def algorithm(chromatograms, 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", 'false', '')
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().rm_outliers(pairs, 0.95, 0.6)
pairs_corrected = [list(p) for p in pairs_corrected]
# // store transformation, using a linear model as default
trafo_out = pyopenms.TransformationDescription()
trafo_out.setDataPoints(pairs_corrected)
model_params = pyopenms.Param()
model_params.setValue("symmetric_regression", 'false', '')
model_type = "linear"
trafo_out.fitModel(model_type, model_params)
return trafo_out
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 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 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)
