def bulk_background_correction(fragment_dict, list_of_replicates,
sample_background, isotope_dict, decimals):
input_fragments = []
unlabeled_fragment = []
corrected_fragments_dict = {}
for key, value in fragment_dict.iteritems():
if value.unlabeled:
unlabeled_fragment.append((key, value))
input_fragments.append((key, value))
else:
input_fragments.append((key, value))
try:
assert len(unlabeled_fragment) == 1
except AssertionError:
raise AssertionError(
'The input should contain atleast and only one unlabeled fragment data'
'Please check metadata or raw data files')
for input_fragment in input_fragments:
replicate_value = background(list_of_replicates, input_fragment[1],
unlabeled_fragment[0][1], isotope_dict)
#corrected_sample_data = bacground_correction(
# replicate_value, sample_background, input_fragment[1].data, decimals)
sample_data = input_fragment[1].data
corrected_sample_data = {}
for key, value in sample_data.iteritems():
background_value = replicate_value[sample_background[key]]
new_value = np.around(value - background_value, decimals)
corrected_sample_data[key] = new_value
corrected_fragments_dict[input_fragment[0]] = Infopacket(
input_fragment[1].frag, corrected_sample_data,
input_fragment[1].unlabeled, input_fragment[1].name)
return corrected_fragments_dict
def na_correction_mimosa_by_fragment(fragments_dict, isotope_dict, decimals):
fragment_dict_mass = change_fragment_keys_to_mass(fragments_dict)
corrected_dict_mass = {}
for key, value in fragment_dict_mass.iteritems():
m_1_n = (key[0] - 1, key[1])
m_1_n_1 = (key[0] - 1, key[1] - 1)
parent_frag_m, daughter_frag_n = value.frag
isotope = parent_frag_m.isotracer
na = helpers.get_isotope_na(isotope, isotope_dict)
corrected_data = {}
for sample_name, intensity_m_n in value.data.iteritems():
try:
intensity_m_1_n = fragment_dict_mass[m_1_n].data[sample_name]
except KeyError:
intensity_m_1_n = 0
try:
intensity_m_1_n_1 = fragment_dict_mass[m_1_n_1].data[
sample_name]
except KeyError:
intensity_m_1_n_1 = 0
corrected_data[sample_name] = na_correct_mimosa_algo_array(
parent_frag_m, daughter_frag_n, intensity_m_n, intensity_m_1_n,
intensity_m_1_n_1, isotope, na, decimals)
corrected_dict_mass[key] = Infopacket(value.frag, corrected_data,
value.unlabeled, value.name)
return corrected_dict_mass
def test_fragmentdict_model():
cit_frag = Fragment('Citruline', 'C6H13N3O3', label_dict={'C13': 0})
fragments_dict = {
'Citruline_C13_0_N15_0':
Infopacket(frag=cit_frag,
data={'sample_1': np.array([0.3624])},
unlabeled=True,
name='Acetic')
}
lab_sam_dict = {(0, 0): {'sample_1': 0.3624}}
assert algo.fragmentdict_model(['C13', 'N15'], fragments_dict,
lab_sam_dict) == {
'Citruline_C13_0_N15_0':
Infopacket(frag=cit_frag,
data={'sample_1': 0.3624},
unlabeled=True,
name='Acetic')
}
def test_unique_samples_for_dict():
A = {
'Acetic_C13_0':
Infopacket(frag='H4C2O2',
data={'sample_1': 0.3624},
unlabeled=True,
name='Acetic'),
'Acetic_C13_1':
Infopacket(frag='H4C2O2',
data={'sample_1': 0.040349999999999997},
unlabeled=False,
name='Acetic'),
'Acetic_C13_2':
Infopacket(frag='H4C2O2',
data={'sample_1': 0.59724999999999995},
unlabeled=False,
name='Acetic')
}
assert algo.unique_samples_for_dict(A) == ['sample_1']
def test_add_data_fragment():
frag_dict = {('Succinate 121/103_121.0', 'Succinate 121/103_103.0'): ['C4H5O4', 'C4H3O3']}
data = {'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 134)': 5187.6000000000004,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 122)': 0.0,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 190)': 4480.1999999999998}
label_info = False
name = 'Succinate 117/99'
assert iso.add_data_fragment(frag_dict,
data, label_info, name) == {('Succinate 121/103_121.0', 'Succinate 121/103_103.0'):
Infopacket(frag=['C4H5O4', 'C4H3O3'],
data={'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 122)': 0.0,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 190)': 4480.2,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 134)': 5187.6},
unlabeled=False, name='Succinate 117/99')}
def fragmentdict_model(iso_tracers, fragments_dict, lab_samp_dict):
"""
This function creates a model of fragments dictionary on which model functions can
applied
Args:
iso_tracers : list of isotopic tracers
fragments_dict : dictionary of the form, example : {'Aceticacid_C13_1': [C2H4O2,
{'sample_1': array([ 0.0164])}, False, 'Aceticacid']
lab_samp_dict : dictionary of the form {(0, 1): {'sample_1': 0.0619}....}
Returns:
nacorr_fragment_dict : fragment dictionary model
"""
nacorr_fragment_dict = {}
for frag_name, frag_info in fragments_dict.iteritems():
if len(iso_tracers) == 1:
lab_tup_key = frag_info.frag.get_num_labeled_atoms_isotope(
iso_tracers[0])
elif len(iso_tracers) > 1:
try:
lab_tup_key = []
for isotope in iso_tracers:
lab_tup_key.append(
frag_info.frag.get_num_labeled_atoms_isotope(isotope))
lab_tup_key = tuple(lab_tup_key)
except KeyError:
raise KeyError(
'Name, Formula or Sample not found in input data file')
nacorr_fragment_dict[frag_name] = Infopacket(
frag_info.frag, lab_samp_dict[lab_tup_key], frag_info.unlabeled,
frag_info.name)
return nacorr_fragment_dict
def output_constants():
nest_dict = {
out.OutKey(name='L-Methionine', formula='C5H10NO2S'): {
'N15_0_C13_5': {
'sample_1': 3.156529191428407e-08
},
'N15_0_C13_4': {
'sample_1': -4.6457232333485042e-06
},
'N15_0_C13_1': {
'sample_1': 0.055358607526132128
},
'N15_0_C13_0': {
'sample_1': 0.26081351241574013
},
'N15_0_C13_3': {
'sample_1': 0.00010623115401093528
},
'N15_0_C13_2': {
'sample_1': 0.0045440397693722904
},
'N15_1_C13_0': {
'sample_1': 0.064545716018271096
},
'N15_1_C13_1': {
'sample_1': 0.013283380798059349
},
'N15_1_C13_2': {
'sample_1': 0.00084379489120955248
},
'N15_1_C13_3': {
'sample_1': 6.1161350126415117e-05
},
'N15_1_C13_4': {
'sample_1': -1.8408545189878132e-06
},
'N15_1_C13_5': {
'sample_1': 0.60114017085422033
}
}
}
acetic_frag = Fragment('Acetic', 'H4C2O2', label_dict={'C13': 1, 'C13': 2})
fragment_dict = {
'Acetic_C13_1':
Infopacket(frag='H4C2O2',
data={'sample_1': 1},
unlabeled=False,
name='Acetic'),
'Acetic_C13_2':
Infopacket(frag='H4C2O2',
data={'sample_1': 0},
unlabeled=False,
name='Acetic')
}
metabolite_dict = {('Acetic', 'H4C2O2'): fragment_dict}
metabolite_dict = {
('L-Methionine', 'C5H10NO2S'): {
'C13_1': {
'sample_1': 3.18407678e-07
},
'C13_0': {
'sample_1': 0.48557866
}
}
}
df = pd.DataFrame({
'Sample': {
0: 'sample_1',
1: 'sample_1',
2: 'sample_1',
3: 'sample_1',
4: 'sample_1',
5: 'sample_1',
6: 'sample_1',
7: 'sample_1',
8: 'sample_1',
9: 'sample_1',
10: 'sample_1',
11: 'sample_1'
},
'Formula': {
0: 'C5H10NO2S',
1: 'C5H10NO2S',
2: 'C5H10NO2S',
3: 'C5H10NO2S',
4: 'C5H10NO2S',
5: 'C5H10NO2S',
6: 'C5H10NO2S',
7: 'C5H10NO2S',
8: 'C5H10NO2S',
9: 'C5H10NO2S',
10: 'C5H10NO2S',
11: 'C5H10NO2S'
},
'Intensity': {
0: 3.156529191428407e-08,
1: -4.6457232333485042e-06,
2: 0.055358607526132128,
3: 0.26081351241574013,
4: 0.00010623115401093528,
5: 0.0045440397693722904,
6: 0.064545716018271096,
7: 0.013283380798059349,
8: 0.00084379489120955248,
9: 6.1161350126415117e-05,
10: -1.8408545189878132e-06,
11: 0.60114017085422033
},
'Name': {
0: 'L-Methionine',
1: 'L-Methionine',
2: 'L-Methionine',
3: 'L-Methionine',
4: 'L-Methionine',
5: 'L-Methionine',
6: 'L-Methionine',
7: 'L-Methionine',
8: 'L-Methionine',
9: 'L-Methionine',
10: 'L-Methionine',
11: 'L-Methionine'
},
'Label': {
0: 'N15_0_C13_5',
1: 'N15_0_C13_4',
2: 'N15_0_C13_1',
3: 'N15_0_C13_0',
4: 'N15_0_C13_3',
5: 'N15_0_C13_2',
6: 'N15_1_C13_0',
7: 'N15_1_C13_1',
8: 'N15_1_C13_2',
9: 'N15_1_C13_3',
10: 'N15_1_C13_4',
11: 'N15_1_C13_5'
}
})
return nest_dict, df
def algorithms_yale_constants():
data_input = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 1967.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 1446.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 747.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 1926.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 1969.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 661.77
}
data_unlabel = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 62610,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 58640,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 59950,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 62520,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 59690,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 57200
}
corrected_data_input = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': -55.07,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': -456.52,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': -1214.13,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': -94.,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 43.33,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': -1211.27
}
corrected_data_unlabel = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 64676.13,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 60575.12,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 61928.35,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 64583.16,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 61659.77,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 59087.6
}
unlabeled_fragment = iso.insert_data_to_fragment_mass(
Multiquantkey('2PG 185/79', 'O3P', '2PG 185/79', 'C3H6O7P'),
'C13_185.0_79.0', data_unlabel)
input_fragment = iso.insert_data_to_fragment_mass(
Multiquantkey('2PG 186/79', 'O3P', '2PG 185/79', 'C3H6O7P'),
'C13_186.0_79.0', data_input)
parent_frag_input = input_fragment[('2PG 186/79_186.0',
'2PG 186/79_79.0')][0][0]
daughter_frag_input = input_fragment[('2PG 186/79_186.0',
'2PG 186/79_79.0')][0][1]
parent_frag_unlabeled = unlabeled_fragment[('2PG 185/79_185.0',
'2PG 185/79_79.0')][0][0]
daughter_frag_unlabeled = unlabeled_fragment[('2PG 185/79_185.0',
'2PG 185/79_79.0')][0][1]
fragment_dict = dict(unlabeled_fragment, **input_fragment)
corrected_fragment_dict = {
(186.0, 79.0):
Infopacket(
frag=['C3H6O7P', 'O3P'],
data={
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)':
-73.459999999999994,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)':
-473.81999999999999,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': -1231.96,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': -112.37,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 25.82,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': -1228.3
},
unlabeled=False,
name='2PG 185/79'),
(185.0, 79.0):
Infopacket(frag=['C3H6O7P', 'O3P'],
data={
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)':
64694.910000000003,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)':
60592.709999999999,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)':
61946.339999999997,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)':
64601.919999999998,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)':
61677.68,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)':
59104.760000000002
},
unlabeled=True,
name='2PG 185/79')
}
unlabeled_fragment_dhap = iso.insert_data_to_fragment_mass(
Multiquantkey('DHAP 169/97', 'H2O4P', 'DHAP 169/97', 'C3H6O6P'),
'C13_169.0_97.0', {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 51670,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 52360,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 52540,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 56580,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 52580,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 58990
})
input_fragment_dhap = iso.insert_data_to_fragment_mass(
Multiquantkey('DHAP 170/97', 'H2O4P', 'DHAP 169/97', 'C3H6O6P'),
'C13_170.0_97.0', {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 1292.67,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 901.67,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 1292.67,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 1468.67,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 1511.67,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 1946.67
})
corrected_data_unlabel_dhap = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': 53390.610000000001,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': 54103.589999999997,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': 54289.580000000002,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': 58464.110000000001,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': 54330.910000000003,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 60954.370000000003
}
corrected_data_input_dhap = {
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 1)': -399.24000000000001,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 17)': -821.89999999999998,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 33)': -428.20999999999998,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 49)': -382.83999999999997,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 65)': -205.68000000000001,
'TA_SCS-ATP BCH_19May16_1June16.wiff (sample 81)': 25.52
}
parent_frag_input_dhap = input_fragment_dhap[('DHAP 170/97_170.0',
'DHAP 170/97_97.0')][0][0]
daughter_frag_input_dhap = input_fragment_dhap[('DHAP 170/97_170.0',
'DHAP 170/97_97.0')][0][1]
parent_frag_unlabeled_dhap = unlabeled_fragment_dhap[(
'DHAP 169/97_169.0', 'DHAP 169/97_97.0')][0][0]
daughter_frag_unlabeled_dhap = unlabeled_fragment_dhap[(
'DHAP 169/97_169.0', 'DHAP 169/97_97.0')][0][1]
corrected_fragment_dict_dhap = {
(169.0, 97.0):
iso.Infopacket(
frag=[parent_frag_unlabeled_dhap, daughter_frag_unlabeled_dhap],
data=corrected_data_unlabel_dhap,
unlabeled=True,
name='DHAP 169/97'),
(170.0, 97.0):
iso.Infopacket(frag=[parent_frag_input_dhap, daughter_frag_input_dhap],
data=corrected_data_input_dhap,
unlabeled=False,
name='DHAP 169/97')
}
fragment_dict_dhap = dict(unlabeled_fragment_dhap, **input_fragment_dhap)
metabolite_frag_dict = {'dhap': fragment_dict_dhap, '2pg': fragment_dict}
corrected_metabolite_dict = {
'dhap': corrected_fragment_dict_dhap,
'2pg': corrected_fragment_dict
}
return parent_frag_input, daughter_frag_input, \
fragment_dict, data_input, parent_frag_unlabeled, \
daughter_frag_unlabeled, data_unlabel, corrected_fragment_dict, metabolite_frag_dict, corrected_fragment_dict_dhap, corrected_fragment_dict