diff options
Diffstat (limited to 'gn3')
| -rw-r--r-- | gn3/computations/partial_correlations.py | 114 |
1 files changed, 43 insertions, 71 deletions
diff --git a/gn3/computations/partial_correlations.py b/gn3/computations/partial_correlations.py index 519dce9..4b0cf30 100644 --- a/gn3/computations/partial_correlations.py +++ b/gn3/computations/partial_correlations.py @@ -11,6 +11,7 @@ from typing import Any, Tuple, Union, Sequence from scipy.stats import pearsonr, spearmanr import pandas +import pingouin from gn3.settings import TEXTDIR from gn3.data_helpers import parse_csv_line @@ -248,7 +249,7 @@ def compute_partial_correlations_fast(# pylint: disable=[R0913, R0914] all_target_trait_names: Tuple[str, ...] = processed_trait_names_values[0] all_target_trait_values: Tuple[float, ...] = processed_trait_names_values[1] - all_correlations = determine_partials( + all_correlations = compute_partial( primary_vals, control_vals, all_target_trait_names, all_target_trait_values, method) ## Line 772 to 779 in GN1 are the cause of the weird complexity in the @@ -278,79 +279,50 @@ def build_data_frame( return interm_df.rename(columns={"z0": "z"}) return interm_df -def partial_correlation_matrix( - xdata: Tuple[float, ...], ydata: Tuple[float, ...], - zdata: Union[Tuple[float, ...], Tuple[Tuple[float, ...], ...]], - method: str = "pearson", omit_nones: bool = True) -> float: +def compute_partial( + primary_val, control_vals, target_vals, target_names, + method: str) -> Tuple[ + Union[ + Tuple[str, int, float, float, float, float], None], + ...]: """ - Computes the partial correlation coefficient using the - 'variance-covariance matrix' method + Compute the partial correlations. - This is a partial migration of the - `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in - GeneNetwork1, specifically the `pcor.mat` function written in the R - programming language. - """ - return 0 + This is a re-implementation of the + `web.webqtl.correlation.correlationFunction.determinePartialsByR` function + in GeneNetwork1. -def partial_correlation_recursive( - xdata: Tuple[float, ...], ydata: Tuple[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 + This implementation reworks the child function `compute_partial` which will + then be used in the place of `determinPartialsByR`. - This is a partial migration of the - `web.webqtl.correlation.correlationFunction.determinPartialsByR` function in - GeneNetwork1, specifically the `pcor.rec` function written in the R - programming language. + TODO: moving forward, we might need to use the multiprocessing library to + speed up the computations, in case they are found to be slow. """ - 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() - } + # replace the R code with `pingouin.partial_corr` + def __compute_trait_info__(target): + df = build_data_frame( + [prim for targ, prim in zip(target, primary_vals) + if targ is not None], + [targ for targ in target if targ is not None], + [cont for i, cont in enumerate(control) if target[i] is not None]) + covariates = "z" if df.shape[1] == 3 else [ + col for col in df.columns if col not in ("x", "y")] + ppc = pingouin.partial_corr( + data=df, x="x", y="y", covar=covariates, method=method) + pc_coeff = ppc["r"] + + zero_order_corr = pingouin.corr(df["x"], df["y"], method=method) + + if math.isnan(pc_coeff): + return ( + target[1], len(primary), pc_coeff, 1, zero_order_corr["r"], + zero_order_corr["p-val"]) return ( - (corrs["rxy"] - corrs["rxz"] * corrs["ryz"]) / - (math.sqrt(1 - corrs["rxz"]**2) * - math.sqrt(1 - corrs["ryz"]**2))) - - remaining_cols = [ - colname for colname, series in data.items() - if colname not in ("x", "y", "z0") - ] - - new_xdata = tuple(data["x"]) - new_ydata = tuple(data["y"]) - zc = tuple(tuple(data[colname]) for colname in data[remaining_cols].columns) - - rxy_zc = partial_correlation_recursive( - new_xdata, new_ydata, zc, method=method, - omit_nones=omit_nones) - rxz0_zc = partial_correlation_recursive( - new_xdata, tuple(data["z0"]), zc, method=method, - omit_nones=omit_nones) - ryz0_zc = partial_correlation_recursive( - new_ydata, tuple(data["z0"]), zc, method=method, - omit_nones=omit_nones) - - return ((rxy_zc - rxz0_zc * ryz0_zc) /( - math.sqrt(1 - rxz0_zc**2) * math.sqrt(1 - ryz0_zc**2))) + target[1], len(primary), pc_coeff, + (ppc["p-val"] if not math.isnan(ppc["p-val"]) else ( + 0 if (abs(pc_coeff - 1) < 0.0000001) else 1)), + zero_order_corr["r"], zero_order_corr["p-val"]) + + return tuple( + __compute_trait_info__(target) + for target in zip(target_vals, target_names)) |