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-rw-r--r--gn3/computations/partial_correlations.py41
1 files changed, 34 insertions, 7 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py
index ffdf0c5..bd127a7 100644
--- a/gn3/computations/partial_correlations.py
+++ b/gn3/computations/partial_correlations.py
@@ -5,13 +5,14 @@ It is an attempt to migrate over the partial correlations feature from
GeneNetwork1.
"""
+import math
from functools import reduce
-from typing import Any, Tuple, Sequence
+from typing import Any, Tuple, Union, Sequence
from scipy.stats import pearsonr, spearmanr
-from gn3.settings import TEXTDIR
import pandas
+from gn3.settings import TEXTDIR, ROUND_TO
from gn3.data_helpers import parse_csv_line
def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]):
@@ -276,8 +277,8 @@ def build_data_frame(
def partial_correlation_matrix(
xdata: Tuple[float, ...], ydata: Tuple[float, ...],
- zdata: Tuple[float, ...], method: str = "pearsons",
- omit_nones: bool = True) -> float:
+ zdata: Union[Tuple[float, ...], Tuple[Tuple[float, ...], ...]],
+ method: str = "pearson", omit_nones: bool = True) -> float:
"""
Computes the partial correlation coefficient using the
'variance-covariance matrix' method
@@ -291,8 +292,8 @@ def partial_correlation_matrix(
def partial_correlation_recursive(
xdata: Tuple[float, ...], ydata: Tuple[float, ...],
- zdata: Tuple[float, ...], method: str = "pearsons",
- omit_nones: bool = True) -> float:
+ zdata: Union[Tuple[float, ...], Tuple[Tuple[float, ...], ...]],
+ method: str = "pearson", omit_nones: bool = True) -> float:
"""
Computes the partial correlation coefficient using the 'recursive formula'
method
@@ -302,4 +303,30 @@ def partial_correlation_recursive(
GeneNetwork1, specifically the `pcor.rec` function written in the R
programming language.
"""
- return 0
+ assert method in ("pearson", "spearman", "kendall")
+ data = (
+ build_data_frame(xdata, ydata, zdata).dropna(axis=0)
+ if omit_nones else
+ build_data_frame(xdata, ydata, zdata))
+
+ if data.shape[1] == 3: # z is a vector, not matrix
+ fields = {
+ "rxy": ("x", "y"),
+ "rxz": ("x", "z"),
+ "ryz": ("y", "z")}
+ tdata = {
+ corr_type: pandas.DataFrame(
+ {cols[0]: data[cols[0]],
+ cols[1]: data[cols[1]]}).dropna(axis=0)
+ for corr_type, cols in fields.items()
+ }
+ corrs = {
+ corr_type: tdata[corr_type][cols[0]].corr(
+ tdata[corr_type][cols[1]], method=method)
+ for corr_type, cols in fields.items()
+ }
+ return round((
+ (corrs["rxy"] - corrs["rxz"] * corrs["ryz"]) /
+ (math.sqrt(1 - corrs["rxz"]**2) *
+ math.sqrt(1 - corrs["ryz"]**2))), ROUND_TO)
+ return round(0, ROUND_TO)