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from gn2.base import data_set
from gn2.base.trait import create_trait, retrieve_sample_data
from gn2.wqflask.marker_regression import gemma_mapping, rqtl_mapping
from gn2.wqflask.show_trait.show_trait import normf
def do_mapping_for_api(start_vars):
if ('db' not in start_vars) or ("trait_id" not in start_vars):
raise ValueError("Mapping: db and trait_id are not in start_vars")
dataset = data_set.create_dataset(dataset_name=start_vars['db'])
dataset.group.get_markers()
this_trait = create_trait(dataset=dataset, name=start_vars['trait_id'])
this_trait = retrieve_sample_data(this_trait, dataset)
samples = []
vals = []
mapping_params = initialize_parameters(start_vars, dataset, this_trait)
genofile_samplelist = []
if mapping_params.get('genofile'):
dataset.group.genofile = mapping_params['genofile']
genofile_samplelist = get_genofile_samplelist(dataset)
if (len(genofile_samplelist) > 0):
samplelist = genofile_samplelist
for sample in samplelist:
in_trait_data = False
for item in this_trait.data:
if this_trait.data[item].name == sample:
value = str(this_trait.data[item].value)
samples.append(item)
vals.append(value)
in_trait_data = True
break
if not in_trait_data:
vals.append("x")
else:
samplelist = dataset.group.samplelist
for sample in samplelist:
in_trait_data = False
for item in this_trait.data:
if this_trait.data[item].name == sample:
value = str(this_trait.data[item].value)
samples.append(item)
vals.append(value)
in_trait_data = True
break
if not in_trait_data:
vals.append("x")
if mapping_params.get('transform') == "qnorm":
vals_minus_x = [float(val) for val in vals if val != "x"]
qnorm_vals = normf(vals_minus_x)
qnorm_vals_with_x = []
counter = 0
for val in vals:
if val == "x":
qnorm_vals_with_x.append("x")
else:
qnorm_vals_with_x.append(qnorm_vals[counter])
counter += 1
vals = qnorm_vals_with_x
# It seems to take an empty string as default. This should probably be changed.
covariates = ""
if mapping_params.get('mapping_method') == "gemma":
header_row = ["name", "chr", "Mb", "lod_score", "p_value"]
# gemma_mapping returns both results and the filename for LOCO, so need to only grab the former for api
if mapping_params.get('use_loco') == "True":
result_markers = gemma_mapping.run_gemma(
this_trait, dataset, samples, vals, covariates, mapping_params['use_loco'], mapping_params['maf'])[0]
else:
result_markers = gemma_mapping.run_gemma(
this_trait, dataset, samples, vals, covariates, mapping_params['use_loco'], mapping_params['maf'])
elif mapping_params.get('mapping_method') == "rqtl":
header_row = ["name", "chr", "cM", "lod_score"]
if mapping_params['num_perm'] > 0:
_sperm_output, _suggestive, _significant, result_markers = rqtl_mapping.run_rqtl(this_trait.name, vals, samples, dataset, None, "Mb", mapping_params['rqtl_model'],
mapping_params['rqtl_method'], mapping_params['num_perm'], None,
mapping_params['do_control'], mapping_params['control_marker'],
mapping_params['manhattan_plot'], None)
else:
result_markers = rqtl_mapping.run_rqtl(this_trait.name, vals, samples, dataset, None, "Mb", mapping_params['rqtl_model'],
mapping_params['rqtl_method'], mapping_params['num_perm'], None,
mapping_params['do_control'], mapping_params['control_marker'],
mapping_params['manhattan_plot'], None)
if mapping_params.get('limit_to'):
result_markers = result_markers[:mapping_params['limit_to']]
if mapping_params.get('format') == "csv":
output_rows = []
output_rows.append(header_row)
for marker in result_markers:
this_row = [marker[header] for header in header_row]
output_rows.append(this_row)
return output_rows, mapping_params['format']
elif mapping_params['format'] == "json":
return result_markers, mapping_params['format']
else:
return result_markers, None
def initialize_parameters(start_vars, dataset, this_trait):
mapping_params = {}
mapping_params['format'] = "json"
if 'format' in start_vars:
mapping_params['format'] = start_vars['format']
mapping_params['limit_to'] = False
if 'limit_to' in start_vars:
if start_vars['limit_to'].isdigit():
mapping_params['limit_to'] = int(start_vars['limit_to'])
mapping_params['mapping_method'] = "gemma"
if 'method' in start_vars:
mapping_params['mapping_method'] = start_vars['method']
if mapping_params['mapping_method'] == "rqtl":
mapping_params['rqtl_method'] = "hk"
mapping_params['rqtl_model'] = "normal"
mapping_params['do_control'] = False
mapping_params['control_marker'] = ""
mapping_params['manhattan_plot'] = True
mapping_params['pair_scan'] = False
if 'rqtl_method' in start_vars:
mapping_params['rqtl_method'] = start_vars['rqtl_method']
if 'rqtl_model' in start_vars:
mapping_params['rqtl_model'] = start_vars['rqtl_model']
if 'control_marker' in start_vars:
mapping_params['control_marker'] = start_vars['control_marker']
mapping_params['do_control'] = True
if 'pair_scan' in start_vars:
if start_vars['pair_scan'].lower() == "true":
mapping_params['pair_scan'] = True
if 'interval_mapping' in start_vars:
if start_vars['interval_mapping'].lower() == "true":
mapping_params['manhattan_plot'] = False
elif 'manhattan_plot' in start_vars:
if start_vars['manhattan_plot'].lower() != "true":
mapping_params['manhattan_plot'] = False
mapping_params['maf'] = 0.01
if 'maf' in start_vars:
mapping_params['maf'] = start_vars['maf'] # Minor allele frequency
mapping_params['use_loco'] = True
if 'use_loco' in start_vars:
if (start_vars['use_loco'].lower() == "false") or (start_vars['use_loco'].lower() == "no"):
mapping_params['use_loco'] = False
mapping_params['num_perm'] = 0
mapping_params['perm_check'] = False
if 'num_perm' in start_vars:
try:
mapping_params['num_perm'] = int(start_vars['num_perm'])
mapping_params['perm_check'] = "ON"
except:
mapping_params['perm_check'] = False
mapping_params['transform'] = False
if 'transform' in start_vars:
mapping_params['transform'] = start_vars['transform']
mapping_params['genofile'] = False
if 'genofile' in start_vars:
mapping_params['genofile'] = start_vars['genofile']
return mapping_params
def get_genofile_samplelist(dataset):
genofile_samplelist = []
genofile_json = dataset.group.get_genofiles()
for genofile in genofile_json:
if genofile['location'] == dataset.group.genofile and 'sample_list' in genofile:
genofile_samplelist = genofile['sample_list']
return genofile_samplelist
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