Discrete Gene Enrichment Analysis (indra_cogex.client.enrichment.discrete
)
A collection of analyses possible on gene lists (of HGNC identifiers).
- go_ora(client, gene_ids, background_gene_ids=None, **kwargs)[source]
Calculate over-representation on all GO terms.
- Parameters:
client (
Neo4jClient
) – Neo4jClientbackground_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq
- indra_downstream_ora(client, gene_ids, background_gene_ids=None, *, minimum_evidence_count=1, minimum_belief=0.0, **kwargs)[source]
Calculate a p-value for each entity in the INDRA database based on the genes that are causally upstream of it and how they compare to the query gene set.
- Parameters:
client (
Neo4jClient
) – Neo4jClientbackground_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.minimum_evidence_count (
Optional
[int
]) – Minimum number of evidences to consider a causal relationshipminimum_belief (
Optional
[float
]) – Minimum belief to consider a causal relationship**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq
- indra_upstream_ora(client, gene_ids, background_gene_ids=None, *, minimum_evidence_count=1, minimum_belief=0.0, **kwargs)[source]
Calculate a p-value for each entity in the INDRA database based on the set of genes that it regulates and how they compare to the query gene set.
- Parameters:
client (
Neo4jClient
) – Neo4jClientbackground_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.minimum_evidence_count (
Optional
[int
]) – Minimum number of evidences to consider a causal relationshipminimum_belief (
Optional
[float
]) – Minimum belief to consider a causal relationship**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq
- phenotype_ora(gene_ids, background_gene_ids=None, *, client, **kwargs)[source]
Calculate over-representation on all HP phenotypes.
- Parameters:
background_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.client (
Neo4jClient
) – Neo4jClient**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq
- reactome_ora(client, gene_ids, background_gene_ids=None, **kwargs)[source]
Calculate over-representation on all Reactome pathways.
- Parameters:
client (
Neo4jClient
) – Neo4jClientbackground_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq
- wikipathways_ora(client, gene_ids, background_gene_ids=None, **kwargs)[source]
Calculate over-representation on all WikiPathway pathways.
- Parameters:
client (
Neo4jClient
) – Neo4jClientbackground_gene_ids (
Optional
[Collection
[str
]]) – List of HGNC gene identifiers for the background gene set. If not given, all genes with HGNC IDs are used as the background.**kwargs – Additional keyword arguments to pass to _do_ora
- Return type:
DataFrame
- Returns:
DataFrame with columns: curie, name, p, q, mlp, mlq