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-rw-r--r--gn3/computations/partial_correlations.py32
1 files changed, 23 insertions, 9 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index 0d4394b..e6056d5 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -9,6 +9,7 @@ import math
from functools import reduce, partial
from typing import Any, Tuple, Union, Sequence
+import numpy
import pandas
import pingouin
from scipy.stats import pearsonr, spearmanr
@@ -538,6 +539,11 @@ def trait_for_output(trait):
`None`, because it is a waste of network resources to transmit the key-value
pair just to indicate it does not exist.
"""
+ def __nan_to_none__(val):
+ if math.isnan(val) or numpy.isnan(val):
+ return None
+ return val
+
trait = {
"trait_type": trait["db"]["dataset_type"],
"dataset_name": trait["db"]["dataset_name"],
@@ -562,19 +568,27 @@ def trait_for_output(trait):
"geneid": trait.get("geneid"),
"homologeneid": trait.get("homologeneid"),
"noverlap": trait.get("noverlap"),
- "partial_corr": trait.get("partial_corr"),
- "partial_corr_p_value": trait.get("partial_corr_p_value"),
- "corr": trait.get("corr"),
- "corr_p_value": trait.get("corr_p_value"),
- "rank_order": trait.get("rank_order"),
+ "partial_corr": __nan_to_none__(trait.get("partial_corr")),
+ "partial_corr_p_value": __nan_to_none__(
+ trait.get("partial_corr_p_value")),
+ "corr": __nan_to_none__(trait.get("corr")),
+ "corr_p_value": __nan_to_none__(trait.get("corr_p_value")),
+ "rank_order": __nan_to_none__(trait.get("rank_order")),
"delta": (
None if trait.get("partial_corr") is None
else (trait.get("partial_corr") - trait.get("corr"))),
- "l_corr": trait.get("l_corr"),
- "tissue_corr": trait.get("tissue_corr"),
- "tissue_p_value": trait.get("tissue_p_value")
+ "l_corr": __nan_to_none__(trait.get("l_corr")),
+ "tissue_corr": __nan_to_none__(trait.get("tissue_corr")),
+ "tissue_p_value": __nan_to_none__(trait.get("tissue_p_value"))
}
- return {key: val for key, val in trait.items() if val is not None}
+ return {
+ key: val
+ for key, val in trait.items()
+ if (
+ val is not None
+ or key in (
+ "partial_corr_p_value", "corr", "corr_p_value", "rank_order",
+ "delta", "l_corr", "tissue_corr", "tissue_p_value"))}
def partial_correlations_entry(# pylint: disable=[R0913, R0914, R0911]
conn: Any, primary_trait_name: str,