diff options
Diffstat (limited to 'gn3/computations')
| -rw-r--r-- | gn3/computations/partial_correlations.py | 41 |
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) |