"""
Idea:
- take an allele where MS vs. no-MS trained predictors are very different. One
possiblility is DLA-88*501:01 but human would be better
- generate synethetic multi-allele MS by combining single-allele MS for differnet
alleles, including the selected allele
- train ligandome predictor based on the no-ms pan-allele models on theis
synthetic dataset
- see if the pan-allele predictor learns the "correct" motif for the selected
allele, i.e. updates to become more similar to the with-ms pan allele predictor.
"""
from sklearn.metrics import roc_auc_score
import pandas
import argparse
import sys
from numpy.testing import assert_, assert_equal
import numpy
from random import shuffle
from mhcflurry import Class1AffinityPredictor,Class1NeuralNetwork
from mhcflurry.allele_encoding import AlleleEncoding
from mhcflurry.class1_ligandome_predictor import Class1LigandomePredictor
from mhcflurry.downloads import get_path
from mhcflurry.testing_utils import cleanup, startup
from mhcflurry.amino_acid import COMMON_AMINO_ACIDS
COMMON_AMINO_ACIDS = sorted(COMMON_AMINO_ACIDS)
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC = None
PAN_ALLELE_MOTIFS_WITH_MASS_SPEC_DF = None
PAN_ALLELE_MOTIFS_NO_MASS_SPEC_DF = None
def setup():
global PAN_ALLELE_PREDICTOR_NO_MASS_SPEC
global PAN_ALLELE_MOTIFS_WITH_MASS_SPEC_DF
global PAN_ALLELE_MOTIFS_NO_MASS_SPEC_DF
startup()
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC = Class1AffinityPredictor.load(
get_path("models_class1_pan", "models.no_mass_spec"))
PAN_ALLELE_MOTIFS_WITH_MASS_SPEC_DF = pandas.read_csv(
get_path(
"models_class1_pan",
"models.with_mass_spec/frequency_matrices.csv.bz2"))
PAN_ALLELE_MOTIFS_NO_MASS_SPEC_DF = pandas.read_csv(
get_path(
"models_class1_pan",
"models.no_mass_spec/frequency_matrices.csv.bz2"))
def teardown():
global PAN_ALLELE_PREDICTOR_NO_MASS_SPEC
global PAN_ALLELE_MOTIFS_WITH_MASS_SPEC_DF
global PAN_ALLELE_MOTIFS_NO_MASS_SPEC_DF
PAN_ALLELE_PREDICTOR_NO_MASS_SPEC = None
PAN_ALLELE_MOTIFS_WITH_MASS_SPEC_DF = None
PAN_ALLELE_MOTIFS_NO_MASS_SPEC_DF = None
cleanup()
def sample_peptides_from_pssm(pssm, count):
result = pandas.DataFrame(
index=numpy.arange(count),
columns=pssm.index,
dtype=object,
)
for (position, vector) in pssm.iterrows():
result.loc[:, position] = numpy.random.choice(
pssm.columns,
size=count,
replace=True,
p=vector.values)
return result.apply("".join, axis=1)
def scramble_peptide(peptide):
lst = list(peptide)
shuffle(lst)
return "".join(lst)
def test_synthetic_allele_refinement():
refine_allele = "HLA-C*01:02"
alleles = [
"HLA-A*02:01", "HLA-B*27:01", "HLA-C*07:01",
"HLA-A*03:01", "HLA-B*15:01", refine_allele
]
peptides_per_allele = [
2000, 1000, 500,
1500, 1200, 800,
]
allele_to_peptides = dict(zip(alleles, peptides_per_allele))
length = 9
train_with_ms = pandas.read_csv(
get_path("data_curated", "curated_training_data.with_mass_spec.csv.bz2"))
train_no_ms = pandas.read_csv(get_path("data_curated",
"curated_training_data.no_mass_spec.csv.bz2"))
def filter_df(df):
df = df.loc[
(df.allele.isin(alleles)) &
(df.peptide.str.len() == length)
]
return df
train_with_ms = filter_df(train_with_ms)
train_no_ms = filter_df(train_no_ms)
ms_specific = train_with_ms.loc[
~train_with_ms.peptide.isin(train_no_ms.peptide)
]
train_peptides = []
train_true_alleles = []
for allele in alleles:
peptides = ms_specific.loc[ms_specific.allele == allele].peptide.sample(
n=allele_to_peptides[allele])
train_peptides.extend(peptides)
train_true_alleles.extend([allele] * len(peptides))
hits_df = pandas.DataFrame({"peptide": train_peptides})
hits_df["true_allele"] = train_true_alleles
hits_df["hit"] = 1.0
decoys_df = hits_df.copy()
decoys_df["peptide"] = decoys_df.peptide.map(scramble_peptide)
decoys_df["true_allele"] = ""
decoys_df["hit"] = 0.0
train_df = pandas.concat([hits_df, decoys_df], ignore_index=True)
predictor = Class1LigandomePredictor(PAN_ALLELE_PREDICTOR_NO_MASS_SPEC)
predictor.fit(
peptides=train_df.peptide.values,
labels=train_df.hit.values,
experiment_names=["experiment1"] * len(train_df),
experiment_name_to_alleles={
"experiment1": alleles,
}
)
predictions = predictor.predict(
peptides=train_df.peptide.values,
alleles=alleles,
output_format="concatenate"
)
print(predictions)
import ipdb ; ipdb.set_trace()
"""
def test_simple_synethetic(
num_peptide_per_allele_and_length=100, lengths=[8,9,10,11]):
alleles = [
"HLA-A*02:01", "HLA-B*52:01", "HLA-C*07:01",
"HLA-A*03:01", "HLA-B*57:02", "HLA-C*03:01",
]
cutoff = PAN_ALLELE_MOTIFS_DF.cutoff_fraction.min()
peptides_and_alleles = []
for allele in alleles:
sub_df = PAN_ALLELE_MOTIFS_DF.loc[
(PAN_ALLELE_MOTIFS_DF.allele == allele) &
(PAN_ALLELE_MOTIFS_DF.cutoff_fraction == cutoff)
]
assert len(sub_df) > 0, allele
for length in lengths:
pssm = sub_df.loc[
sub_df.length == length
].set_index("position")[COMMON_AMINO_ACIDS]
peptides = sample_peptides_from_pssm(pssm, num_peptide_per_allele_and_length)
for peptide in peptides:
peptides_and_alleles.append((peptide, allele))
hits_df = pandas.DataFrame(
peptides_and_alleles,
columns=["peptide", "allele"]
)
hits_df["hit"] = 1
decoys = hits_df.copy()
decoys["peptide"] = decoys.peptide.map(scramble_peptide)
decoys["hit"] = 0.0
train_df = pandas.concat([hits_df, decoys], ignore_index=True)
return train_df
return
pass
"""
parser = argparse.ArgumentParser(usage=__doc__)
parser.add_argument(
"--alleles",
nargs="+",
default=None,
help="Which alleles to test")
if __name__ == '__main__':
# If run directly from python, leave the user in a shell to explore results.
setup()
args = parser.parse_args(sys.argv[1:])
result = test_synthetic_allele_refinement()
# Leave in ipython
import ipdb # pylint: disable=import-error
ipdb.set_trace()