def generate_krona(): """Generate Krona plot with all the results via Krona 2.0 XML spec""" print(gray('\nBuilding the taxonomy multiple tree... '), end='') sys.stdout.flush() krona: KronaTree = KronaTree( samples, num_raw_samples=len(raw_samples), stats=stats, min_score=Score( min([ min(scores[sample].values()) for sample in samples if len(scores[sample]) ])), max_score=Score( max([ max(scores[sample].values()) for sample in samples if len(scores[sample]) ])), scoring=scoring, ) polytree.grow(ontology=ncbi, abundances=counts, accs=accs, scores=scores) print(green('OK!')) print(gray('Generating final plot (') + magenta(htmlfile) + gray(')... '), end='') sys.stdout.flush() polytree.toxml(ontology=ncbi, krona=krona) krona.tohtml(htmlfile, pretty=False) print(green('OK!'))
def check_debug(): """Check debugging mode""" if args.debug: print(blue('INFO:'), gray('Debugging mode activated')) print(blue('INFO:'), gray('Active parameters:')) for key, value in vars(args).items(): if value: print(gray(f'\t{key} ='), f'{value}')
def check_controls(): """Check and info about the control samples""" if args.controls: if args.controls > len(input_files): print(red(' ERROR!'), gray('More controls than samples')) exit(1) print(gray('Control(s) sample(s) for subtractions:')) for i in range(args.controls): print(blue(f'\t{input_files[i]}'))
def summarize_analysis( *args, **kwargs) -> Tuple[Optional[Sample], Counter[Id], Counter[Id], Scores]: """ Summarize for a cross-analysis (to be usually called in parallel!). """ # Recover input and parameters analysis: str = args[0] ontology: Ontology = kwargs['ontology'] # TODO: Delete the following comment lines in a future release # including = ontology.including # See comment below for the reason # excluding = ontology.excluding # in/excluding are not used anymore counts: Dict[Sample, Counter[Id]] = kwargs['counts'] scores: Dict[Sample, Dict[Id, Score]] = kwargs['scores'] samples: List[Sample] = kwargs['samples'] output: io.StringIO = io.StringIO(newline='') # Declare/define variables summary_counts: Counter[Id] = col.Counter() summary_acc: Counter[Id] = col.Counter() summary_score: Scores = Scores({}) summary: Optional[Sample] = None output.write(gray('Summary for ') + analysis + gray('... ')) target_samples: List[Sample] = [ smpl for smpl in samples if smpl.startswith(analysis) ] assert len(target_samples) >= 1, \ red('ERROR! ') + analysis + gray(' has no samples to summarize!') for smpl in target_samples: summary_counts += counts[smpl] summary_score.update(scores[smpl]) tree = TaxTree() tree.grow(ontology=ontology, counts=summary_counts, scores=summary_score) tree.subtract() tree.shape() summary_counts.clear() summary_score.clear() # Avoid including/excluding here as get_taxa is not as 'clever' as allin1 # and taxa are already included/excluded in the derived samples tree.get_taxa(counts=summary_counts, accs=summary_acc, scores=summary_score) summary_counts = +summary_counts # remove counts <= 0 if summary_counts: # Avoid returning empty sample (summary would be None) summary = Sample(f'{analysis}_{STR_SUMMARY}') output.write( gray('(') + cyan(f'{len(target_samples)}') + gray(' samples)') + green(' OK!\n')) else: output.write(yellow(' VOID\n')) # Print output and return print(output.getvalue(), end='') sys.stdout.flush() return summary, summary_counts, summary_acc, summary_score
def summarize_analysis( *args, **kwargs) -> Tuple[Sample, Counter[Id], Counter[Id], Scores]: """ Summarize for a cross-analysis (to be usually called in parallel!). """ # Recover input and parameters analysis: str = args[0] ontology: Ontology = kwargs['ontology'] including = ontology.including excluding = ontology.excluding counts: Dict[Sample, Counter[Id]] = kwargs['counts'] scores: Dict[Sample, Dict[Id, Score]] = kwargs['scores'] samples: List[Sample] = kwargs['samples'] output: io.StringIO = io.StringIO(newline='') # Declare/define variables summary_counts: Counter[Id] = Counter() summary_acc: Counter[Id] = Counter() summary_score: Scores = Scores({}) summary: Sample = None output.write(gray('Summary for ') + analysis + gray('... ')) target_samples: List[Sample] = [ smpl for smpl in samples if smpl.startswith(analysis) ] assert len(target_samples) >= 1, \ red('ERROR! ') + analysis + gray(' has no samples to summarize!') for smpl in target_samples: summary_counts += counts[smpl] summary_score.update(scores[smpl]) tree = TaxTree() tree.grow(ontology=ontology, counts=summary_counts, scores=summary_score) tree.subtract() tree.shape() summary_counts.clear() summary_score.clear() tree.get_taxa(counts=summary_counts, accs=summary_acc, scores=summary_score, include=including, exclude=excluding) summary_counts = +summary_counts # remove counts <= 0 if summary_counts: # Avoid returning empty sample (summary would be None) summary = Sample(f'{analysis}_{STR_SUMMARY}') output.write( gray('(') + cyan(f'{len(target_samples)}') + gray(' samples)') + green(' OK!\n')) else: output.write(yellow(' VOID\n')) # Print output and return print(output.getvalue(), end='') sys.stdout.flush() return summary, summary_counts, summary_acc, summary_score
def summarize_samples(): """Summary of samples in parallel by type of cross-analysis""" # timing initialization summ_start_time: float = time.perf_counter() print(gray('Please, wait. Generating summaries in parallel...')) # Update kwargs with more parameters for the followings func calls kwargs.update({'samples': samples}) # Get list of set of samples to summarize (note pylint bug #776) # pylint: disable=unsubscriptable-object target_analysis: col.OrderedDict[str, None] = col.OrderedDict({ f'{raw}_{study}': None for study in [STR_EXCLUSIVE, STR_CONTROL] for raw in raw_samples for smpl in samples if smpl.startswith(f'{raw}_{study}') }) # pylint: enable=unsubscriptable-object # Add shared and control_shared analysis if they exist (are not void) for study in [STR_SHARED, STR_CONTROL_SHARED]: for smpl in samples: if smpl.startswith(study): target_analysis[study] = None break if platform.system() and not args.sequential: # Only for known systems mpctx = mp.get_context('fork') with mpctx.Pool( processes=min(os.cpu_count(), len(input_files))) as pool: async_results = [ pool.apply_async(summarize_analysis, args=[analysis], kwds=kwargs) for analysis in target_analysis ] for analysis, (summary, abund, acc, score) in zip(target_analysis, [r.get() for r in async_results]): if summary: # Avoid adding empty samples summaries.append(summary) counts[summary] = abund accs[summary] = acc scores[summary] = score else: # sequential processing of each selected rank for analysis in target_analysis: (summary, abund, acc, score) = summarize_analysis(analysis, **kwargs) if summary: # Avoid adding empty samples summaries.append(summary) counts[summary] = abund accs[summary] = acc scores[summary] = score # Timing results print(gray('Summary elapsed time:'), f'{time.perf_counter() - summ_start_time:.3g}', gray('sec'))
def read_mock_files(mock: Filename) -> Counter[Id]: """Read a mock layout (.mck) file""" mock_layout: Counter[Id] = col.Counter() with open(mock, 'r') as file: vprint(gray('\nProcessing'), blue(mock), gray('file:\n')) for line in file: if line.startswith('#'): continue _tid, _num = line.split('\t') tid = Id(_tid) num = int(_num) mock_layout[tid] = num vprint(num, gray('\treads for taxid\t'), tid, '\t(', cyan(ncbi.get_name(tid)), ')\n') return mock_layout
def _debug_dummy_plot( taxonomy: Taxonomy, htmlfile: Filename, scoring: Scoring = Scoring.SHEL, ): """ Generate dummy Krona plot via Krona 2.0 XML spec and exit """ print(gray(f'Generating dummy Krona plot {htmlfile}...'), end='') sys.stdout.flush() samples: List[Sample] = [ Sample('SINGLE'), ] krona: KronaTree = KronaTree( samples, min_score=Score(35), max_score=Score(100), scoring=scoring, ) polytree: MultiTree = MultiTree(samples=samples) polytree.grow(ontology=taxonomy) polytree.toxml(ontology=taxonomy, krona=krona) krona.tohtml(htmlfile, pretty=True) print(green('OK!'))
def read_report(report_file: str) -> Tuple[str, Counter[Id], Dict[Id, Rank]]: """ Read Centrifuge/Kraken report file Args: report_file: report file name Returns: log string, abundances counter, taxlevel dict """ # TODO: Discontinued method, to be erased in a future release output: io.StringIO = io.StringIO(newline='') abundances: Counter[Id] = col.Counter() level_dic = {} output.write(f'\033[90mLoading report file {report_file}...\033[0m') try: with open(report_file, 'r') as file: for report_line in file: _, _, taxnum, taxlev, _tid, _ = report_line.split('\t') tid = Id(_tid) abundances[tid] = int(taxnum) level_dic[tid] = Rank.centrifuge(taxlev) except KeyboardInterrupt: print(gray(' User'), yellow('interrupted!')) raise except Exception: print(red('ERROR!'), 'Cannot read "' + report_file + '"') raise else: output.write('\033[92m OK! \033[0m\n') return output.getvalue(), abundances, level_dic
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'))
def analyze_samples(): """Cross analysis of samples in parallel by taxlevel""" print(gray('Please, wait. Performing cross analysis in parallel...\n')) # Update kwargs with more parameters for the followings func calls kwargs.update({ 'taxids': taxids, 'counts': counts, 'scores': scores, 'accs': accs, 'raw_samples': raw_samples }) if platform.system() and not args.sequential: # Only for known systems mpctx = mp.get_context('fork') # Important for OSX&Win with mpctx.Pool(processes=min(os.cpu_count(), len(Rank.selected_ranks))) as pool: async_results = [ pool.apply_async(process_rank, args=[level], kwds=kwargs) for level in Rank.selected_ranks ] for level, (smpls, abunds, accumulators, score) in zip(Rank.selected_ranks, [r.get() for r in async_results]): samples.extend(smpls) counts.update(abunds) accs.update(accumulators) scores.update(score) else: # sequential processing of each selected rank for level in Rank.selected_ranks: (smpls, abunds, accumulators, score) = process_rank(level, **kwargs) samples.extend(smpls) counts.update(abunds) accs.update(accumulators) scores.update(score)
def mock_from_scratch(out: Filename, mock_layout: Counter[TaxId]) -> None: """Generate a mock Centrifuge output file from scratch""" with open(out, 'w') as fout: vprint(gray('Generating'), blue(out), gray('file... ')) fout.write('readID\tseqID\ttaxID\tscore\t2ndBestScore\t' 'hitLength\tqueryLength\tnumMatches\n') reads_writen: int = 0 for tid in mock_layout: maxhl: int = random.randint(args.random + 1, MAX_HIT_LENGTH) rank: str = str(ncbi.get_rank(tid)).lower() for _ in range(mock_layout[tid]): hit_length = random.randint(args.random + 1, maxhl) fout.write(f'test{reads_writen}\t{rank}\t' f'{tid}\t{(hit_length-15)**2}\t' f'0\t{hit_length}\t{MAX_HIT_LENGTH}\t1\n') reads_writen += 1 vprint(reads_writen, 'reads', green('OK!\n'))
def select_lmat_inputs(lmats: List[Filename]) -> None: """"LMAT files processing specific stuff""" if lmats == ['.']: lmats.clear() with os.scandir() as dir_entry: for entry in dir_entry: if not entry.name.startswith('.') and entry.is_dir(): if entry.name != os.path.basename(TAXDUMP_PATH): lmats.append(Filename(entry.name)) lmats.sort() print(gray('LMAT subdirs to analyze:'), lmats)
def mock_from_scratch(out: Filename, mock_layout: Counter[Id]) -> None: """Generate a mock Centrifuge output file from scratch""" with open(out, 'w') as fout: vprint(gray('Generating'), blue(out), gray('file... ')) fout.write('readID\tseqID\ttaxID\tscore\t2ndBestScore\t' 'hitLength\tqueryLength\tnumMatches\n') reads_writen: int = 0 for numtid in mock_layout: tid = Id(numtid) # Convert to Id the excel integer maxhl: int = random.randint(rnd + 1, MAX_HIT_LENGTH) rank: str = str(ncbi.get_rank(tid)).lower() for _ in range(int(mock_layout[numtid])): hit_length = random.randint(rnd + 1, maxhl) fout.write(f'test{reads_writen}\t{rank}\t' f'{tid}\t{(hit_length - 15) ** 2}\t' f'0\t{hit_length}\t{MAX_HIT_LENGTH}\t1\n') reads_writen += 1 vprint(reads_writen, 'reads', green('OK!\n')) if out == TEST_REXT_SMPL: # Test mode: create mock FASTQ for smpl mock_fastq(reads_writen)
def mock_fastq(num_reads: int) -> None: """Do the job in case of Excel file with all the details""" def fastq_seqs(alphabet=single_letter_alphabet): """Generator function that creates mock fastq sequences """ for seq in range(num_reads): yield SeqRecord(Seq('AGTC', alphabet), id=f'test{seq}', name=f'test{seq}', description=f'test{seq}', annotations={'quality': '@@@@'}) print(gray('Writing'), magenta(f'{num_reads}'), gray('reads in'), TEST_REXT_FSTQ, gray('...'), end='', flush=True) SeqIO.write((sq for sq in fastq_seqs()), TEST_REXT_FSTQ, 'quickfastq') print(green(' OK!'))
def mock_from_source(out: Filename, mock_layout: Counter[Id]) -> None: """Generate a mock Centrifuge output file from source file""" with open(out, 'w') as fout, open(args.file) as fcfg: vprint(gray('Generating'), blue(out), gray('file... ')) fout.write(fcfg.readline()) # copy cfg output file header reads_writen: int = 0 for line in fcfg: tid = Id(line.split('\t')[2]) if mock_layout[tid]: fout.write(line) mock_layout[tid] -= 1 reads_writen += 1 if not sum(mock_layout.values()): vprint(reads_writen, 'reads', green('OK!\n')) break if sum(mock_layout.values()): print(red('ERROR!\n')) print(gray('Incomplete read copy by taxid:')) mock_layout = +mock_layout # Delete zero counts elements for tid in mock_layout: print(yellow(mock_layout[tid]), gray('reads missing for tid'), tid, '(', cyan(ncbi.get_name(tid)), ')\n')
def select_kraken_inputs(krakens: List[Filename], ext: str = '.krk') -> None: """Search for Kraken files to analyze""" dir_name = krakens[0] krakens.clear() with os.scandir(dir_name) as dir_entry: for fil in dir_entry: if not fil.name.startswith('.') and fil.name.endswith(ext): if dir_name != '.': krakens.append(Filename(os.path.join(dir_name, fil.name))) else: # Avoid sample names starting with just the dot krakens.append(Filename(fil.name)) krakens.sort() print(gray(f'Kraken {ext} files to analyze:'), krakens)
def select_clark_inputs(clarks: List[Filename], ext: str = '.csv') -> None: """Search for CLARK, CLARK-l, CLARK-S files to analyze""" dir_name = clarks[0] clarks.clear() with os.scandir(dir_name) as dir_entry: for fil in dir_entry: if not fil.name.startswith('.') and fil.name.endswith(ext): if dir_name != '.': clarks.append(Filename(os.path.join(dir_name, fil.name))) else: # Avoid sample names starting with just the dot clarks.append(Filename(fil.name)) clarks.sort() print(gray(f'CLARK {ext} files to analyze:'), clarks)
def select_centrifuge_inputs(outputs: List[Filename], ext: str = '.out') -> None: """Centrifuge output files processing specific stuff""" dir_name = outputs[0] outputs.clear() with os.scandir(dir_name) as dir_entry: for fil in dir_entry: if not fil.name.startswith('.') and fil.name.endswith(ext): if dir_name != '.': outputs.append(Filename(os.path.join(dir_name, fil.name))) else: # Avoid sample names starting with just the dot outputs.append(Filename(fil.name)) outputs.sort() print(gray(f'Centrifuge {ext} files to analyze:'), outputs)
def read_samples(): """Read samples""" print(gray('\nPlease, wait, processing files in parallel...\n')) # Enable parallelization with 'spawn' under known platforms if platform.system() and not args.sequential: # Only for known systems mpctx = mp.get_context('fork') with mpctx.Pool( processes=min(os.cpu_count(), len(input_files))) as pool: async_results = [ pool.apply_async( process, args=[ input_files[num], # file name True if num < args.controls else False ], # is ctrl? kwds=kwargs) for num in range(len(input_files)) ] for file, (sample, tree, out, stat, err) in zip(input_files, [r.get() for r in async_results]): if err is Err.NO_ERROR: samples.append(sample) trees[sample] = tree taxids[sample] = out.get_taxlevels() counts[sample] = out.counts accs[sample] = out.accs scores[sample] = out.scores stats[sample] = stat elif err is Err.VOID_CTRL: print('There were void controls.', red('Aborting!')) exit(1) else: # sequential processing of each sample for num, file in enumerate(input_files): (sample, tree, out, stat, err) = process(file, True if num < args.controls else False, **kwargs) if err is Err.NO_ERROR: samples.append(sample) trees[sample] = tree taxids[sample] = out.get_taxlevels() counts[sample] = out.counts accs[sample] = out.accs scores[sample] = out.scores stats[sample] = stat elif err is Err.VOID_CTRL: print('There were void controls.', red('Aborting!')) exit(1) raw_samples.extend(samples) # Store raw sample names
def by_excel_file() -> None: """Do the job in case of Excel file with all the details""" dirname = os.path.dirname(args.xcel) # Expected index (taxids) in column after taxa name, and last row will # be removed (reserved for sum of reads in Excel file) mock_df = pd.read_excel(args.xcel, index_col=1, skip_footer=1) del mock_df['RECENTRIFUGE MOCK'] vprint(gray('Layout to generate the mock files:\n'), mock_df, '\n') for name, series in mock_df.iteritems(): mock_layout: Counter[TaxId] = col.Counter(series.to_dict(dict)) # In prev, series.to_dict(col.Counter) fails, so this is workaround test: Filename = Filename(os.path.join(dirname, name + '.out')) if args.file: mock_from_source(test, mock_layout) else: mock_from_scratch(test, mock_layout)
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)
def read_clark_output( output_file: Filename, scoring: Scoring = Scoring.CLARK_C, minscore: Score = None, ) -> Tuple[str, SampleStats, Counter[Id], Dict[Id, Score]]: """ Read CLARK(-l)(-S) full mode output file Args: output_file: output file name scoring: type of scoring to be applied (see Scoring class) minscore: minimum confidence level for the classification Returns: log string, statistics, abundances counter, scores dict """ output: io.StringIO = io.StringIO(newline='') all_scores: Dict[Id, List[Score]] = {} all_confs: Dict[Id, List[Score]] = {} all_gammas: Dict[Id, List[Score]] = {} all_length: Dict[Id, List[int]] = {} taxids: Set[Id] = set() num_read: int = 0 nt_read: int = 0 num_uncl: int = 0 last_error_read: int = -1 # Number of read of the last error num_errors: int = 0 # Number or reads discarded due to error output.write(gray(f'Loading output file {output_file}... ')) try: with open(output_file, 'r') as file: # Check number of cols in header header = file.readline().split(',') if len(header) != 8: print( red('\nERROR! ') + 'CLARK output format of ', yellow(f'"{output_file}"'), 'not supported.') print(magenta('Expected:'), 'ID,Length,Gamma,1st,score1,2nd,score2,conf') print(magenta('Found:'), ','.join(header), end='') print(blue('HINT:'), 'Use CLARK, CLARK-l, or CLARK-S ' 'with full mode (', blue('-m 0'), ')') raise Exception('Unsupported file format. Aborting.') for raw_line in file: try: output_line = raw_line.strip() (_label, _length, _gamma, _tid1, _score1, _tid2, _score2, _conf) = output_line.split(',') except ValueError: print( yellow('Failure'), 'parsing line elements:' f' {output_line} in {output_file}' '. Ignoring line!') last_error_read = num_read + 1 num_errors += 1 continue try: length: int = int(_length) gamma: Score = Score(float(_gamma)) tid1: Id = Id(_tid1) score1: Score = Score(float(_score1)) tid2: Id = Id(_tid2) score2: Score = Score(float(_score2)) conf: Score = Score(float(_conf)) except ValueError: print( yellow('Failure'), 'parsing line elements:' f' {output_line} in {output_file}' '. Ignoring line!') last_error_read = num_read + 1 num_errors += 1 continue num_read += 1 nt_read += length # Select tid and score between CLARK assignments 1 and 2 tid: Id = tid1 score: Score = score1 if tid1 == UNCLASSIFIED: if tid2 == UNCLASSIFIED: # Just count unclassified reads num_uncl += 1 continue else: # Majority of read unclassified tid = tid2 score = score2 conf = Score(1 - conf) # Get CLARK's h2/(h1+h2) # From CLARK_C(S) score get "single hit equivalent length" shel: Score = Score(score + K_MER_SIZE) taxids.add(tid) # Save all the selected tids (tid1 or tid2) if minscore is not None: # Decide if ignore read if low score if scoring is Scoring.CLARK_C: if conf < minscore: continue elif scoring is Scoring.CLARK_G: if gamma < minscore: continue else: if shel < minscore: continue try: all_scores[tid].append(shel) except KeyError: all_scores[tid] = [ shel, ] try: all_confs[tid].append(conf) except KeyError: all_confs[tid] = [ conf, ] try: all_gammas[tid].append(gamma) except KeyError: all_gammas[tid] = [ gamma, ] try: all_length[tid].append(length) except KeyError: all_length[tid] = [ length, ] except FileNotFoundError: raise Exception(red('\nERROR! ') + f'Cannot read "{output_file}"') if last_error_read == num_read + 1: # Check error in last line: truncated! print(yellow('Warning!'), f'{output_file} seems truncated!') counts: Counter[Id] = col.Counter( {tid: len(all_scores[tid]) for tid in all_scores}) output.write(green('OK!\n')) if num_read == 0: raise Exception( red('\nERROR! ') + f'Cannot read any sequence from "{output_file}"') filt_seqs: int = sum([len(scores) for scores in all_scores.values()]) if filt_seqs == 0: raise Exception(red('\nERROR! ') + 'No sequence passed the filter!') # Get statistics stat: SampleStats = SampleStats(minscore=minscore, nt_read=nt_read, lens=all_length, scores=all_scores, scores2=all_confs, scores3=all_gammas, seq_read=num_read, seq_unclas=num_uncl, seq_filt=filt_seqs, tid_clas=len(taxids)) # Output statistics if num_errors: output.write( gray(' Seqs fail: ') + red(f'{num_errors:_d}\t') + gray('(Last error in read ') + red(f'{last_error_read}') + gray(')\n')) output.write( gray(' Seqs read: ') + f'{stat.seq.read:_d}\t' + gray('[') + f'{stat.nt_read}' + gray(']\n')) output.write( gray(' Seqs clas: ') + f'{stat.seq.clas:_d}\t' + gray('(') + f'{stat.get_unclas_ratio():.2%}' + gray(' unclassified)\n')) output.write( gray(' Seqs pass: '******'{stat.seq.filt:_d}\t' + gray('(') + f'{stat.get_reject_ratio():.2%}' + gray(' rejected)\n')) output.write( gray(' Hit (score): min = ') + f'{stat.sco.mini:.1f},' + gray(' max = ') + f'{stat.sco.maxi:.1f},' + gray(' avr = ') + f'{stat.sco.mean:.1f}\n') output.write( gray(' Conf. score: min = ') + f'{stat.sco2.mini:.1f},' + gray(' max = ') + f'{stat.sco2.maxi:.1f},' + gray(' avr = ') + f'{stat.sco2.mean:.1f}\n') output.write( gray(' Gamma score: min = ') + f'{stat.sco3.mini:.1f},' + gray(' max = ') + f'{stat.sco3.maxi:.1f},' + gray(' avr = ') + f'{stat.sco3.mean:.1f}\n') output.write( gray(' Read length: min = ') + f'{stat.len.mini},' + gray(' max = ') + f'{stat.len.maxi},' + gray(' avr = ') + f'{stat.len.mean}\n') output.write( gray(' TaxIds: by classifier = ') + f'{stat.tid.clas}' + gray(', by filter = ') + f'{stat.tid.filt}\n') # Select score output out_scores: Dict[Id, Score] if scoring is Scoring.SHEL: out_scores = {tid: Score(mean(all_scores[tid])) for tid in all_scores} elif scoring is Scoring.CLARK_C: out_scores = { tid: Score(mean(all_confs[tid]) * 100) for tid in all_confs } elif scoring is Scoring.CLARK_G: out_scores = {tid: Score(mean(all_gammas[tid])) for tid in all_gammas} elif scoring is Scoring.LENGTH: out_scores = {tid: Score(mean(all_length[tid])) for tid in all_length} elif scoring is Scoring.LOGLENGTH: out_scores = { tid: Score(log10(mean(all_length[tid]))) for tid in all_length } elif scoring is Scoring.NORMA: scores: Dict[Id, Score] = { tid: Score(mean(all_scores[tid])) for tid in all_scores } lengths: Dict[Id, Score] = { tid: Score(mean(all_length[tid])) for tid in all_length } out_scores = { tid: Score(scores[tid] / lengths[tid] * 100) for tid in scores } else: print(red('ERROR!'), f'clark: Unsupported Scoring "{scoring}"') raise Exception('Unsupported scoring') # Return return output.getvalue(), stat, counts, out_scores
def read_lmat_output( output_file: Filename, scoring: Scoring = Scoring.LMAT, minscore: Score = None, ) -> Tuple[str, SampleStats, Counter[Id], Dict[Id, Score]]: """ Read LMAT output (iterate over all the output files) Args: output_file: output file name (prefix) scoring: type of scoring to be applied (see Scoring class) minscore: minimum confidence level for the classification Returns: log string, abundances counter, scores dict """ output: io.StringIO = io.StringIO(newline='') all_scores: Dict[Id, List[Score]] = {} all_length: Dict[Id, List[int]] = {} nt_read: int = 0 matchings: Counter[Match] = Counter() output_files: List[Filename] = [] # Select files to process depending on if the output files are explicitly # given or directory name is provided (all the output files there) if os.path.isdir(output_file): # Just the directory name is provided dirname = os.path.normpath(output_file) for file in os.listdir(dirname): # Add all LMAT output files in dir if ('_output' in file and file.endswith('.out') and 'canVfin' not in file and 'pyLCA' not in file): output_files.append(Filename(file)) else: # Explicit path and file name prefix is given dirname, basename = os.path.split(output_file) for file in os.listdir(dirname): # Add selected output files in dir if (file.startswith(basename) and file.endswith('.out') and 'canVfin' not in file and 'pyLCA' not in file): output_files.append(Filename(file)) if not output_files: raise Exception( f'\n\033[91mERROR!\033[0m Cannot read from "{output_file}"') # Read LMAT output files for output_name in output_files: path: Filename = Filename(os.path.join(dirname, output_name)) output.write(f'\033[90mLoading output file {path}...\033[0m') try: with open(path, 'r') as io_file: for seq in SeqIO.parse(io_file, "lmat"): tid: Id = seq.annotations['final_taxid'] score: Score = seq.annotations['final_score'] match: Match = Match.lmat(seq.annotations['final_match']) matchings[match] += 1 length: int = len(seq) nt_read += length if minscore is not None: if score < minscore: # Ignore read if low score continue if match in [Match.DIRECTMATCH, Match.MULTIMATCH]: try: all_scores[tid].append(score) except KeyError: all_scores[tid] = [ score, ] try: all_length[tid].append(length) except KeyError: all_length[tid] = [ length, ] except FileNotFoundError: raise Exception(red('\nERROR!') + f'Cannot read "{path}"') output.write(green('OK!\n')) abundances: Counter[Id] = Counter( {tid: len(all_scores[tid]) for tid in all_scores}) # Basic output statistics read_seqs: int = sum(matchings.values()) if read_seqs == 0: raise Exception( red('\nERROR! ') + f'Cannot read any sequence from"{output_file}"') filt_seqs: int = sum([len(scores) for scores in all_scores.values()]) if filt_seqs == 0: raise Exception(red('\nERROR! ') + 'No sequence passed the filter!') stat: SampleStats = SampleStats(minscore=minscore, nt_read=nt_read, scores=all_scores, lens=all_length, seq_read=read_seqs, seq_filt=filt_seqs, seq_clas=matchings[Match.DIRECT] + matchings[Match.MULTI]) output.write( gray(' Seqs read: ') + f'{stat.seq.read:_d}\t' + gray('[') + f'{stat.nt_read}' + gray(']\n')) output.write( gray(' Seqs clas: ') + f'{stat.seq.clas:_d}\t' + gray('(') + f'{stat.get_unclas_ratio():.2%}' + gray(' unclassified)\n')) output.write( gray(' Seqs pass: '******'{stat.seq.filt:_d}\t' + gray('(') + f'{stat.get_reject_ratio():.2%}' + gray(' rejected)\n')) multi_rel: float = matchings[Match.MULTI] / read_seqs direct_rel: float = matchings[Match.DIRECT] / read_seqs nodbhits_rel: float = matchings[Match.NODBHITS] / read_seqs tooshort_rel: float = matchings[Match.READTOOSHORT] / read_seqs lowscore_rel: float = matchings[Match.LOWSCORE] / read_seqs output.write(f'\033[90m DB Matching: ' f'Multi =\033[0m {multi_rel:.1%}\033[90m ' f'Direct =\033[0m {direct_rel:.1%}\033[90m ' f'ReadTooShort =\033[0m {tooshort_rel:.1%}\033[90m ' f'LowScore =\033[0m {lowscore_rel:.1%}\033[90m ' f'NoDbHits =\033[0m {nodbhits_rel:.1%}\033[90m\n') output.write( gray(' Scores: min = ') + f'{stat.sco.mini:.1f},' + gray(' max = ') + f'{stat.sco.maxi:.1f},' + gray(' avr = ') + f'{stat.sco.mean:.1f}\n') output.write( gray(' Length: min = ') + f'{stat.len.mini},' + gray(' max = ') + f'{stat.len.maxi},' + gray(' avr = ') + f'{stat.len.mean}\n') output.write(f' {stat.num_taxa}' + gray(f' taxa with assigned reads\n')) # Select score output out_scores: Dict[Id, Score] if scoring is Scoring.LMAT: out_scores = {tid: Score(mean(all_scores[tid])) for tid in all_scores} else: print(red('ERROR!'), f' LMAT: Unsupported Scoring "{scoring}"') raise Exception('Unsupported scoring') # Return return output.getvalue(), stat, abundances, out_scores
def fltlst2str(lst: List[float]) -> str: """Convert a list of floats into a nice string""" return '[' + gray((', '.join(f'{elm:.1g}' for elm in lst))) + ']'
def process_rank( *args, **kwargs ) -> Tuple[List[Sample], Dict[Sample, UnionCounter], Dict[Sample, Counter[Id]], Dict[Sample, UnionScores]]: """ Process results for a taxlevel (to be usually called in parallel!). """ # Recover input and parameters rank: Rank = args[0] controls: int = kwargs['controls'] mintaxas: Dict[Sample, int] = kwargs['mintaxas'] ontology: Ontology = kwargs['ontology'] including = ontology.including excluding = ontology.excluding taxids: Dict[Sample, TaxLevels] = kwargs['taxids'] counts: Dict[Sample, UnionCounter] = kwargs['counts'] accs: Dict[Sample, Counter[Id]] = kwargs['accs'] scores: Dict[Sample, UnionScores] = kwargs['scores'] raws: List[Sample] = kwargs['raw_samples'] output: io.StringIO = io.StringIO(newline='') def vwrite(*args) -> None: """Print only if verbose/debug mode is enabled""" if kwargs['debug']: output.write(' '.join(str(item) for item in args)) def fltlst2str(lst: List[float]) -> str: """Convert a list of floats into a nice string""" return '[' + gray((', '.join(f'{elm:.1g}' for elm in lst))) + ']' def blst2str(lst: List[bool]) -> str: """Convert a list of booleans into a nice string""" return ('[' + (', '.join(magenta('T') if elm else 'F' for elm in lst)) + ']') def get_shared_mintaxa() -> int: """Give a value of mintaxa for shared derived samples This value is currently the minimum of the mintaxa of all the (non control) raw samples. """ return min([mintaxas[smpl] for smpl in raws[controls:]]) # Declare/define variables samples: List[Sample] = [] # pylint: disable = unused-variable shared_counts: SharedCounter = SharedCounter() shared_score: SharedCounter = SharedCounter() shared_ctrl_counts: SharedCounter = SharedCounter() shared_ctrl_score: SharedCounter = SharedCounter() # pylint: enable = unused-variable output.write(f'\033[90mAnalysis for taxonomic rank "' f'\033[95m{rank.name.lower()}\033[90m":\033[0m\n') 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 shared_analysis(): """Perform last steps of shared taxa analysis""" shared_tree: TaxTree = TaxTree() shared_out: SampleDataById = SampleDataById(['shared', 'accs']) shared_tree.allin1(ontology=ontology, counts=shared_counts, scores=shared_score, min_taxa=get_shared_mintaxa(), include=including, exclude=excluding, out=shared_out) shared_out.purge_counters() out_counts: SharedCounter = shared_out.get_shared_counts() output.write( gray(f' Shared: Including {len(out_counts)}' ' shared taxa. Generating sample... ')) if out_counts: sample = Sample(f'{STR_SHARED}_{rank.name.lower()}') samples.append(sample) counts[Sample(sample)] = out_counts accs[Sample(sample)] = shared_out.get_accs() scores[sample] = shared_out.get_shared_scores() output.write(green('OK!\n')) else: output.write(yellow('VOID\n')) 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')) # Cross analysis iterating by output: exclusive and part of shared&ctrl for num_file, raw_sample_name in enumerate(raws): cross_analysis(num_file, raw_sample_name) # Shared taxa final analysis shared_counts = +shared_counts # remove counts <= 0 if shared_counts: # Normalize scaled scores by total abundance (after eliminating zeros) shared_score /= (+shared_counts) # Get averaged abundance by number of samples shared_counts //= len(raws) shared_analysis() else: output.write( gray(' Shared: No shared taxa! ') + yellow('VOID') + gray(' sample.\n')) # Control sample subtraction if controls: control_analysis() # Print output and return print(output.getvalue()) sys.stdout.flush() return samples, counts, accs, scores
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 main(): """Main entry point to script.""" # Argument Parser Configuration parser = argparse.ArgumentParser( description='Extract reads following Centrifuge/Kraken output', epilog=f'%(prog)s - {__author__} - {__date__}') parser.add_argument('-V', '--version', action='version', version=f'%(prog)s release {__version__} ({__date__})') parser.add_argument('-f', '--file', action='store', metavar='FILE', required=True, help='Centrifuge output file.') parser.add_argument('-l', '--limit', action='store', metavar='NUMBER', type=int, default=None, help=('Limit of FASTQ reads to extract. ' 'Default: no limit')) parser.add_argument( '-m', '--maxreads', action='store', metavar='NUMBER', type=int, default=None, help=('Maximum number of FASTQ reads to search for the taxa. ' 'Default: no maximum')) parser.add_argument( '-n', '--nodespath', action='store', metavar='PATH', default=TAXDUMP_PATH, help=('path for the nodes information files (nodes.dmp and names.dmp' + ' from NCBI')) parser.add_argument( '-i', '--include', action='append', metavar='TAXID', type=TaxId, default=[], help=('NCBI taxid code to include a taxon and all underneath ' + '(multiple -i is available to include several taxid). ' + 'By default all the taxa is considered for inclusion.')) parser.add_argument( '-x', '--exclude', action='append', metavar='TAXID', type=TaxId, default=[], help=('NCBI taxid code to exclude a taxon and all underneath ' + '(multiple -x is available to exclude several taxid)')) parser.add_argument( '-y', '--minscore', action='store', metavar='NUMBER', type=lambda txt: Score(float(txt)), default=None, help=('minimum score/confidence of the classification of a read ' 'to pass the quality filter; all pass by default')) filein = parser.add_mutually_exclusive_group(required=True) filein.add_argument('-q', '--fastq', action='store', metavar='FILE', default=None, help='Single FASTQ file (no paired-ends)') filein.add_argument('-1', '--mate1', action='store', metavar='FILE', default=None, help='Paired-ends FASTQ file for mate 1s ' '(filename usually includes _1)') parser.add_argument('-2', '--mate2', action='store', metavar='FILE', default=None, help='Paired-ends FASTQ file for mate 2s ' '(filename usually includes _2)') # timing initialization start_time: float = time.time() # Program header print(f'\n=-= {sys.argv[0]} =-= v{__version__} =-= {__date__} =-=\n') sys.stdout.flush() # Parse arguments args = parser.parse_args() output_file = args.file nodesfile: Filename = Filename(os.path.join(args.nodespath, NODES_FILE)) namesfile: Filename = Filename(os.path.join(args.nodespath, NAMES_FILE)) excluding: Set[TaxId] = set(args.exclude) including: Set[TaxId] = set(args.include) fastq_1: Filename fastq_2: Filename = args.mate2 if not fastq_2: fastq_1 = args.fastq else: fastq_1 = args.mate1 # Load NCBI nodes, names and build children plasmidfile: Filename = None ncbi: Taxonomy = Taxonomy(nodesfile, namesfile, plasmidfile, False, excluding, including) # Build taxonomy tree print(gray('Building taxonomy tree...'), end='') sys.stdout.flush() tree = TaxTree() tree.grow(taxonomy=ncbi, look_ancestors=False) print(green(' OK!')) # Get the taxa print(gray('Filtering taxa...'), end='') sys.stdout.flush() ranks: Ranks = Ranks({}) tree.get_taxa(ranks=ranks, include=including, exclude=excluding) print(green(' OK!')) taxids: Set[TaxId] = set(ranks) taxlevels: TaxLevels = Rank.ranks_to_taxlevels(ranks) num_taxlevels = Counter({rank: len(taxlevels[rank]) for rank in taxlevels}) num_taxlevels = +num_taxlevels # Statistics about including taxa print(f' {len(taxids)}\033[90m taxid selected in \033[0m', end='') print(f'{len(num_taxlevels)}\033[90m different taxonomical levels:\033[0m') for rank in num_taxlevels: print(f' Number of different {rank}: {num_taxlevels[rank]}') assert taxids, red('ERROR! No taxids to search for!') # Get the records records: List[SeqRecord] = [] num_seqs: int = 0 # timing initialization start_time_load: float = time.perf_counter() print(gray(f'Loading output file {output_file}...'), end='') sys.stdout.flush() try: with open(output_file, 'rU') as file: file.readline() # discard header for num_seqs, record in enumerate(SeqIO.parse(file, 'centrifuge')): tid: TaxId = record.annotations['taxID'] if tid not in taxids: continue # Ignore read if low confidence score: Score = Score(record.annotations['score']) if args.minscore is not None and score < args.minscore: continue records.append(record) except FileNotFoundError: raise Exception(red('ERROR!') + 'Cannot read "' + output_file + '"') print(green(' OK!')) # Basic records statistics print( gray(' Load elapsed time: ') + f'{time.perf_counter() - start_time_load:.3g}' + gray(' sec')) print(f' \033[90mMatching reads: \033[0m{len(records):_d} \033[90m\t' f'(\033[0m{len(records)/num_seqs:.4%}\033[90m of sample)') sys.stdout.flush() # FASTQ sequence dealing # records_ids: List[SeqRecord] = [record.id for record in records] records_ids: Set[SeqRecord] = {record.id for record in records} seqs1: List[SeqRecord] = [] seqs2: List[SeqRecord] = [] extracted: int = 0 i: int = 0 if fastq_2: print( f'\033[90mLoading FASTQ files {fastq_1} and {fastq_2}...\n' f'Mseqs: \033[0m', end='') sys.stdout.flush() try: with open(fastq_1, 'rU') as file1, open(fastq_2, 'rU') as file2: for i, (rec1, rec2) in enumerate( zip(SeqIO.parse(file1, 'quickfastq'), SeqIO.parse(file2, 'quickfastq'))): if not records_ids or (args.maxreads and i >= args.maxreads ) or (args.limit and extracted >= args.limit): break elif not i % 1000000: print(f'{i//1000000:_d}', end='') sys.stdout.flush() elif not i % 100000: print('.', end='') sys.stdout.flush() try: records_ids.remove(rec1.id) except KeyError: pass else: seqs1.append(rec1) seqs2.append(rec2) extracted += 1 except FileNotFoundError: raise Exception('\n\033[91mERROR!\033[0m Cannot read FASTQ files') else: print(f'\033[90mLoading FASTQ files {fastq_1}...\n' f'Mseqs: \033[0m', end='') sys.stdout.flush() try: with open(fastq_1, 'rU') as file1: for i, rec1 in enumerate(SeqIO.parse(file1, 'quickfastq')): if not records_ids or (args.maxreads and i >= args.maxreads ) or (args.limit and extracted >= args.limit): break elif not i % 1000000: print(f'{i//1000000:_d}', end='') sys.stdout.flush() elif not i % 100000: print('.', end='') sys.stdout.flush() try: records_ids.remove(rec1.id) except KeyError: pass else: seqs1.append(rec1) extracted += 1 except FileNotFoundError: raise Exception('\n\033[91mERROR!\033[0m Cannot read FASTQ file') print(cyan(f' {i/1e+6:.3g} Mseqs'), green('OK! ')) def format_filename(fastq: Filename) -> Filename: """Auxiliary function to properly format the output filenames. Args: fastq: Complete filename of the fastq input file Returns: Filename of the rextracted fastq output file """ fastq_filename, _ = os.path.splitext(fastq) output_list: List[str] = [fastq_filename, '_rxtr'] if including: output_list.append('_incl') output_list.extend('_'.join(including)) if excluding: output_list.append('_excl') output_list.extend('_'.join(excluding)) output_list.append('.fastq') return Filename(''.join(output_list)) filename1: Filename = format_filename(fastq_1) SeqIO.write(seqs1, filename1, 'quickfastq') print(gray('Wrote'), magenta(f'{len(seqs1)}'), gray('reads in'), filename1) if fastq_2: filename2: Filename = format_filename(fastq_2) SeqIO.write(seqs2, filename2, 'quickfastq') print(gray('Wrote'), magenta(f'{len(seqs1)}'), gray('reads in'), filename2) # Timing results print(gray('Total elapsed time:'), time.strftime("%H:%M:%S", time.gmtime(time.time() - start_time)))
def read_output( output_file: Filename, scoring: Scoring = Scoring.SHEL, minscore: Score = None, ) -> Tuple[str, SampleStats, Counter[Id], Dict[Id, Score]]: """ Read Centrifuge output file Args: output_file: output file name scoring: type of scoring to be applied (see Scoring class) minscore: minimum confidence level for the classification Returns: log string, statistics, abundances counter, scores dict """ output: io.StringIO = io.StringIO(newline='') all_scores: Dict[Id, List[Score]] = {} all_length: Dict[Id, List[int]] = {} taxids: Set[Id] = set() num_read: int = 0 nt_read: int = 0 num_uncl: int = 0 last_error_read: int = -1 # Number of read of the last error num_errors: int = 0 # Number or reads discarded due to error output.write(gray(f'Loading output file {output_file}... ')) try: with open(output_file, 'r') as file: file.readline() # discard header for output_line in file: try: _, _, _tid, _score, _, _, _length, *_ = output_line.split( '\t') except ValueError: print( yellow('Failure'), 'parsing line elements:' f' {output_line} in {output_file}' '. Ignoring line!') last_error_read = num_read + 1 num_errors += 1 continue tid = Id(_tid) try: # From Centrifuge score get "single hit equivalent length" shel = Score(float(_score)**0.5 + 15) length = int(_length) except ValueError: print(yellow('Failure'), f'parsing score ({_score}) for ', f'query length {_length} for taxid {_tid}', f'in {output_file}. Ignoring line!') last_error_read = num_read + 1 num_errors += 1 continue num_read += 1 nt_read += length if tid == UNCLASSIFIED: # Just count unclassified reads num_uncl += 1 continue else: taxids.add(tid) # Save all the tids of classified reads if minscore is not None and shel < minscore: continue # Ignore read if low confidence try: all_scores[tid].append(shel) except KeyError: all_scores[tid] = [ shel, ] try: all_length[tid].append(length) except KeyError: all_length[tid] = [ length, ] except FileNotFoundError: raise Exception(red('\nERROR! ') + f'Cannot read "{output_file}"') if last_error_read == num_read + 1: # Check error in last line: truncated! print(yellow('Warning!'), f'{output_file} seems truncated!') counts: Counter[Id] = col.Counter( {tid: len(all_scores[tid]) for tid in all_scores}) output.write(green('OK!\n')) if num_read == 0: raise Exception( red('\nERROR! ') + f'Cannot read any sequence from "{output_file}"') filt_seqs: int = sum([len(scores) for scores in all_scores.values()]) if filt_seqs == 0: raise Exception(red('\nERROR! ') + 'No sequence passed the filter!') # Get statistics stat: SampleStats = SampleStats(minscore=minscore, nt_read=nt_read, scores=all_scores, lens=all_length, seq_read=num_read, seq_unclas=num_uncl, seq_filt=filt_seqs, tid_clas=len(taxids)) # Output statistics if num_errors: output.write( gray(' Seqs fail: ') + red(f'{num_errors:_d}\t') + gray('(Last error in read ') + red(f'{last_error_read}') + gray(')\n')) output.write( gray(' Seqs read: ') + f'{stat.seq.read:_d}\t' + gray('[') + f'{stat.nt_read}' + gray(']\n')) output.write( gray(' Seqs clas: ') + f'{stat.seq.clas:_d}\t' + gray('(') + f'{stat.get_unclas_ratio():.2%}' + gray(' unclassified)\n')) output.write( gray(' Seqs pass: '******'{stat.seq.filt:_d}\t' + gray('(') + f'{stat.get_reject_ratio():.2%}' + gray(' rejected)\n')) output.write( gray(' Scores: min = ') + f'{stat.sco.mini:.1f}' + gray(', max = ') + f'{stat.sco.maxi:.1f}' + gray(', avr = ') + f'{stat.sco.mean:.1f}\n') output.write( gray(' Length: min = ') + f'{stat.len.mini}' + gray(', max = ') + f'{stat.len.maxi}' + gray(', avr = ') + f'{stat.len.mean}\n') output.write( gray(' TaxIds: by classifier = ') + f'{stat.tid.clas}' + gray(', by filter = ') + f'{stat.tid.filt}\n') # Select score output out_scores: Dict[Id, Score] if scoring is Scoring.SHEL: out_scores = {tid: Score(mean(all_scores[tid])) for tid in all_scores} elif scoring is Scoring.LENGTH: out_scores = {tid: Score(mean(all_length[tid])) for tid in all_length} elif scoring is Scoring.LOGLENGTH: out_scores = { tid: Score(log10(mean(all_length[tid]))) for tid in all_length } elif scoring is Scoring.NORMA: scores: Dict[Id, Score] = { tid: Score(mean(all_scores[tid])) for tid in all_scores } lengths: Dict[Id, Score] = { tid: Score(mean(all_length[tid])) for tid in all_length } out_scores = { tid: Score(scores[tid] / lengths[tid] * 100) for tid in scores } else: print(red('ERROR!'), f' Centrifuge: Unsupported Scoring "{scoring}"') raise Exception('Unsupported scoring') # Return return output.getvalue(), stat, counts, out_scores
def process_report( *args, **kwargs ) -> Tuple[Sample, TaxTree, SampleDataByTaxId, SampleStats, Err]: """ Process Centrifuge/Kraken report files (to be usually called in parallel!). """ # TODO: Full review to report support # Recover input and parameters filerep: Filename = args[0] taxonomy: Taxonomy = kwargs['taxonomy'] mintaxa: int = kwargs['mintaxa'] collapse: bool = taxonomy.collapse including: Set[TaxId] = taxonomy.including excluding: Set[TaxId] = taxonomy.excluding debug: bool = kwargs['debug'] 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(filerep) # Read Centrifuge/Kraken report file to get abundances log: str abundances: Counter[TaxId] log, abundances, _ = read_report(filerep) output.write(log) # Remove root counts, in case if kwargs['root']: vwrite(gray('Removing'), abundances[ROOT], gray('"ROOT" reads... ')) abundances[ROOT] = 0 vwrite(green('OK!'), '\n') # Build taxonomy tree output.write(' \033[90mBuilding taxonomy tree...\033[0m') tree = TaxTree() tree.grow(taxonomy=taxonomy, counts=abundances) # Grow tax tree from root node output.write('\033[92m OK! \033[0m\n') # Prune the tree output.write(' \033[90mPruning taxonomy tree...\033[0m') tree.prune(mintaxa, None, collapse, debug) tree.shape() output.write('\033[92m OK! \033[0m\n') # Get the taxa with their abundances and taxonomical levels output.write(' \033[90mFiltering taxa...\033[0m') new_abund: Counter[TaxId] = col.Counter() new_accs: Counter[TaxId] = col.Counter() ranks: Ranks = Ranks({}) tree.get_taxa(abundance=new_abund, accs=new_accs, ranks=ranks, mindepth=0, maxdepth=0, include=including, exclude=excluding) new_abund = +new_abund # remove zero and negative counts if including or excluding: # Recalculate accumulated counts new_tree = TaxTree() new_tree.grow(taxonomy, new_abund) # Grow tree with new abund new_tree.shape() new_abund = col.Counter() # Reset abundances new_accs = col.Counter() # Reset accumulated new_tree.get_taxa(new_abund, new_accs) # Get new accumulated counts out: SampleDataByTaxId = SampleDataByTaxId() out.set(counts=new_abund, ranks=ranks, accs=new_accs) output.write('\033[92m OK! \033[0m\n') print(output.getvalue()) sys.stdout.flush() return sample, tree, out, SampleStats(), Err.NO_ERROR