def test_SyntaxCheck():
cvt = qc.CvParameter(cvRef="REF",
accession="TEST:123",
name="testname",
value=99)
infi = qc.InputFile(name="file.raw",
location="file:///dev/null",
fileFormat=qc.CvParameter("MS", "MS:1000584",
"mzML format"),
fileProperties=[
qc.CvParameter(cvRef="MS",
accession="MS:1000747",
name="completion time",
value="2017-12-08-T15:38:57Z")
])
anso = qc.AnalysisSoftware(
cvRef="QC",
accession="QC:9999999",
name="bigwhopqc",
version="1.2.3",
uri="file:///dev/null") # isn't requiring a uri a bit too much?
meta = qc.MetaDataParameters(inputFiles=[infi], analysisSoftware=[anso])
qm = qc.QualityMetric(cvRef="QC",
accession="QC:4000053",
name="RT duration",
value=99)
qm2 = qc.QualityMetric(cvRef="QC",
accession="QC:4000061",
name="Maximal MS2 frequency",
value=999)
qm3 = qc.QualityMetric(cvRef="QC",
accession="QC:4000055",
name="MS1 quantiles RT fraction",
value=9)
rq = qc.RunQuality(metadata=meta, qualityMetrics=[qm, qm2])
sq = qc.SetQuality(metadata=meta, qualityMetrics=[qm3])
cv = qc.ControlledVocabulary(ref="QC", name="QCvocab", uri="www.qc.ml")
cv2 = qc.ControlledVocabulary(ref="REF", name="TEST", uri="www.eff.off")
mzqc = qc.MzQcFile(version="0.0.11",
runQualities=[rq],
setQualities=[sq],
controlledVocabularies=[cv, cv2])
# with open('tests/mzqc_lib_out.mzqc', 'w') as f:
# f.write("{ \"mzQC\": " + qc.JsonSerialisable.ToJson(mzqc) + " }")
syn_check = sy.SyntacticCheck()
syn_check.validate("{ \"mzQC\": " + qc.JsonSerialisable.ToJson(mzqc) +
" }")
Unit tests for the MZQCFile library
"""
# String comparison -as in TestSerialisation- needs the 'empty' attributes, too, whereas Object comparison -as in TestDeserialisation- only compares 'non-empty' attributes
QM = '{"cvRef": "QC", "accession": "QC:4000053", "name": "RT duration", "description": "", "value": 99, "unit": ""}'
CV = '{"ref": "REF", "name": "TEST", "uri": "www.eff.off", "version": ""}'
CVT = '{"cvRef": "REF", "accession": "TEST:123", "name": "testname", "description": "", "value": 99, "unit": ""}'
ANSO = '{"cvRef": "QC", "accession": "QC:9999999", "name": "bigwhopqc", "description": "", "value": "", "unit": "", "version": "1.2.3", "uri": "file:///dev/null"}'
INFI = '{"location": "file:///dev/null", "name": "file.raw", "fileFormat": {"cvRef": "MS", "accession": "MS:1000584", "name": "mzML format"}, "fileProperties": [{"cvRef": "MS", "accession": "MS:1000747", "name": "completion time", "value": "2017-12-08-T15:38:57Z"}]}'
META = '{"inputFiles": [{"location": "file:///dev/null", "name": "file.raw", "fileFormat": {"cvRef": "MS", "accession": "MS:1000584", "name": "mzML format"}, "fileProperties": [{"cvRef": "MS", "accession": "MS:1000747", "name": "completion time", "value": "2017-12-08-T15:38:57Z"}]}], "analysisSoftware": [{"cvRef": "QC", "accession": "QC:9999999", "name": "bigwhopqc", "version": "1.2.3", "uri": "file:///dev/null"}]}'
RUQU = '{"metadata": {"inputFiles": [{"location": "file:///dev/null", "name": "file.raw", "fileFormat": {"cvRef": "MS", "accession": "MS:1000584", "name": "mzML format"}, "fileProperties": [{"cvRef": "MS", "accession": "MS:1000747", "name": "completion time", "value": "2017-12-08-T15:38:57Z"}]}], "analysisSoftware": [{"cvRef": "QC", "accession": "QC:9999999", "name": "bigwhopqc", "version": "1.2.3", "uri": "file:///dev/null"}]}, "qualityMetrics": [{"cvRef": "QC", "accession": "QC:4000053", "name": "RT duration", "value": 99}]}'
SEQU = '{"metadata": {"inputFiles": [{"location": "file:///dev/null", "name": "file.raw", "fileFormat": {"cvRef": "MS", "accession": "MS:1000584", "name": "mzML format"}, "fileProperties": [{"cvRef": "MS", "accession": "MS:1000747", "name": "completion time", "value": "2017-12-08-T15:38:57Z"}]}], "analysisSoftware": [{"cvRef": "QC", "accession": "QC:9999999", "name": "bigwhopqc", "version": "1.2.3", "uri": "file:///dev/null"}]}, "qualityMetrics": [{"cvRef": "QC", "accession": "QC:4000053", "name": "RT duration", "value": 99}]}'
NPQM = '{"cvRef": "", "accession": "QC:123", "name": "einszweidrei", "description": "", "value": {"np": [0.1111111119389534, 0.25, 0.4285714328289032]}, "unit": ""}'
cvt = qc.CvParameter(cvRef="REF",
accession="TEST:123",
name="testname",
value=99)
infi = qc.InputFile(name="file.raw",
location="file:///dev/null",
fileFormat=qc.CvParameter("MS", "MS:1000584",
"mzML format"),
fileProperties=[
qc.CvParameter(cvRef="MS",
accession="MS:1000747",
name="completion time",
value="2017-12-08-T15:38:57Z")
])
anso = qc.AnalysisSoftware(
cvRef="QC",
accession="QC:9999999",
name="bigwhopqc",
def getBasicQuality(exp: oms.MSExperiment, verbose: bool=False) -> mzqc.RunQuality:
"""
getBasicQuality calculates the basic QualityMetrics from a mass spectrometry peak file and creates the related RunQuality object.
Calculated basic QC metrics and proto-metrics necessary to calculate more elaborate QC metrics with additional data (e.g. ID).
Parameters
----------
exp : oms.MSExperiment
The mass spectrometry peak file to calculate metrics from
verbose : bool, optional
switches on verbose logging, by default False
Returns
-------
mzqc.RunQuality
A RunQuality object containing the list of metrics calculated and metadata collected, ready for integration into a mzQC file object.
"""
metrics: List[mzqc.QualityMetric] = list()
if exp.getExperimentalSettings().getSourceFiles():
parent_base_name: str = basename(exp.getExperimentalSettings().getSourceFiles()[0].getNameOfFile())
parent_chksm: str = exp.getExperimentalSettings().getSourceFiles()[0].getChecksum()
parent_chksm_type: str = exp.getExperimentalSettings().getSourceFiles()[0].getChecksumType()
instr_srl: str = exp.getInstrument().getMetaValue('instrument serial number') \
if exp.getInstrument().metaValueExists('instrument serial number') else 'unknown' # MS:1000529 in mzML
input_loc: str = exp.getExperimentalSettings().getLoadedFilePath()
base_name: str = basename(input_loc)
chksm: str = utils.sha256fromfile(exp.getExperimentalSettings().getLoadedFilePath())
cmpltn: str = exp.getDateTime().get()
# strt:datetime.datetime = datetime.datetime.strptime(cmpltn, '%Y-%m-%d %H:%M:%S') - datetime.timedelta(seconds=exp.getChromatograms()[0][exp.getChromatograms()[0].size()-1].getRT()*60)
meta: mzqc.MetaDataParameters = mzqc.MetaDataParameters(
inputFiles=[
mzqc.InputFile(name=base_name,location=input_loc,
fileFormat=mzqc.CvParameter("MS", "MS:1000584", "mzML format"),
fileProperties=[
mzqc.CvParameter(cvRef="MS",
accession="MS:1000747",
name="completion time",
value=cmpltn
),
mzqc.CvParameter(cvRef="MS",
accession="MS:1000569",
name="SHA-256",
value=chksm
),
mzqc.CvParameter(cvRef="MS",
accession="MS:1000031",
name="instrument model",
value=exp.getInstrument().getName()
),
mzqc.CvParameter(cvRef="MS",
accession="MS:1000529",
name="instrument serial number",
value=instr_srl
)
# TODO integrate parent location and checksum
# id: MS:1002846 (Associated raw file URI) N.B. definition is PRIDE specific - WTF
# fitting checksum cv missing
]
)
],
analysisSoftware=[
mzqc.AnalysisSoftware(cvRef="MS", accession="MS:1000752", name="TOPP software", version=oms.__version__, uri="openms.de")
]
)
# this is mighty important to sort by RT
exp.sortSpectra()
min_mz: float = sys.maxsize
max_mz: float = 0
mslevelcounts: Dict[int,int] = defaultdict(int)
spectrum_acquisition_metrics_MS1: Dict[str,List[Any]] = defaultdict(list)
spectrum_acquisition_metrics_MS2: Dict[str,List[Any]] = defaultdict(list)
spectrum_topn: Dict[str,List[Any]] = defaultdict(list)
tandem_spectrum_metrics_MS2: Dict[str,List[Any]] = defaultdict(list)
trap_metrics_MS1: Dict[str,List[Any]] = defaultdict(list)
trap_metrics_MS2: Dict[str,List[Any]] = defaultdict(list)
isolation_window_metrics: Dict[str,List[Any]] = defaultdict(list)
tic_tab: Dict[str,List[Any]] = defaultdict(list)
# ActivationMethod look-up dict
ams = {getattr(ActivationMethod,i): i for i in dir(ActivationMethod) if type(getattr(ActivationMethod,i))==int }
intens_sum: np.float = 0
last_surveyscan_index:int = 0
for spin, spec in enumerate(exp):
mslevelcounts[spec.getMSLevel()] += 1
iontraptime = utils.getTrapTime(spec)
intens_max = spec.get_peaks()[1].max()
intens_min = spec.get_peaks()[1].min()
intens_sum = spec.get_peaks()[1].sum()
if spec.getMSLevel() == 1:
last_surveyscan_index = spin
last_surveyscan_intensity = intens_sum
last_surveyscan_max = intens_max
spectrum_acquisition_metrics_MS1['RT'].append(spec.getRT())
spectrum_acquisition_metrics_MS1['SN'].append(noiseqc.getSN_medianmethod(spec))
spectrum_acquisition_metrics_MS1['peakcount'].append(spec.size())
spectrum_acquisition_metrics_MS1['int'].append(intens_sum.item()) # .item() for dtype to pytype
trap_metrics_MS1['RT'].append(spec.getRT())
trap_metrics_MS1['traptime'].append(iontraptime)
tic_tab['RT'].append(spec.getRT())
tic_tab['int'].append(intens_sum)
if (spec.getMSLevel() == 2):
if (spec.getPrecursors()[0].getMZ() < min_mz):
min_mz = spec.getPrecursors()[0].getMZ()
if (spec.getPrecursors()[0].getMZ() > max_mz):
max_mz = spec.getPrecursors()[0].getMZ()
spectrum_acquisition_metrics_MS2['RT'].append(spec.getRT())
spectrum_acquisition_metrics_MS2['SN'].append(noiseqc.getSN_medianmethod(spec))
spectrum_acquisition_metrics_MS2['peakcount'].append(spec.size())
spectrum_acquisition_metrics_MS2['int'].append(intens_sum.item()) # .item() for dtype to pytype
spectrum_acquisition_metrics_MS2['native_id'].append(utils.spec_native_id(spec))
rank = spin - last_surveyscan_index
spectrum_acquisition_metrics_MS2['rank'].append(rank)
trap_metrics_MS2['RT'].append(spec.getRT())
trap_metrics_MS2['traptime'].append(iontraptime)
trap_metrics_MS2['activation_method'].append(ams.get(next(iter(spec.getPrecursors()[0].getActivationMethods()), None),'unknown'))
trap_metrics_MS2['activation_energy'].append(spec.getPrecursors()[0].getMetaValue('collision energy') if \
spec.getPrecursors()[0].metaValueExists('collision energy') else -1)
precursor_index = np.searchsorted(exp[last_surveyscan_index].get_peaks()[0], [exp[spin].getPrecursors()[0].getMZ()])[0]
if precursor_index != np.array(exp[last_surveyscan_index].get_peaks()).shape[1]:
precursor_err = spec.getPrecursors()[0].getMZ() - np.array(exp[last_surveyscan_index].get_peaks())[:,precursor_index][0]
precursor_int = np.array(exp[last_surveyscan_index].get_peaks())[:,precursor_index][1]
else:
precursor_err = np.nan
precursor_int = np.nan
tandem_spectrum_metrics_MS2['RT'].append(spec.getRT())
tandem_spectrum_metrics_MS2['precursor_intensity'].append(precursor_int) # TODO different from mzid->mzml getPrecursors[0].getIntensity() ? YES, latter one usually zero
tandem_spectrum_metrics_MS2['precursor_error'].append(precursor_err)
tandem_spectrum_metrics_MS2['precursor_mz'].append(spec.getPrecursors()[0].getMZ())
tandem_spectrum_metrics_MS2['precursor_c'].append(spec.getPrecursors()[0].getCharge())
tandem_spectrum_metrics_MS2['surveyscan_intensity_sum'].append(last_surveyscan_intensity)
tandem_spectrum_metrics_MS2['surveyscan_intensity_max'].append(last_surveyscan_max)
isolation_window_metrics['RT'].append(spec.getRT())
isolation_window_metrics['isolation_target'].append(spec.getPrecursors()[0].getMZ()) # https://github.com/OpenMS/OpenMS/blob/d17cc251fd0c4068eb253b03c9fb107897771fdc/src/openms/source/FORMAT/HANDLERS/MzMLHandler.cpp#L1992
isolation_window_metrics['isolation_lower'].append(spec.getPrecursors()[0].getIsolationWindowLowerOffset())
isolation_window_metrics['isolation_upper'].append(spec.getPrecursors()[0].getIsolationWindowUpperOffset())
lower = spec.getPrecursors()[0].getMZ() - spec.getPrecursors()[0].getIsolationWindowLowerOffset()
upper = spec.getPrecursors()[0].getMZ() + spec.getPrecursors()[0].getIsolationWindowUpperOffset()
s = np.array([(i.getMZ(),i.getIntensity()) for i in exp[last_surveyscan_index]], ndmin = 2)
s = s[np.where(np.logical_and(s[:, 0]>=lower, s[:, 0]<=upper))[0]]
isolation_window_metrics['peaks_in_window'].append(np.shape(s)[0])
int_sort_desc = np.flip(np.argsort(s[:,1]))
if np.shape(s)[0] > 1:
isolation_window_metrics['int_ratio_ranked_peaks_in_window'].append(
s[int_sort_desc][:-1,1]/s[int_sort_desc][1:,1][0]) # intensity ratio between top1&2, 2&3, ...
else:
isolation_window_metrics['int_ratio_ranked_peaks_in_window'].append(0) # bigger is better, though best is 0
isolation_window_metrics['summed_window_intensity'].append(np.sum(s[int_sort_desc][:,1]))
isolation_window_metrics['isolation_target_intensity'].append(spec.getPrecursors()[0].getIntensity())
# TODO this needs to go outside
tol = 0.5
if spec.metaValueExists('filter string'):
if 'FTMS' in spec.getMetaValue('filter string'):
tol = 0.05
elif 'ITMS' in spec.getMetaValue('filter string'):
tol = 0.5
elif 'QTOF' in spec.getMetaValue('filter string'): #TOFMS, SQMS, TQMS, SectorMS
tol = 0.1
# ms2 peaks directly from isolation window?
unfragmented = np.any([np.isclose(i[0],[x.getMZ() for x in spec], atol=tol) for i in s])
isolation_window_metrics['peaks_in_window_in_ms2'].append(str(unfragmented))
## Spectra detail numbers
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Spectrum acquisition metric values - MS1",
value=spectrum_acquisition_metrics_MS1)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Spectrum acquisition metric values - MS2",
value=spectrum_acquisition_metrics_MS2)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Spectra topn ranks",
value=spectrum_topn)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Tandem spectrum metric values - MS2",
value=tandem_spectrum_metrics_MS2)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Trap metric values - MS1",
value=trap_metrics_MS1)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Trap metric values - MS2",
value=trap_metrics_MS2)
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="isolation window metrics",
value=isolation_window_metrics)
)
## Spectra numbers
for levels in mslevelcounts.keys():
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Number of MS{l} spectra".format(l=str(levels)),
value=mslevelcounts[levels])
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Number of chromatograms",
value=len(exp.getChromatograms()))
)
## Ranges
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="MZ aquisition range",
value=[min_mz,max_mz])
)
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="RT aquisition range",
value=[exp[0].getRT(),exp[exp.size()-1].getRT()])
)
# TIC
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Total ion current",
value=tic_tab)
)
# Chrom
chrom_tab: Dict[str,List[Any]] = defaultdict(list)
chroms = exp.getChromatograms()
for t in chroms:
if t.getChromatogramType() == oms.ChromatogramSettings.ChromatogramType.TOTAL_ION_CURRENT_CHROMATOGRAM:
for chro_peak in t:
chrom_tab['RT'].append(chro_peak.getRT())
chrom_tab['int'].append(chro_peak.getIntensity())
break
metrics.append(
mzqc.QualityMetric(cvRef="QC",
accession="QC:0000000",
name="Chromatogram",
value=chrom_tab)
)
# TODO is there a difference between TIC as defined in MS:1000235 and the chromatogram you get from TRP?? In MZML it says its a MS:1000235 (ion current detected in each of a series of mass spectra) but is it?
# TODO consider collection of spectrum_native_id
return mzqc.RunQuality(metadata=meta, qualityMetrics=metrics)