def filter_mutations(self,mutations_I=[],sample_names_I=[], mutation_id_exclusion_list=[],max_position=4000000,
row_pdist_metric_I='euclidean',row_linkage_method_I='complete',
col_pdist_metric_I='euclidean',col_linkage_method_I='complete',
order_sampleNameByMutationID_I=False):
'''Execute hierarchical cluster on row and column data'''
print('executing heatmap...');
from sequencing_analysis.genome_diff import genome_diff
genomediff = genome_diff();
# partition into variables:
if mutations_I: mutation_data = mutations_I;
else: mutation_data = self.mutations;
if sample_names_I: sample_names = sample_names_I;
else: sample_names = self.sample_names;
mutation_data_O = [];
mutation_ids_all = [];
for end_cnt,mutation in enumerate(mutation_data):
if int(mutation['mutation_position']) > max_position: #ignore positions great than 4000000
continue;
# mutation id
mutation_id = '';
mutation_id = genomediff._make_mutationID(mutation['mutation_genes'],mutation['mutation_type'],int(mutation['mutation_position']))
tmp = {};
tmp.update(mutation);
tmp.update({'mutation_id':mutation_id});
mutation_data_O.append(tmp);
mutation_ids_all.append(mutation_id);
mutation_ids_all_unique = list(set(mutation_ids_all));
mutation_ids = [x for x in mutation_ids_all_unique if not x in mutation_id_exclusion_list];
def get_AllMutationLocations_experimentIDAndSampleName_dataStage01ResequencingMutationsAnnotated(self,experiment_id_I,sample_name_I):
'''Query mutation_locations from data_stage01_resequencing_mutationsAnnotated'''
try:
data = self.session.query(data_stage01_resequencing_mutationsAnnotated.mutation_locations).filter(
data_stage01_resequencing_mutationsAnnotated.experiment_id.like(experiment_id_I),
data_stage01_resequencing_mutationsAnnotated.sample_name.like(sample_name_I)).order_by(
data_stage01_resequencing_mutationsAnnotated.mutation_locations.asc()).all();
data_O = [];
genomediff = genome_diff();
for cnt,d in enumerate(data):
data_tmp_str = genomediff._make_mutationLocationsStr(d.mutation_locations);
data_O.append(data_tmp_str);
return data_O;
except SQLAlchemyError as e:
print(e);
def get_allMutationClasses_experimentIDAndSampleName_dataStage01ResequencingMutationsSeqChanges(self,experiment_id_I,sample_name_I):
'''Query mutation data'''
try:
data = self.session.query(data_stage01_resequencing_mutationsSeqChanges.mutation_class).filter(
data_stage01_resequencing_mutationsSeqChanges.experiment_id.like(experiment_id_I),
data_stage01_resequencing_mutationsSeqChanges.sample_name.like(sample_name_I)).order_by(
data_stage01_resequencing_mutationsSeqChanges.mutation_class.asc()).all();
data_O = [];
genomediff = genome_diff();
for d in data:
data_tmp_str = genomediff._make_mutationClassStr(d.mutation_class);
data_O.append(data_tmp_str);
return data_O;
except SQLAlchemyError as e:
print(e);
from sequencing_analysis.gff_coverage import gff_coverage
#import and parse .gd files, filter the mutations, and export the filtered mutations tables to .csv
filenames_I = ['C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/tests/analysis_resequencing/breseq/ALEevo04-tpiA/output.gd',
'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/tests/analysis_resequencing/breseq/Evo04tpiAEvo01EP/output.gd',
'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/tests/analysis_resequencing/breseq/Evo04tpiAEvo02EP/output.gd',
'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/tests/analysis_resequencing/breseq/Evo04tpiAEvo03EP/output.gd',
'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/tests/analysis_resequencing/breseq/Evo04tpiAEvo04EP/output.gd',
];
samplenames = ['Evo04tpiA',
'Evo04tpiAEvo01EP',
'Evo04tpiAEvo02EP',
'Evo04tpiAEvo03EP',
'Evo04tpiAEvo04EP',
];
gd = genome_diff();
for cnt,filename in enumerate(filenames_I):
gd.import_gd(filename,experiment_id='ALEsKOs01',sample_name=samplenames[cnt]);
#use default settings to filter the data
gd.filter_mutations_population();
#annotate the genome using genbank and MG1655
gd.annotate_mutations(table_I = 'mutationsFiltered',
ref_genome_I = 'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas/sbaas/data/U00096.2.gb',
ref_I = 'genbank',
geneReference_I = 'C:/Users/dmccloskey-sbrg/Documents/GitHub/sbaas_workspace/sbaas_workspace/workspace_data/_input/150527_MG1655_geneReference.csv',
biologicalmaterial_id_I = 'MG1655')
#export the filtered and annotated data for later use
filename_O = samplenames[cnt] + ".csv"
gd.export_mutationsAnnotated(filename_O);
gd.clear_data();
def export_dataStage01ResequencingMutationsAnnotatedLineageArea_js(self,analysis_id_I,mutation_id_exclusion_list=[],frequency_threshold=0.1,max_position=4630000,data_dir_I="tmp"):
'''export data_stage01_resequencing_mutationsAnnotated to js file
Visualization: scatterlineplot of mutation frequency across jump time
'''
genomediff = genome_diff();
print('exportingdataStage01ResequencingMutationsAnnotated...')
# get the analysis information
experiment_ids,sample_names,time_points = [],[],[];
experiment_ids,sample_names,time_points = self.get_experimentIDAndSampleNameAndTimePoint_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
# convert time_point to intermediates
time_points_int = [int(x) for x in time_points];
intermediates,time_points,experiment_ids,sample_names = (list(t) for t in zip(*sorted(zip(time_points_int,time_points,experiment_ids,sample_names))))
intermediates = [i for i,x in enumerate(intermediates)];
mutation_data_O = [];
mutation_ids = [];
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);
for end_cnt,mutation in enumerate(mutations):
if mutation['mutation_position'] > max_position: #ignore positions great than 4000000
continue;
if mutation['mutation_frequency']<frequency_threshold:
continue;
# mutation id
mutation_id = genomediff._make_mutationID(mutation['mutation_genes'],mutation['mutation_type'],mutation['mutation_position']);
#mutation_id = self._make_mutationID(mutation['mutation_genes'],mutation['mutation_type'],mutation['mutation_position']);
tmp = {};
tmp.update(mutation);
tmp.update({'mutation_id':mutation_id});
mutation_data_O.append(tmp);
mutation_ids.append(mutation_id);
# screen out mutations
mutation_ids_screened = [x for x in mutation_ids if not x in mutation_id_exclusion_list];
mutation_ids_unique = list(set(mutation_ids_screened));
# get mutation information for all unique mutations
mutation_ids_uniqueInfo = [];
for mutation_id in mutation_ids_unique:
for mutation in mutation_data_O:
if mutation_id == mutation['mutation_id']:
tmp = {};
#tmp['mutation_id']=mutation['mutation_id']
#tmp['mutation_frequency']=mutation['mutation_frequency'];
#tmp['mutation_genes']=mutation['mutation_genes'];
#tmp['mutation_position']=mutation['mutation_position'];
#tmp['mutation_annotations']=mutation['mutation_annotations'];
#tmp['mutation_locations']=mutation['mutation_locations'];
#tmp['mutation_links']=mutation['mutation_links'];
#tmp['mutation_type']=mutation['mutation_type'];
tmp['mutation_id']=mutation['mutation_id'];
tmp['mutation_frequency']=mutation['mutation_frequency'];
if mutation['mutation_genes']:
tmp['mutation_genes']=";".join([x for x in mutation['mutation_genes'] if x is not None]);
else: tmp['mutation_genes']=mutation['mutation_genes'];
if mutation['mutation_position']:
tmp['mutation_position']=mutation['mutation_position'];
else: tmp['mutation_position']=mutation['mutation_position'];
if mutation['mutation_annotations']:
tmp['mutation_annotations']=";".join([x for x in mutation['mutation_annotations'] if x is not None]);
else: tmp['mutation_annotations']=mutation['mutation_annotations'];
if mutation['mutation_locations']:
tmp['mutation_locations']=";".join([x for x in mutation['mutation_locations'] if x is not None]);
else: tmp['mutation_locations']=mutation['mutation_locations'];
if mutation['mutation_links']:
tmp['mutation_links']=";".join([x for x in mutation['mutation_links'] if x is not None]);
else: tmp['mutation_links']=mutation['mutation_links'];
tmp['mutation_type']=mutation['mutation_type'];
tmp['used_']=mutation['used_'];
tmp['comment_']=mutation['comment_'];
mutation_ids_uniqueInfo.append(tmp);
break;
data_O = [];
# add in 0.0 frequency for mutations that are not found
for sample_name_cnt,sample_name in enumerate(sample_names):
for mutation_id in mutation_ids_uniqueInfo:
tmp = {};
tmp_fitted = {};
tmp['mutation_id']=mutation_id['mutation_id']
tmp['time_point']=time_points[sample_name_cnt]
tmp['intermediate']=intermediates[sample_name_cnt]
tmp['experiment_id']=experiment_ids[sample_name_cnt]
tmp['sample_name']=sample_name
tmp['mutation_frequency']=0.0;
tmp['mutation_genes']=mutation_id['mutation_genes'];
tmp['mutation_position']=mutation_id['mutation_position'];
tmp['mutation_annotations']=mutation_id['mutation_annotations'];
tmp['mutation_locations']=mutation_id['mutation_locations'];
tmp['mutation_links']=mutation_id['mutation_links'];
tmp['mutation_type']=mutation_id['mutation_type'];
tmp['used_']=mutation_id['used_'];
tmp['comment_']=mutation_id['comment_'];
for mutation in mutation_data_O:
if sample_name == mutation['sample_name'] and mutation_id['mutation_id'] == mutation['mutation_id']:
tmp['mutation_frequency']=mutation['mutation_frequency'];
tmp['comment_']=mutation['comment_'];
break;
data_O.append(tmp);
# dump chart parameters to a js files
data1_keys = [
#'experiment_id',
#'sample_name',
'mutation_id',
#'mutation_frequency',
'mutation_type',
'mutation_position',
#'mutation_data',
#'mutation_annotations',
'mutation_genes',
#'mutation_links',
'mutation_locations'
];
data1_nestkeys = ['mutation_id']; #horizontalareaplot2d_01
#data1_nestkeys = ['intermediate']; #verticalbarschart2d_01
data1_keymap = {'xdata':'intermediate',
'ydata':'mutation_frequency',
'serieslabel':'mutation_id',
'featureslabel':''};
parameters = {"chart1margin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"chart1width":500,"chart1height":350,
"chart1title":"Population mutation frequency", "chart1x1axislabel":"jump_time_point","chart1y1axislabel":"frequency"}
# make the data object
dataobject_O = [{"data":data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},{"data":data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys}];
# make the tile parameter objects
formtileparameters_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
formparameters_O = {'htmlid':'filtermenuform1','htmltype':'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters_O.update(formparameters_O);
svgparameters_O = {
#"svgtype":'verticalbarschart2d_01',
"svgtype":'horizontalareaplot2d_01',
"svgkeymap":[data1_keymap,data1_keymap],
'svgid':'svg1',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":500,"svgheight":350,
"svgstackoffset":"extend",
"svgx1axislabel":"jump_time_point","svgy1axislabel":"frequency",
'svgformtileid':'filtermenu1','svgresetbuttonid':'reset1','svgsubmitbuttonid':'submit1'};
svgtileparameters_O = {'tileheader':'Population mutation frequency','tiletype':'svg','tileid':"tile2",'rowid':"row1",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-8"};
svgtileparameters_O.update(svgparameters_O);
tableparameters_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_O = {'tileheader':'Population mutation frequency','tiletype':'table','tileid':"tile3",'rowid':"row2",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters_O,svgtileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"tile2":[0],"tile3":[0]};
# dump the data to a json file
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = None);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='project':
filename_str = self.settings['visualization_data'] + '/project/' + analysis_id_I + '_data_stage01_resequencing_mutationsAnnotated' + '.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
def export_dataStage01ResequencingMutationsAnnotated_js(self,analysis_id_I,mutation_id_exclusion_list=[],frequency_threshold=0.1,max_position=4630000,data_dir_I="tmp"):
'''export data_stage01_resequencing_mutationsAnnotated to js file
Visualization: treemap of the mutations
'''
print('exportingdataStage01ResequencingMutationsAnnotated...')
genomediff = genome_diff();
# get the analysis information
experiment_ids,sample_names,time_points = [],[],[];
experiment_ids,sample_names,time_points = self.get_experimentIDAndSampleNameAndTimePoint_analysisID_dataStage01ResequencingAnalysis(analysis_id_I);
mutation_data_O = [];
mutation_ids = [];
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);
for end_cnt,mutation in enumerate(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['analysis_id']=analysis_id_I;
tmp['time_point']=time_points[sample_name_cnt]
tmp['experiment_id']=experiment_ids[sample_name_cnt]
tmp['sample_name']=sample_name
tmp['mutation_id']=mutation_id;
if mutation['mutation_annotations']:
tmp['mutation_annotations']=";".join([x for x in mutation['mutation_annotations'] if x is not None]);
else: tmp['mutation_annotations']=mutation['mutation_annotations'];
if mutation['mutation_links']:
tmp['mutation_links']=";".join([x for x in mutation['mutation_links'] if x is not None]);
else: tmp['mutation_links']=mutation['mutation_links'];
if mutation['mutation_genes']:
tmp['mutation_genes']=";".join([x for x in mutation['mutation_genes'] if x is not None]);
else: tmp['mutation_genes']=mutation['mutation_genes'];
if mutation['mutation_position']:
tmp['mutation_position']=mutation['mutation_position'];
else: tmp['mutation_position']=mutation['mutation_position'];
if mutation['mutation_locations']:
tmp['mutation_locations']=";".join([x for x in mutation['mutation_locations'] if x is not None]);
else: tmp['mutation_locations']=mutation['mutation_locations'];
tmp.update(mutation);
mutation_data_O.append(tmp);
# dump chart parameters to a js files
data1_keys = [
'experiment_id',
'sample_name',
'mutation_id',
#'mutation_frequency',
'mutation_type',
'mutation_position',
#'mutation_data',
'mutation_annotations',
'mutation_genes',
#'mutation_links',
'mutation_locations'
];
data1_nestkeys = ['analysis_id',
'mutation_genes',
'mutation_position',
'mutation_type',
#'sample_name'
];
data1_keymap = {'xdata':'intermediate',
'ydata':'mutation_frequency',
'serieslabel':'mutation_id',
'featureslabel':''};
# make the data object
dataobject_O = [{"data":mutation_data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys},
{"data":mutation_data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys}
];
# make the tile parameter objects
# tile 0: form
formtileparameters_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
formparameters_O = {'htmlid':'filtermenuform1','htmltype':'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters_O.update(formparameters_O);
# tile 1: treelayout2d_01
svgparameters_O = {"svgtype":'treelayout2d_01',
"svgkeymap":[data1_keymap],
'svgid':'svg1',
"svgmargin":{ 'top': 100, 'right': 100, 'bottom': 100, 'left': 100 },
"svgwidth":1000,
"svgheight":1000,
"svgduration":750,
"datalastchild":'sample_name'
};
svgtileparameters_O = {
'tileheader':'Mutations annotated',
'tiletype':'svg',
'tileid':"tile1",
'rowid':"row2",
'colid':"col1",
'tileclass':"panel panel-default",
'rowclass':"row",
'colclass':"col-sm-12"
};
svgtileparameters_O.update(svgparameters_O);
# tile 2: table
tableparameters_O = {
"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
};
tabletileparameters_O = {
'tileheader':'Mutations annotated',
'tiletype':'table',
'tileid':"tile2",
'rowid':"row3",
'colid':"col1",
'tileclass':"panel panel-default",
'rowclass':"row",
'colclass':"col-sm-12"
};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters_O,svgtileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"tile1":[0],"tile2":[1]}; #pass two identical data objects to the treemap and table
# dump the data to a json file
data_str = 'var ' + 'data' + ' = ' + json.dumps(dataobject_O) + ';';
parameters_str = 'var ' + 'parameters' + ' = ' + json.dumps(parametersobject_O) + ';';
tile2datamap_str = 'var ' + 'tile2datamap' + ' = ' + json.dumps(tile2datamap_O) + ';';
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = None);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
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();
def export_dataStage01ResequencingEndpointLineages_js(self,analysis_id_I,
query_I={},
data_dir_I='tmp'):
'''
Pie chart or bar chart of mutation counts
INPUT:
analysis_id_I = string,
query_I = additional query deliminators
'''
data_O = self.getGroupAndCount_analysisIDAndMutationTypeAndMutationPositionAndMutationGenes_analysisID_dataStage01ResequencingEndpointLineages(
analysis_id_I,
column_name_I = 'analysis_id',
aggregate_function_I='count',
aggregate_label_I='count_1',
query_I=query_I,
output_O='listDict',
dictColumn_I=None);
#add in the mutationID
genomediff = genome_diff();
for d in data_O:
d['mutation_id'] = genomediff._make_mutationID(
d['mutation_genes'],d['mutation_type'],d['mutation_position']
);
if d['mutation_genes']:
d['mutation_genes']=";".join([x for x in d['mutation_genes'] if x is not None]);
else: d['mutation_genes']=d['mutation_genes'];
# make the tile objects
#data1 = filter menu and table
data1_keys = ['analysis_id',
'mutation_type',
'mutation_position',
'mutation_genes',
'count_1',
'mutation_id'
];
data1_nestkeys = ['mutation_id'];
data1_keymap = {
'xdata':'mutation_id',
'ydata':'count_1',
'serieslabel':'analysis_id',
'featureslabel':'mutation_id',
'tooltiplabel':'mutation_id',
};
nsvgtable = ddt_container_filterMenuAndChart2dAndTable();
nsvgtable.make_filterMenuAndChart2dAndTable(
data_filtermenu=data_O,
data_filtermenu_keys=data1_keys,
data_filtermenu_nestkeys=data1_nestkeys,
data_filtermenu_keymap=data1_keymap,
data_svg_keys=None,
data_svg_nestkeys=None,
data_svg_keymap=None,
data_table_keys=None,
data_table_nestkeys=None,
data_table_keymap=None,
data_svg=None,
data_table=None,
svgtype='verticalbarschart2d_01',
tabletype='responsivetable_01',
svgx1axislabel='',
svgy1axislabel='',
tablekeymap = [data1_keymap],
svgkeymap = [data1_keymap],
formtile2datamap=[0],
tabletile2datamap=[0],
svgtile2datamap=[0], #calculated on the fly
svgfilters=None,
svgtileheader='Mutation counts',
tablefilters=None,
tableheaders=None
);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = nsvgtable.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(nsvgtable.get_allObjects());
