def test_result_exporter(self):
polarity = -1
exp_settings = ExperimentalSettings(10, polarity, is_dims_exp=False)
peakels = PeakListReader(op.normcase("mzos/ressources/peaks_matrix_NEG.tsv"), exp_settings).get_peakels()
# annotation
peakels_annotator = PeakelsAnnotator(peakels, exp_settings)
peakels_annotator.annotate()
# database finding
db_search = DatabaseSearch('hmdb', exp_settings)
adducts_l = ['H1']
db_search.assign_formula(peakels, adducts_l, exp_settings.mz_tol_ppm)
# scoring
# first simplistic
model = StatsModel(peakels, exp_settings.mz_tol_ppm * 1.5)
# populate annotations objects
model.calculate_score()
# scoring 2
bi = BayesianInferer(peakels, exp_settings)
# populate annotations object
bi.infer_assignment_probabilities()
exporter = ResultsExporter(op.normcase("annotations.tsv"), peakels)
exporter.write()
def run_analysis(**kwargs):
"""
function to run analysis (all the pipeline)
:return:
"""
xcms_pkl = kwargs['xcms_pkl']
kwargs.pop('xcms_pkl')
polarity = kwargs['polarity']
kwargs.pop('polarity')
mz_tol_ppm = kwargs['mz_tol_ppm']
kwargs.pop('mz_tol_ppm')
is_dims = kwargs['is_dims']
kwargs.pop('is_dims')
db_search = kwargs['db_search']
kwargs.pop('db_search')
output = kwargs['output']
kwargs.pop('output')
bayes = kwargs['bayes']
kwargs.pop('bayes')
if xcms_pkl is None or not xcms_pkl:
raise ValueError("Supply a XCMS peaklist.")
if not os.path.isfile(xcms_pkl):
raise ValueError("XCMS peaklist path does not exist.")
if polarity is None:
raise ValueError("polarity must be 'negative' or 'positive'.")
ionisation_mode = IONISATION_MODE.NEG if polarity == 'negative' else IONISATION_MODE.POS
frag_conf = kwargs.get('frag_conf')
neg_adducts_conf = kwargs.get('neg_adducts_conf')
pos_adducts_conf = kwargs.get('pos_adducts_conf')
exp_settings = ExperimentalSettings(mz_tol_ppm,
ionisation_mode,
is_dims,
frag_conf=frag_conf,
neg_adducts_conf=neg_adducts_conf,
pos_adducts_conf=pos_adducts_conf)
# clear logging
logging.getLogger('').handlers = []
logging.basicConfig(level=logging.INFO)
t1 = time.clock()
peakels = PeakListReader(xcms_pkl, exp_settings).get_peakels()
logging.info("Peaklist loaded.")
# annotation
peakels_annotator = PeakelsAnnotator(peakels, exp_settings)
logging.info("Annotating...")
best_monos = peakels_annotator.annotate()
logging.info("Monoisotopic found: #{0}".format(len(best_monos)))
# database finding
db = []
for d in db_search:
if d not in ('hmdb', 'lmsd'):
logging.warn(
'Error specifying db (got {}), only hmdb and lmsd are supported...'
.format(d))
else:
db.append(d)
db = '+'.join(db)
search = DatabaseSearch(db, exp_settings)
logging.info("Searching in database...")
adducts_l = ['H1']
nb_metabs, not_found = search.assign_formula(peakels, adducts_l,
exp_settings.mz_tol_ppm)
logging.info("Found #{} metabolites, #{} "
"elution peak with no metabolite assignments".format(
nb_metabs, not_found))
# scoring first simplistic
model = StatsModel(peakels, exp_settings.mz_tol_ppm * 1.5)
logging.info("Compute score 1....")
# populate annotations objects
model.calculate_score()
logging.info("Done.")
# scoring bayesian inferer
if bayes:
bi = BayesianInferer(peakels, exp_settings)
logging.info("Compute score 2...")
# populate annotations object
bi.infer_assignment_probabilities()
# logging.info("Finished")
logging.info('Exporting results...')
exporter = ResultsExporter(output, sorted(peakels, key=lambda _: _.id))
exporter.write()
logging.info("Done.")
def run_analysis(**kwargs):
"""
function to run analysis (all the pipeline)
:return:
"""
xcms_pkl = kwargs['xcms_pkl']
kwargs.pop('xcms_pkl')
polarity = kwargs['polarity']
kwargs.pop('polarity')
mz_tol_ppm = kwargs['mz_tol_ppm']
kwargs.pop('mz_tol_ppm')
is_dims = kwargs['is_dims']
kwargs.pop('is_dims')
db_search = kwargs['db_search']
kwargs.pop('db_search')
output = kwargs['output']
kwargs.pop('output')
bayes = kwargs['bayes']
kwargs.pop('bayes')
if xcms_pkl is None or not xcms_pkl:
raise ValueError("Supply a XCMS peaklist.")
if not os.path.isfile(xcms_pkl):
raise ValueError("XCMS peaklist path does not exist.")
if polarity is None:
raise ValueError("polarity must be 'negative' or 'positive'.")
ionisation_mode = IONISATION_MODE.NEG if polarity == 'negative' else IONISATION_MODE.POS
frag_conf = kwargs.get('frag_conf')
neg_adducts_conf = kwargs.get('neg_adducts_conf')
pos_adducts_conf = kwargs.get('pos_adducts_conf')
exp_settings = ExperimentalSettings(mz_tol_ppm, ionisation_mode, is_dims,
frag_conf=frag_conf,
neg_adducts_conf=neg_adducts_conf,
pos_adducts_conf=pos_adducts_conf)
# clear logging
logging.getLogger('').handlers = []
logging.basicConfig(level=logging.INFO)
t1 = time.clock()
peakels = PeakListReader(xcms_pkl, exp_settings).get_peakels()
logging.info("Peaklist loaded.")
# annotation
peakels_annotator = PeakelsAnnotator(peakels, exp_settings)
logging.info("Annotating...")
best_monos = peakels_annotator.annotate()
logging.info("Monoisotopic found: #{0}".format(len(best_monos)))
# database finding
db = []
for d in db_search:
if d not in ('hmdb', 'lmsd'):
logging.warn('Error specifying db (got {}), only hmdb and lmsd are supported...'.format(d))
else:
db.append(d)
db = '+'.join(db)
search = DatabaseSearch(db, exp_settings)
logging.info("Searching in database...")
adducts_l = ['H1']
nb_metabs, not_found = search.assign_formula(peakels, adducts_l, exp_settings.mz_tol_ppm)
logging.info("Found #{} metabolites, #{} "
"elution peak with no metabolite assignments".format(nb_metabs, not_found))
# scoring first simplistic
model = StatsModel(peakels, exp_settings.mz_tol_ppm * 1.5)
logging.info("Compute score 1....")
# populate annotations objects
model.calculate_score()
logging.info("Done.")
# scoring bayesian inferer
if bayes:
bi = BayesianInferer(peakels, exp_settings)
logging.info("Compute score 2...")
# populate annotations object
bi.infer_assignment_probabilities()
# logging.info("Finished")
logging.info('Exporting results...')
exporter = ResultsExporter(output, sorted(peakels, key=lambda _: _.id))
exporter.write()
logging.info("Done.")