def test_create_kmers_from_sequence(self):
kmers = KmerHelper.create_kmers_from_sequence(ReceptorSequence(amino_acid_sequence="ABCDEFG"), 3)
self.assertTrue("ABC" in kmers and "BCD" in kmers and "CDE" in kmers and "DEF" in kmers and "EFG" in kmers)
self.assertEqual(5, len(kmers))
kmers = KmerHelper.create_kmers_from_sequence(ReceptorSequence(amino_acid_sequence="AB"), 3)
self.assertTrue(len(kmers) == 0)
def test_create_kmers_from_string(self):
kmers = KmerHelper.create_kmers_from_string("ABCDEFG", 3)
self.assertTrue("ABC" in kmers and "BCD" in kmers and "CDE" in kmers and "DEF" in kmers and "EFG" in kmers)
self.assertEqual(5, len(kmers))
kmers = KmerHelper.create_kmers_from_string("AB", 3)
self.assertTrue(len(kmers) == 0)
def encode_sequence(sequence: ReceptorSequence, params: EncoderParams):
"""
creates all overlapping gapped k-mers and IMGT position pairs from a sequence as features for use in KmerFrequencyEncoder.
this gap length goes from min_gap to max_gap inclusive, and there is a k-mer of length k_left on the left
side of the gap and a k-mer of length k_right on the right side of the gap.
:param sequence: ReceptorSequence
:param params: EncoderParams (within the "model", the following keys are used: "k_left", "k_right", "max_gap",
"min_gap")
:return: SequenceEncodingResult
"""
k_left = params.model.get('k_left')
k_right = params.model.get('k_right', k_left)
max_gap = params.model.get('max_gap')
min_gap = params.model.get('min_gap', 0)
length = len(sequence.get_sequence())
if length < k_left + k_right + max_gap:
warnings.warn(
'Sequence length is less than k_left + k_right + max_gap. Ignoring sequence'
)
return None
gapped_kmers = KmerHelper.create_IMGT_gapped_kmers_from_sequence(
sequence,
k_left=k_left,
max_gap=max_gap,
min_gap=min_gap,
k_right=k_right)
gapped_kmers = [
Constants.FEATURE_DELIMITER.join([str(mer) for mer in kmer])
for kmer in gapped_kmers
]
return gapped_kmers
def create_model(self, dataset: RepertoireDataset, k: int, vector_size: int, batch_size: int, model_path: str):
model = Word2Vec(size=vector_size, min_count=1, window=5) # creates an empty model
all_kmers = KmerHelper.create_all_kmers(k=k, alphabet=EnvironmentSettings.get_sequence_alphabet())
all_kmers = [[kmer] for kmer in all_kmers]
model.build_vocab(all_kmers)
for kmer in all_kmers:
sentences = KmerHelper.create_kmers_within_HD(kmer=kmer[0],
alphabet=EnvironmentSettings.get_sequence_alphabet(),
distance=1)
model.train(sentences=sentences, total_words=len(all_kmers), epochs=model.epochs)
model.save(model_path)
return model
def test_create_kmers_within_HD(self):
kmers = KmerHelper.create_kmers_within_HD("ACT", list("ACTEF"), 1)
self.assertEqual(15, len(kmers))
for i in range(15):
self.assertTrue(set("ACT").intersection(set(kmers[i][1])))
def test_create_all_kmers(self):
alphabet = list("ABCD")
k = 2
kmers = KmerHelper.create_all_kmers(k=k, alphabet=alphabet)
self.assertEqual(len(kmers), 16)
self.assertTrue("BD" in kmers)
self.assertTrue("DA" in kmers)
def test_create_IMGT_kmers_from_sequence(self):
kmers = KmerHelper.create_IMGT_kmers_from_sequence(ReceptorSequence("CASSRYUF"), 3)
self.assertTrue(("CAS", 105) in kmers)
self.assertTrue(("ASS", 106) in kmers)
self.assertTrue(("SSR", 107) in kmers)
self.assertTrue(("SRY", 108) in kmers)
self.assertTrue(("RYU", 114) in kmers)
self.assertTrue(("YUF", 115) in kmers)
def create_model(self, dataset: RepertoireDataset, k: int,
vector_size: int, batch_size: int, model_path: str):
model = Word2Vec(size=vector_size, min_count=1,
window=5) # creates an empty model
all_kmers = KmerHelper.create_all_kmers(
k=k, alphabet=EnvironmentSettings.get_sequence_alphabet())
all_kmers = [[kmer] for kmer in all_kmers]
model.build_vocab(all_kmers)
for repertoire in dataset.get_data(batch_size=batch_size):
sentences = KmerHelper.create_sentences_from_repertoire(
repertoire=repertoire, k=k)
model.train(sentences=sentences,
total_words=len(all_kmers),
epochs=15)
model.save(model_path)
return model
def _encode_repertoire(self, repertoire, vectors):
repertoire_vector = np.zeros(vectors.vector_size)
for (index2, sequence) in enumerate(repertoire.sequences):
kmers = KmerHelper.create_kmers_from_sequence(sequence=sequence, k=self.k)
sequence_vector = np.zeros(vectors.vector_size)
for kmer in kmers:
try:
word_vector = vectors.get_vector(kmer)
sequence_vector = np.add(sequence_vector, word_vector)
except KeyError:
pass
repertoire_vector = np.add(repertoire_vector, sequence_vector)
return repertoire_vector
def test_create_sentences_from_repertoire(self):
path = EnvironmentSettings.tmp_test_path + "kmer/"
PathBuilder.build(path)
rep = Repertoire.build_from_sequence_objects([ReceptorSequence(amino_acid_sequence="AACT"),
ReceptorSequence(amino_acid_sequence="ACCT"),
ReceptorSequence(amino_acid_sequence="AACT")], path, {})
sentences = KmerHelper.create_sentences_from_repertoire(rep, 3)
self.assertEqual(3, len(sentences))
self.assertTrue(len(sentences[0]) == 2 and "AAC" in sentences[0] and "ACT" in sentences[0])
shutil.rmtree(path)
def compute_tcrb_relative_abundance(sequences: np.ndarray,
counts: np.ndarray, k: int) -> dict:
"""
Computes the relative abundance of k-mers in the repertoire per following equations where C is the template count for the given receptor
sequence, T is the total count across all receptor sequences. The relative abundance per receptor sequence is then computed and only the
maximum sequence abudance was used for the k-mer so that the k-mer's relative abundance is equal to the abundance of the most frequent
receptor sequence in which the receptor appears:
.. math::
T^{TCR \\beta} = \\sum_{TCR\\beta} C^{TCR\\beta}
RA^{TCR\\beta} = \\frac{C^{TCR\\beta}}{T^{TCR\\beta}}
RA = \\max_{\\underset{with \\, kmer}{TCR\\beta}} {RA^{TCR \\beta}}
For more details, please see the original publication: Ostmeyer J, Christley S, Toby IT, Cowell LG. Biophysicochemical motifs in T cell
receptor sequences distinguish repertoires from tumor-infiltrating lymphocytes and adjacent healthy tissue. Cancer Res. Published online
January 1, 2019:canres.2292.2018. `doi:10.1158/0008-5472.CAN-18-2292 <https://cancerres.aacrjournals.org/content/canres/79/7/1671.full.pdf>`_
Arguments:
sequences: an array of (amino acid) sequences (corresponding to a repertoire)
counts: an array of counts for each of the sequences
k: the length of the k-mer (in the publication referenced above, k is 4)
Returns:
a dictionary where keys are k-mers and values are their relative abundances in the given list of sequences
"""
relative_abundance = {}
total_count = np.sum(counts)
relative_abundance_per_sequence = counts / total_count
for index, sequence in enumerate(sequences):
kmers = KmerHelper.create_kmers_from_string(sequence, k)
for kmer in kmers:
if kmer not in relative_abundance or relative_abundance[
kmer] < relative_abundance_per_sequence[index]:
relative_abundance[kmer] = relative_abundance_per_sequence[
index]
return relative_abundance
def encode_sequence(sequence: ReceptorSequence, params: EncoderParams):
"""
creates overlapping continuous k-mers and IMGT position pairs from a sequence as features for use in
KmerFrequencyEncoder object of type EncoderParams, same object as passed into KmerFrequencyEncoder.
:param sequence: ReceptorSequence
:param params: EncoderParams (where params["model"]["k"] is used)
:return: SequenceEncodingResult
"""
k = params.model["k"]
length = len(sequence.get_sequence())
if length < k:
logging.warning(
'KmerSequenceEncoder: Sequence length is less than k. Ignoring sequence...'
)
return None
kmers = KmerHelper.create_kmers_from_sequence(sequence, k)
return kmers
def compute_relative_abundance(sequences: np.ndarray, counts: np.ndarray,
k: int) -> dict:
"""
Computes the relative abundance of k-mers in the repertoire per following equations where C is the template count, T is the total count and
RA is relative abundance (the output of the function for each k-mer separately):
.. math::
C^{kmer}=\\sum_{\\underset{with kmer}{TCR \\beta}} C^{TCR \\beta}
T^{kmer} = \\sum_{kmer} C^{kmer}
RA = \\frac{C^{kmer}}{T^{kmer}}
For more details, please see the original publication: Ostmeyer J, Christley S, Toby IT, Cowell LG. Biophysicochemical motifs in T cell
receptor sequences distinguish repertoires from tumor-infiltrating lymphocytes and adjacent healthy tissue. Cancer Res. Published online
January 1, 2019:canres.2292.2018. `doi:10.1158/0008-5472.CAN-18-2292 <https://cancerres.aacrjournals.org/content/canres/79/7/1671.full.pdf>`_
Arguments:
sequences: an array of (amino acid) sequences (corresponding to a repertoire)
counts: an array of counts for each of the sequences
k: the length of the k-mer (in the publication referenced above, k is 4)
Returns:
a dictionary where keys are k-mers and values are their relative abundances in the given list of sequences
"""
c_kmers = Counter()
for index, sequence in enumerate(sequences):
kmers = KmerHelper.create_kmers_from_string(sequence, k)
c_kmers += {kmer: counts[index] for kmer in kmers}
t_kmers = sum(c_kmers.values())
return {kmer: c_kmers[kmer] / t_kmers for kmer in c_kmers.keys()}
def encode_sequence(sequence: ReceptorSequence, params: EncoderParams):
"""
creates overlapping continuous k-mers from a sequence as features for use in KmerFrequencyEncoder
object of type EncoderParams, same object as passed into KmerFrequencyEncoder
:param sequence: ReceptorSequence
:param params: EncoderParams (where params["model"]["k"] is used)
:return: SequenceEncodingResult consisting of features and feature information names
"""
k = params.model["k"]
length = len(sequence.get_sequence())
if length < k:
logging.warning(
'Sequence length is less than k. Ignoring sequence')
return None
kmers = KmerHelper.create_IMGT_kmers_from_sequence(sequence, k)
kmers = [
Constants.FEATURE_DELIMITER.join([str(mer) for mer in kmer])
for kmer in kmers
]
return kmers
def test_create_gapped_kmers_from_string(self):
kmers = KmerHelper.create_gapped_kmers_from_string("CASSRYUF", 2, 1, 1, 1)
self.assertTrue(all([k in kmers for k in ['CA.S', 'AS.R', 'SS.Y', 'SR.U', 'RY.F']]))
def test_create_IMGT_gapped_kmers_from_sequence(self):
kmers = KmerHelper.create_IMGT_gapped_kmers_from_sequence(ReceptorSequence("CASSRYUF"), 2, 1, 1, 1)
self.assertTrue(all([k in kmers for k in [('CA.S', 105), ('AS.R', 106), ('SS.Y', 107), ('SR.U', 108), ('RY.F', 114)]]))