deeprvat.deeprvat.evaluate

Module Contents

Functions

count_unique

get_baseline

get_baseline_results

combine_results

get_pvals

min_Bonferroni_aggregate

aggregate_pvals_per_gene

process_results

evaluate_

evaluate

Data

logger

BASELINE_GROUPS

BURDEN_SKAT_RENAME

VARIANT_TYPE_RENAME

METHOD_NAMES

API

deeprvat.deeprvat.evaluate.logger = 'getLogger(...)'
deeprvat.deeprvat.evaluate.BASELINE_GROUPS = None
deeprvat.deeprvat.evaluate.BURDEN_SKAT_RENAME = None
deeprvat.deeprvat.evaluate.VARIANT_TYPE_RENAME = None
deeprvat.deeprvat.evaluate.METHOD_NAMES = None
deeprvat.deeprvat.evaluate.count_unique(result: pandas.DataFrame, query: str)
deeprvat.deeprvat.evaluate.get_baseline(paths, experiment_name, deeprvat_genes, phenotype=None, min_eaf=50, alpha: float = 0.05, correction_method: str = 'Bonferroni') pandas.DataFrame
deeprvat.deeprvat.evaluate.get_baseline_results(config: Dict, pheno, deeprvat_genes: numpy.ndarray, alpha: float = 0.05, correction_method: str = 'Bonferroni')
deeprvat.deeprvat.evaluate.combine_results(deeprvat_results: pandas.DataFrame, baseline_results: pandas.DataFrame, correction_method: str = 'Bonferroni', alpha: float = 0.05, combine_pval: str = 'Bonferroni')
deeprvat.deeprvat.evaluate.get_pvals(results, method_mapping=None, phenotype_mapping={})
deeprvat.deeprvat.evaluate.min_Bonferroni_aggregate(pvals)
deeprvat.deeprvat.evaluate.aggregate_pvals_per_gene(df, agg_method)
deeprvat.deeprvat.evaluate.process_results(results: pandas.DataFrame, alpha: float = 0.05, correction_method: str = 'Bonferroni', combine_pval: str = 'Bonferroni') Tuple[pandas.DataFrame, pandas.DataFrame]
deeprvat.deeprvat.evaluate.evaluate_(associations: pandas.DataFrame, alpha: float, baseline_results: Optional[pandas.DataFrame] = None, debug: bool = False, correction_method: str = 'Bonferroni', combine_pval: str = 'Bonferroni')
deeprvat.deeprvat.evaluate.evaluate(debug: bool, phenotype: Optional[str], use_baseline_results: bool, association_files: Tuple[str], config_file: str, out_dir: str, combine_pval)