def main(options):
# load TraML file
targeted = pyopenms.TargetedExperiment()
pyopenms.TraMLFile().load(options.traml_in, targeted)
# Create empty files as input and finally as output
empty_swath = pyopenms.MSExperiment()
trafo = pyopenms.TransformationDescription()
output = pyopenms.MSExperiment()
# load input
for infile in options.infiles:
exp = pyopenms.MSExperiment()
pyopenms.FileHandler().loadExperiment(infile, exp)
transition_exp_used = pyopenms.TargetedExperiment()
do_continue = True
if options.is_swath:
do_continue = pyopenms.OpenSwathHelper(
).checkSwathMapAndSelectTransitions(exp, targeted,
transition_exp_used,
options.min_upper_edge_dist)
else:
transition_exp_used = targeted
if do_continue:
# set up extractor and run
tmp_out = pyopenms.MSExperiment()
extractor = pyopenms.ChromatogramExtractor()
extractor.extractChromatograms(exp, tmp_out, targeted,
options.extraction_window,
options.ppm, trafo,
options.rt_extraction_window,
options.extraction_function)
# add all chromatograms to the output
for chrom in tmp_out.getChromatograms():
output.addChromatogram(chrom)
dp = pyopenms.DataProcessing()
pa = pyopenms.ProcessingAction().SMOOTHING
dp.setProcessingActions(set([pa]))
chromatograms = output.getChromatograms()
for chrom in chromatograms:
this_dp = chrom.getDataProcessing()
this_dp.append(dp)
chrom.setDataProcessing(this_dp)
output.setChromatograms(chromatograms)
pyopenms.MzMLFile().store(options.outfile, output)
def main(options):
precursor_tolerance = options.precursor_tolerance
product_tolerance = options.product_tolerance
out = options.outfile
chromat_in = options.infile
traml_in = options.traml_in
# precursor_tolerance = 0.05
# product_tolerance = 0.05
# out = "/tmp/out.mzML"
# chromat_in = "../source/TEST/TOPP/MRMMapping_input.chrom.mzML"
# traml_in = "../source/TEST/TOPP/MRMMapping_input.TraML"
ff = pyopenms.MRMFeatureFinderScoring()
chromatogram_map = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(chromat_in, chromatogram_map)
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(traml_in, targeted)
output = algorithm(chromatogram_map, targeted, precursor_tolerance,
product_tolerance)
pyopenms.MzMLFile().store(out, output)
def test_run_mrmfeaturefinder(self):
# load chromatograms
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(self.chromatograms, chromatograms)
# load TraML file
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(self.tramlfile, targeted)
# Create empty files as input and finally as output
empty_swath = pyopenms.MSExperiment()
trafo = pyopenms.TransformationDescription()
output = pyopenms.FeatureMap()
# set up featurefinder and run
featurefinder = pyopenms.MRMFeatureFinderScoring()
featurefinder.pickExperiment(chromatograms, output, targeted, trafo,
empty_swath)
self.assertAlmostEqual(output.size(), 3)
self.assertAlmostEqual(output[0].getRT(), 3119.092041015, eps)
self.assertAlmostEqual(output[0].getIntensity(), 3614.99755859375, eps)
self.assertAlmostEqual(
output[0].getMetaValue(b"var_xcorr_shape_weighted"),
0.997577965259552, eps)
self.assertAlmostEqual(output[0].getMetaValue(b"sn_ratio"),
86.00413513183594, eps)
def test_extractor(self):
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(self.filename, targeted)
exp = pyopenms.MSExperiment()
pyopenms.MzMLFile().load(self.filename_mzml, exp)
trafo = pyopenms.TransformationDescription()
tmp_out = pyopenms.MSExperiment()
extractor = pyopenms.ChromatogramExtractor()
extractor.extractChromatograms(exp, tmp_out, targeted, 10, False,
trafo, -1, "tophat")
# Basically test that the output is non-zero (e.g. the data is
# correctly relayed to python)
# The functionality is not tested here!
self.assertEqual(len(tmp_out.getChromatograms()),
len(targeted.getTransitions()))
self.assertNotEqual(len(tmp_out.getChromatograms()), 0)
self.assertEqual(tmp_out.getChromatograms()[0].size(), exp.size())
self.assertNotEqual(tmp_out.getChromatograms()[0].size(), 0)
self.assertNotEqual(tmp_out.getChromatograms()[0][0].getRT(), 0)
self.assertNotEqual(tmp_out.getChromatograms()[0][0].getIntensity(), 0)
def main(options):
out = options.outfile
chromat_in = options.infile
traml_in = options.traml_in
trafo_in = options.trafo_in
pp = pyopenms.MRMTransitionGroupPicker()
metabolomics = False
# this is an important weight for RT-deviation -- the larger the value, the less importance will be given to exact RT matches
# for proteomics data it tends to be a good idea to set it to the length of
# the RT space (e.g. for 100 second RT space, set it to 100)
rt_normalization_factor = 100.0
pp_params = pp.getDefaults();
pp_params.setValue("PeakPickerMRM:remove_overlapping_peaks", options.remove_overlapping_peaks, '')
pp_params.setValue("PeakPickerMRM:method", options.method, '')
if (metabolomics):
# Need to change those for metabolomics and very short peaks!
pp_params.setValue("PeakPickerMRM:signal_to_noise", 0.01, '')
pp_params.setValue("PeakPickerMRM:peak_width", 0.1, '')
pp_params.setValue("PeakPickerMRM:gauss_width", 0.1, '')
pp_params.setValue("resample_boundary", 0.05, '')
pp_params.setValue("compute_peak_quality", "true", '')
pp.setParameters(pp_params)
scorer = pyopenms.MRMFeatureFinderScoring()
scoring_params = scorer.getDefaults();
# Only report the top 5 features
scoring_params.setValue("stop_report_after_feature", 5, '')
scoring_params.setValue("rt_normalization_factor", rt_normalization_factor, '')
scorer.setParameters(scoring_params);
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(chromat_in, chromatograms)
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(traml_in, targeted);
trafoxml = pyopenms.TransformationXMLFile()
trafo = pyopenms.TransformationDescription()
if trafo_in is not None:
model_params = pyopenms.Param()
model_params.setValue("symmetric_regression", "false", "", [])
model_type = "linear"
trafoxml.load(trafo_in, trafo, True)
trafo.fitModel(model_type, model_params);
light_targeted = pyopenms.LightTargetedExperiment();
pyopenms.OpenSwathDataAccessHelper().convertTargetedExp(targeted, light_targeted)
output = algorithm(chromatograms, light_targeted, pp, scorer, trafo)
pyopenms.FeatureXMLFile().store(out, output);
def tramlInferMapping(rawdata_files,
aligned_pg_files,
mapping,
precursors_mapping,
sequences_mapping,
protein_mapping,
verbose=False):
try:
import pyopenms
except ImportError as e:
print("\nError!")
print(
"Could not import pyOpenMS while trying to load a TraML file - please make sure pyOpenMS is installed."
)
print(
"pyOpenMS is available from https://pypi.python.org/pypi/pyopenms")
print()
raise e
assert len(
aligned_pg_files) == 1, "There should only be one file in simple mode"
f = aligned_pg_files[0]
# Produce simple mapping between runs and files (assume each file is one run)
for i, raw in enumerate(rawdata_files):
mapping[str(i)] = [raw]
targexp = pyopenms.TargetedExperiment()
pyopenms.TraMLFile().load(f, targexp)
for peptide_precursor in targexp.getPeptides():
# Fill the protein mapping
protein_id = peptide_precursor.protein_refs
if len(protein_id) > 0:
protein_id = protein_id[0] # take the first one ...
tmp = protein_mapping.get(protein_id, [])
if peptide_precursor.sequence not in tmp:
tmp.append(peptide_precursor.sequence)
protein_mapping[protein_id] = tmp
# Fill the sequence mapping
tmp = sequences_mapping.get(peptide_precursor.sequence, [])
if peptide_precursor.id not in tmp:
tmp.append(peptide_precursor.id)
sequences_mapping[peptide_precursor.sequence] = tmp
for transition in targexp.getTransitions():
# Fill the precursor mapping
tmp = precursors_mapping.get(transition.getPeptideRef(), [])
if transition.getPeptideRef() not in tmp:
tmp.append(transition.getNativeID())
precursors_mapping[transition.getPeptideRef()] = tmp
def test_readfile_content(self):
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(self.filename, targeted);
self.assertEqual(len( targeted.getTransitions() ), 3 )
self.assertAlmostEqual(targeted.getTransitions()[0].getPrecursorMZ(), 500.0)
self.assertAlmostEqual(targeted.getTransitions()[0].getProductMZ(), 628.45, places=4)
self.assertEqual(targeted.getTransitions()[0].getName(), "tr1" )
self.assertEqual(targeted.getTransitions()[0].getNativeID(), "tr1" )
self.assertEqual(targeted.getTransitions()[0].getPeptideRef(), "tr_gr1")
def main(options):
# load chromatograms
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(options.infile, chromatograms)
# load TraML file
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(options.traml_in, targeted)
trafo_out = algorithm(chromatograms, targeted)
pyopenms.TransformationXMLFile().store(options.outfile, trafo_out)
def test_run_mrmrtnormalizer(self):
# load chromatograms
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(self.chromatograms, chromatograms)
# load TraML file
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(self.tramlfile, targeted)
# Create empty files as input and finally as output
empty_swath = pyopenms.MSExperiment()
trafo = pyopenms.TransformationDescription()
output = pyopenms.FeatureMap()
# set up featurefinder and run
featurefinder = pyopenms.MRMFeatureFinderScoring()
# set the correct rt use values
scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults()
scoring_params.setValue("Scores:use_rt_score".encode(),
'false'.encode(), ''.encode())
featurefinder.setParameters(scoring_params)
featurefinder.pickExperiment(chromatograms, output, targeted, trafo,
empty_swath)
# get the pairs
pairs = []
simple_find_best_feature(output, pairs, targeted)
pairs_corrected = pyopenms.MRMRTNormalizer().removeOutliersIterative(
pairs, 0.95, 0.6, True, "iter_jackknife")
pairs_corrected = [list(p) for p in pairs_corrected]
expected = [(1497.56884765625, 1881.0), (2045.9776611328125, 2409.0),
(2151.4814453125, 2509.0), (1924.0750732421875, 2291.0),
(612.9832153320312, 990.0), (1086.2474365234375, 1470.0),
(1133.89404296875, 1519.0), (799.5291137695312, 1188.0),
(1397.1541748046875, 1765.0)]
for exp, res in zip(expected, pairs_corrected):
self.assertAlmostEqual(exp[0], res[0], eps)
self.assertAlmostEqual(exp[1], res[1], eps)
def test_TargetedExperiment(self):
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(self.filename, targeted);
self.assertEqual(len( targeted.getTransitions() ), 3 )
targeted.setCVs(targeted.getCVs())
targeted.setTargetCVTerms(targeted.getTargetCVTerms())
targeted.setPeptides(targeted.getPeptides())
targeted.setProteins(targeted.getProteins())
targeted.setTransitions(targeted.getTransitions())
first_transition = targeted.getTransitions()[0]
first_peptide = targeted.getPeptides()[0]
targeted.addTransition(first_transition)
targeted.addPeptide(first_peptide)
self.assertTrue( targeted.getPeptideByRef(first_transition.getPeptideRef()) is not None)
self.assertTrue( targeted.getProteinByRef(first_peptide.protein_refs[0]) is not None)
def main(options):
# load featureXML
features = pyopenms.FeatureMap()
fh = pyopenms.FileHandler()
fh.loadFeatures(options.infile, features)
# load TraML file
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(options.traml_in, targeted);
# write TSV file
filename = options.infile.split(".")[0]
fh = open(options.outfile, "w")
wr = csv.writer(fh, delimiter='\t')
header = get_header(features)
wr.writerow(header)
for f in features:
keys = []
f.getKeys(keys)
row = convert_to_row(f,targeted,filename,keys,filename)
wr.writerow(row)
def main(options):
out = options.outfile
chromat_in = options.infile
traml_in = options.traml_in
pp = pyopenms.MRMTransitionGroupPicker()
pp_params = pp.getDefaults()
pp_params.setValue("PeakPickerMRM:remove_overlapping_peaks",
options.remove_overlapping_peaks, '')
pp_params.setValue("PeakPickerMRM:method", options.method, '')
pp.setParameters(pp_params)
chromatograms = pyopenms.MSExperiment()
fh = pyopenms.FileHandler()
fh.loadExperiment(chromat_in, chromatograms)
targeted = pyopenms.TargetedExperiment()
tramlfile = pyopenms.TraMLFile()
tramlfile.load(traml_in, targeted)
output = algorithm(chromatograms, targeted, pp)
pyopenms.FeatureXMLFile().store(out, output)
def test_readfile(self):
targeted = pyopenms.TargetedExperiment();
tramlfile = pyopenms.TraMLFile();
tramlfile.load(self.filename, targeted);
#!/usr/bin/env python
#!/usr/bin/env python
