diff options
Diffstat (limited to 'gn3/partial_correlations.py')
| -rw-r--r-- | gn3/partial_correlations.py | 30 |
1 files changed, 28 insertions, 2 deletions
diff --git a/gn3/partial_correlations.py b/gn3/partial_correlations.py index 4db4807..c556d10 100644 --- a/gn3/partial_correlations.py +++ b/gn3/partial_correlations.py @@ -5,8 +5,8 @@ It is an attempt to migrate over the partial correlations feature from GeneNetwork1. """ -from typing import Sequence from functools import reduce +from typing import Any, Sequence def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]): """ @@ -45,7 +45,7 @@ def control_samples(controls: Sequence[dict], sampleslist: Sequence[str]): [__process_control__(trait_data) for trait_data in controls], (tuple(), tuple(), tuple(), tuple())) -def dictify_by_samples(samples_vals_vars: Sequence[Sequence]) -> dict: +def dictify_by_samples(samples_vals_vars: Sequence[Sequence]) -> Sequence[dict]: """ Build a sequence of dictionaries from a sequence of separate sequences of samples, values and variances. @@ -60,3 +60,29 @@ def dictify_by_samples(samples_vals_vars: Sequence[Sequence]) -> dict: sample: {"sample_name": sample, "value": val, "variance": var} for sample, val, var in zip(*trait_line) } for trait_line in zip(*(samples_vals_vars[0:3]))) + +def fix_samples(primary_trait: dict, control_traits: Sequence[dict]) -> Sequence[Sequence[Any]]: + """ + Corrects sample_names, values and variance such that they all contain only + those samples that are common to the reference trait and all control traits. + + This is a partial migration of the + `web.webqtl.correlation.correlationFunction.fixStrain` function in GN1. + """ + primary_samples = tuple( + present[0] for present in + ((sample, all(sample in control.keys() for control in control_traits)) + for sample in primary_trait.keys()) + if present[1]) + control_vals_vars: tuple = reduce( + lambda acc, x: (acc[0] + (x[0],), acc[1] + (x[1],)), + ((item["value"], item["variance"]) + for sublist in [tuple(control.values()) for control in control_traits] + for item in sublist), + (tuple(), tuple())) + return ( + primary_samples, + tuple(primary_trait[sample]["value"] for sample in primary_samples), + control_vals_vars[0], + tuple(primary_trait[sample]["variance"] for sample in primary_samples), + control_vals_vars[1]) |