm), decreasing=TRUE))[1:100]
mostAbundantORFs = subsetORFs(Hadza, mostAbundantORFnames)