import random
from immuneML.data_model.repertoire.Repertoire import Repertoire
from immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy import SignalImplantingStrategy
[docs]
class ReceptorImplanting(SignalImplantingStrategy):
"""
This class represents a :py:obj:`~immuneML.simulation.signal_implanting_strategy.SignalImplantingStrategy.SignalImplantingStrategy`
where signals will be implanted in both chains of immune receptors. This class should be used only when simulating paired chain data.
Arguments:
implanting: name of the implanting strategy, here Receptor
sequence_position_weights (dict): A dictionary describing the relative weights for implanting a signal at each given IMGT position in the receptor sequence. If sequence_position_weights are not set, then SequenceImplantingStrategy will make all of the positions equally likely for each receptor sequence.
YAML specification:
.. indent with spaces
.. code-block:: yaml
motifs:
my_motif:
...
signals:
my_signal:
motifs:
- my_motif
- ...
implanting: Receptor
sequence_position_weights:
109: 1
110: 2
111: 5
112: 1
"""
[docs]
def implant_in_receptor(self, receptor, signal, is_noise: bool):
new_receptor = receptor.clone()
motif = random.choice(signal.motifs)
sequence1 = self.implant_in_sequence(getattr(new_receptor, motif.name_chain1.name.lower()), signal, motif, motif.name_chain1)
sequence2 = self.implant_in_sequence(getattr(new_receptor, motif.name_chain2.name.lower()), signal, motif, motif.name_chain2)
setattr(new_receptor, motif.name_chain1.name.lower(), sequence1)
setattr(new_receptor, motif.name_chain2.name.lower(), sequence2)
new_receptor.metadata[signal.id] = True if not is_noise else False
return new_receptor
[docs]
def implant_in_repertoire(self, repertoire: Repertoire, repertoire_implanting_rate: float, signal, path):
raise RuntimeError("ReceptorImplanting was called on a repertoire object. Check the simulation parameters.")