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from nose.tools import eq_, assert_less
import numpy
import pandas
from mhcflurry import amino_acid
from mhcflurry.antigen_presentation import (
decoy_strategies,
presentation_component_models,
presentation_model)
######################
# Helper functions
def make_random_peptides(num, length=9):
return [
''.join(peptide_sequence)
for peptide_sequence in
numpy.random.choice(
amino_acid.common_amino_acid_letters, size=(num, length))
]
def hit_criterion(experiment_name, peptide):
# Peptides with 'A' are always hits. Easy for model to learn.
return 'A' in peptide
######################
# Small test dataset
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PEPTIDES = make_random_peptides(100, 9)
TRANSCRIPTS = [
"transcript-%d" % i
for i in range(1, 10)
]
EXPERIMENT_TO_ALLELES = {
'exp1': ['HLA-A*01:01'],
'exp2': ['HLA-A*02:01', 'HLA-B*51:01'],
}
EXPERIMENT_TO_EXPRESSION_GROUP = {
'exp1': 'group1',
'exp2': 'group2',
}
EXPERESSION_GROUPS = sorted(set(EXPERIMENT_TO_EXPRESSION_GROUP.values()))
TRANSCIPTS_DF = pandas.DataFrame(index=PEPTIDES, columns=EXPERESSION_GROUPS)
TRANSCIPTS_DF[:] = numpy.random.choice(TRANSCRIPTS, size=TRANSCIPTS_DF.shape)
PEPTIDES_AND_TRANSCRIPTS_DF = TRANSCIPTS_DF.stack().to_frame().reset_index()
PEPTIDES_AND_TRANSCRIPTS_DF.columns = ["peptide", "group", "transcript"]
del PEPTIDES_AND_TRANSCRIPTS_DF["group"]
PEPTIDES_DF = pandas.DataFrame({"peptide": PEPTIDES})
PEPTIDES_DF["experiment_name"] = "exp1"
PEPTIDES_DF["hit"] = [
hit_criterion(row.experiment_name, row.peptide)
for _, row in
PEPTIDES_DF.iterrows()
]
HITS_DF = PEPTIDES_DF.ix[PEPTIDES_DF.hit].reset_index().copy()
del HITS_DF["hit"]
def test_mhcflurry_trained_on_hits():
mhcflurry_model = presentation_component_models.MHCflurryTrainedOnHits(
"basic",
experiment_to_alleles=EXPERIMENT_TO_ALLELES,
experiment_to_expression_group=EXPERIMENT_TO_EXPRESSION_GROUP,
transcripts=TRANSCIPTS_DF,
peptides_and_transcripts=PEPTIDES_AND_TRANSCRIPTS_DF,
random_peptides_for_percent_rank=make_random_peptides(10000, 9),
)
peptides = PEPTIDES_DF.copy()
predictions = mhcflurry_model.predict(peptides)
peptides["affinity"] = predictions["mhcflurry_basic_affinity"]
peptides["percent_rank"] = predictions["mhcflurry_basic_percentile_rank"]
assert_less(
peptides.affinity[peptides.hit].mean(),
peptides.affinity[~peptides.hit].mean())
assert_less(
peptides.percent_rank[peptides.hit].mean(),
peptides.percent_rank[~peptides.hit].mean())
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def test_presentation_model():
mhcflurry_model = presentation_component_models.MHCflurryTrainedOnHits(
"basic",
experiment_to_alleles=EXPERIMENT_TO_ALLELES,
experiment_to_expression_group=EXPERIMENT_TO_EXPRESSION_GROUP,
transcripts=TRANSCIPTS_DF,
peptides_and_transcripts=PEPTIDES_AND_TRANSCRIPTS_DF,
random_peptides_for_percent_rank=make_random_peptides(10000, 9),
)
decoys = decoy_strategies.UniformRandom(
make_random_peptides(10000, 9),
decoys_per_hit=50)
terms = {
'A_ms': (
[mhcflurry_model],
["log1p(mhcflurry_basic_affinity)"]),
}
models = presentation_model.build_presentation_models(
terms,
["A_ms"],
decoy_strategy=decoys)
eq_(len(models), 1)
model = models["A_ms"]
model.fit(HITS_DF.ix[HITS_DF.experiment_name == "exp1"])
peptides = PEPTIDES_DF.copy()
peptides["prediction"] = model.predict(peptides)
assert_less(
peptides.prediction[~peptides.hit].mean(),
peptides.prediction[peptides.hit].mean())