class Cluster(object):
"""Analyzes a group of samples."""
all_paths = """
/reads/all_reads.fasta
/otus/
/logs/
/metadata.csv
"""
def __repr__(self):
return '<%s object "%s" with %i samples>' % (
self.__class__.__name__, self.name, len(self.samples))
def __iter__(self):
return iter(self.samples)
def __len__(self):
return len(self.samples)
def __getitem__(self, key):
return self.samples[key]
@property
def first(self):
return self.children[0]
@property
def count_seq(self):
return sum([len(sample) for sample in self])
def __init__(self, samples, name, base_dir=None):
# Save samples #
self.name = name
self.samples, self.children = samples, samples
# Check names are unique #
names = [s.short_name for s in samples if s.used]
assert len(names) == len(set(names))
# Figure out pools #
self.pools = list(set([s.pool for s in self.samples]))
self.pools.sort(key=lambda x: x.id_name)
# Load them #
for p in self.pools:
p.load()
# Dir #
if base_dir: self.base_dir = base_dir
else:
self.base_dir = illumitag.view_dir + "clusters/" + self.name + '/'
self.p = AutoPaths(self.base_dir, self.all_paths)
# Runner #
self.runner = ClusterRunner(self)
# FASTA #
self.reads = FASTA(self.p.all_reads_fasta)
# OTU picking #
self.otu_uparse = UparseOTUs(self)
self.otu_uclust = UclustOTUs(self)
self.otu_cdhit = CdhitOTUs(self)
# Reporting #
self.reporter = ClusterReporter(self)
def run(self, *args, **kwargs):
self.runner.run(*args, **kwargs)
def run_slurm(self, *args, **kwargs):
self.runner.run_slurm(*args, **kwargs)
def process_samples(self):
for sample in tqdm(self):
sample.process()
def combine_reads(self):
paths = [sample.fasta.path for sample in self]
shell_output('cat %s > %s' % (' '.join(paths), self.reads))
def set_size(self, length):
"""Trim all sequences to a specific length starting from the end"""
self.size_trimmed = FASTA(new_temp_path())
def trim_iterator(reads):
for read in reads:
if len(read) < length: continue
yield read[-length:]
self.size_trimmed.write(trim_iterator(self.reads))
self.size_trimmed.close()
# Replace it #
self.reads.remove()
shutil.move(self.size_trimmed, self.reads)
def run_uparse(self):
self.otu_uparse.run()
@property
def metadata(self):
return pandas.DataFrame([s.info for s in self],
index=[s.short_name for s in self])
def export_metadata(self):
self.metadata.to_csv(self.p.metadata, sep='\t', encoding='utf-8')
# Make fraction graph #
proj.graphs[-1].plot()
# Get statistics #
proj.reporter.fraction_discarded
# Get clustering values #
r1, r2 = list(set([p.run for p in proj]))
r1.parse_report_xml()
r2.parse_report_xml()
print float(r1.report_stats['fwd']['DensityPF']) / float(r1.report_stats['fwd']['DensityRaw'])
print float(r2.report_stats['fwd']['DensityPF']) / float(r2.report_stats['fwd']['DensityRaw'])
# Check below 400 bp sequences #
folder = DirectoryPath(illumitag.projects['evaluation'].base_dir + "below_400/")
over = FASTA(folder + "reads.fasta")
def over_iterator(reads, max_length=400):
for read in reads:
if len(read) <= max_length: yield read
over.create()
for pool in pools: over.add_iterator(over_iterator(pool.good_barcodes.assembled.good_primers.qual_filtered))
over.close()
over.graphs[-1].plot()
crest = SimpleCrestTaxonomy(over, folder)
crest.assign()
crest.composition.graph.plot()
rdp = SimpleRdpTaxonomy(over, folder)
rdp.assign()
rdp.composition.graph.plot()