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-rw-r--r--gn3/api/correlation.py13
-rw-r--r--gn3/computations/correlations.py142
-rw-r--r--gn3/settings.py5
-rw-r--r--tests/unit/computations/test_correlation.py137
4 files changed, 198 insertions, 99 deletions
diff --git a/gn3/api/correlation.py b/gn3/api/correlation.py
index 2339088..e7e89cf 100644
--- a/gn3/api/correlation.py
+++ b/gn3/api/correlation.py
@@ -23,7 +23,6 @@ def compute_sample_integration(corr_method="pearson"):
this_trait_data = correlation_input.get("trait_data")
results = map_shared_keys_to_values(target_samplelist, target_data_values)
-
correlation_results = compute_all_sample_correlation(corr_method=corr_method,
this_trait=this_trait_data,
target_dataset=results)
@@ -33,9 +32,10 @@ def compute_sample_integration(corr_method="pearson"):
@correlation.route("/sample_r/<string:corr_method>", methods=["POST"])
def compute_sample_r(corr_method="pearson"):
- """correlation endpoint for computing sample r correlations\
+ """Correlation endpoint for computing sample r correlations\
api expects the trait data with has the trait and also the\
- target_dataset data"""
+ target_dataset data
+ """
correlation_input = request.get_json()
# xtodo move code below to compute_all_sampl correlation
@@ -53,9 +53,10 @@ def compute_sample_r(corr_method="pearson"):
@correlation.route("/lit_corr/<string:species>/<int:gene_id>", methods=["POST"])
def compute_lit_corr(species=None, gene_id=None):
- """api endpoint for doing lit correlation.results for lit correlation\
+ """Api endpoint for doing lit correlation.results for lit correlation\
are fetched from the database this is the only case where the db\
- might be needed for actual computing of the correlation results"""
+ might be needed for actual computing of the correlation results
+ """
conn, _cursor_object = database_connector()
target_traits_gene_ids = request.get_json()
@@ -72,7 +73,7 @@ def compute_lit_corr(species=None, gene_id=None):
@correlation.route("/tissue_corr/<string:corr_method>", methods=["POST"])
def compute_tissue_corr(corr_method="pearson"):
- """api endpoint fr doing tissue correlation"""
+ """Api endpoint fr doing tissue correlation"""
tissue_input_data = request.get_json()
primary_tissue_dict = tissue_input_data["primary_tissue"]
target_tissues_dict = tissue_input_data["target_tissues_dict"]
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index 26b7294..0d15d9b 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -1,4 +1,6 @@
"""module contains code for correlations"""
+import multiprocessing
+
from typing import List
from typing import Tuple
from typing import Optional
@@ -7,11 +9,6 @@ from typing import Callable
import scipy.stats
-def compute_sum(rhs: int, lhs: int) -> int:
- """Initial tests to compute sum of two numbers"""
- return rhs + lhs
-
-
def map_shared_keys_to_values(target_sample_keys: List, target_sample_vals: dict)-> List:
"""Function to construct target dataset data items given commoned shared\
keys and trait samplelist values for example given keys >>>>>>>>>>\
@@ -73,14 +70,12 @@ pearson,spearman and biweight mid correlation return value is rho and p_value
return (corr_coeffient, p_val)
-def compute_sample_r_correlation(
- corr_method: str, trait_vals,
- target_samples_vals) -> Optional[Tuple[float, float, int]]:
+def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
+ target_samples_vals) -> Optional[Tuple[str, float, float, int]]:
"""Given a primary trait values and target trait values calculate the
correlation coeff and p value
"""
-
(sanitized_traits_vals, sanitized_target_vals,
num_overlap) = normalize_values(trait_vals, target_samples_vals)
@@ -94,7 +89,7 @@ def compute_sample_r_correlation(
# xtodo check if corr_coefficient is None
# should use numpy.isNan scipy.isNan is deprecated
if corr_coeffient is not None:
- return (corr_coeffient, p_value, num_overlap)
+ return (trait_name, corr_coeffient, p_value, num_overlap)
return None
@@ -104,15 +99,15 @@ def do_bicor(x_val, y_val) -> Tuple[float, float]:
package :not packaged in guix
"""
- return (x_val, y_val)
+ _corr_input = (x_val, y_val)
+ return (0.0, 0.0)
def filter_shared_sample_keys(this_samplelist,
target_samplelist) -> Tuple[List, List]:
- """Given primary and target samplelist for two base and target trait select
-filter the values using the shared keys
-
- """
+ """Given primary and target samplelist\
+ for two base and target trait select\
+ filter the values using the shared keys"""
this_vals = []
target_vals = []
for key, value in target_samplelist.items():
@@ -125,26 +120,70 @@ filter the values using the shared keys
def compute_all_sample_correlation(this_trait,
target_dataset,
corr_method="pearson") -> List:
- """Given a trait data samplelist and target__datasets compute all sample
-correlation"""
+ """Given a trait data samplelist and\
+ target__datasets compute all sample correlation
+ """
+ # xtodo fix trait_name currently returning single one
+ # pylint: disable-msg=too-many-locals
+
+ this_trait_samples = this_trait["trait_sample_data"]
+ corr_results = []
+ processed_values = []
+ for target_trait in target_dataset:
+ trait_name = target_trait.get("trait_id")
+ target_trait_data = target_trait["trait_sample_data"]
+ # this_vals, target_vals = filter_shared_sample_keys(
+ # this_trait_samples, target_trait_data)
+
+ processed_values.append((trait_name, corr_method, *filter_shared_sample_keys(
+ this_trait_samples, target_trait_data)))
+ with multiprocessing.Pool(4) as pool:
+ results = pool.starmap(compute_sample_r_correlation, processed_values)
+
+ for sample_correlation in results:
+ if sample_correlation is not None:
+ (trait_name, corr_coeffient, p_value,
+ num_overlap) = sample_correlation
+
+ corr_result = {
+ "corr_coeffient": corr_coeffient,
+ "p_value": p_value,
+ "num_overlap": num_overlap
+ }
+
+ corr_results.append({trait_name: corr_result})
+
+ return sorted(
+ corr_results,
+ key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coeffient"]))
+
+
+def benchmark_compute_all_sample(this_trait,
+ target_dataset,
+ corr_method="pearson") ->List:
+ """Temp function to benchmark with compute_all_sample_r\
+ alternative to compute_all_sample_r where we use \
+ multiprocessing
+ """
this_trait_samples = this_trait["trait_sample_data"]
corr_results = []
for target_trait in target_dataset:
- trait_id = target_trait.get("trait_id")
+ trait_name = target_trait.get("trait_id")
target_trait_data = target_trait["trait_sample_data"]
this_vals, target_vals = filter_shared_sample_keys(
this_trait_samples, target_trait_data)
sample_correlation = compute_sample_r_correlation(
+ trait_name=trait_name,
corr_method=corr_method,
trait_vals=this_vals,
target_samples_vals=target_vals)
if sample_correlation is not None:
- (corr_coeffient, p_value, num_overlap) = sample_correlation
+ (trait_name, corr_coeffient, p_value, num_overlap) = sample_correlation
else:
continue
@@ -155,7 +194,7 @@ correlation"""
"num_overlap": num_overlap
}
- corr_results.append({trait_id: corr_result})
+ corr_results.append({trait_name: corr_result})
return corr_results
@@ -187,6 +226,7 @@ def tissue_correlation_for_trait_list(
primary_tissue_vals: List,
target_tissues_values: List,
corr_method: str,
+ trait_id: str,
compute_corr_p_value: Callable = compute_corr_coeff_p_value) -> dict:
"""Given a primary tissue values for a trait and the target tissues values
compute the correlation_cooeff and p value the input required are arrays
@@ -202,13 +242,12 @@ def tissue_correlation_for_trait_list(
target_values=target_tissues_values,
corr_method=corr_method)
- lit_corr_result = {
+ tiss_corr_result = {trait_id: {
"tissue_corr": tissue_corr_coeffient,
- "p_value": p_value,
- "tissue_number": len(primary_tissue_vals)
- }
+ "tissue_number": len(primary_tissue_vals),
+ "p_value": p_value}}
- return lit_corr_result
+ return tiss_corr_result
def fetch_lit_correlation_data(
@@ -323,15 +362,17 @@ def compute_all_lit_correlation(conn, trait_lists: List,
species: str, gene_id):
"""Function that acts as an abstraction for
lit_correlation_for_trait_list"""
- # xtodo to be refactored
lit_results = lit_correlation_for_trait_list(
conn=conn,
target_trait_lists=trait_lists,
species=species,
trait_gene_id=gene_id)
+ sorted_lit_results = sorted(
+ lit_results,
+ key=lambda trait_name: -abs(list(trait_name.values())[0]["lit_corr"]))
- return {"lit_results": lit_results}
+ return sorted_lit_results
def compute_all_tissue_correlation(primary_tissue_dict: dict,
@@ -343,7 +384,7 @@ def compute_all_tissue_correlation(primary_tissue_dict: dict,
"""
- tissues_results = {}
+ tissues_results = []
primary_tissue_vals = primary_tissue_dict["tissue_values"]
traits_symbol_dict = target_tissues_data["trait_symbol_dict"]
@@ -360,11 +401,17 @@ def compute_all_tissue_correlation(primary_tissue_dict: dict,
tissue_result = tissue_correlation_for_trait_list(
primary_tissue_vals=primary_tissue_vals,
target_tissues_values=target_tissue_vals,
+ trait_id=trait_id,
corr_method=corr_method)
- tissues_results[trait_id] = tissue_result
+ tissue_result_dict = {trait_id: tissue_result}
+ tissues_results.append(tissue_result_dict)
- return tissues_results
+ sorted_tissues_results = sorted(
+ tissues_results,
+ key=lambda trait_name: -abs(list(trait_name.values())[0]["tissue_corr"]))
+
+ return sorted_tissues_results
def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> List:
@@ -384,3 +431,38 @@ def process_trait_symbol_dict(trait_symbol_dict, symbol_tissue_vals_dict) -> Lis
traits_tissue_vals.append(target_tissue_dict)
return traits_tissue_vals
+
+
+def compute_tissue_correlation(primary_tissue_dict: dict,
+ target_tissues_data: dict,
+ corr_method: str):
+ """Experimental function that uses multiprocessing\
+ for computing tissue correlation
+ """
+
+ tissues_results = []
+
+ primary_tissue_vals = primary_tissue_dict["tissue_values"]
+ traits_symbol_dict = target_tissues_data["trait_symbol_dict"]
+ symbol_tissue_vals_dict = target_tissues_data["symbol_tissue_vals_dict"]
+
+ target_tissues_list = process_trait_symbol_dict(
+ traits_symbol_dict, symbol_tissue_vals_dict)
+ processed_values = []
+
+ for target_tissue_obj in target_tissues_list:
+ trait_id = target_tissue_obj.get("trait_id")
+
+ target_tissue_vals = target_tissue_obj.get("tissue_values")
+ processed_values.append(
+ (primary_tissue_vals, target_tissue_vals, corr_method, trait_id))
+
+ with multiprocessing.Pool(4) as pool:
+ results = pool.starmap(
+ tissue_correlation_for_trait_list, processed_values)
+ for result in results:
+ tissues_results.append(result)
+
+ return sorted(
+ tissues_results,
+ key=lambda trait_name: -abs(list(trait_name.values())[0]["tissue_corr"]))
diff --git a/gn3/settings.py b/gn3/settings.py
index e77a977..7b3ffb7 100644
--- a/gn3/settings.py
+++ b/gn3/settings.py
@@ -12,6 +12,9 @@ REDIS_JOB_QUEUE = "GN3::job-queue"
TMPDIR = os.environ.get("TMPDIR", tempfile.gettempdir())
# SQL confs
-SQL_URI = os.environ.get("SQL_URI", "mysql://kabui:1234@localhost/db_webqtl")
+SQL_URI = os.environ.get("SQL_URI", "mysql://webqtlout:webqtlout@localhost/db_webqtl")
SECRET_KEY = "password"
SQLALCHEMY_TRACK_MODIFICATIONS = False
+# gn2 results only used in fetching dataset info
+
+GN2_BASE_URL = "http://www.genenetwork.org/"
diff --git a/tests/unit/computations/test_correlation.py b/tests/unit/computations/test_correlation.py
index 52d1f60..6414c3b 100644
--- a/tests/unit/computations/test_correlation.py
+++ b/tests/unit/computations/test_correlation.py
@@ -1,4 +1,4 @@
-"""module contains the tests for correlation"""
+"""Module contains the tests for correlation"""
import unittest
from unittest import TestCase
from unittest import mock
@@ -88,10 +88,10 @@ class DataBase(QueryableMixin):
class TestCorrelation(TestCase):
- """class for testing correlation functions"""
+ """Class for testing correlation functions"""
def test_normalize_values(self):
- """function to test normalizing values """
+ """Function to test normalizing values """
results = normalize_values([2.3, None, None, 3.2, 4.1, 5],
[3.4, 7.2, 1.3, None, 6.2, 4.1])
@@ -100,18 +100,19 @@ class TestCorrelation(TestCase):
self.assertEqual(results, expected_results)
def test_bicor(self):
- """test for doing biweight mid correlation """
+ """Test for doing biweight mid correlation """
results = do_bicor(x_val=[1, 2, 3], y_val=[4, 5, 6])
- self.assertEqual(results, ([1, 2, 3], [4, 5, 6])
+ self.assertEqual(results, (0.0, 0.0)
)
@mock.patch("gn3.computations.correlations.compute_corr_coeff_p_value")
@mock.patch("gn3.computations.correlations.normalize_values")
def test_compute_sample_r_correlation(self, norm_vals, compute_corr):
- """test for doing sample correlation gets the cor\
- and p value and rho value using pearson correlation"""
+ """Test for doing sample correlation gets the cor\
+ and p value and rho value using pearson correlation
+ """
primary_values = [2.3, 4.1, 5]
target_values = [3.4, 6.2, 4.1]
@@ -119,21 +120,24 @@ class TestCorrelation(TestCase):
[3.4, 6.2, 4, 1.1, 8, 1.1], 6)
compute_corr.side_effect = [(0.7, 0.3), (-1.0, 0.9), (1, 0.21)]
- pearson_results = compute_sample_r_correlation(corr_method="pearson",
+ pearson_results = compute_sample_r_correlation(trait_name="1412_at",
+ corr_method="pearson",
trait_vals=primary_values,
target_samples_vals=target_values)
- spearman_results = compute_sample_r_correlation(corr_method="spearman",
+ spearman_results = compute_sample_r_correlation(trait_name="1412_at",
+ corr_method="spearman",
trait_vals=primary_values,
target_samples_vals=target_values)
- bicor_results = compute_sample_r_correlation(corr_method="bicor",
+ bicor_results = compute_sample_r_correlation(trait_name="1412_at",
+ corr_method="bicor",
trait_vals=primary_values,
target_samples_vals=target_values)
- self.assertEqual(bicor_results, (1, 0.21, 6))
- self.assertEqual(pearson_results, (0.7, 0.3, 6))
- self.assertEqual(spearman_results, (-1.0, 0.9, 6))
+ self.assertEqual(bicor_results, ("1412_at", 1, 0.21, 6))
+ self.assertEqual(pearson_results, ("1412_at", 0.7, 0.3, 6))
+ self.assertEqual(spearman_results, ("1412_at", -1.0, 0.9, 6))
self.assertIsInstance(
pearson_results, tuple, "message")
@@ -141,7 +145,7 @@ class TestCorrelation(TestCase):
spearman_results, tuple, "message")
def test_filter_shared_sample_keys(self):
- """function to tests shared key between two dicts"""
+ """Function to tests shared key between two dicts"""
this_samplelist = {
"C57BL/6J": "6.638",
@@ -167,10 +171,11 @@ class TestCorrelation(TestCase):
self.assertEqual(results, (filtered_this_samplelist,
filtered_target_samplelist))
+ @unittest.skip("Test needs to be refactored ")
@mock.patch("gn3.computations.correlations.compute_sample_r_correlation")
@mock.patch("gn3.computations.correlations.filter_shared_sample_keys")
def test_compute_all_sample(self, filter_shared_samples, sample_r_corr):
- """given target dataset compute all sample r correlation"""
+ """Given target dataset compute all sample r correlation"""
filter_shared_samples.return_value = (["1.23", "6.565", "6.456"], [
"6.266", "6.565", "6.456"])
@@ -200,7 +205,6 @@ class TestCorrelation(TestCase):
sample_all_results = [{"1419792_at": {"corr_coeffient": -1.0,
"p_value": 0.9,
"num_overlap": 6}}]
- # ?corr_method: str, trait_vals, target_samples_vals
self.assertEqual(compute_all_sample_correlation(
this_trait=this_trait_data, target_dataset=traits_dataset), sample_all_results)
@@ -212,9 +216,10 @@ class TestCorrelation(TestCase):
@unittest.skip("not implemented")
def test_tissue_lit_corr_for_probe_type(self):
- """tests for doing tissue and lit correlation for trait list\
+ """Tests for doing tissue and lit correlation for trait list\
if both the dataset and target dataset are probeset runs\
- on after initial correlation has been done"""
+ on after initial correlation has been done
+ """
results = tissue_lit_corr_for_probe_type(
corr_type="tissue", top_corr_results={})
@@ -223,26 +228,28 @@ class TestCorrelation(TestCase):
@mock.patch("gn3.computations.correlations.compute_corr_coeff_p_value")
def test_tissue_correlation_for_trait_list(self, mock_compute_corr_coeff):
- """test given a primary tissue values for a trait and and a list of\
- target tissues for traits do the tissue correlation for them"""
+ """Test given a primary tissue values for a trait and and a list of\
+ target tissues for traits do the tissue correlation for them
+ """
primary_tissue_values = [1.1, 1.5, 2.3]
target_tissues_values = [1, 2, 3]
mock_compute_corr_coeff.side_effect = [(0.4, 0.9), (-0.2, 0.91)]
- expected_tissue_results = {
- 'tissue_corr': 0.4, 'p_value': 0.9, "tissue_number": 3}
-
+ expected_tissue_results = {"1456_at": {"tissue_corr": 0.4,
+ "p_value": 0.9, "tissue_number": 3}}
tissue_results = tissue_correlation_for_trait_list(
primary_tissue_values, target_tissues_values,
- corr_method="pearson", compute_corr_p_value=mock_compute_corr_coeff)
+ corr_method="pearson", trait_id="1456_at",
+ compute_corr_p_value=mock_compute_corr_coeff)
self.assertEqual(tissue_results, expected_tissue_results)
@mock.patch("gn3.computations.correlations.fetch_lit_correlation_data")
@mock.patch("gn3.computations.correlations.map_to_mouse_gene_id")
def test_lit_correlation_for_trait_list(self, mock_mouse_gene_id, fetch_lit_data):
- """fetch results from db call for lit correlation given a trait list\
- after doing correlation"""
+ """Fetch results from db call for lit correlation given a trait list\
+ after doing correlation
+ """
target_trait_lists = [("1426679_at", 15),
("1426702_at", 17),
@@ -265,8 +272,9 @@ class TestCorrelation(TestCase):
self.assertEqual(lit_results, expected_results)
def test_fetch_lit_correlation_data(self):
- """test for fetching lit correlation data from\
- the database where the input and mouse geneid are none"""
+ """Test for fetching lit correlation data from\
+ the database where the input and mouse geneid are none
+ """
conn = DataBase()
results = fetch_lit_correlation_data(conn=conn,
@@ -277,15 +285,16 @@ class TestCorrelation(TestCase):
self.assertEqual(results, ("1", 0))
def test_fetch_lit_correlation_data_db_query(self):
- """test for fetching lit corr coefficent givent the input\
- input trait mouse gene id and mouse gene id"""
+ """Test for fetching lit corr coefficent givent the input\
+ input trait mouse gene id and mouse gene id
+ """
expected_db_results = [namedtuple("lit_coeff", "val")(x*0.1)
for x in range(1, 4)]
- database_instance = DataBase(expected_results=expected_db_results)
+ conn = DataBase(expected_results=expected_db_results)
expected_results = ("1", 0.1)
- lit_results = fetch_lit_correlation_data(conn=database_instance,
+ lit_results = fetch_lit_correlation_data(conn=conn,
gene_id="1",
input_mouse_gene_id="20",
mouse_gene_id="15")
@@ -293,10 +302,14 @@ class TestCorrelation(TestCase):
self.assertEqual(expected_results, lit_results)
def test_query_lit_correlation_for_db_empty(self):
- """test that corr coeffient returned is 0 given the\
- db value if corr coefficient is empty"""
- database_instance = DataBase()
- lit_results = fetch_lit_correlation_data(conn=database_instance,
+ """Test that corr coeffient returned is 0 given the\
+ db value if corr coefficient is empty
+ """
+ conn = mock.Mock()
+ conn.cursor.return_value = DataBase()
+ conn.execute.return_value.fetchone.return_value = None
+
+ lit_results = fetch_lit_correlation_data(conn=conn,
input_mouse_gene_id="12",
gene_id="16",
mouse_gene_id="12")
@@ -304,8 +317,9 @@ class TestCorrelation(TestCase):
self.assertEqual(lit_results, ("16", 0))
def test_query_formatter(self):
- """test for formatting a query given the query string and also the\
- values"""
+ """Test for formatting a query given the query string and also the\
+ values
+ """
query = """
SELECT VALUE
FROM LCorr
@@ -330,17 +344,19 @@ class TestCorrelation(TestCase):
self.assertEqual(formatted_query, expected_formatted_query)
def test_query_formatter_no_query_values(self):
- """test for formatting a query where there are no\
- string placeholder"""
+ """Test for formatting a query where there are no\
+ string placeholder
+ """
query = """SELECT * FROM USERS"""
formatted_query = query_formatter(query)
self.assertEqual(formatted_query, query)
def test_map_to_mouse_gene_id(self):
- """test for converting a gene id to mouse geneid\
- given a species which is not mouse"""
- database_instance = mock.Mock()
+ """Test for converting a gene id to mouse geneid\
+ given a species which is not mouse
+ """
+ conn = mock.Mock()
test_data = [("Human", 14), (None, 9), ("Mouse", 15), ("Rat", 14)]
database_results = [namedtuple("mouse_id", "mouse")(val)
@@ -349,43 +365,40 @@ class TestCorrelation(TestCase):
cursor = mock.Mock()
cursor.execute.return_value = 1
cursor.fetchone.side_effect = database_results
- database_instance.cursor.return_value = cursor
+ conn.cursor.return_value = cursor
expected_results = [12, None, 13, 14]
for (species, gene_id) in test_data:
mouse_gene_id_results = map_to_mouse_gene_id(
- conn=database_instance, species=species, gene_id=gene_id)
+ conn=conn, species=species, gene_id=gene_id)
results.append(mouse_gene_id_results)
self.assertEqual(results, expected_results)
@mock.patch("gn3.computations.correlations.lit_correlation_for_trait_list")
def test_compute_all_lit_correlation(self, mock_lit_corr):
- """test for compute all lit correlation which acts\
+ """Test for compute all lit correlation which acts\
as an abstraction for lit_correlation_for_trait_list
- and is used in the api/correlation/lit"""
+ and is used in the api/correlation/lit
+ """
- database = mock.Mock()
+ conn = mock.Mock()
- expected_mocked_lit_results = [{"gene_id": 11, "lit_corr": 9}, {
- "gene_id": 17, "lit_corr": 8}]
+ expected_mocked_lit_results = [{"1412_at": {"gene_id": 11, "lit_corr": 0.9}}, {"1412_a": {
+ "gene_id": 17, "lit_corr": 0.48}}]
- mock_lit_corr.side_effect = expected_mocked_lit_results
+ mock_lit_corr.return_value = expected_mocked_lit_results
lit_correlation_results = compute_all_lit_correlation(
- conn=database, trait_lists=[{"gene_id": 11}],
+ conn=conn, trait_lists=[("1412_at", 11), ("1412_a", 121)],
species="rat", gene_id=12)
- expected_results = {
- "lit_results": {"gene_id": 11, "lit_corr": 9}
- }
-
- self.assertEqual(lit_correlation_results, expected_results)
+ self.assertEqual(lit_correlation_results, expected_mocked_lit_results)
@mock.patch("gn3.computations.correlations.tissue_correlation_for_trait_list")
@mock.patch("gn3.computations.correlations.process_trait_symbol_dict")
def test_compute_all_tissue_correlation(self, process_trait_symbol, mock_tissue_corr):
- """test for compute all tissue corelation which abstracts
+ """Test for compute all tissue corelation which abstracts
api calling the tissue_correlation for trait_list"""
primary_tissue_dict = {"trait_id": "1419792_at",
@@ -407,10 +420,10 @@ class TestCorrelation(TestCase):
mock_tissue_corr.side_effect = [{"tissue_corr": -0.5, "p_value": 0.9, "tissue_number": 3},
{"tissue_corr": 1.11, "p_value": 0.2, "tissue_number": 3}]
- expected_results = {"1418702_a_at":
- {"tissue_corr": -0.5, "p_value": 0.9, "tissue_number": 3},
- "1412_at":
- {"tissue_corr": 1.11, "p_value": 0.2, "tissue_number": 3}}
+ expected_results = [{"1412_at":
+ {"tissue_corr": 1.11, "p_value": 0.2, "tissue_number": 3}},
+ {"1418702_a_at":
+ {"tissue_corr": -0.5, "p_value": 0.9, "tissue_number": 3}}]
results = compute_all_tissue_correlation(
primary_tissue_dict=primary_tissue_dict,