def add_resolution_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates resolutions metric for two ions (at around 200 m/z and 700 m/z).
It's m/z divided by width of the peak at 50% height."""
# get resolution at mz ~200
mz200, width200 = resolution_200_features_names
mz200_value = ms_run['features_values'][ms_run['features_names'].index(mz200)]
width200_value = ms_run['features_values'][ms_run['features_names'].index(width200)]
if mz200_value == -1. or width200_value == -1.:
resolution200 = -1.
else:
resolution200 = int((193.0725512871 + mz200_value) / width200_value)
# get resolution at mz ~700
mz700, width700 = resolution_700_features_names
mz700_value = ms_run['features_values'][ms_run['features_names'].index(mz700)]
width700_value = ms_run['features_values'][ms_run['features_names'].index(width700)]
if mz700_value == -1. or width700_value == -1.:
resolution700 = -1.
else:
resolution700 = int((712.94671694 + mz700_value) / width700_value)
qc_values.extend([resolution200, resolution700])
qc_names.extend(['resolution_200', 'resolution_700'])
if in_debug_mode:
qcm_validator.print_qcm_names('resolution_200', ['ion_mz', mz200, width200, 'resolution_200'])
qcm_validator.print_qcm_values('resolution_200', [193.0725512871, mz200_value, width200_value, resolution200])
qcm_validator.print_qcm_names('resolution_700', ['ion_mz', mz700, width700, 'resolution_700'])
qcm_validator.print_qcm_values('resolution_700', [712.94671694, mz700_value, width700_value, resolution700])
def add_baseline_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method saves the metrics of baseline (nothing is calculated, just directly passed).
25th and 50th percentiles are taken from two chemical noise scan frames: [150, 250], [650, 750]. """
percentile_25_from_150, percentile_50_from_150 = baseline_150_250_features_names
percentile_25_from_650, percentile_50_from_650 = baseline_650_750_features_names
baseline_25_from_150 = int(ms_run['features_values'][ms_run['features_names'].index(percentile_25_from_150)])
baseline_50_from_150 = int(ms_run['features_values'][ms_run['features_names'].index(percentile_50_from_150)])
baseline_25_from_650 = int(ms_run['features_values'][ms_run['features_names'].index(percentile_25_from_650)])
baseline_50_from_650 = int(ms_run['features_values'][ms_run['features_names'].index(percentile_50_from_650)])
qc_values.extend([baseline_25_from_150, baseline_50_from_150, baseline_25_from_650, baseline_50_from_650])
qc_names.extend(['baseline_25_150', 'baseline_50_150', 'baseline_25_650', 'baseline_50_650'])
if in_debug_mode:
qcm_validator.print_qcm_names('baseline_25_150', [percentile_25_from_150])
qcm_validator.print_qcm_values('baseline_25_150', [baseline_25_from_150])
qcm_validator.print_qcm_names('baseline_50_150', [percentile_50_from_150])
qcm_validator.print_qcm_values('baseline_50_150', [baseline_50_from_150])
qcm_validator.print_qcm_names('baseline_25_650', [percentile_25_from_650])
qcm_validator.print_qcm_values('baseline_25_650', [baseline_25_from_650])
qcm_validator.print_qcm_names('baseline_50_650', [percentile_50_from_650])
qcm_validator.print_qcm_values('baseline_50_650', [baseline_50_from_650])
def add_fragmentation_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method saves the metrics of fragmentation (nothing is calculated, just directly passed).
Fragmentation intensity ratios of two ions are taken: mz305(to mz191) & mz712 (to mz668). """
ratio305, ratio712 = fragmentation_features_names
fragmentation305 = ms_run['features_values'][ms_run['features_names'].index(ratio305)]
fragmentation712 = ms_run['features_values'][ms_run['features_names'].index(ratio712)]
qc_values.extend([fragmentation305, fragmentation712])
qc_names.extend(['fragmentation_305', 'fragmentation_712'])
if in_debug_mode:
qcm_validator.print_qcm_names('fragmentation_305', [ratio305])
qcm_validator.print_qcm_values('fragmentation_305', [fragmentation305])
qcm_validator.print_qcm_names('fragmentation_712', [ratio712])
qcm_validator.print_qcm_values('fragmentation_712', [fragmentation712])
def add_noise_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates metric of the instrument noise.
It sums up absolute intensities of the instrument noise scan."""
instrument_noise_signal_sum = 0
values = [] # for debugging only
for feature in noise_features_names:
instrument_noise_signal_sum += ms_run['features_values'][ms_run['features_names'].index(feature)]
if in_debug_mode:
values.append(ms_run['features_values'][ms_run['features_names'].index(feature)])
qc_values.append(int(instrument_noise_signal_sum))
qc_names.append('instrument_noise')
if in_debug_mode:
qcm_validator.print_qcm_names('instrument_noise', [*noise_features_names, 'instrument_noise'])
qcm_validator.print_qcm_values('instrument_noise', [*values, int(instrument_noise_signal_sum)])
def add_dirt_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates metric of the dirtiness.
It sums up absolute intensities of the chemical noise scan. All the expected peaks there are excluded. """
chem_noise_signal_sum = 0
values = [] # for debugging only
for feature in dirt_features_names:
chem_noise_signal_sum += ms_run['features_values'][ms_run['features_names'].index(feature)]
if in_debug_mode:
values.append(ms_run['features_values'][ms_run['features_names'].index(feature)])
qc_values.append(int(chem_noise_signal_sum))
qc_names.append('chemical_dirt')
if in_debug_mode:
qcm_validator.print_qcm_names('chemical_dirt', [*dirt_features_names, 'chemical_dirt'])
qcm_validator.print_qcm_values('chemical_dirt', [*values, int(chem_noise_signal_sum)])
def add_signal_to_background_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates metric of signal to background as follows:
intensity at mz510 / 25th percentile in [500, 550]. """
intensity510, percentile_25_from_500 = s2b_features_names
intensity510_value = ms_run['features_values'][ms_run['features_names'].index(intensity510)]
percentile_25_from_500_value = ms_run['features_values'][ms_run['features_names'].index(percentile_25_from_500)]
if intensity510_value != -1.:
s2b = intensity510_value / percentile_25_from_500_value
else:
s2b = -1.
qc_values.append(s2b)
qc_names.append('s2b')
if in_debug_mode:
qcm_validator.print_qcm_names('s2b', [intensity510, percentile_25_from_500, 's2b'])
qcm_validator.print_qcm_values('s2b', [intensity510_value, percentile_25_from_500_value, s2b])
def add_transmission_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates the metric of transmission.
It finds the ratio of the intensities of two ions: the light one (~ mz305) and the heavy one (~ mz712). """
intensity712, intensity305 = transmission_features_names
intensity712_value = ms_run['features_values'][ms_run['features_names'].index(intensity712)]
intensity305_value = ms_run['features_values'][ms_run['features_names'].index(intensity305)]
if intensity305_value != -1. and intensity712_value != -1.:
transmission = intensity712_value / intensity305_value
else:
transmission = -1.
qc_values.append(transmission)
qc_names.append('transmission')
if in_debug_mode:
qcm_validator.print_qcm_names('transmission', [intensity712, intensity305, 'transmission'])
qcm_validator.print_qcm_values('transmission', [intensity712_value, intensity305_value, transmission])
def add_signal_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates metric of the overall signal.
It sums up absolute intensities of all the expected peaks."""
values = []
signal_sum = 0.
for feature in signal_features_names:
value = ms_run['features_values'][ms_run['features_names'].index(feature)]
values.append(value)
if value != -1.:
signal_sum += value
signal_sum = int(signal_sum)
total_non_missing = sum(numpy.array(values) != -1.)
qc_values.append(signal_sum)
qc_names.append('signal')
if in_debug_mode:
qcm_validator.print_qcm_names('signal', [*signal_features_names, 'total_non_missing', 'signal'])
qcm_validator.print_qcm_values('signal', [*values, total_non_missing, signal_sum])
def add_isotopic_abundance_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates metrics of the isotopic presence.
It finds the average of isotopic intensities ratios diffs (absolute percent diffs for all the isotopes). """
values = []
values_sum = 0.
for feature in isotopic_presence_features_names:
value = ms_run['features_values'][ms_run['features_names'].index(feature)]
values.append(value)
if value != -1.:
values_sum += abs(value)
total_non_missing = sum(numpy.array(values) != -1.)
ratios_diffs_mean = values_sum / total_non_missing
qc_values.append(ratios_diffs_mean)
qc_names.append('isotopic_presence')
if in_debug_mode:
qcm_validator.print_qcm_names('isotopic_presence', [*isotopic_presence_features_names, 'total_non_missing', 'isotopic_presence'])
qcm_validator.print_qcm_values('isotopic_presence', [*values, total_non_missing, ratios_diffs_mean])
def add_accuracy_metrics(qc_values, qc_names, ms_run, in_debug_mode=False):
""" This method calculates accuracy metrics for QC run.
It's average of the absolute m/z diff values for all the expected ions. """
values = []
values_sum = 0.
for feature in accuracy_features_names:
value = ms_run['features_values'][ms_run['features_names'].index(feature)]
values.append(value)
if value != -1.: # if this is not a missing value
values_sum += abs(value) # abs for absolute mass accuracy values
total_non_missing = sum(numpy.array(values) != -1.)
average_accuracy = values_sum / total_non_missing
qc_values.append(average_accuracy)
qc_names.append('average_accuracy')
if in_debug_mode:
qcm_validator.print_qcm_names('average_accuracy', [*accuracy_features_names, 'total_non_missing', 'average_accuracy'])
qcm_validator.print_qcm_values('average_accuracy', [*values, total_non_missing, average_accuracy])