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-rw-r--r--wqflask/base/data_set.py2
-rw-r--r--wqflask/wqflask/correlation/correlation_gn3_api.py115
2 files changed, 7 insertions, 110 deletions
diff --git a/wqflask/base/data_set.py b/wqflask/base/data_set.py
index 62afdb63..d31161ec 100644
--- a/wqflask/base/data_set.py
+++ b/wqflask/base/data_set.py
@@ -672,6 +672,8 @@ class DataSet:
return results
def get_probeset_data(self, sample_list=None, trait_ids=None):
+
+ # improvement of get trait data--->>>
if sample_list:
self.samplelist = sample_list
diff --git a/wqflask/wqflask/correlation/correlation_gn3_api.py b/wqflask/wqflask/correlation/correlation_gn3_api.py
index 3e1ce1dc..eb986655 100644
--- a/wqflask/wqflask/correlation/correlation_gn3_api.py
+++ b/wqflask/wqflask/correlation/correlation_gn3_api.py
@@ -27,13 +27,11 @@ def create_target_this_trait(start_vars):
return (this_dataset, this_trait, target_dataset, sample_data)
-
-def test_process_data(this_trait,dataset,start_vars):
+def test_process_data(this_trait, dataset, start_vars):
"""test function for bxd,all and other sample data"""
corr_samples_group = start_vars["corr_samples_group"]
-
primary_samples = dataset.group.samplelist
if dataset.group.parlist != None:
primary_samples += dataset.group.parlist
@@ -51,10 +49,12 @@ def test_process_data(this_trait,dataset,start_vars):
if corr_samples_group == 'samples_other':
primary_samples = [x for x in primary_samples if x not in (
dataset.group.parlist + dataset.group.f1list)]
- sample_data = process_samples(start_vars, list(this_trait.data.keys()), primary_samples)
+ sample_data = process_samples(start_vars, list(
+ this_trait.data.keys()), primary_samples)
return sample_data
+
def process_samples(start_vars, sample_names, excluded_samples=None):
"""process samples"""
sample_data = {}
@@ -149,7 +149,7 @@ def fetch_sample_data(start_vars, this_trait, this_dataset, target_dataset):
# sample_data = test_process_data(this_trait,this_dataset,start_vars)
- if target_dataset.type =="ProbeSet":
+ if target_dataset.type == "ProbeSet":
# pass
target_dataset.get_probeset_data(list(sample_data.keys()))
else:
@@ -238,7 +238,6 @@ def compute_correlation(start_vars, method="pearson"):
"target_dataset": start_vars['corr_dataset'],
"return_results": corr_return_results}
-
return correlation_data
@@ -299,107 +298,3 @@ def get_tissue_correlation_input(this_trait, trait_symbol_dict):
}
return (primary_tissue_data, target_tissue_data)
return None
-
-
-def generate_corr_data(corr_results, target_dataset):
- counter = 0
- results_list = []
- for (index, trait_corr) in enumerate(corr_results):
- trait_name = list(trait_corr.keys())[0]
- trait = create_trait(dataset=target_dataset,
- name=trait_name)
-
- trait_corr_data = trait_corr[trait_name]
-
- if trait.view == False:
- continue
- results_dict = {}
- results_dict['index'] = index + 1
- results_dict['trait_id'] = trait.name
- results_dict['dataset'] = trait.dataset.name
- # results_dict['hmac'] = hmac.data_hmac(
- # '{}:{}'.format(trait.name, trait.dataset.name))
- if target_dataset.type == "ProbeSet":
- results_dict['symbol'] = trait.symbol
- results_dict['description'] = "N/A"
- results_dict['location'] = trait.location_repr
- results_dict['mean'] = "N/A"
- results_dict['additive'] = "N/A"
- if bool(trait.description_display):
- results_dict['description'] = trait.description_display
- if bool(trait.mean):
- results_dict['mean'] = f"{float(trait.mean):.3f}"
- try:
- results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
- except:
- results_dict['lod_score'] = "N/A"
- results_dict['lrs_location'] = trait.LRS_location_repr
- if bool(trait.additive):
- results_dict['additive'] = f"{float(trait.additive):.3f}"
- results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}"
- results_dict['num_overlap'] = trait.num_overlap
- results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
- results_dict['lit_corr'] = "--"
- results_dict['tissue_corr'] = "--"
- results_dict['tissue_pvalue'] = "--"
- tissue_corr = trait_corr_data.get('tissue_corr',0)
- lit_corr = trait_corr_data.get('lit_corr',0)
- if bool(lit_corr):
- results_dict['lit_corr'] = f"{float(trait_corr_data.get('lit_corr',0)):.3f}"
- if bool(tissue_corr):
- results_dict['tissue_corr'] = f"{float(trait_corr_data.get('tissue_corr',0)):.3f}"
- results_dict['tissue_pvalue'] = f"{float(trait_corr_data.get('tissue_pvalue',0)):.3e}"
- elif target_dataset.type == "Publish":
- results_dict['abbreviation_display'] = "N/A"
- results_dict['description'] = "N/A"
- results_dict['mean'] = "N/A"
- results_dict['authors_display'] = "N/A"
- results_dict['additive'] = "N/A"
- if for_api:
- results_dict['pubmed_id'] = "N/A"
- results_dict['year'] = "N/A"
- else:
- results_dict['pubmed_link'] = "N/A"
- results_dict['pubmed_text'] = "N/A"
-
- if bool(trait.abbreviation):
- results_dict['abbreviation_display'] = trait.abbreviation
- if bool(trait.description_display):
- results_dict['description'] = trait.description_display
- if bool(trait.mean):
- results_dict['mean'] = f"{float(trait.mean):.3f}"
- if bool(trait.authors):
- authors_list = trait.authors.split(',')
- if len(authors_list) > 6:
- results_dict['authors_display'] = ", ".join(
- authors_list[:6]) + ", et al."
- else:
- results_dict['authors_display'] = trait.authors
- if bool(trait.pubmed_id):
- if for_api:
- results_dict['pubmed_id'] = trait.pubmed_id
- results_dict['year'] = trait.pubmed_text
- else:
- results_dict['pubmed_link'] = trait.pubmed_link
- results_dict['pubmed_text'] = trait.pubmed_text
- try:
- results_dict['lod_score'] = f"{float(trait.LRS_score_repr) / 4.61:.1f}"
- except:
- results_dict['lod_score'] = "N/A"
- results_dict['lrs_location'] = trait.LRS_location_repr
- if bool(trait.additive):
- results_dict['additive'] = f"{float(trait.additive):.3f}"
- results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}"
- results_dict['num_overlap'] = trait.num_overlap
- results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
- else:
- results_dict['location'] = trait.location_repr
- results_dict['sample_r'] = f"{float(trait_corr_data.get('sample_r',0)):.3f}"
- results_dict['num_overlap'] = trait.num_overlap
- results_dict['sample_p'] = f"{float(trait_corr_data.get('sample_p',0)):.3e}"
-
- results_list.append(results_dict)
-
- return results_list
-
-