def cross_analysis(iteration, raw):
"""Cross analysis: exclusive and part of shared&ctrl"""
nonlocal shared_counts, shared_score
nonlocal shared_ctrl_counts, shared_ctrl_score
def partial_shared_update(i):
"""Perform shared and shared-control taxa partial evaluations"""
nonlocal shared_counts, shared_score
nonlocal shared_ctrl_counts, shared_ctrl_score
if i == 0: # 1st iteration: Initialize shared abundance and score
shared_counts.update(sub_shared_counts)
shared_score.update(sub_shared_score)
elif i < controls: # Just update shared abundance and score
shared_counts &= sub_shared_counts
shared_score &= sub_shared_score
elif i == controls: # Initialize shared-control counters
shared_counts &= sub_shared_counts
shared_score &= sub_shared_score
shared_ctrl_counts.update(sub_shared_counts)
shared_ctrl_score.update(sub_shared_score)
elif controls: # Both: Accumulate shared abundance and score
shared_counts &= sub_shared_counts
shared_score &= sub_shared_score
shared_ctrl_counts &= sub_shared_counts
shared_ctrl_score &= sub_shared_score
else: # Both: Accumulate shared abundance and score (no controls)
shared_counts &= sub_shared_counts
shared_score &= sub_shared_score
exclude: Set[Id] = set()
# Get taxids at this rank that are present in the other samples
for sample in (smpl for smpl in raws if smpl != raw):
exclude.update(taxids[sample][rank])
exclude.update(excluding) # Add explicit excluding taxa if any
output.write(f' \033[90mExclusive: From \033[0m{raw}\033[90m '
f'excluding {len(exclude)} taxa. '
f'Generating sample...\033[0m')
exclude_tree = TaxTree()
exclude_out = SampleDataById(['counts', 'scores', 'accs'])
exclude_tree.allin1(ontology=ontology,
counts=counts[raw],
scores=scores[raw],
min_taxa=mintaxas[raw],
min_rank=rank,
just_min_rank=True,
include=including,
exclude=exclude,
out=exclude_out)
exclude_out.purge_counters()
if exclude_out.counts: # Avoid adding empty samples
sample = Sample(f'{raw}_{STR_EXCLUSIVE}_{rank.name.lower()}')
samples.append(sample)
counts[sample] = exclude_out.get_counts()
accs[sample] = exclude_out.get_accs()
scores[sample] = exclude_out.get_scores()
output.write('\033[92m OK! \033[0m\n')
else:
output.write('\033[93m VOID \033[0m\n')
# Get partial abundance and score for the shared analysis
sub_shared_tree = TaxTree()
sub_shared_out = SampleDataById(['shared', 'accs'])
sub_shared_tree.allin1(ontology=ontology,
counts=counts[raw],
scores=scores[raw],
min_taxa=mintaxas[raw],
min_rank=rank,
just_min_rank=True,
include=including,
exclude=excluding,
out=sub_shared_out)
sub_shared_out.purge_counters()
# Scale scores by abundance
sub_shared_counts: SharedCounter = sub_shared_out.get_shared_counts()
sub_shared_score: SharedCounter = sub_shared_out.get_shared_scores()
sub_shared_score *= sub_shared_counts
partial_shared_update(iteration)
def process_output(
*args, **kwargs
) -> Tuple[Sample, TaxTree, SampleDataByTaxId, SampleStats, Err]:
"""
Process Centrifuge/LMAT output files (to be usually called in parallel!).
"""
# timing initialization
start_time: float = time.perf_counter()
# Recover input and parameters
target_file: Filename = args[0]
debug: bool = kwargs['debug']
is_ctrl: bool = args[1]
if debug:
print(gray('Processing'), blue('ctrl' if is_ctrl else 'sample'),
target_file, gray('...'))
sys.stdout.flush()
taxonomy: Taxonomy = kwargs['taxonomy']
mintaxa: int = kwargs['ctrlmintaxa'] if is_ctrl else kwargs['mintaxa']
minscore: Score = kwargs['ctrlminscore'] if is_ctrl else kwargs['minscore']
including: Set[TaxId] = taxonomy.including
excluding: Set[TaxId] = taxonomy.excluding
scoring: Scoring = kwargs['scoring']
lmat: bool = kwargs['lmat']
output: io.StringIO = io.StringIO(newline='')
def vwrite(*args):
"""Print only if verbose/debug mode is enabled"""
if kwargs['debug']:
output.write(' '.join(str(item) for item in args))
sample: Sample = Sample(os.path.splitext(target_file)[0])
error: Err = Err.NO_ERROR
# Read Centrifuge/LMAT output files to get abundances
read_method: Callable[[Filename, Scoring, Optional[Score]], # Input
Tuple[str, SampleStats, Counter[TaxId],
Dict[TaxId, Score]] # Output
]
if lmat:
read_method = read_lmat_output
else:
read_method = read_output
log: str
counts: Counter[TaxId]
scores: Dict[TaxId, Score]
log, stat, counts, scores = read_method(target_file, scoring, minscore)
output.write(log)
# Update field in stat about control nature of the sample
stat.is_ctrl = is_ctrl
# Move cellular_organisms counts to root, in case
if taxonomy.collapse and counts[CELLULAR_ORGANISMS]:
vwrite(gray('Moving'), counts[CELLULAR_ORGANISMS],
gray('"CELLULAR_ORGANISMS" reads to "ROOT"... '))
if counts[ROOT]:
stat.num_taxa -= 1
scores[ROOT] = (
(scores[CELLULAR_ORGANISMS] * counts[CELLULAR_ORGANISMS] +
scores[ROOT] * counts[ROOT]) /
(counts[CELLULAR_ORGANISMS] + counts[ROOT]))
else:
scores[ROOT] = scores[CELLULAR_ORGANISMS]
counts[ROOT] += counts[CELLULAR_ORGANISMS]
counts[CELLULAR_ORGANISMS] = 0
scores[CELLULAR_ORGANISMS] = NO_SCORE
# Remove root counts, in case
if kwargs['root'] and counts[ROOT]:
vwrite(gray('Removing'), counts[ROOT], gray('"ROOT" reads... '))
stat.seq = stat.seq._replace(filt=stat.seq.filt - counts[ROOT])
stat.num_taxa -= 1
counts[ROOT] = 0
scores[ROOT] = NO_SCORE
vwrite(green('OK!'), '\n')
# Building taxonomy tree
output.write(gray('Building from raw data... '))
vwrite(gray('\n Building taxonomy tree with all-in-1... '))
tree = TaxTree()
ancestors: Set[TaxId]
orphans: Set[TaxId]
ancestors, orphans = taxonomy.get_ancestors(counts.keys())
out = SampleDataByTaxId(['all'])
tree.allin1(taxonomy=taxonomy,
counts=counts,
scores=scores,
ancestors=ancestors,
min_taxa=mintaxa,
include=including,
exclude=excluding,
out=out)
out.purge_counters()
vwrite(green('OK!'), '\n')
# Give stats about orphan taxid
if debug:
vwrite(gray(' Checking taxid loss (orphans)... '))
lost: int = 0
if orphans:
for orphan in orphans:
vwrite(yellow('Warning!'), f'Orphan taxid={orphan}\n')
lost += counts[orphan]
vwrite(
yellow('WARNING!'), f'{len(orphans)} orphan taxids ('
f'{len(orphans)/len(counts):.2%} of total)\n'
f'{lost} orphan sequences ('
f'{lost/sum(counts.values()):.3%} of total)\n')
else:
vwrite(green('OK!\n'))
# Check the lost of taxids (plasmids typically) under some conditions
if debug and not excluding and not including:
vwrite(gray(' Additional checking of taxid loss... '))
lost = 0
for taxid in counts:
if not out.counts[taxid]:
lost += 1
vwrite(yellow('Warning!'), f'Lost taxid={taxid}: '
f'{taxonomy.get_name(taxid)}\n')
if lost:
vwrite(
yellow('WARNING!'), f'Lost {lost} taxids ('
f'{lost/len(counts):.2%} of total)'
'\n')
else:
vwrite(green('OK!\n'))
# Print last message and check if the sample is void
if out.counts:
output.write(sample + blue(' ctrl ' if is_ctrl else ' sample ') +
green('OK!\n'))
elif is_ctrl:
output.write(sample + red(' ctrl VOID!\n'))
error = Err.VOID_CTRL
else:
output.write(sample + blue(' sample ') + yellow('VOID\n'))
error = Err.VOID_SAMPLE
# Timing results
output.write(
gray('Load elapsed time: ') +
f'{time.perf_counter() - start_time:.3g}' + gray(' sec\n'))
print(output.getvalue())
sys.stdout.flush()
return sample, tree, out, stat, error
def control_analysis():
"""Perform last steps of control and shared controls analysis"""
nonlocal shared_ctrl_counts, shared_ctrl_score
def robust_contamination_removal():
"""Implement robust contamination removal algorithm."""
nonlocal exclude_sets, shared_crossover
def compute_qn(data: List[float], dist: str = "Gauss") -> float:
"""Compute Qn robust estimator of scale (Rousseeuw, 1993)"""
c_d: float # Select d parameter depending on the distribution
if dist == "Gauss":
c_d = 2.2219
elif dist == "Cauchy": # Heavy-tailed distribution
c_d = 1.2071
elif dist == "NegExp": # Negative exponential (asymetric)
c_d = 3.4760
else:
raise Exception(red('\nERROR! ') + 'Unknown distribution')
num: int = len(data)
sort_data = sorted(data)
pairwisedifs: List[float] = []
for (i, x_val) in enumerate(sort_data):
for y_val in sort_data[i + 1:]:
pairwisedifs.append(abs(x_val - y_val))
k: int = int(num * (num / 2 + 1) / 4)
return c_d * sorted(pairwisedifs)[k - 1]
exclude_sets = {smpl: set() for smpl in raws[controls:]}
vwrite(
gray('Robust contamination removal: '
'Searching for contaminants...\n'))
for tid in exclude_candidates:
relfreq_ctrl: List[float] = [
accs[ctrl][tid] / accs[ctrl][ontology.ROOT]
for ctrl in raws[:controls]
]
relfreq_smpl: List[float] = [
accs[smpl][tid] / accs[smpl][ontology.ROOT]
for smpl in raws[controls:]
]
relfreq: List[float] = relfreq_ctrl + relfreq_smpl
crossover: List[bool] # Crossover source (yes/no)
# Just-controls contamination check
if all([rf < EPS for rf in relfreq_smpl]):
vwrite(cyan('just-ctrl:\t'), tid, ontology.get_name(tid),
gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl),
'\n')
continue # Go for next candidate
# Critical contamination check
if all([rf > SEVR_CONTM_MIN_RELFREQ for rf in relfreq_ctrl]):
vwrite(red('critical:\t'), tid, ontology.get_name(tid),
gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl),
'\n')
for exclude_set in exclude_sets.values():
exclude_set.add(tid)
continue # Go for next candidate
# Severe contamination check
if any([rf > SEVR_CONTM_MIN_RELFREQ for rf in relfreq_ctrl]):
vwrite(yellow('severe: \t'), tid, ontology.get_name(tid),
gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl),
'\n')
for exclude_set in exclude_sets.values():
exclude_set.add(tid)
continue # Go for next candidate
# Mild contamination check
if all([rf > MILD_CONTM_MIN_RELFREQ for rf in relfreq_ctrl]):
vwrite(blue('mild cont:\t'), tid, ontology.get_name(tid),
gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl),
'\n')
for exclude_set in exclude_sets.values():
exclude_set.add(tid)
continue # Go for next candidate
# Calculate median and MAD median but including controls
mdn: float = statistics.median(relfreq)
# mad:float=statistics.mean([abs(mdn - rf) for rf in relfreq])
q_n: float = compute_qn(relfreq, dist="NegExp")
# Calculate crossover in samples
outlier_lim: float = mdn + ROBUST_XOVER_OUTLIER * q_n
ordomag_lim: float = max(
relfreq_ctrl) * 10**ROBUST_XOVER_ORD_MAG
crossover = [
rf > outlier_lim and rf > ordomag_lim
for rf in relfreq[controls:]
]
# Crossover contamination check
if any(crossover):
vwrite(
magenta('crossover:\t'), tid, ontology.get_name(tid),
green(f'lims: [{outlier_lim:.1g}]' +
('<' if outlier_lim < ordomag_lim else '>') +
f'[{ordomag_lim:.1g}]'), gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl),
gray('crossover:'), blst2str(crossover), '\n')
# Exclude just for contaminated samples (not the source)
vwrite(magenta('\t->'), gray(f'Include {tid} just in:'))
for i in range(len(raws[controls:])):
if not crossover[i]:
exclude_sets[raws[i + controls]].add(tid)
else:
vwrite(f' {raws[i + controls]}')
if all(crossover): # Shared taxon contaminating control(s)
vwrite(' (', yellow('Shared crossover taxon!'), ')')
shared_crossover.add(tid)
vwrite('\n')
continue
# Other contamination: remove from all samples
vwrite(
gray('other cont:\t'), tid, ontology.get_name(tid),
green(f'lims: [{outlier_lim:.1g}]' +
('<' if outlier_lim < ordomag_lim else '>') +
f'[{ordomag_lim:.1g}]'), gray('relfreq:'),
fltlst2str(relfreq_ctrl) + fltlst2str(relfreq_smpl), '\n')
for exclude_set in exclude_sets.values():
exclude_set.add(tid)
# Get taxids at this rank that are present in the control samples
exclude_candidates: Set[Id] = set()
for i in range(controls):
exclude_candidates.update(taxids[raws[i]][rank])
exclude_sets: Dict[Sample, Set[Id]]
shared_crossover: Set[Id] = set() # Shared taxa contaminating controls
if controls and (len(raws) - controls >= ROBUST_MIN_SAMPLES):
robust_contamination_removal()
else: # If this case, just apply strict control
exclude_sets = {
file: exclude_candidates
for file in raws[controls::]
}
# Add explicit excluding taxa (if any) to exclude sets
for exclude_set in exclude_sets.values():
exclude_set.update(excluding)
exclude_candidates.update(excluding)
# Process each sample excluding control taxa
for raw in raws[controls:]:
output.write(
gray(' Ctrl: From') + f' {raw} ' +
gray(f'excluding {len(exclude_sets[raw])} ctrl taxa. '
f'Generating sample... '))
ctrl_tree = TaxTree()
ctrl_out = SampleDataById(['counts', 'scores', 'accs'])
ctrl_tree.allin1(ontology=ontology,
counts=counts[raw],
scores=scores[raw],
min_taxa=mintaxas[raw],
min_rank=rank,
just_min_rank=True,
include=including,
exclude=exclude_sets[raw],
out=ctrl_out)
ctrl_out.purge_counters()
if ctrl_out.counts: # Avoid adding empty samples
sample = Sample(f'{raw}_{STR_CONTROL}_{rank.name.lower()}')
samples.append(sample)
counts[sample] = ctrl_out.get_counts()
accs[sample] = ctrl_out.get_accs()
scores[sample] = ctrl_out.get_scores()
output.write(green('OK!\n'))
else:
output.write(yellow('VOID\n'))
def shared_ctrl_analysis():
"""Perform last steps of shared taxa analysis"""
shared_ctrl_tree: TaxTree = TaxTree()
shared_ctrl_out: SampleDataById = SampleDataById(
['shared', 'accs'])
shared_ctrl_tree.allin1(ontology=ontology,
counts=shared_ctrl_counts,
scores=shared_ctrl_score,
min_taxa=get_shared_mintaxa(),
include=including,
exclude=(exclude_candidates -
shared_crossover),
out=shared_ctrl_out)
shared_ctrl_out.purge_counters()
out_counts: SharedCounter = shared_ctrl_out.get_shared_counts()
output.write(
gray(f' Ctrl-shared: Including {len(out_counts)}'
' shared taxa. Generating sample... '))
if out_counts:
sample = Sample(f'{STR_CONTROL_SHARED}_{rank.name.lower()}')
samples.append(sample)
counts[Sample(sample)] = out_counts
accs[Sample(sample)] = shared_ctrl_out.get_accs()
scores[sample] = shared_ctrl_out.get_shared_scores()
output.write(green('OK!\n'))
else:
output.write(yellow('VOID\n'))
# Shared-control taxa final analysis
if shared_ctrl_counts:
# Normalize scaled scores by total abundance
shared_ctrl_score /= (+shared_ctrl_counts)
# Get averaged abundance by number of samples minus ctrl samples
shared_ctrl_counts //= (len(raws) - controls)
shared_ctrl_analysis()
else:
output.write(
gray(' Ctrl-shared: No taxa! ') + yellow('VOID') +
gray(' sample.\n'))
def process_output(
*args,
**kwargs) -> Tuple[Sample, TaxTree, SampleDataById, SampleStats, Err]:
"""
Process classifiers output files (to be usually called in parallel!).
"""
# timing initialization
start_time: float = time.perf_counter()
# Recover input and parameters
target_file: Filename = args[0]
debug: bool = kwargs['debug']
is_ctrl: bool = args[1]
if debug:
print(gray('Processing'), blue('ctrl' if is_ctrl else 'sample'),
target_file, gray('...'))
sys.stdout.flush()
ontology: Ontology = kwargs['ontology']
mintaxa: Optional[int] = (kwargs['ctrlmintaxa']
if is_ctrl else kwargs['mintaxa'])
minscore: Score = kwargs['ctrlminscore'] if is_ctrl else kwargs['minscore']
including: Union[Tuple, Set[Id]] = ontology.including
excluding: Union[Tuple, Set[Id]] = ontology.excluding
scoring: Scoring = kwargs['scoring']
classifier: Classifier = kwargs['classifier']
genfmt: GenericFormat = kwargs['genfmt']
output: io.StringIO = io.StringIO(newline='')
def vwrite(*args):
"""Print only if verbose/debug mode is enabled"""
if kwargs['debug']:
output.write(' '.join(str(item) for item in args))
sample: Sample = Sample(os.path.splitext(target_file)[0])
error: Err = Err.NO_ERROR
# Read taxonomic classifier output files to get abundances
read_method: Callable[ # Format: [[Input], Output]
[Filename, Scoring, Optional[Score]],
Tuple[str, SampleStats, Counter[Id], Dict[Id, Score]]]
log: str
stat: SampleStats
counts: Counter[Id]
scores: Dict[Id, Score]
if classifier is Classifier.GENERIC: # Direct call to generic method
log, stat, counts, scores = read_generic_output(
target_file, scoring, minscore, genfmt)
else: # Use read_method
if classifier is Classifier.KRAKEN:
read_method = read_kraken_output
elif classifier is Classifier.CLARK:
read_method = read_clark_output
elif classifier is Classifier.LMAT:
read_method = read_lmat_output
elif classifier is Classifier.CENTRIFUGE:
read_method = read_output
else:
raise Exception(red('\nERROR!'),
f'taxclass: Unknown classifier "{classifier}".')
log, stat, counts, scores = read_method(target_file, scoring, minscore)
output.write(log)
# Complete/Update fields in stats
stat.is_ctrl = is_ctrl # set control nature of the sample
if mintaxa is not None: # manual mintaxa has precedence over automatic
stat.mintaxa = mintaxa
else: # update local value with the automatically guessed value
mintaxa = stat.mintaxa
# Move cellular_organisms counts to root, in case
if ontology.collapse and counts[CELLULAR_ORGANISMS]:
vwrite(gray('Moving'), counts[CELLULAR_ORGANISMS],
gray('"CELLULAR_ORGANISMS" reads to "ROOT"... \n'))
if counts[ontology.ROOT]:
stat.decrease_filtered_taxids()
scores[ontology.ROOT] = Score(
(scores[CELLULAR_ORGANISMS] * counts[CELLULAR_ORGANISMS] +
scores[ontology.ROOT] * counts[ontology.ROOT]) /
(counts[CELLULAR_ORGANISMS] + counts[ontology.ROOT]))
else:
scores[ontology.ROOT] = scores[CELLULAR_ORGANISMS]
counts[ontology.ROOT] += counts[CELLULAR_ORGANISMS]
counts[CELLULAR_ORGANISMS] = 0
scores[CELLULAR_ORGANISMS] = NO_SCORE
# Remove root counts, in case
if kwargs['root'] and counts[ontology.ROOT]:
vwrite(gray('Removing'), counts[ontology.ROOT],
gray('"ROOT" reads... '))
stat.seq = stat.seq._replace(filt=stat.seq.filt -
counts[ontology.ROOT])
stat.decrease_filtered_taxids()
counts[ontology.ROOT] = 0
scores[ontology.ROOT] = NO_SCORE
vwrite(green('OK!'), '\n')
# Building ontology tree
output.write(
gray('Building from raw data with mintaxa = ') + f'{mintaxa:_d}' +
gray(' ... \n'))
vwrite(gray(' Building ontology tree with all-in-1... '))
tree = TaxTree()
ancestors: Set[Id]
orphans: Set[Id]
ancestors, orphans = ontology.get_ancestors(counts.keys())
out = SampleDataById(['all'])
tree.allin1(ontology=ontology,
counts=counts,
scores=scores,
ancestors=ancestors,
min_taxa=mintaxa,
include=including,
exclude=excluding,
out=out)
out.purge_counters()
vwrite(green('OK!'), '\n')
# Stats: Complete final value for TaxIDs after tree building and folding
final_taxids: int = len(out.counts) if out.counts is not None else 0
stat.set_final_taxids(final_taxids)
# Check for additional loss of reads (due to include/exclude an orphans)
output.write(gray(' Check for more seqs lost ([in/ex]clude affects)... '))
if out.counts is not None:
discard: int = sum(counts.values()) - sum(out.counts.values())
if discard:
output.write(
blue('\n Info:') + f' {discard} ' +
gray('additional seqs discarded (') +
f'{discard/sum(counts.values()):.3%} ' +
gray('of accepted)\n'))
else:
output.write(green('OK!\n'))
else:
output.write(red('No counts in sample tree!\n'))
# Warn or give detailed stats about orphan taxid and orphan seqs
if debug:
vwrite(gray(' Checking taxid loss (orphans)... '))
lost: int = 0
if orphans:
for orphan in orphans:
vwrite(yellow(' Warning!'), gray('Orphan taxid'),
f'{orphan}\n')
lost += counts[orphan]
vwrite(
yellow(' WARNING!'), f'{len(orphans)} orphan taxids ('
f'{len(orphans)/len(counts):.2%} of accepted)\n'
f' and {lost} orphan sequences ('
f'{lost/sum(counts.values()):.3%} of accepted)\n')
else:
vwrite(green('OK!\n'))
elif orphans:
output.write(
yellow('\n Warning!') + f' {len(orphans)} orphan taxids' +
gray(' (rerun with --debug for details)\n'))
# Check the removal of TaxIDs (accumulation of leaves in parents)
if debug and not excluding and including == {ontology.ROOT}:
vwrite(gray(' Assess accumulation due to "folding the tree"...\n'))
migrated: int = 0
if out.counts is not None:
for taxid in counts:
if out.counts[taxid] == 0:
migrated += 1
vwrite(
blue(' Info:'),
gray(f'Folded TaxID {taxid} (') +
f'{ontology.get_name(taxid)}' + gray(') with ') +
f'{counts[taxid]}' + gray(' original seqs\n'))
if migrated:
vwrite(
blue(' INFO:'), f'{migrated} TaxIDs folded ('
f'{migrated/len(+counts):.2%} of TAF —TaxIDs after filtering—)'
'\n')
vwrite(
blue(' INFO:'), f'Final assigned TaxIDs: {final_taxids} '
f'(reduced to {final_taxids/len(+counts):.2%} of '
'number of TAF)\n')
else:
vwrite(blue(' INFO:'), gray('No migration!'), green('OK!\n'))
# Print last message and check if the sample is void
if out.counts:
output.write(sample + blue(' ctrl ' if is_ctrl else ' sample ') +
green('OK!\n'))
elif is_ctrl:
output.write(sample + red(' ctrl VOID!\n'))
error = Err.VOID_CTRL
else:
output.write(sample + blue(' sample ') + yellow('VOID\n'))
error = Err.VOID_SAMPLE
# Timing results
output.write(
gray('Load elapsed time: ') +
f'{time.perf_counter() - start_time:.3g}' + gray(' sec\n'))
print(output.getvalue())
sys.stdout.flush()
return sample, tree, out, stat, error