import copy
import math
import random
from pathlib import Path
from typing import List
from immuneML.data_model.receptor.receptor_sequence.ReceptorSequence import ReceptorSequence
from immuneML.data_model.receptor.receptor_sequence.SequenceAnnotation import SequenceAnnotation
from immuneML.data_model.receptor.receptor_sequence.SequenceMetadata import SequenceMetadata
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.simulation.implants.ImplantAnnotation import ImplantAnnotation
from immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy import SignalImplantingStrategy
[docs]class FullSequenceImplanting(SignalImplantingStrategy):
"""
This class represents a :py:obj:`~immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy.SignalImplantingStrategy`
where signals will be implanted in the repertoire by replacing `repertoire_implanting_rate` percent of the sequences with sequences
generated from the motifs of the signal. Motifs here cannot include gaps and the motif instances are the full sequences and will be
a part of the repertoire.
Note: when providing the sequence to be implanted, check if the import setting regarding the sequence type (CDR3 vs IMGT junction) matches
the sequence to be implanted. For example, if importing would convert junction sequences to CDR3, but the sequence specified here for implanting
would be the junction, the results of the simulation could be inconsistent.
Arguments: this signal implanting strategy has no arguments.
YAML specification:
.. indent with spaces
.. code-block:: yaml
motifs:
my_motif: # cannot include gaps
...
signals:
my_signal:
motifs:
- my_motif
implanting: FullSequence
"""
[docs] def implant_in_repertoire(self, repertoire: Repertoire, repertoire_implanting_rate: float, signal, path: Path):
assert all("/" not in motif.seed for motif in signal.motifs), \
f'FullSequenceImplanting: motifs cannot include gaps. Check motifs {[motif.identifier for motif in signal.motifs]}.'
sequences = repertoire.sequences
new_sequence_count = math.ceil(len(sequences) * repertoire_implanting_rate)
assert new_sequence_count > 0, \
f"FullSequenceImplanting: there are too few sequences ({len(sequences)}) in the repertoire with identifier {repertoire.identifier} " \
f"to have the given repertoire implanting rate ({repertoire_implanting_rate}). Please consider increasing the repertoire implanting rate."
new_sequences = self._create_new_sequences(sequences, new_sequence_count, signal)
metadata = copy.deepcopy(repertoire.metadata)
metadata[signal.id] = True
return Repertoire.build_from_sequence_objects(new_sequences, path, metadata)
def _create_new_sequences(self, sequences, new_sequence_count, signal) -> List[ReceptorSequence]:
new_sequences = sequences[:-new_sequence_count]
for _ in range(new_sequence_count):
motif = random.choice(signal.motifs)
motif_instance = motif.instantiate_motif()
annotation = SequenceAnnotation([ImplantAnnotation(signal_id=signal.id, motif_id=motif.identifier,
motif_instance=motif_instance.instance, position=0)])
metadata = SequenceMetadata(v_gene="TRBV6-1", j_gene="TRBJ2-7", count=1, chain="B")
new_sequences.append(ReceptorSequence(amino_acid_sequence=motif_instance.instance, annotation=annotation, metadata=metadata))
return new_sequences
[docs] def implant_in_receptor(self, receptor, signal, is_noise: bool):
raise RuntimeError("FullSequenceImplanting was called on a receptor object. Check the simulation parameters.")