def execute_heatmap(self,
analysis_id_I,simulation_ids_I=[],simulation_dateAndTimes_I=[],
flux_units_I=[],rxn_ids_I=[],
row_pdist_metric_I='euclidean',row_linkage_method_I='complete',
col_pdist_metric_I='euclidean',col_linkage_method_I='complete',
observable_only_I = False,
order_rxnBySim_I = True,
order_simulation_ids_I=False,
order_rxn_ids_I=False,
rxn_id_reverse_I=[]):
'''Execute hierarchical cluster on row and column data
INPUT:
analysis_id_I = string, analysis id
simulation_ids_I = list of simulation_ids
simulation_dataAndTimes_I = list of simulation_dateAndTimes_I
flux_units = list of flux units
rxn_ids_I = list of rxn_ids
observable_only_I = include only observable reactions
order_rxnBySim_I = if True, rows will represent the fluxes and columns will represent the simulations
if False, rows will represent the simulations and columns will represent the fluxes
order_simulation_ids_I = if True, order of the simulation_ids will be kept
order_rxn_ids_I = if True, order of the rxn_ids will be kept
rxn_id_reverse_I = list of rxn_ids to reverse the flux direction
Assumptions:
all simulation_ids must be unique (i.e., 1 simulation but 2 simulation_dateAndTimes will break the algorithm)
all simulation_ids must have the same flux units (i.e.,
'''
#print('executing heatmap...');
calculateheatmap = calculate_heatmap();
## Pass 1: get all the data
data_O = {};
rxn_ids_all = [];
unobservable_fu_rxn_ids = {};
# get the simulation_id and simulation_id dateAndTimes
if simulation_ids_I and simulation_dateAndTimes_I:
simulation_ids = simulation_ids_I;
simulation_dateAndTimes = simulation_dateAndTimes_I;
else:
simulation_ids = [];
simulation_ids_unique = [];
simulation_dateAndTimes = [];
# get the simulation unique ids
simulation_ids_unique = self.get_simulationID_analysisID_dataStage02IsotopomerAnalysis(analysis_id_I);
for simulation_id in simulation_ids_unique:
# get the simulation dateAndTimes
simulation_dateAndTimes_tmp = []
simulation_dateAndTimes_tmp = self.get_simulationDateAndTimes_simulationID_dataStage02IsotopomerfittedNetFluxes(simulation_id);
simulation_ids_tmp = [simulation_id for x in simulation_dateAndTimes_tmp];
simulation_dateAndTimes.extend(simulation_dateAndTimes_tmp)
simulation_ids.extend(simulation_ids_tmp)
for simulation_cnt_1, simulation_id_1 in enumerate(simulation_ids):
#print('generating a heatmap for simulation_id ' + simulation_id_1);
# get the units
if flux_units_I:
flux_units = flux_units_I;
else:
flux_units = [];
flux_units = self.get_fluxUnits_simulationIDAndSimulationDateAndTime_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1])
for fu_cnt,fu in enumerate(flux_units):
# initialize only on first iteration
if simulation_cnt_1==0:
data_O[fu] = {};
unobservable_fu_rxn_ids[fu] = set();
#print('generating a heatmap for flux_units ' + fu);
# get the rxn_ids
if rxn_ids_I:
rxn_ids = rxn_ids_I;
else:
rxn_ids = [];
rxn_ids = self.get_rxnIDs_simulationIDAndSimulationDateAndTimeAndFluxUnits_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1],fu);
for rxn_id in rxn_ids:
if simulation_cnt_1==0:
data_O[fu][rxn_id] = [];
if simulation_cnt_1!=0 and not rxn_id in data_O[fu].keys():
continue;
rxn_ids_all.append(rxn_id);
#print('generating a heatmap for rxn_id ' + rxn_id);
# get the fluxes
row = {};
row = self.get_row_simulationIDAndSimulationDateAndTimeAndFluxUnitsAndRxnID_dataStage02IsotopomerfittedNetFluxes(simulation_id_1,simulation_dateAndTimes[simulation_cnt_1],fu,rxn_id);
if row:
# change the direction of specified fluxes
if row['rxn_id'] in rxn_id_reverse_I:
row['flux']=-row['flux'];
row['flux_lb']=-row['flux_lb'];
row['flux_ub']=-row['flux_ub'];
if observable_only_I:
observable_1 = mfamethods.check_observableNetFlux(row['flux'],row['flux_lb'],row['flux_ub'])
if observable_1:
data_O[fu][rxn_id].append(dendrogram_row_1row);
unobservable_fu_rxn_ids[fu].add(rxn_id);
else:
data_O[fu][rxn_id].append(row);
## Pass 2: data integrity check
rxn_ids_unique = list(set(rxn_ids_all));
rxn_ids_unique.sort();
data_heatmap = {};
rxn_ids_dict = {};
for fu_cnt,fu in enumerate(list(data_O.keys())):
data_heatmap[fu] = {};
rxn_ids_dict[fu] = set();
for rxn_id in rxn_ids_unique:
if rxn_id in unobservable_fu_rxn_ids[fu]:
continue;
data_tmp = [];
for simulation_cnt_1, simulation_id_1 in enumerate(simulation_ids):
for d in data_O[fu][rxn_id]:
if d['simulation_id'] == simulation_id_1 and d['simulation_dateAndTime'] == simulation_dateAndTimes[simulation_cnt_1]:
data_tmp.append(d);
break;
# check that the length matches
if len(data_tmp) == len(simulation_ids):
data_heatmap[fu][rxn_id]= data_tmp;
rxn_ids_dict[fu].add(rxn_id);
## Pass 3: generate the heatmap for each flux_unit
heatmap_O = [];
dendrogram_col_O = [];
dendrogram_row_O = [];
for fu_cnt,fu in enumerate(list(data_heatmap.keys())):
# generate the clustering for the heatmap
heatmap_1 = [];
dendrogram_col_1 = {};
dendrogram_row_1 = {};
# extract out the data {rxn_id:[{},...],...} -> [[{},...],...] -> [{},...]
data1 = [v for v in data_heatmap[fu].values()];
data2=[];
for d in data1:
data2.extend(d);
if order_rxnBySim_I:
heatmap_1,dendrogram_col_1,dendrogram_row_1 = calculateheatmap.make_heatmap(data2,
'rxn_id','simulation_id','flux',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=rxn_ids_I,
filter_columns_I=simulation_ids_I,
order_rowsFromTemplate_I=rxn_ids_I,
order_columnsFromTemplate_I=simulation_ids_I,);
else:
heatmap_1,dendrogram_col_1,dendrogram_row_1 = calculateheatmap.make_heatmap(data2,
'simulation_id','rxn_id','flux',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=simulation_ids_I,
filter_columns_I=rxn_ids_I,
order_rowsFromTemplate_I=simulation_ids_I,
order_columnsFromTemplate_I=rxn_ids_I,);
# add data to to the database for the heatmap
for d in heatmap_1:
d['analysis_id']=analysis_id_I;
d['value_units']=fu;
d['used_']=True;
d['comment_']=None;
heatmap_O.append(d);
# add data to the database for the dendrograms
dendrogram_col_1['analysis_id']=analysis_id_I;
dendrogram_col_1['value_units']=fu;
dendrogram_col_1['used_']=True;
dendrogram_col_1['comment_']=None;
dendrogram_col_O.append(dendrogram_col_1);
dendrogram_row_1['analysis_id']=analysis_id_I;
dendrogram_row_1['value_units']=fu;
dendrogram_row_1['used_']=True;
dendrogram_row_1['comment_']=None;
dendrogram_row_O.append(dendrogram_row_1);
self.add_rows_table('data_stage02_isotopomer_heatmap',heatmap_O);
self.add_rows_table('data_stage02_isotopomer_dendrogram',dendrogram_col_O);
self.add_rows_table('data_stage02_isotopomer_dendrogram',dendrogram_row_O);
def execute_heatmap_lineage(self, analysis_id_I,
row_pdist_metric_I='euclidean',row_linkage_method_I='complete',
col_pdist_metric_I='euclidean',col_linkage_method_I='complete',
mutation_id_exclusion_list = []):
'''Execute hierarchical cluster on row and column data'''
calculateheatmap = calculate_heatmap();
print('executing heatmap...');
# get the analysis information
experiment_ids,lineage_names = [],[];
experiment_ids,lineage_names = self.get_experimentIDAndLineageName_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
# partition into variables:
intermediates_lineage = [];
mutation_data_lineage_all = [];
rows_lineage = [];
n_lineages = len(lineage_names)
cnt_sample_names = 0;
for lineage_name_cnt,lineage_name in enumerate(lineage_names):
# get ALL intermediates by experiment_id and lineage name
intermediates = [];
intermediates = self.get_intermediates_experimentIDAndLineageName_dataStage01ResequencingLineage(experiment_ids[lineage_name_cnt],lineage_name);
intermediates_lineage.append(intermediates);
cnt_sample_names += len(intermediates)
# get ALL mutation data by experiment_id and lineage name
mutation_data = [];
mutation_data = self.get_mutationData_experimentIDAndLineageName_dataStage01ResequencingLineage(experiment_ids[lineage_name_cnt],lineage_name);
mutation_data_lineage_all.extend(mutation_data);
# get ALL mutation frequencies by experiment_id and lineage name
rows = [];
rows = self.get_row_experimentIDAndLineageName_dataStage01ResequencingLineage(experiment_ids[lineage_name_cnt],lineage_name)
rows_lineage.extend(rows);
mutation_data_lineage_unique = list(set(mutation_data_lineage_all));
mutation_data_lineage = [x for x in mutation_data_lineage_unique if not x in mutation_id_exclusion_list];
min_inter = min(intermediates_lineage)
max_inter = max(intermediates_lineage);
# generate the frequency matrix data structure (mutation x intermediate)
data_O = numpy.zeros((cnt_sample_names,len(mutation_data_lineage)));
labels_O = {};
lineages=[];
col_cnt = 0;
# order 2: groups each lineage by mutation (intermediate x mutation)
for lineage_name_cnt,lineage_name in enumerate(lineage_names): #all lineages for intermediate j / mutation i
for intermediate_cnt,intermediate in enumerate(intermediates_lineage[lineage_name_cnt]):
if intermediate_cnt == min(intermediates_lineage[lineage_name_cnt]):
lineages.append(lineage_name+": "+"start"); # corresponding label from hierarchical clustering (in this case, arbitrary)
elif intermediate_cnt == max(intermediates_lineage[lineage_name_cnt]):
lineages.append(lineage_name+": "+"end"); # corresponding label from hierarchical clustering (in this case, arbitrary)
else:
lineages.append(lineage_name+": "+str(intermediate)); # corresponding label from hierarchical clustering (in this case, arbitrary)
for mutation_cnt,mutation in enumerate(mutation_data_lineage): #all mutations i for intermediate j
for row in rows_lineage:
if row['mutation_id'] == mutation and row['intermediate'] == intermediate and row['lineage_name'] == lineage_name:
data_O[col_cnt,mutation_cnt] = row['mutation_frequency'];
#print col_cnt,mutation_cnt
col_cnt+=1;
# generate the clustering for the heatmap
heatmap_O = [];
dendrogram_col_O = {};
dendrogram_row_O = {};
heatmap_O,dendrogram_col_O,dendrogram_row_O = calculateheatmap.heatmap(data_O,lineages,mutation_data_lineage,
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I);
# add data to to the database for the heatmap
for d in heatmap_O:
row = None;
row = data_stage01_resequencing_heatmap(
analysis_id_I,
d['col_index'],
d['row_index'],
d['value'],
d['col_leaves'],
d['row_leaves'],
d['col_label'],
d['row_label'],
d['col_pdist_metric'],
d['row_pdist_metric'],
d['col_linkage_method'],
d['row_linkage_method'],
'frequency',True, None);
self.session.add(row);
# add data to the database for the dendrograms
row = None;
row = data_stage01_resequencing_dendrogram(
analysis_id_I,
dendrogram_col_O['leaves'],
dendrogram_col_O['icoord'],
dendrogram_col_O['dcoord'],
dendrogram_col_O['ivl'],
dendrogram_col_O['colors'],
dendrogram_col_O['pdist_metric'],
dendrogram_col_O['pdist_metric'],
'frequency',True, None);
self.session.add(row);
row = None;
row = data_stage01_resequencing_dendrogram(
analysis_id_I,
dendrogram_row_O['leaves'],
dendrogram_row_O['icoord'],
dendrogram_row_O['dcoord'],
dendrogram_row_O['ivl'],
dendrogram_row_O['colors'],
dendrogram_row_O['pdist_metric'],
dendrogram_row_O['pdist_metric'],
'frequency',True, None);
self.session.add(row);
self.session.commit();
def execute_heatmap(self, analysis_id_I,gene_exclusion_list=[],
row_pdist_metric_I='euclidean',row_linkage_method_I='complete',
col_pdist_metric_I='euclidean',col_linkage_method_I='complete',
order_sampleNameByGeneNameShort_I=False,
sample_names_I=[],
gene_name_shorts_I=[],):
'''Execute hierarchical cluster on row and column data'''
print('executing heatmap...');
calculateheatmap = calculate_heatmap();
#fpkmsheatmap = fpkms_heatmap();
# get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
fpkms_all = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
# query fpkm data:
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesFpkmTracking(experiment_ids[sample_name_cnt],sample_name);
fpkms_all.extend(fpkms);
heatmap_O = [];
dendrogram_col_O = [];
dendrogram_row_O = [];
if order_sampleNameByGeneNameShort_I:
heatmap_1,dendrogram_col_1,dendrogram_row_1 = calculateheatmap.make_heatmap(fpkms_all,
'sample_name','gene_short_name','FPKM',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=sample_names_I,
filter_columns_I=gene_name_shorts_I,
order_rowsFromTemplate_I=sample_names_I,
order_columnsFromTemplate_I=gene_name_shorts_I,);
else:
heatmap_1,dendrogram_col_1,dendrogram_row_1 = calculateheatmap.make_heatmap(fpkms_all,
'gene_short_name','sample_name','FPKM',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=gene_name_shorts_I,
filter_columns_I=sample_names_I,
order_rowsFromTemplate_I=gene_name_shorts_I,
order_columnsFromTemplate_I=sample_names_I);
# add data to to the database for the heatmap
for d in heatmap_1:
d['analysis_id']=analysis_id_I;
d['value_units']='FPKM';
d['used_']=True;
d['comment_']=None;
heatmap_O.append(d);
## add data to the database for the dendrograms
dendrogram_col_1['analysis_id']=analysis_id_I;
dendrogram_col_1['value_units']='FPKM';
dendrogram_col_1['used_']=True;
dendrogram_col_1['comment_']=None;
dendrogram_col_O.append(dendrogram_col_1);
dendrogram_row_1['analysis_id']=analysis_id_I;
dendrogram_row_1['value_units']='FPKM';
dendrogram_row_1['used_']=True;
dendrogram_row_1['comment_']=None;
dendrogram_row_O.append(dendrogram_row_1);
self.add_rows_table('data_stage01_rnasequencing_heatmap',heatmap_O);
#self.add_rows_table('data_stage01_rnasequencing',dendrogram_col_O);
#self.add_rows_table('data_stage01_rnasequencing',dendrogram_row_O);
def execute_heatmap_mutationsAnnotated(self, analysis_id_I,mutation_id_exclusion_list=[],frequency_threshold=0.1,max_position=4630000,
row_pdist_metric_I='euclidean',row_linkage_method_I='complete',
col_pdist_metric_I='euclidean',col_linkage_method_I='complete',
order_sampleNameByMutationID_I=False,
sample_names_I=[],
mutationIDs_I=[],
):
'''Execute hierarchical cluster on row and column data'''
calculateheatmap = calculate_heatmap();
mutationsheatmap = mutations_heatmap();
genomediff = genome_diff();
print('executing heatmap...');
# get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
#mutations_all = [];
mutation_data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
# query mutation data:
mutations = [];
mutations = self.get_mutations_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(experiment_ids[sample_name_cnt],sample_name);
#mutations_all.extend(mutations);
for mutation in mutations:
if mutation['mutation_position'] > max_position:
continue;
if mutation['mutation_frequency']<frequency_threshold:
continue;
#if not mutation['mutation_genes']:
# mutation['mutation_genes'] = ['unknown'];
# mutation id
mutation_id = genomediff._make_mutationID(mutation['mutation_genes'],mutation['mutation_type'],mutation['mutation_position']);
if mutation_id in mutation_id_exclusion_list:
continue;
tmp = {};
tmp.update(mutation);
tmp.update({'mutation_id':mutation_id});
mutation_data_O.append(tmp);
heatmap_O = [];
dendrogram_col_O = {};
dendrogram_row_O = {};
if order_sampleNameByMutationID_I:
heatmap_O,dendrogram_col_O,dendrogram_row_O = calculateheatmap.make_heatmap(mutation_data_O,
'sample_name','mutation_id','mutation_frequency',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=sample_names_I,
filter_columns_I=mutationIDs_I,
order_rowsFromTemplate_I=sample_names_I,
order_columnsFromTemplate_I=mutationIDs_I,);
else:
heatmap_O,dendrogram_col_O,dendrogram_row_O = calculateheatmap.make_heatmap(mutation_data_O,
'mutation_id','sample_name','mutation_frequency',
row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I,
filter_rows_I=mutationIDs_I,
filter_columns_I=sample_names_I,
order_rowsFromTemplate_I=mutationIDs_I,
order_columnsFromTemplate_I=sample_names_I);
## generate the clustering for the heatmap
#mutationsheatmap.mutations = mutations_all;
#mutationsheatmap.sample_names = sample_names;
#mutationsheatmap.make_heatmap(mutation_id_exclusion_list=mutation_id_exclusion_list,max_position=max_position,
# row_pdist_metric_I=row_pdist_metric_I,row_linkage_method_I=row_linkage_method_I,
# col_pdist_metric_I=col_pdist_metric_I,col_linkage_method_I=col_linkage_method_I)
#heatmap_O = mutationsheatmap.heatmap;
#dendrogram_col_O = mutationsheatmap.dendrogram_col;
#dendrogram_row_O = mutationsheatmap.dendrogram_row;
# add data to to the database for the heatmap
for d in heatmap_O:
row = None;
row = data_stage01_resequencing_heatmap(
analysis_id_I,
d['col_index'],
d['row_index'],
d['value'],
d['col_leaves'],
d['row_leaves'],
d['col_label'],
d['row_label'],
d['col_pdist_metric'],
d['row_pdist_metric'],
d['col_linkage_method'],
d['row_linkage_method'],
'frequency',True, None);
self.session.add(row);
# add data to the database for the dendrograms
row = None;
row = data_stage01_resequencing_dendrogram(
analysis_id_I,
dendrogram_col_O['leaves'],
dendrogram_col_O['icoord'],
dendrogram_col_O['dcoord'],
dendrogram_col_O['ivl'],
dendrogram_col_O['colors'],
dendrogram_col_O['pdist_metric'],
dendrogram_col_O['pdist_metric'],
'frequency',True, None);
self.session.add(row);
row = None;
row = data_stage01_resequencing_dendrogram(
analysis_id_I,
dendrogram_row_O['leaves'],
dendrogram_row_O['icoord'],
dendrogram_row_O['dcoord'],
dendrogram_row_O['ivl'],
dendrogram_row_O['colors'],
dendrogram_row_O['pdist_metric'],
dendrogram_row_O['pdist_metric'],
'frequency',True, None);
self.session.add(row);
self.session.commit();
