def chain_fusion_samples(dirs,
names,
group_filename,
gff_filename,
count_filename,
field_to_use='count_fl',
fuzzy_junction=0,
fastq_filename=None):
for d in dirs.values():
sample_sanity_check(os.path.join(d, group_filename),\
os.path.join(d, gff_filename),\
os.path.join(d, count_filename),\
os.path.join(d, fastq_filename) if fastq_filename is not None else None)
count_header, count_info = read_count_info(count_filename, dirs,
field_to_use)
# some names may already start with "tmp_" which means they are intermediate results that have already been chained
# find the first non "tmp_" and start from there
if names[0].startswith('tmp_'):
chain = []
for start_i, name in enumerate(names):
if name.startswith('tmp_'):
chain.append(name[4:])
else:
break
# start_i, name now points at the first "non-tmp" sample
# we want to go to the last tmp_ sample and read it
name = names[start_i -
1][4:] # this is the last tmp_ sample, let's read it
o = sp.MegaPBTreeFusion('tmp_'+name+'.gff', 'tmp_'+name+'.group.txt', self_prefix='tmp_'+name, \
internal_fuzzy_max_dist=fuzzy_junction, \
fastq_filename='tmp_'+name+'.rep.fq' if fastq_filename is not None else None)
#chain.append(name) # no need, already done above
else: # everything is new, start fresh
name = names[0]
d = dirs[name]
chain = [name]
o = sp.MegaPBTreeFusion(os.path.join(d, gff_filename), os.path.join(d, group_filename), \
self_prefix=name, internal_fuzzy_max_dist=fuzzy_junction, \
fastq_filename=os.path.join(d, fastq_filename) if fastq_filename is not None else None)
start_i = 1
for name in names[start_i:]:
assert not name.startswith('tmp_')
d = dirs[name]
o.add_sample(os.path.join(d, gff_filename), os.path.join(d, group_filename), \
sample_prefix=name, output_prefix='tmp_'+name, \
fastq_filename=os.path.join(d, fastq_filename) if fastq_filename is not None else None)
o = sp.MegaPBTreeFusion('tmp_'+name+'.gff', 'tmp_'+name+'.group.txt', self_prefix='tmp_'+name, \
internal_fuzzy_max_dist=fuzzy_junction, \
fastq_filename='tmp_'+name+'.rep.fq' if fastq_filename is not None else None)
chain.append(name)
# now recursively chain back by looking at mega_info.txt!!!
d = {} # ex: (tmp_1009, PB.1.1) --> mega info dict
for c in chain[1:]:
for r in DictReader(open('tmp_' + c + '.mega_info.txt'),
delimiter='\t'):
d['tmp_' + c, r['pbid']] = r
f1 = open('all_samples.chained_ids.txt', 'w')
f1.write("superPBID")
f2 = open('all_samples.chained_count.txt', 'w')
f2.write("superPBID")
for c in chain:
f1.write('\t' + c)
f2.write('\t' + c)
f1.write('\n')
f2.write('\n')
reader = DictReader(open('tmp_' + chain[-1] + '.mega_info.txt'),
delimiter='\t')
for r in reader:
saw_NA = False
r0 = r
answer = defaultdict(lambda: 'NA') # ex: 1009 --> PB.1.1
answer2 = defaultdict(lambda: 'NA') # ex: 1009 --> count
answer[chain[-1]] = r[chain[-1]]
if r[chain[-1]] != 'NA':
answer2[chain[-1]] = count_info[chain[-1], answer[chain[-1]]]
for c in chain[::-1][
1:
-1]: # the first sample does not have tmp_, because it's not a chain
if r['tmp_' + c] == 'NA':
saw_NA = True
break
else:
r2 = d['tmp_' + c, r['tmp_' + c]]
answer[c] = r2[c]
if answer[c] != 'NA':
answer2[c] = count_info[c, answer[c]]
r = r2
if not saw_NA:
answer[chain[0]] = r[chain[0]]
if answer[chain[0]] != 'NA':
answer2[chain[0]] = count_info[chain[0], answer[chain[0]]]
f1.write(r0['pbid'])
f2.write(r0['pbid'])
for c in chain:
f1.write("\t" +
answer[c]) # each tissue still share the same PB id
f2.write("\t" + str(answer2[c]))
f1.write('\n')
f2.write('\n')
f1.close()
f2.close()
shutil.copyfile('tmp_' + chain[-1] + '.gff', 'all_samples.chained.gff')
if fastq_filename is not None:
shutil.copyfile('tmp_' + chain[-1] + '.rep.fq',
'all_samples.chained.rep.fq')
print("Chained output written to:", file=sys.stderr)
print("all_samples.chained.gff", file=sys.stderr)
print(f1.name, file=sys.stderr)
print(f2.name, file=sys.stderr)
if fastq_filename is not None:
print("all_samples.chained.rep.fq", file=sys.stderr)
def chain_fusion_samples(
dirs: List[str],
names: List[str],
group_filename: Union[str, Path],
gff_filename: Union[str, Path],
count_filename: Union[str, Path],
field_to_use: str = "count_fl",
fuzzy_junction: int = 0,
fastq_filename: Optional[Union[str, Path]] = None,
) -> None:
for d in dirs.values():
sample_sanity_check(
Path(d, group_filename),
Path(d, gff_filename),
Path(d, count_filename),
Path(d, fastq_filename) if fastq_filename is not None else None,
)
count_info = read_count_info(count_filename, dirs, field_to_use)
# some names may already start with "tmp_" which means they are intermediate results that have already been chained
# find the first non "tmp_" and start from there
if names[0].startswith("tmp_"):
chain = []
for start_i, name in enumerate(names):
if name.startswith("tmp_"):
chain.append(name[4:])
else:
break
# start_i, name now points at the first "non-tmp" sample
# we want to go to the last tmp_ sample and read it
name = names[start_i -
1][4:] # this is the last tmp_ sample, let's read it
o = sp.MegaPBTreeFusion(
gff_filename=f"tmp_{name}.gff",
group_filename=f"tmp_{name}.group.txt",
self_prefix=f"tmp_{name}",
internal_fuzzy_max_dist=fuzzy_junction,
fastq_filename=f"tmp_{name}.rep.fq"
if fastq_filename is not None else None,
)
# chain.append(name) # no need, already done above
else: # everything is new, start fresh
name = names[0]
d = dirs[name]
chain = [name]
o = sp.MegaPBTreeFusion(
gff_filename=Path(d, gff_filename),
group_filename=Path(d, group_filename),
self_prefix=name,
internal_fuzzy_max_dist=fuzzy_junction,
fastq_filename=Path(d, fastq_filename)
if fastq_filename is not None else None,
)
start_i = 1
for name in names[start_i:]:
assert not name.startswith("tmp_")
d = dirs[name]
o.add_sample(
gff_filename=Path(d, gff_filename),
group_filename=Path(d, group_filename),
sample_prefix=name,
output_prefix=f"tmp_{name}",
fastq_filename=Path(d, fastq_filename)
if fastq_filename is not None else None,
)
o = sp.MegaPBTreeFusion(
gff_filename=f"tmp_{name}.gff",
group_filename=f"tmp_{name}.group.txt",
self_prefix=f"tmp_{name}",
internal_fuzzy_max_dist=fuzzy_junction,
fastq_filename=f"tmp_{name}.rep.fq"
if fastq_filename is not None else None,
)
chain.append(name)
# now recursively chain back by looking at mega_info.txt!!!
d = {} # ex: (tmp_1009, PB.1.1) --> mega info dict
for c in chain[1:]:
for r in DictReader(open(f"tmp_{c}.mega_info.txt"), delimiter="\t"):
d[f"tmp_{c}", r["pbid"]] = r
with open("all_samples.chained_ids.txt",
"w") as f1, open("all_samples.chained_count.txt", "w") as f2:
f1.write("superPBID")
f2.write("superPBID")
for c in chain:
f1.write(f" {c}")
f2.write(f" {c}")
f1.write("\n")
f2.write("\n")
reader = DictReader(Path(f"tmp_{chain[-1]}.mega_info.txt").open(),
delimiter="\t")
for r in reader:
saw_NA = False
r0 = r
answer = defaultdict(lambda: "NA") # ex: 1009 --> PB.1.1
answer2 = defaultdict(lambda: "NA") # ex: 1009 --> count
answer[chain[-1]] = r[chain[-1]]
if r[chain[-1]] != "NA":
answer2[chain[-1]] = count_info[chain[-1], answer[chain[-1]]]
for c in chain[::-1][
1:
-1]: # the first sample does not have tmp_, because it's not a chain
if r[f"tmp_{c}"] == "NA":
saw_NA = True
break
else:
r2 = d[f"tmp_{c}", r[f"tmp_{c}"]]
answer[c] = r2[c]
if answer[c] != "NA":
answer2[c] = count_info[c, answer[c]]
r = r2
if not saw_NA:
answer[chain[0]] = r[chain[0]]
if answer[chain[0]] != "NA":
answer2[chain[0]] = count_info[chain[0], answer[chain[0]]]
f1.write(r0["pbid"])
f2.write(r0["pbid"])
for c in chain:
f1.write(
f" {answer[c]}") # each tissue still share the same PB id
f2.write(f" {str(answer2[c])}")
f1.write("\n")
f2.write("\n")
shutil.copyfile(f"tmp_{chain[-1]}.gff", "all_samples.chained.gff")
if fastq_filename is not None:
shutil.copyfile(f"tmp_{chain[-1]}.rep.fq",
"all_samples.chained.rep.fq")
logger.info("Chained output written to:")
logger.info("all_samples.chained.gff")
logger.info(f1.name)
logger.info(f2.name)
if fastq_filename is not None:
logger.info("all_samples.chained.rep.fq")