batch_evaluate() processes proteins in bulk, using one digest and score
call per serial or parallel chunk, and returns a flat tibble with one row
per protein. Columns include protein_id, protein_length, and all
component scores, including composite_score, verdict, n_peptides,
n_valid_peptides, and median_peptide_length, plus four
sequence-level difficulty flags. Pass the result to summarize_batch() for
aggregate statistics or to triage_proteins() for action labels.
Usage
batch_evaluate(
sequences,
enzyme = "trypsin",
missed_cleavages = 1L,
include_cleavage_efficiency = FALSE,
proteome = NULL,
weights = NULL,
cores = 1L,
...
)Arguments
- sequences
Multi-protein input. Accepts the same forms as
digest_protein(). Must resolve to at least one protein. IfNULLor empty, raises an error.- enzyme
Enzyme name passed to
digest_protein(). Defaults to"trypsin".- missed_cleavages
Maximum missed cleavages. Defaults to
1L.- include_cleavage_efficiency
Logical flag passed to
digest_protein(). Defaults toFALSE. WhenTRUE, each per-protein peptide table includes acleavage_efficiencycolumn (does not affect the flat batch tibble columns).- proteome
Optional proteome digest tibble passed to
score_peptides()for every protein evaluation. WhenNULL(default), no uniqueness scoring is performed and theS_uniquecolumn is omitted.- weights
Optional scoring weight vector passed to
score_peptides(). WhenNULL(default), uses pepVet's default scoring weights.- cores
Number of parallel workers for protein-level chunking.
1L(default) runs sequentially with no extra dependencies. Values greater than1Lsplit the input into equal chunks and process each chunk withparallel::mclapply()on Unix or aparallel::parLapply()socket cluster on Windows.- ...
Additional scoring arguments passed to
score_peptides(), such asgravy_rangeandlength_range.
Value
A tibble with one row per protein. Fixed columns:
protein_id, protein_length, n_peptides, n_valid_peptides,
all available component scores (S_length, S_coverage, S_count,
S_hydro, S_charge; S_unique when proteome is provided),
composite_score, verdict, median_peptide_length,
flag_short_protein, flag_hydrophobic, flag_low_complexity,
flag_no_valid_peptides.
Details
The returned tibble carries a scoring_config attribute containing the
resolved ranges, weights, proteome-aware mode, and pI mode. Batch
inputs must have unique protein identifiers. Difficulty flags use the
active scoring ranges; flag_short_protein and flag_low_complexity
remain sequence-level heuristics. Rows follow the supplied input-record
order. Reordering uniquely named records changes row order but not the
named per-protein results.
Limitations
Windows socket workers receive serialized copies of their input, while Unix fork workers use copy-on-write memory. If a worker or socket cluster fails, the affected chunks are retried sequentially with a warning.
Examples
small_proteome <- system.file(
"extdata", "small_proteome_50_proteins.fasta",
package = "pepVet"
)
batch <- batch_evaluate(small_proteome, enzyme = "trypsin")
nrow(batch)
#> [1] 50
batch[, c("protein_id", "composite_score", "verdict")]
#> # A tibble: 50 × 3
#> protein_id composite_score verdict
#> <chr> <dbl> <chr>
#> 1 sp|O15162|PLS1_HUMAN Phospholipid scramblase 1 OS=Ho… 0.624 Modera…
#> 2 sp|O15393|TMPS2_HUMAN Transmembrane protease serine … 0.683 Good
#> 3 sp|O60502|OGA_HUMAN Protein O-GlcNAcase OS=Homo sapi… 0.746 Good
#> 4 sp|O75475|PSIP1_HUMAN PC4 and SFRS1-interacting prot… 0.736 Good
#> 5 sp|O75970|MPDZ_HUMAN Multiple PDZ domain protein OS=… 0.724 Good
#> 6 sp|O95273|CCDB1_HUMAN Cyclin-D1-binding protein 1 OS… 0.563 Modera…
#> 7 sp|O95905|ECD_HUMAN Protein ecdysoneless homolog OS=… 0.709 Good
#> 8 sp|P01889|HLAB_HUMAN HLA class I histocompatibility … 0.739 Good
#> 9 sp|P01911|DRB1_HUMAN HLA class II histocompatibility… 0.742 Good
#> 10 sp|P04439|HLAA_HUMAN HLA class I histocompatibility … 0.744 Good
#> # ℹ 40 more rows
