Signed Gene Enrichment Analysis (indra_cogex.client.enrichment.signed)

A collection of analyses possible on pairs of gene lists (of HGNC identifiers).

main()[source]

Demonstrate signed gene list functions.

reverse_causal_reasoning(positive_hgnc_ids, negative_hgnc_ids, minimum_size=4, alpha=None, keep_insignificant=True, *, client, minimum_evidence_count=None, minimum_belief=None)[source]

Implement the Reverse Causal Reasoning algorithm from [catlett2013].

Parameters:

  • client (Neo4jClient) – A neo4j client

  • positive_hgnc_ids (Iterable[str]) – A list of positive-signed HGNC gene identifiers (e.g., up-regulated genes in a differential gene expression analysis)

  • negative_hgnc_ids (Iterable[str]) – A list of negative-signed HGNC gene identifiers (e.g., down-regulated genes in a differential gene expression analysis)

  • minimum_size (int) – The minimum number of entities marked as downstream of an entity for it to be usable as a hyp

  • alpha (Optional[float]) – The cutoff for significance. Defaults to 0.05

  • keep_insignificant (bool) – If false, removes results with a p value less than alpha.

  • minimum_evidence_count (Optional[int]) – The minimum number of evidences for a relationship to count it as a regulator. Defaults to 1 (i.e., cutoff not applied).

  • minimum_belief (Optional[float]) – The minimum belief for a relationship to count it as a regulator. Defaults to 0.0 (i.e., cutoff not applied).

Return type:

DataFrame

Returns:

  • A pandas DataFrame with results for each entity in the graph database

  • .. [catlett2013] Catlett, N. L., *et al.* (2013). `Reverse causal reasoning (applying) – qualitative causal knowledge to the interpretation of high-throughput data <https://doi.org/10.1186/1471-2105-14-340>`_. BMC Bioinformatics, **14**(1), 340.