1 files changed, 122 insertions, 0 deletions
diff --git a/wqflask/wqflask/api/mapping.py b/wqflask/wqflask/api/mapping.py
new file mode 100644
index 00000000..83c61796
--- /dev/null
+++ b/wqflask/wqflask/api/mapping.py
@@ -0,0 +1,122 @@
+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", "Mb", "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'])
+
+    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
+
+
+def initialize_parameters(start_vars, dataset, this_trait):
+    mapping_params = {}
+    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'] = False
+    if 'use_loco' in start_vars:
+        if start_vars['use_loco'].lower() != "false":
+            mapping_params['use_loco'] = start_vars['use_loco']
+
+    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
+    
+