diff options
Diffstat (limited to 'wqflask/wqflask/api/mapping.py')
| -rw-r--r-- | wqflask/wqflask/api/mapping.py | 141 |
1 files changed, 141 insertions, 0 deletions
diff --git a/wqflask/wqflask/api/mapping.py b/wqflask/wqflask/api/mapping.py new file mode 100644 index 00000000..d830cefc --- /dev/null +++ b/wqflask/wqflask/api/mapping.py @@ -0,0 +1,141 @@ +from __future__ import absolute_import, division, print_function + +import string + +from base import data_set +from base import webqtlConfig +from base.trait import GeneralTrait, retrieve_sample_data + +from utility import helper_functions +from wqflask.marker_regression import gemma_mapping, rqtl_mapping, qtlreaper_mapping, plink_mapping + +import utility.logger +logger = utility.logger.getLogger(__name__ ) + +def do_mapping_for_api(start_vars): + assert('db' in start_vars) + assert('trait_id' in start_vars) + + dataset = data_set.create_dataset(dataset_name = start_vars['db']) + dataset.group.get_markers() + this_trait = GeneralTrait(dataset = dataset, name = start_vars['trait_id']) + this_trait = retrieve_sample_data(this_trait, dataset) + + samples = [] + vals = [] + + for sample in dataset.group.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") + + mapping_params = initialize_parameters(start_vars, dataset, this_trait) + + covariates = "" #ZS: It seems to take an empty string as default. This should probably be changed. + + if mapping_params['mapping_method'] == "gemma": + header_row = ["name", "chr", "Mb", "lod_score", "p_value"] + if mapping_params['use_loco'] == "True": #ZS: gemma_mapping returns both results and the filename for LOCO, so need to only grab the former for api + 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['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_geno(vals, dataset, mapping_params['rqtl_method'], mapping_params['rqtl_model'], + mapping_params['perm_check'], mapping_params['num_perm'], + mapping_params['do_control'], mapping_params['control_marker'], + mapping_params['manhattan_plot'], mapping_params['pair_scan']) + else: + result_markers = rqtl_mapping.run_rqtl_geno(vals, dataset, mapping_params['rqtl_method'], mapping_params['rqtl_model'], + mapping_params['perm_check'], mapping_params['num_perm'], + mapping_params['do_control'], mapping_params['control_marker'], + mapping_params['manhattan_plot'], mapping_params['pair_scan']) + + if mapping_params['limit_to']: + result_markers = result_markers[:mapping_params['limit_to']] + + if mapping_params['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 + + return mapping_params + + |