def export_allMetaboliteFragmentMappings_csv(filename_I='ms_method_fragments.csv',mode_I='-',msmethodtype_I='tuning'):
'''export all metabolite fragment mappings to .csv
INPUT:
filename_I = string, filename
mode_I = string, MS mode (default=-)
msmethodtype_I = string, ms_methodtype (default=tuning)
'''
mfautil = MFA_utilities();
#Query met_ids
met_ids = [];
met_ids = msmethodquery.get_metIDs_msModeAndMsMethodType('-','tuning');
data_O = [];
for met in met_ids:
# fragments
fragment_formulas = [];
parent,product=[],[];
parent,product= msmethodquery.get_precursorAndProductFormulas_metID(met,'-','tuning');
parent = list(set(parent));
fragment_formulas.extend(parent);
fragment_formulas.extend(product);
# fragment carbon mappings
frag_cmap = {};
frag_cmap = msmethodquery.get_precursorFormulaAndProductFormulaAndCMapsAndPositions_metID(met,'-','tuning');
for frag in fragment_formulas:
if frag_cmap[frag]['fragment'] is None: continue;
# combine into a structure
positions,elements = [],[];
positions,elements = mfautil.convert_fragmentAndElements2PositionAndElements(frag_cmap[frag]['fragment'],frag_cmap[frag]['fragment_elements']);
if positions:
tmp = {'met_id':met,'positions':positions,'elements':elements,'formula':frag};
data_O.append(tmp);
# export the data
if data_O:
export = base_exportData(data_O)
export.write_dict2csv(filename_I);
def execute_makeMeasuredFragments(self,experiment_id_I, sample_name_abbreviations_I = [], time_points_I = [], scan_types_I = [], met_ids_I = []):
'''Collect and format MS data from data_stage01_isotopomer_averagesNormSum for fluxomics simulation'''
mfautilities = MFA_utilities();
# get experiment information:
met_id_conv_dict = {'Hexose_Pool_fru_glc-D':'glc-D',
'Pool_2pg_3pg':'3pg',
'23dpg':'13dpg'};
data_O = [];
experiment_stdev = [];
# get sample names and sample name abbreviations
if sample_name_abbreviations_I:
sample_abbreviations = sample_name_abbreviations_I;
st = 'Unknown';
sample_types_lst = [];
sample_types_lst.extend([st for i in range(len(sample_abbreviations))]);
else:
sample_abbreviations = [];
sample_types = ['Unknown'];
sample_types_lst = [];
for st in sample_types:
sample_abbreviations_tmp = [];
sample_abbreviations_tmp = self.get_sampleNameAbbreviations_experimentIDAndSampleType_dataStage01AveragesNormSum(experiment_id_I,st);
sample_abbreviations.extend(sample_abbreviations_tmp);
sample_types_lst.extend([st for i in range(len(sample_abbreviations_tmp))]);
for sna_cnt,sna in enumerate(sample_abbreviations):
print('Collecting experimental MS data for sample name abbreviation ' + sna);
# get time points
if time_points_I:
time_points = time_points_I;
else:
time_points = [];
time_points = self.get_timePoint_experimentIDAndSampleNameAbbreviation_dataStage01AveragesNormSum(experiment_id_I,sna);
for tp in time_points:
print('Collecting experimental MS data for time-point ' + str(tp));
# get the scan_types
if scan_types_I:
scan_types = [];
scan_types_tmp = [];
scan_types_tmp = self.get_scanTypes_experimentIDAndTimePointAndSampleAbbreviationsAndSampleType_dataStage01AveragesNormSum(experiment_id_I,tp,sna,sample_types_lst[sna_cnt]);
scan_types = [st for st in scan_types_tmp if st in scan_types_I];
else:
scan_types = [];
scan_types = self.get_scanTypes_experimentIDAndTimePointAndSampleAbbreviationsAndSampleType_dataStage01AveragesNormSum(experiment_id_I,tp,sna,sample_types_lst[sna_cnt]);
for scan_type in scan_types:
print('Collecting experimental MS data for scan type ' + scan_type)
# met_ids
if not met_ids_I:
met_ids = [];
met_ids = self.get_metIDs_experimentIDAndSampleAbbreviationAndTimePointAndSampleTypeAndScanType_dataStage01AveragesNormSum( \
experiment_id_I,sna,tp,sample_types_lst[sna_cnt],scan_type);
else:
met_ids = met_ids_I;
if not(met_ids): continue #no component information was found
for met in met_ids:
print('Collecting experimental MS data for metabolite ' + met);
# format the metabolite
if met in list(met_id_conv_dict.keys()):
met_formatted = met_id_conv_dict[met];
else: met_formatted = met;
met_formatted = re.sub('-','_DASH_',met_formatted)
met_formatted = re.sub('[(]','_LPARANTHES_',met_formatted)
met_formatted = re.sub('[)]','_RPARANTHES_',met_formatted)
# fragments
fragment_formulas = [];
fragment_formulas = self.get_fragmentFormula_experimentIDAndSampleAbbreviationAndTimePointAndSampleTypeAndScanTypeAndMetID_dataStage01AveragesNormSum( \
experiment_id_I,sna,tp,sample_types_lst[sna_cnt],scan_type,met);
# frag c map
frag_cmap = {};
frag_cmap = self.get_precursorFormulaAndProductFormulaAndCMapsAndPositions_metID(met,'-','tuning');
for frag in fragment_formulas:
# data
data_mat = [];
data_mat_cv = [];
data_mat_n = [];
data_mat, data_mat_cv, data_mat_n = self.get_spectrum_experimentIDAndSampleAbbreviationAndTimePointAndSampleTypeAndScanTypeAndMetIDAndFragmentFormula_dataStage01AveragesNormSum( \
experiment_id_I,sna,tp,sample_types_lst[sna_cnt],scan_type,met,frag);
# combine into a structure
positions,elements = [],[];
positions,elements = mfautilities.convert_fragmentAndElements2PositionAndElements(frag_cmap[frag]['fragment'],frag_cmap[frag]['fragment_elements']);
#fragname = met_formatted+'_c'+'_'+ re.sub('[-+]','',frag);
fragname = met_formatted+'_c'+'_'+ re.sub('[-+]','',frag)+'_'+scan_type;
data_names = [];
data_stdev = [];
data_stderr = [];
for i,d in enumerate(data_mat):
stdev = 0.0;
stderr = 0.0;
if data_mat_cv[i]:
if data_mat_n[i]==1:
stdev = 0.05;
else:
stdev = data_mat[i]*data_mat_cv[i]/100;
stderr = stdev/sqrt(data_mat_n[i]);
data_names.append(fragname+str(i));
data_stdev.append(stdev);
data_stderr.append(stderr);
experiment_stdev.append(stdev);
data_tmp = {'experiment_id':experiment_id_I,
'sample_name_abbreviation':sna,
'sample_type':sample_types_lst[sna_cnt],
'time_point':tp,
'met_id':met_formatted+'_c',
'fragment_id':fragname,
'fragment_formula':frag,
'intensity_normalized_average':data_mat,
'intensity_normalized_cv':data_mat_cv,
'intensity_normalized_stdev':data_stdev,
'intensity_normalized_n':data_mat_n,
'intensity_normalized_units':'normSum',
'met_elements':elements,
'met_atompositions':positions};
data_O.append(data_tmp);
#add data to the database
row = [];
row = data_stage02_isotopomer_measuredFragments(
experiment_id_I,
sna,
tp,
met_formatted+'_c',
fragname,
frag,
data_mat,
data_mat_cv,
data_stdev,
'normSum',
scan_type,
elements,
positions,
True,
None);
self.session.add(row);
self.session.commit();
