def open_trackfactory(parser, options):
tf = TrackFactory(options.file, "r+")
if tf.has_track(options.name):
tf.close()
parser.error("trackfactory '%s' already has track named '%s'" %
(options.file, options.name))
return tf
def create_trackfactory(parser, options):
if options.file is None:
parser.error("no filename specified")
if os.path.exists(options.file):
parser.error("file %s exists, cannot overwrite" % (options.file))
# get references
if options.refs_type == "chrom_sizes":
if not os.path.exists(options.refs):
parser.error("references file %s not found" % (options.refs))
refs = [tuple(line.strip().split(None,2)) for line in open(options.refs)]
elif options.refs_type == "sam":
if not os.path.exists(options.refs):
parser.error("references file %s not found" % (options.refs))
refs = get_refs_from_sam(options.refs)
elif options.refs_type == "bam":
if not os.path.exists(options.refs):
parser.error("references file %s not found" % (options.refs))
refs = get_refs_from_bam(options.refs)
elif options.refs_type == "bowtie_index":
if not check_executable("bowtie-inspect"):
parser.error("'bowtie-inspect' executable not found")
refs = get_refs_from_bowtie_index(options.refs)
tf = TrackFactory(options.file, 'w', refs=refs)
tf.close()
logging.info("created trackfactory %s using refs from %s (%s)" %
(options.file, options.refs, options.refs_type))
def list_tracks(parser, options):
if options.file is None:
parser.error("no filename specified")
if not os.path.exists(options.file):
parser.error("file '%s' not found" % (options.file))
tf = TrackFactory(options.file, "r")
print '\t'.join(["track_name", "track_type"])
print '\t'.join(["==========", "=========="])
for t in tf:
print '\t'.join(t)
tf.close()
def setUp(self):
filename = mktemp(prefix="tmp", suffix=".h5")
self.filename = filename
self.length = 100
self.isize_max = 10
self.refs = (('gene1', self.length), ('gene2', 10))
self.tf = TrackFactory(self.filename, 'w', refs=self.refs)
def remove_track(parser, options):
tf = TrackFactory(options.file, "r+")
if not tf.has_track(options.name):
tf.close()
parser.error("trackfactory '%s' does not contain track '%s'" %
(options.file, options.name))
tf.delete_track(options.name)
tf.close()
logging.info("removed track '%s' from trackfactory '%s'" %
(options.name, options.file))
def view_track(parser, options):
tf = TrackFactory(options.file, "r")
if not tf.has_track(options.name):
tf.close()
parser.error("trackfactory '%s' does not contain track '%s'" %
(options.file, options.name))
region = parse_interval(options.region)
t = tf.get_track(options.name)
track_type = t.get_type()
logging.debug("opened track '%s' type '%s'" % (options.name, track_type))
if track_type == SequenceTrack.__name__:
print t[region]
if track_type == ArrayTrack.__name__:
if options.file_type == "bedgraph":
t.tobedgraph(region, sys.stdout)
else:
print t[region]
elif track_type == VectorTrack.__name__:
if options.file_type == "bedgraph":
readnum = options.readnum
allele = options.allele
t.tobedgraph(region, sys.stdout, norm=True,
read=readnum, allele=allele)
else:
print t[region]
elif track_type == RnaseqTrack.__name__:
cov_track = t.get_coverage_track()
print cov_track.density(region)
junc_track = t.get_junction_track()
print junc_track[region]
logging.debug("done")
tf.close()
class TestVectorTrack(unittest.TestCase):
def setUp(self):
filename = mktemp(prefix="tmp", suffix=".h5")
self.filename = filename
self.length = 100
self.isize_max = 10
self.refs = (('gene1', self.length), ('gene2', 10))
self.tf = TrackFactory(self.filename, 'w', refs=self.refs)
def tearDown(self):
self.tf.close()
if os.path.exists(self.filename):
os.remove(self.filename)
def test_get_channel_dict(self):
# default
d = get_channel_dict(is_pe=False, is_strand=False, is_allele=False)
for v in d.values():
self.assertEqual(v, (0,))
# paired end
d = get_channel_dict(is_pe=True, is_strand=False, is_allele=False)
self.assertEqual(d[(0,NO_STRAND,"A")], (0,))
self.assertEqual(d[(0,POS_STRAND,"G")], (0,))
self.assertEqual(d[(1,NEG_STRAND,"C")], (1,))
self.assertEqual(d[(1,POS_STRAND,"T")], (1,))
self.assertEqual(d[(None,NEG_STRAND,"N")], (0,1))
# strand
d = get_channel_dict(is_pe=False, is_strand=True, is_allele=False)
self.assertEqual(d[(0,NO_STRAND,"A")], (0,1))
self.assertEqual(d[(1,POS_STRAND,"G")], (0,))
self.assertEqual(d[(0,NEG_STRAND,"C")], (1,))
# allele
d = get_channel_dict(is_pe=False, is_strand=False, is_allele=True)
self.assertEqual(d[(0,NO_STRAND,"A")], (0,))
self.assertEqual(d[(1,POS_STRAND,"G")], (1,))
self.assertEqual(d[(0,NEG_STRAND,"C")], (2,))
self.assertEqual(d[(0,NEG_STRAND,"T")], (3,))
self.assertEqual(d[(0,NO_STRAND,"N")], (0,1,2,3,))
# pe/strand
d = get_channel_dict(is_pe=True, is_strand=True, is_allele=False)
self.assertEqual(d[(None,NO_STRAND,"A")], (0,1,2,3))
self.assertEqual(d[(1,POS_STRAND,"G")], (1,))
self.assertEqual(d[(0,NEG_STRAND,"C")], (2,))
self.assertEqual(d[(1,NEG_STRAND,"T")], (3,))
self.assertEqual(d[(None,NEG_STRAND,"N")], (2,3))
# pe/allele
d = get_channel_dict(is_pe=True, is_strand=False, is_allele=True)
self.assertEqual(d[(0,NO_STRAND,"A")], (0,))
self.assertEqual(d[(0,POS_STRAND,"G")], (2,))
self.assertEqual(d[(1,NEG_STRAND,"C")], (5,))
self.assertEqual(d[(1,POS_STRAND,"T")], (7,))
self.assertEqual(d[(None,NEG_STRAND,"N")], (0,1,2,3,4,5,6,7))
# strand/allele
d = get_channel_dict(is_pe=False, is_strand=True, is_allele=True)
self.assertEqual(d[(None,NO_STRAND,"A")], (0,1))
self.assertEqual(d[(1,POS_STRAND,"G")], (2,))
self.assertEqual(d[(0,NEG_STRAND,"C")], (5,))
self.assertEqual(d[(1,NEG_STRAND,"T")], (7,))
self.assertEqual(d[(None,NEG_STRAND,"N")], (1,3,5,7))
# pe/strand/allele
d = get_channel_dict(is_pe=True, is_strand=True, is_allele=True)
self.assertEqual(d[(None,NO_STRAND,"A")], (0,1,2,3))
self.assertEqual(d[(1,POS_STRAND,"G")], (5,))
self.assertEqual(d[(0,NEG_STRAND,"C")], (10,))
self.assertEqual(d[(1,NO_STRAND,"T")], (13,15))
self.assertEqual(d[(None,NO_STRAND,"N")], tuple(range(0,16)))
self.assertEqual(d[(None,NEG_STRAND,"N")], (2,3,6,7,10,11,14,15))
self.assertEqual(d[(1,POS_STRAND,"N")], (1,5,9,13))
def test_fromintervals(self):
dtype = "i"
intervals1, correct1 = random_intervals(1000, self.length,
self.isize_max, dtype)
# try one channel array
t = self.tf.create_track("a", VectorTrack, dtype,
pe=False, strand=False, allele=False)
t.fromintervals(iter(intervals1))
self.assertTrue(np.all(t["gene1"][:,0] == correct1))
# try three channel array
intervals2, correct2 = random_intervals(1000, self.length,
self.isize_max, dtype)
intervals3, correct3 = random_intervals(1000, self.length,
self.isize_max, dtype)
t = self.tf.create_track("b", VectorTrack, dtype)
t.fromintervals(iter(intervals1))
self.assertTrue(np.all(t["gene1"][:,0] == correct1))
self.assertFalse(np.all(t["gene1"][:,0] == correct2))
self.assertFalse(np.all(t["gene1"][:,0] == correct3))
#t.fromintervals(iter(intervals2), channel=1)
#t.fromintervals(iter(intervals3), channel=2)
#self.assertTrue(np.all(t["gene1"][:,1] == correct2))
#self.assertFalse(np.all(t["gene1"][:,1] == correct3))
#self.assertTrue(np.all(t["gene1"][:,2] == correct3))
#self.assertFalse(np.all(t["gene1"][:,2] == correct1))
def test_stranded_intervals(self):
"""testing allocating coverage to both strands"""
dtype = "i4"
intervals1, correct1 = random_stranded_intervals(100, self.length, self.isize_max, dtype)
total_cov = correct1.sum()
t = self.tf.create_track("a", VectorTrack, strand=True)
# test loading from intervals
t.fromintervals(iter(intervals1))
self.assertTrue(np.all(t["gene1"] == correct1))
# test count function
intervals2, correct2 = random_intervals(1000, self.length, self.isize_max, dtype)
for ival in intervals2:
ref = ival.ref
start = ival.start
end = ival.end
strand = ival.strand
val = ival.value
# check plus strand
# count
mycount = t.count((ref, start, end, POS_STRAND, val))
correctcount = correct1[start:end,0].sum()
self.assertAlmostEqual(mycount, correctcount)
# coverage
mycov = t.coverage((ref, start, end, POS_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end,0] / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
# density
mydens = t.density((ref, start, end, POS_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
# check minus strand
# count
mycount = t.count((ref, start, end, NEG_STRAND, val))
correctcount = correct1[start:end,1].sum()
self.assertAlmostEqual(mycount, correctcount)
# coverage
mycov = t.coverage((ref, start, end, NEG_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end,1] / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
# density
mydens = t.density((ref, start, end, NEG_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
# check both strands
mycount = t.count((ref, start, end, NO_STRAND, val))
correctcount = correct1[start:end].sum()
self.assertAlmostEqual(mycount, correctcount)
# cov
mycov = t.coverage((ref, start, end, NO_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end].sum(axis=1) / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
# density
mydens = t.density((ref, start, end, NO_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
def test_stranded_allele_intervals(self):
"""testing coverage with allele frequencies"""
dtype = "f"
channel_dict = get_channel_dict(False, True, True)
pos_strand_channels = channel_dict[(None,POS_STRAND,None)]
neg_strand_channels = channel_dict[(None,NEG_STRAND,None)]
intervals1, correct1 = \
random_stranded_allele_intervals(100, self.length,
self.isize_max, dtype)
total_cov = correct1.sum()
t = self.tf.create_track("a", VectorTrack, strand=True, allele=True)
# test loading from intervals
t.fromintervals(iter(intervals1))
self.assertTrue(np.all(t["gene1"] == correct1))
# test count function
intervals2, correct2 = random_intervals(10, self.length, self.isize_max, dtype)
for ival in intervals2:
ref = ival.ref
start = ival.start
end = ival.end
strand = ival.strand
val = ival.value
# check plus strand
mycount = t.count((ref, start, end, POS_STRAND, val))
correctcount = correct1[start:end,pos_strand_channels].sum()
self.assertAlmostEqual(mycount, correctcount)
mycov = t.coverage((ref, start, end, POS_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end,pos_strand_channels].sum(axis=1) / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
mydens = t.density((ref, start, end, POS_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
# check minus strand
mycount = t.count((ref, start, end, NEG_STRAND, val))
correctcount = correct1[start:end,neg_strand_channels].sum()
self.assertAlmostEqual(mycount, correctcount)
mycov = t.coverage((ref, start, end, NEG_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end,neg_strand_channels].sum(axis=1) / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
mydens = t.density((ref, start, end, NEG_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
# check both strands
mycount = t.count((ref, start, end, NO_STRAND, val))
correctcount = correct1[start:end].sum()
self.assertAlmostEqual(mycount, correctcount)
mycov = t.coverage((ref, start, end, NO_STRAND, val), multiplier=1.0)
correctcov = correct1[start:end].sum(axis=1) / float(total_cov)
self.assertTrue(np.allclose(mycov, correctcov, atol=1e-4))
mydens = t.density((ref, start, end, NO_STRAND, val), multiplier=1.0)
correctdens = correctcount / float(total_cov * (end - start))
self.assertTrue(np.allclose(mydens, correctdens, atol=1e-4))
def main():
logging.basicConfig(level=logging.DEBUG,
format="%(asctime)s - %(levelname)s - %(message)s")
parser = argparse.ArgumentParser()
parser.add_argument("--stranded", dest="stranded", action="store_true", default=False)
parser.add_argument("--ambiguous", dest="ambiguous", action="store_true", default=False)
parser.add_argument("--aliases", dest="alias_file", default=None)
parser.add_argument("bed")
parser.add_argument("track_files", nargs="+")
options = parser.parse_args()
alias_dict = {}
alias_header = []
if options.alias_file is not None:
for line in open(options.alias_file):
if line.startswith("#"):
alias_header = line.strip()[1:].split('\t')
continue
fields = line.strip().split('\t')
alias_dict[fields[0]] = fields[1:]
header_fields = alias_header + ["gene_name", "gene_interval", "gene_length"]
tracks = []
for track_path in options.track_files:
name, path = track_path.split("@")
file_path, h5_path = path.split(":")
tf = TrackFactory(file_path, "r")
t = tf.get_track(h5_path)
tracks.append((name, tf, t, set(t.get_rnames())))
if options.stranded:
header_fields.append("%s_sense" % name)
header_fields.append("%s_antisense" % name)
else:
header_fields.append(name)
# output header
print '\t'.join(map(str, header_fields))
# read genes
if options.ambiguous:
genes = list(BedGene.parse(open(options.bed)))
else:
genes = filter_strand_conflicts(options.bed)
# get counts
for g in genes:
alias_fields = alias_dict.get(g.name, ["None"] * len(alias_header))
fields = ([g.name] + alias_fields +
["%s[%s]:%d-%d" % (g.chrom, g.strand, g.tx_start, g.tx_end),
sum((end-start) for start,end in g.exons)])
sense_strand = NEG_STRAND if g.strand == "+" else POS_STRAND
antisense_strand = int(not sense_strand)
rname_found = False
for name, tf, t, rnames in tracks:
if g.chrom not in rnames:
continue
rname_found = True
if options.stranded:
sense_count = 0
antisense_count = 0
for start, end in g.exons:
sense_count += t.count((g.chrom, start, end, sense_strand))
antisense_count += t.count((g.chrom, start, end, antisense_strand))
fields.append(sense_count)
fields.append(antisense_count)
else:
count = 0
for start, end in g.exons:
count += t.count((g.chrom, start, end))
fields.append(count)
if rname_found:
print '\t'.join(map(str, fields))
for name,tf,t,rnames in tracks:
tf.close()
