enrichment_api package

Subpackages

enrichment_api.tests package

Submodules

enrichment_api.enrichment_builder module

class enrichment_api.enrichment_builder.BaseEnrichmentBuilder[source]

Bases: GPFTool

Base class for enrichment builders.

abstract enrichment_test(query: dict[str, Any]) → dict[str, Any][source]: Build enrichment test result.

class enrichment_api.enrichment_builder.EnrichmentBuilder(enrichment_helper: EnrichmentHelper, gene_scores_db: GeneScoresDb, study: WDAEStudy)[source]

Bases: BaseEnrichmentBuilder

Build enrichment tool test.

build(gene_syms: list[str] | None, gene_score: dict[str, Any] | None, background_id: str | None, counting_id: str | None) → list[dict[str, Any]][source]: Build enrichment test result

build_results(gene_syms: Iterable[str], background_id: str | None, counting_id: str | None) → list[dict[str, Any]][source]

Build and return a list of enrichment results.

Returns:: A list of dictionaries representing the enrichment results.

create_enrichment_description(gene_set_id: str | None, gene_score: dict[str, Any] | None, gene_syms: list[str] | None) → str[source]: Build enrichment result description.

enrichment_test(query: dict[str, Any]) → dict[str, Any][source]: Build enrichment test result.

static make_tool(study: WDAEAbstractStudy) → GPFTool | None[source]

enrichment_api.enrichment_helper module

class enrichment_api.enrichment_helper.BaseEnrichmentHelper[source]

Bases: GPFTool

Base helper class to create enrichment helper WDAE study.

abstract get_enrichment_models() → dict[str, Any][source]: Return enrichment models available for the study.

class enrichment_api.enrichment_helper.EnrichmentHelper(grr: GenomicResourceRepo, study: WDAEStudy)[source]

Bases: BaseEnrichmentHelper

Helper Base helper class to create enrichment helper WDAE study.

calc_enrichment_test(psc_id: str, gene_syms: Iterable[str], effect_groups: Iterable[str] | Iterable[Iterable[str]], background_id: str | None = None, counter_id: str | None = None) → dict[str, dict[str, EnrichmentResult]][source]: Perform enrichment test for a genotype data.

collect_genotype_data_backgrounds() → list[BaseEnrichmentBackground][source]: Collect enrichment backgrounds configured for a genotype data.

create_background(background_id: str) → BaseEnrichmentBackground[source]: Construct and return an enrichment background.

create_counter(counter_id: str) → CounterBase[source]: Create counter for a genotype data.

get_default_background_model() → str[source]: Return default background model field from the enrichment config. If it is missing, default to the first selected background model.

get_default_counting_model() → str[source]

get_enrichment_models() → dict[str, Any][source]: Return enrichment models available for the study.

get_selected_counting_models() → list[str][source]: Return selected counting models field from the enrichment config. If it is missing, default to the counting field.

get_selected_person_set_collections() → str[source]: Return selected person set collections field from the enrichment config. If it is missing, default to the first available person set collection in the provided study.

static make_tool(study: WDAEAbstractStudy) → GPFTool | None[source]

enrichment_api.enrichment_serializer module

class enrichment_api.enrichment_serializer.EnrichmentSerializer(enrichment_config: dict[str, Any], results: list[dict[str, Any]])[source]

Bases: EffectTypesMixin

Serializer for enrichment tool results.

serialize() → list[dict][source]

serialize_all(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult) → dict[str, Any][source]

serialize_common_filter(grouping_results: dict[str, dict[str, EnrichmentSingleResult]], effect_type: str, _result: EnrichmentSingleResult, gender: list[str] | None = None) → dict[str, Any][source]: Serialize common filter.

serialize_enrichment_result(result: EnrichmentSingleResult) → dict[str, Any][source]: Serialize enrichment result.

serialize_female(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult) → dict[str, Any][source]

serialize_male(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult) → dict[str, Any][source]

serialize_overlap_filter(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult, *, gender: list[str] | None = None, overlapped_genes: bool = False) → dict[str, Any][source]: Serialize overlapped events filter.

serialize_people_groups(grouping_results: dict[str, Any]) → dict[str, Any][source]: Serialize people group results.

serialize_rec(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult) → dict[str, Any][source]: Serialize recurrent events.

serialize_rec_filter(grouping_results: dict[str, Any], effect_type: str, result: EnrichmentSingleResult, gender: list[str] | None = None) → dict[str, Any][source]: Serialize recurrent events filter.

enrichment_api.urls module

enrichment_api.views module

class enrichment_api.views.EnrichmentModelsView[source]

Bases: QueryBaseView

Select enrichment models view.

get(_request: Request, dataset_id: str) → Response[source]: Return enrichment configuration prepared for choosing.

class enrichment_api.views.EnrichmentTestView[source]