From 0bb51bd78479c05839d7b7f9f878db4b5616cfda Mon Sep 17 00:00:00 2001
From: Frederick Muriuki Muriithi
Date: Fri, 29 Oct 2021 06:34:19 +0300
Subject: Implement `tissue_correlation` function

Issue:
https://github.com/genenetwork/gn-gemtext-threads/blob/main/topics/gn1-migration-to-gn2/partial-correlations.gmi

* gn3/computations/partial_correlations.py: New function (tissue_correlation)
* tests/unit/test_partial_correlations.py ->
  tests/unit/computations/test_partial_correlations.py: Move module. Implement
  tests for new function

  Migrate the `cal_tissue_corr` function embedded in the
  `web.webqtl.correlation.correlationFunction.batchCalTissueCorr` function in
  GN1 and implement tests to ensure it works correctly.
---
 gn3/computations/partial_correlations.py | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)

(limited to 'gn3')

diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index b3de31c..e73edfd 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -7,6 +7,7 @@ GeneNetwork1.
 
 from functools import reduce
 from typing import Any, Tuple, Sequence
+from scipy.stats import pearsonr, spearmanr
 
 def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]):
     """
@@ -123,6 +124,32 @@ def find_identical_traits(
                  if len(item[1]) > 1),
                 tuple()))
 
+def tissue_correlation(
+        primary_trait_values: Tuple[float, ...],
+        target_trait_values: Tuple[float, ...],
+        method: str) -> Tuple[float, float]:
+    """
+    Compute the correlation between the primary trait values, and the values of
+    a single target value.
+
+    This migrates the `cal_tissue_corr` function embedded in the larger
+    `web.webqtl.correlation.correlationFunction.batchCalTissueCorr` function in
+    GeneNetwork1.
+    """
+    def spearman_corr(*args):
+        result = spearmanr(*args)
+        return (result.correlation, result.pvalue)
+
+    method_fns = {"pearson": pearsonr, "spearman": spearman_corr}
+
+    assert len(primary_trait_values) == len(target_trait_values), (
+        "The lengths of the `primary_trait_values` and `target_trait_values` "
+        "must be equal")
+    assert method in method_fns.keys(), (
+        "Method must be one of: {}".format(",".join(method_fns.keys())))
+
+    return method_fns[method](primary_trait_values, target_trait_values)
+
 def batch_computed_tissue_correlation(
         trait_value: str, symbol_value_dict: dict,
         method: str = "pearson") -> Tuple[dict, dict]:
-- 
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