deeprvat.deeprvat.evaluate
Module Contents
Functions
Data
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)