Merge branch 'main' into feature/add_rqtl_pairscan

6 files changed, 561 insertions, 48 deletions

diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_mat_blackbox_test.csv
@@ -0,0 +1,101 @@
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diff --git a/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
new file mode 100644
index 0000000..a1558a0
--- /dev/null
+++ b/tests/unit/computations/partial_correlations_test_data/pcor_rec_blackbox_test.csv
@@ -0,0 +1,101 @@
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+"pcor.mat",12,"73.2413066085428, -14.7738272324204, 50.0003031920642, 45.0548450928181, -48.7516783643514, -23.1134415604174, -87.6020579133183, 30.5087967775762, 82.4424541555345, 72.7492412086576, 32.3323079384863, 85.7122719287872","-9.44385454058647, -7.42488116957247, 3.06755122728646, 35.4948386084288, 69.3501094356179, -19.1765882074833, 95.9793315734714, 29.2394527699798, 53.3461356069893, 11.7002569604665, -71.7540245968848, 20.5835649278015","66.2635098677129, 48.2205909211189, 81.5142437815666, -31.1989811249077, 5.75581910088658, 93.6992627568543, 72.9302003979683, 30.9292090125382, -55.1979901269078, -78.7769370246679, 14.8034851066768, 37.4370891135186","p",TRUE,-0.48727199671024
+"pcor.mat",10,"52.8255486860871, 34.2574729584157, 44.7404673323035, 52.2620148025453, -69.898310629651, -85.9551533591002, -52.2192012518644, -56.3183640595526, -19.7211066260934, -6.08869907446206","-67.6190298050642, 75.8668028283864, 74.6253774967045, -66.1169764585793, -81.1506592668593, -33.249074826017, -21.2896263692528, -97.763530863449, -54.6630097553134, -96.6868148185313","-35.384417232126, 13.2294847629964, 22.9882229119539, -58.4000170230865, 88.3519669994712, 59.8886359948665, -30.7637225370854, 40.6464832834899, -62.5224160030484, -0.506686419248581","p",TRUE,0.492210961830229
+"pcor.mat",8,"18.1551295798272, -8.32464881241322, -99.8132115229964, 18.6201244592667, -53.1589215621352, 70.2180631458759, -36.3900125026703, 92.1965328045189","6.02451944723725, -68.5814322903752, 70.58563423343, 1.00183109752834, 16.1975951399654, 64.5838780794293, -84.6291496884078, -54.131712205708","80.0181794445962, -12.9483319818974, -3.88606782071292, -48.0255630798638, -3.62709653563797, 31.62224679254, 57.1325340308249, -93.3892488945276","p",TRUE,-0.141482085540883
+"pcor.mat",17,"90.1813358999789, -3.33601352758706, -70.5867488868535, -40.8890372607857, 58.0897845327854, 8.4102772641927, 31.2019024044275, 67.8843731526285, -5.85478777065873, 93.743189284578, -21.4788604527712, 48.0386326089501, -42.5454632379115, -78.037649858743, 5.827397108078, -59.5929707866162, 22.9059528559446","46.3524929713458, -42.5642458721995, -69.567626202479, 91.2107714917511, 80.4405058268458, -83.3648740779608, -5.59279890730977, 67.8364232182503, 9.23628495074809, -45.9923652466387, -2.29323050007224, 92.1013004146516, 34.326039114967, -10.8744602650404, 89.988622488454, -23.287376947701, 72.6627949625254","48.8092821557075, -84.7605162765831, -68.7700709793717, -67.7357694599777, 25.1485624350607, 61.709990631789, 29.2394532822073, 47.8424397762865, 37.0008739177138, -47.5637663155794, -14.6964757237583, -69.9305152054876, -54.4029568322003, 80.0897337496281, 9.39622772857547, -1.27441040240228, 74.3666623719037","s",TRUE,0.270551782409472
+"pcor.mat",13,"-68.030820786953, 48.6679933033884, 51.8114904407412, 78.5725246183574, 18.9902665093541, 25.0479029957205, 56.8353782407939, -4.79650879278779, -87.2707166243345, -64.176924712956, -56.511168833822, 41.7948929592967, 79.8323729075491","87.0083963032812, 12.2851114254445, -48.7783022690564, 2.98262075521052, 61.5149905905128, 72.0769232138991, -33.8758388534188, 19.778781151399, -12.315073935315, -95.3089885413647, -40.8825332764536, -50.1593578606844, -29.2145641520619","82.5196304824203, 86.4081613719463, -63.7102510314435, 82.9122426919639, 86.7011873517185, 1.18320896290243, -7.11194663308561, 31.899410719052, -69.8009483516216, -57.3877718299627, 2.83222463913262, -17.2023222781718, -38.1276280619204","p",FALSE,-0.215982700387935
+"pcor.mat",12,"-36.3307877443731, 74.7712590266019, -79.8354192636907, 93.8916243612766, -50.0289557501674, -54.7662570606917, -38.7290718965232, -84.6648510545492, 71.2978508323431, 88.4917345829308, 32.1320930030197, 76.4375317841768","2.62319510802627, -97.154287295416, 21.7567156534642, 95.8672656677663, -0.763413123786449, -9.49476859532297, 83.9058271143585, -38.3374994620681, -16.9302582275122, -85.5358060449362, -83.2731044851243, -18.7622617464513","-76.2503104750067, 36.5515212062746, 48.5381714068353, 72.03823258169, 36.652302602306, 29.980877507478, 21.0754222236574, -96.8017131090164, -66.5918422862887, -10.5546385981143, 91.4459123276174, -84.6182245295495","k",TRUE,-0.161695275103975
+"pcor.mat",13,"65.4542396776378, 2.08085202611983, -88.9037624932826, 52.2526708897203, -38.4136536624283, -13.373964605853, -48.226101975888, 93.4839099645615, 39.563751546666, 69.0497747156769, -94.8003361001611, 24.9876524321735, 25.2306204754859","82.8150092158467, -9.80691406875849, -84.2437265440822, -48.5185658093542, 93.8153511844575, 76.507264515385, 96.1720596533269, 91.2560781463981, -86.2542728893459, -47.9744947515428, 25.5869822110981, -15.71259717457, 99.3623040616512","64.0070253051817, -90.157330268994, 78.4984151832759, 36.6091389209032, -85.7790939509869, -43.4162854682654, -81.61596711725, -7.2117717936635, 69.8510519228876, 87.7512107603252, 60.0242395885289, -77.6109307538718, 33.9131449349225","s",FALSE,0.0846468464958684
+"pcor.mat",14,"6.42584343440831, 89.4456365611404, -94.9723384808749, -4.63838037103415, 82.1529622189701, -72.2434451803565, 21.8717840965837, 13.9814134221524, -70.8967028185725, 99.243081593886, -67.1596728730947, -69.3361242301762, -52.0885898265988, 54.4663844164461","-6.86167716048658, 63.0930243059993, 62.3814635910094, 42.8769947495311, 12.4187188688666, -55.67507436499, 68.4297569096088, 57.5610874220729, -4.82598571106791, -79.5595136005431, -55.5029585491866, -94.9160863645375, 82.0372765418142, -52.3549461271614","-96.7493511270732, 16.427826648578, 26.2085369322449, 89.924060087651, 64.0857739374042, 65.0612049736083, -50.4840951412916, -27.8882106766105, -37.310868408531, -41.0194541793317, -37.8566451836377, -97.9508240241557, -74.5396174024791, 76.8885430879891","p",FALSE,0.0312670434242096
+"pcor.mat",12,"34.8341395147145, 43.1037290487438, 82.2314764373004, -63.9271955937147, 70.3003748320043, 51.0297972708941, 95.4673350322992, 74.5106549467891, -71.5771752875298, 32.5960139743984, 85.8803272712976, -12.2395237442106","-27.241831831634, -76.5246210154146, 86.3751742523164, 74.9675445724279, 63.8482879847288, 14.7356034722179, -30.9328155592084, 73.2200371101499, 26.5261144377291, 42.3744561616331, -80.7972604874521, 95.1648845802993","30.9194989036769, -29.2449895292521, -75.1953874249011, -97.2041368950158, -63.0337142385542, -96.9185112509876, -72.140100877732, 50.4067888017744, 80.1865959074348, 69.9119384400547, 28.0939280521125, -78.1314740888774","p",FALSE,-0.344081701794653
+"pcor.mat",17,"-32.3364136740565, -74.5505046565086, 64.3489994108677, 95.3302425798029, -47.4365599453449, -99.5640191715211, -81.8646350875497, -10.4291386436671, -46.2970128748566, -66.2438121624291, 3.38349812664092, 46.3537188712507, 50.3833524882793, 76.8161817919463, -35.9225623309612, -30.2367287687957, -15.6686681322753","-85.6091414112598, -74.5855379849672, 31.7983427084982, 12.1914628893137, -76.0027903132141, -25.2544173505157, -53.2312866300344, -66.4824201725423, -35.0571169052273, 25.0753607135266, 57.0668096188456, 97.4866581615061, -34.1166456695646, 70.7655222155154, -25.6891251541674, -99.2252895608544, 30.7619682978839","44.1535466350615, 57.7384070493281, -42.7488908171654, -81.2754278071225, 97.9185460601002, 35.2168054319918, -26.9714017864317, -93.0728284176439, -60.7041460927576, -99.858339689672, -53.829790558666, 85.15021414496, -98.6793769989163, -86.0895386897027, 51.4472865033895, -15.630559111014, -28.9994670078158","k",TRUE,0.261679778734634
+"pcor.mat",10,"89.7360242903233, 47.9975900612772, 36.5392371080816, -51.0348361451179, 7.82818463630974, -6.9301129784435, -75.5731589626521, 47.0917036756873, -58.8109106291085, 33.4785438142717","54.0308193303645, 41.3668432738632, 98.2857145369053, -14.9101806804538, 30.7341996114701, 92.3570320941508, -35.8356348704547, -91.0546428058296, 77.7767921797931, 13.7820704840124","-65.3565419837832, -24.2730437777936, 13.4862332604825, -97.8464429732412, 91.0171907860786, -52.4954450316727, -31.7320866975933, 33.8117491919547, 49.1156910546124, -42.7486607804894","p",FALSE,0.109506018207148
diff --git a/tests/unit/computations/test_biweight.py b/tests/unit/computations/test_biweight.py
deleted file mode 100644
index ad404f1..0000000
--- a/tests/unit/computations/test_biweight.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""test for biweight script"""
-from unittest import TestCase
-from unittest import mock
-
-from gn3.computations.biweight import calculate_biweight_corr
-
-
-class TestBiweight(TestCase):
-    """test class for biweight"""
-
-    @mock.patch("gn3.computations.biweight.subprocess.check_output")
-    def test_calculate_biweight_corr(self, mock_check_output):
-        """test for calculate_biweight_corr func"""
-        mock_check_output.return_value = "0.1 0.5"
-        results = calculate_biweight_corr(command="Rscript",
-                                          path_to_script="./r_script.R",
-                                          trait_vals=[
-                                              1.2, 1.1, 1.9],
-                                          target_vals=[1.9, 0.4, 1.1])
-
-        self.assertEqual(results, (0.1, 0.5))
diff --git a/tests/unit/computations/test_correlation.py b/tests/unit/computations/test_correlation.py
index fc52ec1..706520a 100644
--- a/tests/unit/computations/test_correlation.py
+++ b/tests/unit/computations/test_correlation.py
@@ -1,14 +1,15 @@
 """Module contains the tests for correlation"""
 from unittest import TestCase
 from unittest import mock
+import unittest
 
 from collections import namedtuple
 
 from gn3.computations.correlations import normalize_values
-from gn3.computations.correlations import do_bicor
 from gn3.computations.correlations import compute_sample_r_correlation
 from gn3.computations.correlations import compute_all_sample_correlation
 from gn3.computations.correlations import filter_shared_sample_keys
+
 from gn3.computations.correlations import tissue_correlation_for_trait
 from gn3.computations.correlations import lit_correlation_for_trait
 from gn3.computations.correlations import fetch_lit_correlation_data
@@ -94,20 +95,11 @@ class TestCorrelation(TestCase):
         results = normalize_values([2.3, None, None, 3.2, 4.1, 5],
                                    [3.4, 7.2, 1.3, None, 6.2, 4.1])
 
-        expected_results = ([2.3, 4.1, 5], [3.4, 6.2, 4.1], 3)
-
-        self.assertEqual(results, expected_results)
-
-    @mock.patch("gn3.computations.correlations.calculate_biweight_corr")
-    def test_bicor(self, mock_biweight):
-        """Test for doing biweight mid correlation """
-        mock_biweight.return_value = (1.0, 0.0)
-
-        results = do_bicor(x_val=[1, 2, 3], y_val=[4, 5, 6])
+        expected_results = [(2.3, 4.1, 5), (3.4, 6.2, 4.1)]
 
-        self.assertEqual(results, (1.0, 0.0)
-                         )
+        self.assertEqual(list(zip(*list(results))), expected_results)
 
+    @unittest.skip("reason for skipping")
     @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):
@@ -163,22 +155,23 @@ class TestCorrelation(TestCase):
 
         }
 
-        filtered_target_samplelist = ["1.23", "6.565", "6.456"]
-        filtered_this_samplelist = ["6.266", "6.565", "6.456"]
+        filtered_target_samplelist = ("1.23", "6.565", "6.456")
+        filtered_this_samplelist = ("6.266", "6.565", "6.456")
 
         results = filter_shared_sample_keys(
             this_samplelist=this_samplelist, target_samplelist=target_samplelist)
 
-        self.assertEqual(results, (filtered_this_samplelist,
-                                   filtered_target_samplelist))
+        self.assertEqual(list(zip(*list(results))), [filtered_this_samplelist,
+                                                     filtered_target_samplelist])
 
     @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"""
 
-        filter_shared_samples.return_value = (["1.23", "6.565", "6.456"], [
-            "6.266", "6.565", "6.456"])
+        filter_shared_samples.return_value = [iter(val) for val in [(
+            "1.23", "6.266"), ("6.565", "6.565"), ("6.456", "6.456")]]
+
         sample_r_corr.return_value = (["1419792_at", -1.0, 0.9, 6])
 
         this_trait_data = {
@@ -210,10 +203,8 @@ class TestCorrelation(TestCase):
             this_trait=this_trait_data, target_dataset=traits_dataset), sample_all_results)
         sample_r_corr.assert_called_once_with(
             trait_name='1419792_at',
-            corr_method="pearson", trait_vals=['1.23', '6.565', '6.456'],
-            target_samples_vals=['6.266', '6.565', '6.456'])
-        filter_shared_samples.assert_called_once_with(
-            this_trait_data.get("trait_sample_data"), traits_dataset[0].get("trait_sample_data"))
+            corr_method="pearson", trait_vals=('1.23', '6.565', '6.456'),
+            target_samples_vals=('6.266', '6.565', '6.456'))
 
     @mock.patch("gn3.computations.correlations.compute_corr_coeff_p_value")
     def test_tissue_correlation_for_trait(self, mock_compute_corr_coeff):
diff --git a/tests/unit/computations/test_partial_correlations.py b/tests/unit/computations/test_partial_correlations.py
new file mode 100644
index 0000000..83cb9d9
--- /dev/null
+++ b/tests/unit/computations/test_partial_correlations.py
@@ -0,0 +1,335 @@
+"""Module contains tests for gn3.partial_correlations"""
+
+import csv
+from unittest import TestCase, skip
+from gn3.computations.partial_correlations import (
+    fix_samples,
+    control_samples,
+    dictify_by_samples,
+    tissue_correlation,
+    find_identical_traits,
+    partial_correlation_matrix,
+    good_dataset_samples_indexes,
+    partial_correlation_recursive)
+
+sampleslist = ["B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"]
+control_traits = (
+    {
+        "mysqlid": 36688172,
+        "data": {
+            "B6cC3-1": {
+                "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+                "ndata": None},
+            "BXD1": {
+                "sample_name": "BXD1", "value": 7.77141, "variance": None,
+                "ndata": None},
+            "BXD12": {
+                "sample_name": "BXD12", "value": 8.39265, "variance": None,
+                "ndata": None},
+            "BXD16": {
+                "sample_name": "BXD16", "value": 8.17443, "variance": None,
+                "ndata": None},
+            "BXD19": {
+                "sample_name": "BXD19", "value": 8.30401, "variance": None,
+                "ndata": None},
+            "BXD2": {
+                "sample_name": "BXD2", "value": 7.80944, "variance": None,
+                "ndata": None}}},
+    {
+        "mysqlid": 36688172,
+        "data": {
+            "B6cC3-21": {
+                "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+                "ndata": None},
+            "BXD21": {
+                "sample_name": "BXD1", "value": 7.77141, "variance": None,
+                "ndata": None},
+            "BXD12": {
+                "sample_name": "BXD12", "value": 8.39265, "variance": None,
+                "ndata": None},
+            "BXD16": {
+                "sample_name": "BXD16", "value": 8.17443, "variance": None,
+                "ndata": None},
+            "BXD19": {
+                "sample_name": "BXD19", "value": 8.30401, "variance": None,
+                "ndata": None},
+            "BXD2": {
+                "sample_name": "BXD2", "value": 7.80944, "variance": None,
+                "ndata": None}}},
+    {
+        "mysqlid": 36688172,
+        "data": {
+            "B6cC3-1": {
+                "sample_name": "B6cC3-1", "value": 7.51879, "variance": None,
+                "ndata": None},
+            "BXD1": {
+                "sample_name": "BXD1", "value": 7.77141, "variance": None,
+                "ndata": None},
+            "BXD12": {
+                "sample_name": "BXD12", "value": None, "variance": None,
+                "ndata": None},
+            "BXD16": {
+                "sample_name": "BXD16", "value": None, "variance": None,
+                "ndata": None},
+            "BXD19": {
+                "sample_name": "BXD19", "value": None, "variance": None,
+                "ndata": None},
+            "BXD2": {
+                "sample_name": "BXD2", "value": 7.80944, "variance": None,
+                "ndata": None}}})
+
+dictified_control_samples = (
+    {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
+     "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
+     "BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
+     "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
+     "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
+     "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
+    {"BXD12": {"sample_name": "BXD12", "value": 8.39265, "variance": None},
+     "BXD16": {"sample_name": "BXD16", "value": 8.17443, "variance": None},
+     "BXD19": {"sample_name": "BXD19", "value": 8.30401, "variance": None},
+     "BXD2": {"sample_name": "BXD2", "value": 7.80944, "variance": None}},
+    {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879, "variance": None},
+     "BXD1": {"sample_name": "BXD1", "value": 7.77141, "variance": None},
+     "BXD2": {"sample_name": "BXD2", "value":  7.80944, "variance": None}})
+
+def parse_test_data_csv(filename):
+    """
+    Parse test data csv files for R -> Python conversion of some functions.
+    """
+    def __str__to_tuple(line, field):
+        return tuple(float(s.strip()) for s in line[field].split(","))
+
+    with open(filename, newline="\n") as csvfile:
+        reader = csv.DictReader(csvfile, delimiter=",", quotechar='"')
+        lines = tuple(row for row in reader)
+
+    methods = {"p": "pearson", "s": "spearman", "k": "kendall"}
+    return tuple({
+        **line,
+        "x": __str__to_tuple(line, "x"),
+        "y": __str__to_tuple(line, "y"),
+        "z": __str__to_tuple(line, "z"),
+        "method": methods[line["method"]],
+        "rm": line["rm"] == "TRUE",
+        "result": float(line["result"])
+    } for line in lines)
+
+
+class TestPartialCorrelations(TestCase):
+    """Class for testing partial correlations computation functions"""
+
+    def test_control_samples(self):
+        """Test that the control_samples works as expected."""
+        self.assertEqual(
+            control_samples(control_traits, sampleslist),
+            ((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
+              ("BXD12", "BXD16", "BXD19", "BXD2"),
+              ("B6cC3-1", "BXD1", "BXD2")),
+             ((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
+              (8.39265, 8.17443, 8.30401, 7.80944),
+              (7.51879, 7.77141, 7.80944)),
+             ((None, None, None, None, None, None), (None, None, None, None),
+              (None, None, None)),
+             (6, 4, 3)))
+
+    def test_dictify_by_samples(self):
+        """
+        Test that `dictify_by_samples` generates the appropriate dict
+
+        Given:
+            a sequence of sequences with sample names, values and variances, as
+            in the output of `gn3.partial_correlations.control_samples` or
+            the output of `gn3.db.traits.export_informative`
+        When:
+            the sequence is passed as an argument into the
+            `gn3.partial_correlations.dictify_by_sample`
+        Then:
+            return a sequence of dicts with keys being the values of the sample
+            names, and each of who's values being sub-dicts with the keys
+            'sample_name', 'value' and 'variance' whose values correspond to the
+            values passed in.
+        """
+        self.assertEqual(
+            dictify_by_samples(
+                ((("B6cC3-1", "BXD1", "BXD12", "BXD16", "BXD19", "BXD2"),
+                  ("BXD12", "BXD16", "BXD19", "BXD2"),
+                  ("B6cC3-1", "BXD1", "BXD2")),
+                 ((7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944),
+                  (8.39265, 8.17443, 8.30401, 7.80944),
+                  (7.51879, 7.77141, 7.80944)),
+                 ((None, None, None, None, None, None), (None, None, None, None),
+                  (None, None, None)),
+                 (6, 4, 3))),
+            dictified_control_samples)
+
+    def test_fix_samples(self):
+        """
+        Test that `fix_samples` returns only the common samples
+
+        Given:
+            - A primary trait
+            - A sequence of control samples
+        When:
+            - The two arguments are passed to `fix_samples`
+        Then:
+            - Only the names of the samples present in the primary trait that
+              are also present in ALL the control traits are present in the
+              return value
+            - Only the values of the samples present in the primary trait that
+              are also present in ALL the control traits are present in the
+              return value
+            - ALL the values for ALL the control traits are present in the
+              return value
+            - Only the variances of the samples present in the primary trait
+              that are also present in ALL the control traits are present in the
+              return value
+            - ALL the variances for ALL the control traits are present in the
+              return value
+            - The return value is a tuple of the above items, in the following
+              order:
+                ((sample_names, ...), (primary_trait_values, ...),
+                 (control_traits_values, ...), (primary_trait_variances, ...)
+                 (control_traits_variances, ...))
+        """
+        self.assertEqual(
+            fix_samples(
+                {"B6cC3-1": {"sample_name": "B6cC3-1", "value": 7.51879,
+                             "variance": None},
+                 "BXD1": {"sample_name": "BXD1", "value": 7.77141,
+                          "variance": None},
+                 "BXD2": {"sample_name": "BXD2", "value":  7.80944,
+                          "variance": None}},
+                dictified_control_samples),
+            (("BXD2",), (7.80944,),
+             (7.51879, 7.77141, 8.39265, 8.17443, 8.30401, 7.80944, 8.39265,
+              8.17443, 8.30401, 7.80944, 7.51879, 7.77141, 7.80944),
+             (None,),
+             (None, None, None, None, None, None, None, None, None, None, None,
+              None, None)))
+
+    def test_find_identical_traits(self):
+        """
+        Test `gn3.partial_correlations.find_identical_traits`.
+
+        Given:
+            - the name of a primary trait
+            - the value of a primary trait
+            - a sequence of names of control traits
+            - a sequence of values of control traits
+        When:
+            - the arguments above are passed to the `find_identical_traits`
+              function
+        Then:
+            - Return ALL trait names that have the same value when up to three
+              decimal places are considered
+        """
+        for primn, primv, contn, contv, expected in (
+                ("pt", 12.98395, ("ct0", "ct1", "ct2"),
+                 (0.1234, 2.3456, 3.4567), tuple()),
+                ("pt", 12.98395, ("ct0", "ct1", "ct2"),
+                 (12.98354, 2.3456, 3.4567), ("pt", "ct0")),
+                ("pt", 12.98395, ("ct0", "ct1", "ct2", "ct3"),
+                 (0.1234, 2.3456, 0.1233, 4.5678), ("ct0", "ct2"))
+        ):
+            with self.subTest(
+                    primary_name=primn, primary_value=primv,
+                    control_names=contn, control_values=contv):
+                self.assertEqual(
+                    find_identical_traits(primn, primv, contn, contv), expected)
+
+    def test_tissue_correlation_error(self):
+        """
+        Test that `tissue_correlation` raises specific exceptions for particular
+        error conditions.
+        """
+        for primary, target, method, error, error_msg in (
+                ((1, 2, 3), (4, 5, 6, 7), "pearson",
+                 AssertionError,
+                 (
+                     "The lengths of the `primary_trait_values` and "
+                     "`target_trait_values` must be equal")),
+                ((1, 2, 3), (4, 5, 6, 7), "spearman",
+                 AssertionError,
+                 (
+                     "The lengths of the `primary_trait_values` and "
+                     "`target_trait_values` must be equal")),
+                ((1, 2, 3, 4), (5, 6, 7), "pearson",
+                 AssertionError,
+                 (
+                     "The lengths of the `primary_trait_values` and "
+                     "`target_trait_values` must be equal")),
+                ((1, 2, 3, 4), (5, 6, 7), "spearman",
+                 AssertionError,
+                 (
+                     "The lengths of the `primary_trait_values` and "
+                     "`target_trait_values` must be equal")),
+                ((1, 2, 3), (4, 5, 6), "nonexistentmethod",
+                 AssertionError,
+                 (
+                     "Method must be one of: pearson, spearman"))):
+            with self.subTest(primary=primary, target=target, method=method):
+                with self.assertRaises(error, msg=error_msg):
+                    tissue_correlation(primary, target, method)
+
+    def test_tissue_correlation(self):
+        """
+        Test that the correct correlation values are computed for the given:
+        - primary trait
+        - target trait
+        - method
+        """
+        for primary, target, method, expected in (
+                ((12.34, 18.36, 42.51), (37.25, 46.25, 46.56), "pearson",
+                 (0.6761779253, 0.5272701134)),
+                ((1, 2, 3, 4, 5), (5, 6, 7, 8, 7), "spearman",
+                 (0.8207826817, 0.0885870053))):
+            with self.subTest(primary=primary, target=target, method=method):
+                self.assertEqual(
+                    tissue_correlation(primary, target, method), expected)
+
+    def test_good_dataset_samples_indexes(self):
+        """
+        Test that `good_dataset_samples_indexes` returns correct indices.
+        """
+        self.assertEqual(
+            good_dataset_samples_indexes(
+                ("a", "e", "i", "k"),
+                ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l")),
+            (0, 4, 8, 10))
+
+    @skip
+    def test_partial_correlation_matrix(self):
+        """
+        Test that `partial_correlation_matrix` computes the appropriate
+        correlation value.
+        """
+        for sample in parse_test_data_csv(
+                ("tests/unit/computations/partial_correlations_test_data/"
+                 "pcor_mat_blackbox_test.csv")):
+            with self.subTest(
+                    xdata=sample["x"], ydata=sample["y"], zdata=sample["z"],
+                    method=sample["method"], omit_nones=sample["rm"]):
+                self.assertEqual(
+                    partial_correlation_matrix(
+                        sample["x"], sample["y"], sample["z"],
+                        method=sample["method"], omit_nones=sample["rm"]),
+                    sample["result"])
+
+    @skip
+    def test_partial_correlation_recursive(self):
+        """
+        Test that `partial_correlation_recursive` computes the appropriate
+        correlation value.
+        """
+        for sample in parse_test_data_csv(
+                ("tests/unit/computations/partial_correlations_test_data/"
+                 "pcor_rec_blackbox_test.csv")):
+            with self.subTest(
+                    xdata=sample["x"], ydata=sample["y"], zdata=sample["z"],
+                    method=sample["method"], omit_nones=sample["rm"]):
+                self.assertEqual(
+                    partial_correlation_recursive(
+                        sample["x"], sample["y"], sample["z"],
+                        method=sample["method"], omit_nones=sample["rm"]),
+                    sample["result"])
diff --git a/tests/unit/computations/test_wgcna.py b/tests/unit/computations/test_wgcna.py
index ec81d94..5f23a86 100644
--- a/tests/unit/computations/test_wgcna.py
+++ b/tests/unit/computations/test_wgcna.py
@@ -10,13 +10,16 @@ from gn3.computations.wgcna import call_wgcna_script
 class TestWgcna(TestCase):
     """test class for wgcna"""
 
+    @mock.patch("gn3.computations.wgcna.process_image")
     @mock.patch("gn3.computations.wgcna.run_cmd")
     @mock.patch("gn3.computations.wgcna.compose_wgcna_cmd")
     @mock.patch("gn3.computations.wgcna.dump_wgcna_data")
     def test_call_wgcna_script(self,
                                mock_dumping_data,
                                mock_compose_wgcna,
-                               mock_run_cmd):
+                               mock_run_cmd,
+                               mock_img,
+                               ):
         """test for calling wgcna script"""
 
         # pylint: disable = line-too-long
@@ -50,7 +53,7 @@ class TestWgcna(TestCase):
             "output": "Flagging genes and samples with too many missing values...\n  ..step 1\nAllowing parallel execution with up to 3 working processes.\npickSoftThreshold: will use block size 7.\n pickSoftThreshold: calculating connectivity for given powers...\n   ..working on genes 1 through 7 of 7\n   Flagging genes and samples with too many missing values...\n    ..step 1\n ..Working on block 1 .\n    TOM calculation: adjacency..\n    ..will not use multithreading.\nclustering..\n ....detecting modules..\n ....calculating module eigengenes..\n ....checking kME in modules..\n ..merging modules that are too close..\n     mergeCloseModules: Merging modules whose distance is less than 0.15\n     mergeCloseModules: less than two proper modules.\n      ..color levels are turquoise\n      ..there is nothing to merge.\n       Calculating new MEs...\n"
         }
 
-        json_output = "{\"inputdata\":{\"trait_sample_data \":{},\"minModuleSize\":30,\"TOMtype\":\"unsigned\"},\"outputdata\":{\"eigengenes\":[],\"colors\":[]}}"
+        json_output = "{\"inputdata\":{\"trait_sample_data \":{},\"minModuleSize\":30,\"TOMtype\":\"unsigned\"},\"output\":{\"eigengenes\":[],\"imageLoc\":[],\"colors\":[]}}"
 
         expected_output = {
 
@@ -61,9 +64,11 @@ class TestWgcna(TestCase):
                     "TOMtype": "unsigned"
                 },
 
-                "outputdata": {
+                "output": {
                     "eigengenes": [],
-                    "colors": []
+                    "imageLoc": [],
+                    "colors": [],
+                    "image_data": "AFDSFNBSDGJJHH"
                 }
             },
 
@@ -74,6 +79,7 @@ class TestWgcna(TestCase):
         with mock.patch("builtins.open", mock.mock_open(read_data=json_output)):
 
             mock_run_cmd.return_value = mock_run_cmd_results
+            mock_img.return_value = b"AFDSFNBSDGJJHH"
 
             results = call_wgcna_script(
                 "Rscript/GUIX_PATH/scripts/r_file.R", request_data)