def test_one_hot():
seq = "ACGTTTATNT"
assert len(seq) == 10
assert one_hot_dna(seq).shape == (10, 4)
assert one_hot(seq).shape == (10, 4)
assert np.all(one_hot_dna(seq) == one_hot(seq))
assert one_hot(pad(seq, 20)).shape == (20, 4)
assert one_hot(fixed_len(seq, 20)).shape == (20, 4)
assert one_hot(fixed_len(seq, 5)).shape == (5, 4)
assert trim(seq, 5) == 'TTTAT'
assert trim(seq, 5, 'start') == 'ACGTT'
assert trim(seq, 5, 'end') == 'TATNT'
with pytest.raises(Exception):
assert pad(seq, 5, 'end') == 'TATNT'
assert np.all(one_hot(seq)[0] == np.array([1, 0, 0, 0]))
assert np.all(one_hot(seq)[1] == np.array([0, 1, 0, 0]))
assert np.all(one_hot(seq)[2] == np.array([0, 0, 1, 0]))
assert np.all(one_hot(seq)[3] == np.array([0, 0, 0, 1]))
assert np.all(one_hot(seq)[4] == np.array([0, 0, 0, 1]))
assert np.all(one_hot(seq)[-1] == np.array([0, 0, 0, 1]))
assert np.all(one_hot(seq)[-2] == np.array([0.25, 0.25, 0.25, 0.25]))
with pytest.raises(ValueError):
one_hot(['A', 'C'])
with pytest.raises(ValueError):
one_hot_dna(['A', 'C'])
def test_seq_dataset(intervals_file, fasta_file):
dl = SeqIntervalDl(intervals_file, fasta_file)
ret_val = dl[0]
assert np.all(ret_val['inputs'] == one_hot_dna("GT"))
assert isinstance(ret_val["inputs"], np.ndarray)
assert ret_val["inputs"].shape == (2, 4)
def __call__(self, seq):
if self.alphabet == DNA and self.neutral_alphabet == ['N'] and self.neutral_value == 0.25:
return F.one_hot_dna(seq, self.dtype)
else:
return F.one_hot(seq,
alphabet=self.alphabet,
neutral_alphabet=self.neutral_alphabet,
neutral_value=self.neutral_value,
dtype=self.dtype)
def __getitem__(self, idx):
if self.input_data_extractors is None:
self.input_data_extractors = {
"seq":
FastaStringExtractor(self.fasta_file),
"dist_polya_st":
DistToClosestLandmarkExtractor(gtf_file=self.gtf,
landmarks=["polya"])
}
interval = self.bt[idx]
out = {}
out['inputs'] = {
"seq":
one_hot_dna(
FastaStringExtractor(self.fasta_file).extract(interval)),
"dist_polya_st":
np.squeeze(DistToClosestLandmarkExtractor(gtf_file=self.gtf,
landmarks=["polya"
])([interval
]),
axis=0)
}
# use trained spline transformation to transform it
out["inputs"]["dist_polya_st"] = np.squeeze(self.transformer.transform(
out["inputs"]["dist_polya_st"][np.newaxis], warn=False),
axis=0)
if self.target_dataset is not None:
out["targets"] = np.array([self.target_dataset[idx]])
# get metadata
out['metadata'] = {}
out['metadata']['ranges'] = {}
out['metadata']['ranges']['chr'] = interval.chrom
out['metadata']['ranges']['start'] = interval.start
out['metadata']['ranges']['end'] = interval.stop
out['metadata']['ranges']['id'] = interval.name
out['metadata']['ranges']['strand'] = interval.strand
return out
def __getitem__(self, idx):
if self.fasta_extractor is None:
self.fasta_extractor = FastaStringExtractor(self.fasta_file)
interval = self.bt[idx]
# Intervals need to be 1000bp wide
assert interval.stop - interval.start == 1000
if self.targets is not None:
y = self.targets.iloc[idx].values
else:
y = {}
# Run the fasta extractor
seq = one_hot_dna(self.fasta_extractor.extract(interval), dtype=np.float32) # TODO: Remove additional dtype after kipoiseq gets a new release
return {
"inputs": seq,
"targets": y,
"metadata": {
"ranges": GenomicRanges.from_interval(interval)
}
}