def test_getPrecursorRtMz(self):
expectedPrecursorRtMz_mzml = [{
'mz': 337.33999999999997,
'rt': 85.166666666666671
}, {
'mz': 421.76077270507812,
'rt': 0.82813999999999999
}, {
'mz': 462.24014282226562,
'rt': 12.52604
}, {
'mz': 335.222412109375,
'rt': 37.834473333333335
}]
expectedPrecursorRtMz_peaks = [{
'rt': 9.5545000000000009,
'mz': 421.758026123047
}, {
'rt': 49.688400000000001,
'mz': 421.760772705078
}]
msrun_mzml = pymzml.run.Reader(testFolder + 'mzml_test_file_1.mzML')
msrun_peaks = pymzml.run.Reader(testFolder +
'peaksMzmlTestfile.peaks.mzML')
actualPrecursorRtMz_mzml = mzmlFunctions.getPrecursorRtMz(msrun_mzml)
actualPrecursorRtMz_peaks = mzmlFunctions.getPrecursorRtMz(msrun_peaks)
self.assertEqual(expectedPrecursorRtMz_mzml, actualPrecursorRtMz_mzml)
self.assertEqual(expectedPrecursorRtMz_peaks,
actualPrecursorRtMz_peaks)
def test_getPrecursorRtMz(self):
expectedPrecursorRtMz_mzml = [{'mz': 337.33999999999997, 'rt': 85.166666666666671},{'mz': 421.76077270507812, 'rt': 0.82813999999999999},{'mz': 462.24014282226562, 'rt': 12.52604},{'mz': 335.222412109375, 'rt': 37.834473333333335}]
expectedPrecursorRtMz_peaks = [{'rt': 9.5545000000000009, 'mz': 421.758026123047}, {'rt': 49.688400000000001, 'mz': 421.760772705078}]
msrun_mzml = pymzml.run.Reader(testFolder+'mzml_test_file_1.mzML')
msrun_peaks = pymzml.run.Reader(testFolder+'peaksMzmlTestfile.peaks.mzML')
actualPrecursorRtMz_mzml = mzmlFunctions.getPrecursorRtMz(msrun_mzml)
actualPrecursorRtMz_peaks = mzmlFunctions.getPrecursorRtMz(msrun_peaks)
self.assertEqual(expectedPrecursorRtMz_mzml, actualPrecursorRtMz_mzml)
self.assertEqual(expectedPrecursorRtMz_peaks, actualPrecursorRtMz_peaks)
def compareCoordinate(mzmlFile, featureFile, writeCSV=False, writeTo="precursorPerFeature.csv"):
r"""
Compare the precursors scan time and m/z values of a spectrum with all the retention time and m/z values in the convexhull of a feature. The spectrum information can come from
a mzml File or a peaks.mzml file. It returns a dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures
is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and
totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature.
A third input is writeCSV. If this is set to true, totalPrecursorsInFeatures is written out to a CSV file with a column featureID and a column # of precursors.
@type mzmlFile: string
@param mzmlFile: The path of the .mzML file
@type featureFile: string
@param featureFile: The path of the .featureXML file
@type writeCSV: bool
@param writeCSV: Flag if a CSV file has to be written out of the precursor per feature data (default: false)
@type writeTo: string
@param writeTo: The file and path where writeCSV has to be written to, default is precursorPerFeature.csv in the same folder as the script
@rtype: Dictionary
@returns: A dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures
is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and
totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature
B{Examples}:
Print the return value:
>>> print compareCoordinate('example_mzML_file.mzML', 'example_feature_file.featureXML')
{'totalPrecursorsInFeatures': 2, 'featPerPrecursorDict': {'f_43922326584371237334': 1, 'f_8613715360396561740': 0, 'f_13020522388175237334': 1}, 'averagePrecursorsInFeatures': 0.66666666666666663}
Write the results to a csv file:
>>> compareCoordinate(testFolder+'mzmlTestFile.mzML', testFolder+'featurexmlTestFile.featureXML', True, testFolder+'testPrecursorPerFeature.csv') # note the True
"""
fileHandle = fileHandling.FileHandle(os.path.abspath(mzmlFile))
# getting the absolute path of the given mzml file
mzmlFile = os.path.abspath(mzmlFile)
# parsing of mzml file
msrun = pymzml.run.Reader(mzmlFile)
# get the retention times and m/z of all precursors in msrun
retentionTime = mzmlFunctions.getPrecursorRtMz(msrun)
featureFile = os.path.abspath(featureFile)
# make an instance of the parseFeatureXML.Reader object, with file as input
featureXML = parseFeatureXML.Reader(featureFile)
# featPrecursor will hold the amount of precursors per feature, with id as key and amount of precursors as feature
featPrecursor = {}
totalPrecursor = 0
countZero = 0
x = 0
# get all features out of featureXML
for feature in featureXML.getSimpleFeatureInfo():
# set the amount of precursor per feature to 0 at every feature
precursorPerFeature = 0
# get the coordinates of all features
featureCoordinates = featureFunctions.getFeatureConvexhullCoordinates(feature)
# loop for every feature coordinate through every MS/MS precursor coordinate
for mzAndRT in retentionTime:
# if the retention time (*60 to go from minutes to seconds) is larger than xMin and smaller than xMax and the m/z is
# larger than xMin and smaller than xMax, count the precursors
if (
float(mzAndRT["rt"]) * 60 > float(featureCoordinates[feature]["rtMin"])
and float(mzAndRT["rt"] * 60) < float(featureCoordinates[feature]["rtMax"])
and float(mzAndRT["mz"]) > float(featureCoordinates[feature]["mzMin"])
and float(mzAndRT["mz"]) < float(featureCoordinates[feature]["mzMax"])
):
precursorPerFeature += 1
totalPrecursor += 1
if precursorPerFeature == 0:
countZero += 1
featPrecursor[featureXML["id"]] = precursorPerFeature
x += 1
# if writeCSV flag is set to True, write out csv file to the absolute path of writeTo (default: precursorPerFeature.csv in the same folder)
if writeCSV:
compareDataWriter = output.CompareDataWriter(os.path.abspath(writeTo))
compareDataWriter.precursorPerFeatureCsvWriter(featPrecursor)
# calculate the average precursor per feature
averagePrecursFeature = float(totalPrecursor) / float(len(featPrecursor))
return {
"totalPrecursorsInFeatures": totalPrecursor,
"averagePrecursorsInFeatures": averagePrecursFeature,
"featPerPrecursorDict": featPrecursor,
}
def compareCoordinate(mzmlFile,
featureFile,
writeCSV=False,
writeTo='precursorPerFeature.csv'):
r"""
Compare the precursors scan time and m/z values of a spectrum with all the retention time and m/z values in the convexhull of a feature. The spectrum information can come from
a mzml File or a peaks.mzml file. It returns a dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures
is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and
totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature.
A third input is writeCSV. If this is set to true, totalPrecursorsInFeatures is written out to a CSV file with a column featureID and a column # of precursors.
@type mzmlFile: string
@param mzmlFile: The path of the .mzML file
@type featureFile: string
@param featureFile: The path of the .featureXML file
@type writeCSV: bool
@param writeCSV: Flag if a CSV file has to be written out of the precursor per feature data (default: false)
@type writeTo: string
@param writeTo: The file and path where writeCSV has to be written to, default is precursorPerFeature.csv in the same folder as the script
@rtype: Dictionary
@returns: A dictionary with 3 keys: totalPrecursorsInFeatures, averagePrecursorsInFeatures and featPerPrecursorDict. totalPrecursorsInFeatures
is a numeric value: the total amount of precursors that are in all features, averagePrecursorsInFeatures is a numeric value: the average amount of precursors in a feature and
totalPrecursorsInFeatures is a dictionary with as key every feature and as value the amount of precursors per feature
B{Examples}:
Print the return value:
>>> print compareCoordinate('example_mzML_file.mzML', 'example_feature_file.featureXML')
{'totalPrecursorsInFeatures': 2, 'featPerPrecursorDict': {'f_43922326584371237334': 1, 'f_8613715360396561740': 0, 'f_13020522388175237334': 1}, 'averagePrecursorsInFeatures': 0.66666666666666663}
Write the results to a csv file:
>>> compareCoordinate(testFolder+'mzmlTestFile.mzML', testFolder+'featurexmlTestFile.featureXML', True, testFolder+'testPrecursorPerFeature.csv') # note the True
"""
fileHandle = fileHandling.FileHandle(os.path.abspath(mzmlFile))
# getting the absolute path of the given mzml file
mzmlFile = os.path.abspath(mzmlFile)
# parsing of mzml file
msrun = pymzml.run.Reader(mzmlFile)
# get the retention times and m/z of all precursors in msrun
retentionTime = mzmlFunctions.getPrecursorRtMz(msrun)
featureFile = os.path.abspath(featureFile)
# make an instance of the parseFeatureXML.Reader object, with file as input
featureXML = parseFeatureXML.Reader(featureFile)
# featPrecursor will hold the amount of precursors per feature, with id as key and amount of precursors as feature
featPrecursor = {}
totalPrecursor = 0
countZero = 0
x = 0
# get all features out of featureXML
for feature in featureXML.getSimpleFeatureInfo():
# set the amount of precursor per feature to 0 at every feature
precursorPerFeature = 0
# get the coordinates of all features
featureCoordinates = featureFunctions.getFeatureConvexhullCoordinates(
feature)
# loop for every feature coordinate through every MS/MS precursor coordinate
for mzAndRT in retentionTime:
# if the retention time (*60 to go from minutes to seconds) is larger than xMin and smaller than xMax and the m/z is
# larger than xMin and smaller than xMax, count the precursors
if float(mzAndRT['rt']) * 60 > float(featureCoordinates[feature]['rtMin']) and float(mzAndRT['rt'] * 60) < float(featureCoordinates[feature]['rtMax']) \
and float(mzAndRT['mz']) > float(featureCoordinates[feature]['mzMin']) and float(mzAndRT['mz']) < float(featureCoordinates[feature]['mzMax']):
precursorPerFeature += 1
totalPrecursor += 1
if precursorPerFeature == 0:
countZero += 1
featPrecursor[featureXML['id']] = precursorPerFeature
x += 1
# if writeCSV flag is set to True, write out csv file to the absolute path of writeTo (default: precursorPerFeature.csv in the same folder)
if writeCSV:
compareDataWriter = output.CompareDataWriter(os.path.abspath(writeTo))
compareDataWriter.precursorPerFeatureCsvWriter(featPrecursor)
# calculate the average precursor per feature
averagePrecursFeature = float(totalPrecursor) / float(len(featPrecursor))
return {
'totalPrecursorsInFeatures': totalPrecursor,
'averagePrecursorsInFeatures': averagePrecursFeature,
'featPerPrecursorDict': featPrecursor
}
