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Commit 984120cf authored by Tim O'Donnell's avatar Tim O'Donnell
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Support BLOSUM62 encoding

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mhcflurry/class1_affinity_prediction/class1_neural_network.py
+ 27
− 8
View file @ 984120cf
......@@ -16,7 +16,10 @@ from keras.layers.normalization import BatchNormalization
from mhcflurry.hyperparameters import HyperparameterDefaults
from ..encodable_sequences import EncodableSequences
from ..encodable_sequences import (
EncodableSequences,
available_vector_encodings,
vector_encoding_length)
from ..regression_target import to_ic50, from_ic50
from ..common import random_peptides, amino_acid_distribution
......@@ -35,6 +38,7 @@ class Class1NeuralNetwork(object):
network_hyperparameter_defaults = HyperparameterDefaults(
kmer_size=15,
use_embedding=False,
peptide_amino_acid_encoding="one-hot",
embedding_input_dim=21,
embedding_output_dim=8,
pseudosequence_use_embedding=False,
......@@ -256,20 +260,26 @@ class Class1NeuralNetwork(object):
numpy.array
"""
encoder = EncodableSequences.create(peptides)
if self.hyperparameters['use_embedding']:
if (self.hyperparameters['use_embedding'] or
self.hyperparameters['peptide_amino_acid_encoding'] == "embedding"):
encoded = encoder.variable_length_to_fixed_length_categorical(
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
else:
encoded = encoder.variable_length_to_fixed_length_one_hot(
elif (
self.hyperparameters['peptide_amino_acid_encoding'] in
available_vector_encodings()):
encoded = encoder.variable_length_to_fixed_length_vector_encoding(
self.hyperparameters['peptide_amino_acid_encoding'],
max_length=self.hyperparameters['kmer_size'],
**self.input_encoding_hyperparameter_defaults.subselect(
self.hyperparameters))
else:
raise ValueError("Unsupported peptide_amino_acid_encoding: %s" %
self.hyperparameters['peptide_amino_acid_encoding'])
assert len(encoded) == len(peptides)
return encoded
@property
def supported_peptide_lengths(self):
"""
......@@ -492,7 +502,8 @@ class Class1NeuralNetwork(object):
pseudosequences_input = self.pseudosequence_to_network_input(
allele_pseudosequences)
x_dict['pseudosequence'] = pseudosequences_input
(predictions,) = numpy.array(self.network(borrow=True).predict(x_dict)).T
(predictions,) = numpy.array(
self.network(borrow=True).predict(x_dict), dtype="float64").T
return to_ic50(predictions)
def compile(self):
......@@ -507,6 +518,7 @@ class Class1NeuralNetwork(object):
def make_network(
pseudosequence_length,
kmer_size,
peptide_amino_acid_encoding,
use_embedding,
embedding_input_dim,
embedding_output_dim,
......@@ -524,7 +536,7 @@ class Class1NeuralNetwork(object):
"""
Helper function to make a keras network for class1 affinity prediction.
"""
if use_embedding:
if use_embedding or peptide_amino_acid_encoding == "embedding":
peptide_input = Input(
shape=(kmer_size,), dtype='int32', name='peptide')
current_layer = Embedding(
......@@ -535,7 +547,11 @@ class Class1NeuralNetwork(object):
name="peptide_embedding")(peptide_input)
else:
peptide_input = Input(
shape=(kmer_size, 21), dtype='float32', name='peptide')
shape=(
kmer_size,
vector_encoding_length(peptide_amino_acid_encoding)),
dtype='float32',
name='peptide')
current_layer = peptide_input
inputs = [peptide_input]
......@@ -609,4 +625,7 @@ class Class1NeuralNetwork(object):
inputs=inputs,
outputs=[output],
name="predictor")
print("*** ARCHITECTURE ***")
model.summary()
return model
mhcflurry/encodable_sequences.py
+ 98
− 75
View file @ 984120cf
......@@ -22,11 +22,75 @@ import math
import pandas
import numpy
from six import StringIO
import typechecks
from . import amino_acid
AMINO_ACIDS = list(amino_acid.COMMON_AMINO_ACIDS_WITH_UNKNOWN.keys())
BLOSUM62_MATRIX = pandas.read_table(StringIO("""
A R N D C Q E G H I L K M F P S T W Y V X
A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 0
R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 0
N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 0
D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 0
C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 0
Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0
E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 0
G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 0
H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 0
L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 0
K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0
M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 0
F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 0
P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 0
S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0
T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 0
W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 0
Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 0
V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 0
X 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
"""), sep='\s+').loc[AMINO_ACIDS, AMINO_ACIDS]
assert (BLOSUM62_MATRIX == BLOSUM62_MATRIX.T).all().all()
ENCODING_DFS = {
"BLOSUM62": BLOSUM62_MATRIX,
"one-hot": pandas.DataFrame([
[1 if i == j else 0 for i in range(len(AMINO_ACIDS))]
for j in range(len(AMINO_ACIDS))
], index=AMINO_ACIDS, columns=AMINO_ACIDS)
}
def available_vector_encodings():
"""
Return list of supported amino acid vector encodings.
Returns
-------
list of string
"""
return list(ENCODING_DFS)
def vector_encoding_length(name):
"""
Return the length of the given vector encoding.
Parameters
----------
name : string
Returns
-------
int
"""
return ENCODING_DFS[name].shape[1]
def index_encoding(sequences, letter_to_index_dict):
"""
......@@ -47,38 +111,23 @@ def index_encoding(sequences, letter_to_index_dict):
return result.values
def one_hot_encoding(index_encoded, alphabet_size):
def fixed_vectors_encoding(sequences, letter_to_vector_function):
"""
Given an n * k array of integers in the range [0, alphabet_size), return
an n * k * alphabet_size array where element (i, k, j) is 1 if element
(i, k) == j in the input array and zero otherwise.
Given a sequence of n strings all of length k, return a n * k * m array where
the (i, j)th element is letter_to_vector_function(sequence[i][j]).
Parameters
----------
index_encoded : numpy.array of integers with shape (n, k)
alphabet_size : int
sequences : list of length n of strings of length k
letter_to_vector_function : function : string -> vector of length m
Returns
-------
numpy.array of integers of shape (n, k, alphabet_size)
numpy.array of integers with shape (n, k, m)
"""
alphabet_size = int(alphabet_size)
(num_sequences, sequence_length) = index_encoded.shape
result = numpy.zeros(
(num_sequences, sequence_length, alphabet_size),
dtype='int32')
# Transform the index encoded array into an array of indices into the
# flattened result, which we will set to 1.
flattened_indices = (
index_encoded +
(
sequence_length * alphabet_size * numpy.arange(num_sequences)
).reshape((-1, 1)) +
numpy.tile(numpy.arange(sequence_length),
(num_sequences, 1)) * alphabet_size)
result.put(flattened_indices, 1)
arr = numpy.array([list(s) for s in sequences])
result = numpy.vectorize(
letter_to_vector_function, signature='()->(n)')(arr)
return result
......@@ -114,40 +163,6 @@ class EncodableSequences(object):
def __len__(self):
return len(self.sequences)
def fixed_length_categorical(self):
"""
Returns a categorical encoding (i.e. integers 0 <= x < 21) of the
sequences, which must already be all the same length.
Returns
-------
numpy.array of integers
"""
cache_key = ("categorical",)
if cache_key not in self.encoding_cache:
assert self.fixed_sequence_length
self.encoding_cache[cache_key] = index_encoding(
self.sequences, amino_acid.AMINO_ACID_INDEX)
return self.encoding_cache[cache_key]
def fixed_length_one_hot(self):
"""
Returns a binary one-hot encoding of the sequences, which must already
be all the same length.
Returns
-------
numpy.array of integers
"""
cache_key = ("one_hot",)
if cache_key not in self.encoding_cache:
assert self.fixed_sequence_length
encoded = self.categorical_encoding()
result = one_hot_encoding(
encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
self.encoding_cache[cache_key] = result
return self.encoding_cache[cache_key]
def variable_length_to_fixed_length_categorical(
self, left_edge=4, right_edge=4, max_length=15):
"""
......@@ -187,8 +202,8 @@ class EncodableSequences(object):
fixed_length_sequences, amino_acid.AMINO_ACID_INDEX)
return self.encoding_cache[cache_key]
def variable_length_to_fixed_length_one_hot(
self, left_edge=4, right_edge=4, max_length=15):
def variable_length_to_fixed_length_vector_encoding(
self, vector_encoding_name, left_edge=4, right_edge=4, max_length=15):
"""
Encode variable-length sequences using a fixed-length encoding designed
for preserving the anchor positions of class I peptides.
......@@ -198,35 +213,43 @@ class EncodableSequences(object):
Parameters
----------
vector_encoding_name : string
How to represent amino acids.
One of "BLOSUM62", "one-hot", etc. Full list of supported vector
encodings is given by available_vector_encodings().
left_edge : int, size of fixed-position left side
right_edge : int, size of the fixed-position right side
max_length : sequence length of the resulting encoding
Returns
-------
binary numpy.array with shape (num sequences, max_length, 21)
"""
numpy.array with shape (num sequences, max_length, m) where m is
vector_encoding_length(vector_encoding_name)
"""
cache_key = (
"fixed_length_one_hot",
"fixed_length_vector_encoding",
vector_encoding_name,
left_edge,
right_edge,
max_length)
if cache_key not in self.encoding_cache:
encoded = self.variable_length_to_fixed_length_categorical(
left_edge=left_edge,
right_edge=right_edge,
max_length=max_length)
result = one_hot_encoding(
encoded, alphabet_size=len(amino_acid.AMINO_ACID_INDEX))
assert result.shape == (
len(self.sequences),
encoded.shape[1],
len(amino_acid.AMINO_ACID_INDEX))
fixed_length_sequences = [
self.sequence_to_fixed_length_string(
sequence,
left_edge=left_edge,
right_edge=right_edge,
max_length=max_length)
for sequence in self.sequences
]
result = fixed_vectors_encoding(
fixed_length_sequences,
ENCODING_DFS[vector_encoding_name].loc.__getitem__)
assert result.shape[0] == len(self.sequences)
self.encoding_cache[cache_key] = result
return self.encoding_cache[cache_key]
@classmethod
def sequence_to_fixed_length_string(
klass, sequence, left_edge=4, right_edge=4, max_length=15):
......
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