def _drug_combination_heatmap(request, dataset, drug_id, cell_line_id,
template):
if not all(isinstance(d, collections.Sequence) for d in drug_id):
return HttpResponse(
'Please select either one or more individual drugs, or a '
'single drug combination',
status=400)
if len(drug_id) != 1:
return HttpResponse(
'Please select only one drug combination '
'at a time', status=400)
if len(cell_line_id) != 1:
return HttpResponse(
'Please select a single cell line for drug '
'combination plots',
status=400)
color_by = request.GET.get('colorBy', 'off')
if color_by != 'off':
return HttpResponse(
'Color overlay must be set to default for drug '
'combination heat plots',
status=400)
response_metric = request.GET.get('drMetric', 'dip')
if response_metric != 'dip':
return HttpResponse(
'Viability drug combination plots are not '
'supported',
status=400)
dip_absolute = request.GET.get('drcType', 'rel') == 'abs'
if dip_absolute:
return HttpResponse(
'Must use relative DIP rate for drug combination '
'heat plots',
status=400)
try:
ctrl_resp_data, expt_resp_data = df_dip_rates(
dataset_id=dataset.id,
drug_id=drug_id,
cell_line_id=cell_line_id,
use_dataset_names=True)
except NoDataException:
return HttpResponse(
'No data found for this request. This '
'drug/cell line/assay combination may not exist.',
status=400)
return plot_drug_combination_heatmap(ctrl_resp_data,
expt_resp_data,
template=template)
def _generate_dip_rates(dataset, regenerate_cache=False):
file_name = 'dip_rates_{}.h5'.format(dataset.id)
file_type = 'dip_rates'
file_type_protocol = 1
mod_date = timezone.now()
file = _cached_file(dataset, file_type, file_type_protocol)
if file and not regenerate_cache:
full_path = file.file.name
else:
ctrl, expt = df_dip_rates(dataset, cell_line_id=None, drug_id=None)
expt = expt.reset_index()
n_drugs = expt['drug'].apply(len).max()
expt = _unstack_doses(expt).reset_index()
wells = Well.objects.filter(
plate__dataset_id=dataset.id).select_related('plate')
if ctrl is None:
df_data = expt
else:
ctrl = ctrl.reset_index()
df_data = pd.concat([ctrl, expt], ignore_index=True, sort=False)
df_data.drop(columns=['plate', 'dataset'], inplace=True)
well_df = pd.DataFrame(
{
'well_id': well.id,
'plate': well.plate.name,
'well_num': well.well_num,
'well': well.plate.well_id_to_name(well.well_num)
} for well in wells)
df_data = df_data.merge(well_df, on='well_id')
df_data.rename(
columns={f'dose{n+1}': f'drug{n+1}.conc'
for n in range(n_drugs)},
inplace=True)
df_data.rename(columns={'cell_line': 'cell.line'}, inplace=True)
df_data.sort_values(['plate', 'well_num'], inplace=True)
columns = ['plate', 'well', 'cell.line'] + \
[f'drug{n+1}' for n in range(n_drugs)] + \
[f'drug{n+1}.conc' for n in range(n_drugs)] + \
['dip_rate', 'dip_fit_std_err']
df_data = df_data[columns]
full_path = os.path.join(settings.DOWNLOADS_ROOT, file_name)
df_data.to_csv(full_path, sep='\t', index=False)
df, created = HTSDatasetFile.objects.get_or_create(
dataset=dataset,
file_type=file_type,
defaults={
'file_type_protocol': file_type_protocol,
'file': full_path
})
if not created:
df.file_type_protocol = file_type_protocol
df.file = full_path
df.creation_date = mod_date
df.save()
return full_path
def _dose_response_plot(request,
dataset,
dataset2_id,
permission_required,
drug_id,
cell_line_id,
plot_type,
template=default_plotly_template):
if dataset2_id is not None:
try:
dataset2 = HTSDataset.objects.get(pk=dataset2_id)
except HTSDataset.DoesNotExist:
raise Http404()
_assert_has_perm(request, dataset2, permission_required)
if dataset.name == dataset2.name:
return HttpResponse(
'Cannot compare two datasets with the same '
'name. Please rename one of the datasets.',
status=400)
datasets = dataset if not dataset2_id else [dataset, dataset2]
color_by = request.GET.get('colorBy', 'off')
if color_by == 'off':
color_by = None
drug_tag_ids = [int(dt) for dt in request.GET.getlist('dT')]
color_groups = None
aggregate_drugs = request.GET.get('aggregateDrugs', False) == "true"
if not drug_id and drug_tag_ids:
drug_id, drug_groups = _process_aggreate(
request, 'drugs', drug_tag_ids, aggregate_drugs
or color_by == 'dr', datasets)
if aggregate_drugs:
aggregate_drugs = drug_groups
if color_by == 'dr':
color_groups = drug_groups
cell_line_tag_ids = [int(ct) for ct in request.GET.getlist('cT')]
aggregate_cell_lines = request.GET.get('aggregateCellLines', False) \
== "true"
if not cell_line_id and cell_line_tag_ids:
cell_line_id, cell_line_groups = _process_aggreate(
request, 'cell_lines', cell_line_tag_ids, aggregate_cell_lines
or color_by == 'cl', datasets)
if aggregate_cell_lines:
aggregate_cell_lines = cell_line_groups
if color_by == 'cl':
color_groups = cell_line_groups
if color_groups:
color_groups = _make_tags_unique(color_groups)
elif color_by == 'cl':
# The tags will just be the cell lines themselves
color_groups = {
cl.name: [cl.name]
for cl in CellLine.objects.filter(
id__in=cell_line_id).order_by('name')
}
elif color_by == 'dr':
# Ditto for drugs
color_groups = {
dr.name: [dr.name]
for dr in Drug.objects.filter(id__in=drug_id).order_by('name')
}
if color_groups and len(color_groups) > MAX_COLOR_GROUPS:
return HttpResponse(
'Cannot plot using more than {} unique colors. Please remove '
'some entries or turn off coloring to proceed.'.format(
MAX_COLOR_GROUPS),
status=400)
if not cell_line_id:
return HttpResponse('Please enter at least one cell line', status=400)
if not drug_id:
return HttpResponse('Please enter at least one drug', status=400)
response_metric = request.GET.get('drMetric', 'dip')
if response_metric not in ('dip', 'viability', 'compare'):
return HttpResponse(
'Unknown metric. Supported values: dip, '
'viability, compare.',
status=400)
def _setup_dr_par(name, needs_toggle=False):
if needs_toggle and \
request.GET.get(name + 'Toggle', 'off') != 'on':
return None
par_name = request.GET.get(name, None)
if par_name is not None and '_custom' in par_name:
rep_value = request.GET.get(name + 'Custom', None)
if int(rep_value) < 0:
raise ValueError()
par_name = par_name.replace('_custom', rep_value)
return par_name
try:
dr_par = _setup_dr_par('drPar')
except ValueError:
return HttpResponse(
'Parameter custom value '
'needs to be a positive integer',
status=400)
try:
dr_par_two = _setup_dr_par('drParTwo', needs_toggle=True)
except ValueError:
return HttpResponse(
'Parameter two custom value '
'needs to be a positive integer',
status=400)
try:
dr_par_order = _setup_dr_par('drParOrder', needs_toggle=True)
except ValueError:
return HttpResponse(
'Parameter order custom value '
'needs to be a positive integer',
status=400)
# 'compare' plots are only available for one dataset and metric
if response_metric == 'compare':
if dataset2_id is not None:
return HttpResponse(
'"compare" mode not compatible with two '
'datasets',
status=400)
if dr_par_two is not None:
return HttpResponse(
'Parameter two not available with "compare" '
'mode',
status=400)
if dr_par_order is not None:
return HttpResponse(
'Parameter order not available with "compare" '
'mode',
status=400)
if plot_type == 'drc':
return HttpResponse(
'Dose response curves not available with '
'"compare" mode',
status=400)
if dr_par.endswith('_rel'):
return HttpResponse(
'Relative metrics are not available with '
'"compare" mode',
status=400)
# Work out any non-standard parameters we need to calculate
# e.g. non-standard IC concentrations
ic_concentrations = set()
ec_concentrations = set()
e_values = set()
e_rel_values = set()
regexes = {
IC_REGEX: ic_concentrations,
EC_REGEX: ec_concentrations,
E_REGEX: e_values,
E_REL_REGEX: e_rel_values
}
need_aa = False
need_hill = False
need_emax = False
need_einf = False
for param_idx, param in enumerate((dr_par, dr_par_two, dr_par_order)):
if param is None:
continue
if param == 'label' and param_idx == 2:
continue
if param == 'aa_obs':
continue
if param == 'aa':
need_aa = True
continue
if param == 'hill':
need_hill = True
continue
if param.startswith('emax'):
need_emax = True
continue
if param == 'einf':
need_einf = True
continue
for regex, value_list in regexes.items():
match = regex.match(param)
if not match:
continue
try:
value = int(match.groups(0)[0])
if value < 0 or value > 100:
raise ValueError()
value_list.add(value)
break
except ValueError:
return HttpResponse(
'Invalid custom value - must be '
'an integer between 1 and 100',
status=400)
else:
return HttpResponse('Unknown parameter: {}'.format(param),
status=400)
dataset_ids = dataset.id if dataset2_id is None else [
dataset.id, dataset2_id
]
# Fit Hill curves and compute parameters
if response_metric == 'compare':
all_metrics = ('dip', 'viability')
else:
all_metrics = (response_metric, )
try:
base_params = [
df_curve_fits(dataset_ids, metric, drug_id, cell_line_id)
for metric in all_metrics
]
except NoDataException:
return HttpResponse(
'No data found for this request. This drug/cell '
'line/assay combination may not exist.',
status=400)
include_response_values = False
ctrl_resp_data = None
expt_resp_data = None
if plot_type == 'drc':
single_drug = len(
base_params[0].index.get_level_values('drug').unique()) == 1
single_cl = len(
base_params[0].index.get_level_values('cell_line').unique()) \
== 1
if single_cl and single_drug:
try:
if response_metric == 'dip':
ctrl_resp_data, expt_resp_data = df_dip_rates(
dataset_id=dataset_ids,
drug_id=drug_id,
cell_line_id=cell_line_id,
use_dataset_names=True)
else:
expt_resp_data, ctrl_resp_data = _get_viability_scores(
datasets,
drug_id,
cell_line_id,
viability_time=base_params[0]._viability_time)
except NoDataException:
return HttpResponse(
'No data found for this request. This drug/'
'cell line/assay combination may not exist.',
status=400)
include_response_values = True
need_emax = True
ic_concentrations = {50}
ec_concentrations = {50}
with warnings.catch_warnings(record=True) as w:
fit_params = [
fit_params_from_base(
base_param_set,
ctrl_resp_data=ctrl_resp_data,
expt_resp_data=expt_resp_data,
include_response_values=include_response_values,
custom_ic_concentrations=ic_concentrations,
custom_ec_concentrations=ec_concentrations,
custom_e_values=e_values,
include_aa=need_aa,
include_hill=need_hill,
include_emax=need_emax,
include_einf=need_einf) for base_param_set in base_params
]
# Currently only care about warnings if plotting AA
if plot_type == 'drpar' and (dr_par == 'aa' or dr_par_two == 'aa'):
w = [i for i in w if issubclass(i.category, AAFitWarning)]
if w:
return HttpResponse(w[0].message, status=400)
if response_metric == 'compare':
# Create new dataframe
import pandas as pd
fit_params = pd.concat([
fit_params[0]['label'], fit_params[0][dr_par],
fit_params[1][dr_par]
],
join='inner',
axis=1)
fit_params.columns = [
'label', 'dip__{}'.format(dr_par), 'viability__{}'.format(dr_par)
]
fit_params._viability_time = base_params[1]._viability_time
fit_params._drmetric = 'compare'
dr_par, dr_par_two = fit_params.columns[1:]
else:
fit_params = fit_params[0]
if plot_type == 'drpar':
if dr_par is None:
return HttpResponse('Dose response parameter is a required field',
status=400)
try:
plot_fig = plot_drc_params(
fit_params,
fit_param=dr_par,
fit_param_compare=dr_par_two,
fit_param_sort=dr_par_order,
aggregate_cell_lines=aggregate_cell_lines,
aggregate_drugs=aggregate_drugs,
color_by=color_by,
color_groups=color_groups,
multi_dataset=dataset2_id is not None,
template=template)
except CannotPlotError as e:
return HttpResponse(e, status=400)
else:
dip_absolute = request.GET.get('drcType', 'rel') == 'abs'
plot_fig = plot_drc(fit_params,
is_absolute=dip_absolute,
color_by=color_by,
color_groups=color_groups,
template=template)
return plot_fig