def test_ambiguous_mask2(tmpdir):
# only ambigous regions are present
bed_file = write_tmp(
'chr1\t1\t2\t1\t0\nchr2\t1\t3\t0\t1\nchr3\t1\t3\t-1\t-1', tmpdir)
bt = BedDataset(bed_file, ambiguous_mask=-1)
assert len(bt) == 2
assert np.all(bt.get_targets().max(axis=1) >= 0)
def __init__(self,
intervals_file,
fasta_file,
bigwigs,
track_width=2000,
incl_chromosomes=None,
excl_chromosomes=None,
num_chr_fasta=False):
self.num_chr_fasta = num_chr_fasta
self.intervals_file = intervals_file
self.fasta_file = fasta_file
self.bigwigs = bigwigs
self.incl_chromosomes = incl_chromosomes
self.excl_chromosomes = excl_chromosomes
self.track_width = track_width
self.tsv = BedDataset(
self.intervals_file,
num_chr=self.num_chr_fasta,
bed_columns=4,
ignore_targets=True,
incl_chromosomes=incl_chromosomes,
excl_chromosomes=excl_chromosomes,
)
self.fasta_extractor = None
self.bigwig_extractors = None
def test_more_columns(tmpdir):
bed_file = write_tmp(
'chr1\t1\t2\tinterval1\t1\t0\nchr2\t1\t3\tinterval2\t0\t1', tmpdir)
with pytest.raises(Exception):
bt = BedDataset(bed_file, label_dtype=bool)
bt = BedDataset(bed_file, bed_columns=4, label_dtype=bool)
assert bt[0][0].name == 'interval1'
assert bt[1][0].name == 'interval2'
with pytest.raises(Exception):
bt = BedDataset(bed_file)
def test_ambiguous_mask(tmpdir):
bed_file = write_tmp(
'chr1\t1\t2\t1\t0\nchr2\t1\t3\t0\t1\nchr3\t1\t3\t0\t-1', tmpdir)
bt = BedDataset(bed_file)
assert len(bt) == 3
assert np.all(bt[2][1] == np.array([0, -1]))
# same as before
bt = BedDataset(bed_file, ambiguous_mask=-1)
assert len(bt) == 3
assert np.all(bt[2][1] == np.array([0, -1]))
assert np.all(bt.get_targets().max(axis=1) >= 0)
def test_incl_excl_chromosomes(tmpdir):
bed_file = write_tmp(
'chr1\t1\t2\t1\t0\nchr2\t1\t3\t0\t1\nchr3\t1\t3\t0\t1', tmpdir)
bt = BedDataset(bed_file)
assert len(bt) == 3
bt = BedDataset(bed_file, incl_chromosomes=['chr1'])
assert len(bt) == 1
assert bt[0][0] == Interval("chr1", 1, 2)
bt = BedDataset(bed_file, excl_chromosomes=['chr1'])
assert len(bt) == 2
assert bt[0][0] == Interval("chr2", 1, 3)
def test_bed3_labels(tmpdir):
bed_file = write_tmp('chr1\t1\t2\t1\t0\nchr1\t1\t3\t0\t1', tmpdir)
bt = BedDataset(bed_file)
assert np.all(bt.get_targets() == np.array([[1, 0], [0, 1]]))
assert len(bt) == 2
assert bt.n_tasks == 2
assert np.all(bt.df[0] == 'chr1')
assert bt[0][0] == Interval("chr1", 1, 2)
assert np.all(bt[0][1] == np.array([1, 0]))
assert bt[1][0] == Interval("chr1", 1, 3)
assert np.all(bt[1][1] == np.array([0, 1]))
assert len(bt) == 2
def __init__(self, intervals_file, fasta_file, ignore_targets=True):
self.bt = BedDataset(intervals_file,
bed_columns=3,
ignore_targets=ignore_targets)
self.fasta_file = fasta_file
self.fasta_extractor = None
self.transform = OneHot() # one-hot encode DNA sequence
def test_bed3(tmpdir):
bed_file = write_tmp('chr1\t1\t2\nchr1\t1\t3', tmpdir)
bt = BedDataset(bed_file)
assert bt.n_tasks == 0
assert len(bt) == 2
assert np.all(bt.df[0] == 'chr1')
assert bt[0] == (Interval("chr1", 1, 2), {})
assert bt[1] == (Interval("chr1", 1, 3), {})
def test_tsvreader(tsv_file, num_chr, label_dtype):
ds = BedDataset(tsv_file, label_dtype=label_dtype, num_chr=num_chr)
interval, labels = ds[0]
assert isinstance(interval, Interval)
if not num_chr:
assert interval.chrom.startswith("chr")
assert isinstance(labels[0], label_dtype)
assert interval.start == 2
assert interval.end == 4
def test_label_dtype(tmpdir):
bed_file = write_tmp('chr1\t1\t2\t1\t0\nchr2\t1\t3\t0\t1', tmpdir)
bt = BedDataset(bed_file, label_dtype=bool)
assert len(bt) == 2
assert bt[0][1].dtype == bool
assert bt.get_targets().dtype == bool
def test_num_chr(tmpdir):
bed_file = write_tmp(
'chr1\t1\t2\t1\t0\nchr2\t1\t3\t0\t1\nchr3\t1\t3\t0\t-1', tmpdir)
bt = BedDataset(bed_file, num_chr=True)
assert len(bt) == 3
assert bt[0][0].chrom == '1'
class ActivityDataset(Dataset):
"""
Args:
intervals_file: bed4 file containing chrom start end name
fasta_file: file path; Genome sequence
label_dtype: label data type
num_chr_fasta: if True, the tsv-loader will make sure that the chromosomes
don't start with chr
"""
def __init__(self,
intervals_file,
fasta_file,
bigwigs,
track_width=2000,
incl_chromosomes=None,
excl_chromosomes=None,
num_chr_fasta=False):
self.num_chr_fasta = num_chr_fasta
self.intervals_file = intervals_file
self.fasta_file = fasta_file
self.bigwigs = bigwigs
self.incl_chromosomes = incl_chromosomes
self.excl_chromosomes = excl_chromosomes
self.track_width = track_width
self.tsv = BedDataset(
self.intervals_file,
num_chr=self.num_chr_fasta,
bed_columns=4,
ignore_targets=True,
incl_chromosomes=incl_chromosomes,
excl_chromosomes=excl_chromosomes,
)
self.fasta_extractor = None
self.bigwig_extractors = None
def __len__(self):
return len(self.tsv)
def __getitem__(self, idx):
if self.fasta_extractor is None:
self.fasta_extractor = FastaExtractor(self.fasta_file)
self.bigwig_extractors = {
a: [BigwigExtractor(f) for f in self.bigwigs[a]]
for a in self.bigwigs
}
interval, labels = self.tsv[idx]
interval = resize_interval(interval, 1000)
# Intervals need to be 1000bp wide
assert interval.stop - interval.start == 1000
# Run the fasta extractor
seq = np.squeeze(self.fasta_extractor([interval]))
interval_wide = resize_interval(deepcopy(interval), self.track_width)
return {
"inputs": {
"seq": seq
},
"targets": {
a:
sum([e([interval_wide])[0]
for e in self.bigwig_extractors[a]]).sum()
for a in self.bigwig_extractors
},
"metadata": {
"ranges":
GenomicRanges(interval.chrom, interval.start, interval.stop,
str(idx)),
"ranges_wide":
GenomicRanges.from_interval(interval_wide),
"name":
interval.name
}
}
def get_targets(self):
return self.tsv.get_targets()
def __init__(self,
intervals_file,
fasta_file,
vcf_file,
chr_order_file,
vcf_file_tbi=None,
strand_column=6,
id_column=4,
num_chr=True):
# workaround for test
if vcf_file_tbi is not None and vcf_file_tbi.endswith("vcf_file_tbi"):
os.rename(vcf_file_tbi,
vcf_file_tbi.replace("vcf_file_tbi", "vcf_file.tbi"))
self.num_chr_fasta = num_chr
self.intervals_file = intervals_file
self.fasta_file = fasta_file
self.vcf_file = vcf_file
self.chr_order_file = chr_order_file
self.strand_column = strand_column - 1
self.id_column = id_column - 1
self.force_upper = True
# "Parse" bed file
self.bed = BedDataset(self.intervals_file,
num_chr=self.num_chr_fasta,
bed_columns=3,
label_dtype=str,
ignore_targets=False)
# Intersect bed and vcf using bedtools
# bedtools c flag: for each bed interval, counts number of vcf entries it overlaps
bed_tool = pybedtools.BedTool(self.intervals_file)
intersect_counts = list(
bed_tool.intersect(self.vcf_file,
c=True,
sorted=True,
g=self.chr_order_file))
intersect_counts = np.array(
[isect.count for isect in intersect_counts])
# Retain only those transcripts that intersect a variant
utr5_bed = self.bed.df
id_col = utr5_bed.iloc[:, self.id_column]
retain_transcripts = utr5_bed[
intersect_counts > 0].iloc[:, self.id_column]
utr5_bed = utr5_bed[utr5_bed.iloc[:, self.id_column].isin(
retain_transcripts)]
# Aggregate 5utr positions per transcript
tuples = list(zip(utr5_bed.iloc[:, 1], utr5_bed.iloc[:, 2]))
pos = [[x] for x in tuples]
id_chr_strand = list(
zip(utr5_bed.iloc[:, self.id_column], utr5_bed.iloc[:, 0],
utr5_bed.iloc[:, self.strand_column]))
utr5_bed_posaggreg = pd.DataFrame({
"pos": pos,
"id_chr_strand": id_chr_strand
})
utr5_bed_posaggreg = utr5_bed_posaggreg.groupby("id_chr_strand").agg(
{'pos': 'sum'})
# Rebuild "bed"
utr5_bed_posaggreg["id"] = [x[0] for x in utr5_bed_posaggreg.index]
utr5_bed_posaggreg["chr"] = [x[1] for x in utr5_bed_posaggreg.index]
utr5_bed_posaggreg["strand"] = [x[2] for x in utr5_bed_posaggreg.index]
self.bed = utr5_bed_posaggreg.reset_index()[[
"id", "chr", "pos", "strand"
]]
self.fasta_extractor = None
self.vcf = None
self.vcf_extractor = None
