NeuroMMSig

An implementation of the NeuroMMSig mechanism enrichment algorithm [DomingoFernandez2017].

DomingoFernandez2017

Domingo-Fernández, D., et al (2017). Multimodal mechanistic signatures for neurodegenerative diseases (NeuroMMSig): A web server for mechanism enrichment. Bioinformatics, 33(22), 3679–3681.

pybel_tools.analysis.neurommsig.algorithm.get_neurommsig_scores(graph, genes, annotation='Subgraph', ora_weight=None, hub_weight=None, top_percent=None, topology_weight=None, preprocess=False)[source]

Preprocess the graph, stratify by the given annotation, then run the NeuroMMSig algorithm on each.

Parameters

  • graph (BELGraph) – A BEL graph

  • genes (List[Gene]) – A list of gene nodes

  • annotation (str) – The annotation to use to stratify the graph to subgraphs

  • ora_weight (Optional[float]) – The relative weight of the over-enrichment analysis score from neurommsig_gene_ora(). Defaults to 1.0.

  • hub_weight (Optional[float]) – The relative weight of the hub analysis score from neurommsig_hubs(). Defaults to 1.0.

  • top_percent (Optional[float]) – The percentage of top genes to use as hubs. Defaults to 5% (0.05).

  • topology_weight (Optional[float]) – The relative weight of the topolgical analysis core from neurommsig_topology(). Defaults to 1.0.

  • preprocess (bool) – If true, preprocess the graph.

Return type

Optional[Mapping[str, float]]

Returns

A dictionary from {annotation value: NeuroMMSig composite score}

Pre-processing steps:

  1. Infer the central dogma with :func:``

  2. Collapse all proteins, RNAs and miRNAs to genes with :func:``

  3. Collapse variants to genes with :func:``

pybel_tools.analysis.neurommsig.algorithm.get_neurommsig_score(graph, genes, ora_weight=None, hub_weight=None, top_percent=None, topology_weight=None)[source]

Calculate the composite NeuroMMSig Score for a given list of genes.

Parameters

  • graph (BELGraph) – A BEL graph

  • genes (List[Gene]) – A list of gene nodes

  • ora_weight (Optional[float]) – The relative weight of the over-enrichment analysis score from neurommsig_gene_ora(). Defaults to 1.0.

  • hub_weight (Optional[float]) – The relative weight of the hub analysis score from neurommsig_hubs(). Defaults to 1.0.

  • top_percent (Optional[float]) – The percentage of top genes to use as hubs. Defaults to 5% (0.05).

  • topology_weight (Optional[float]) – The relative weight of the topolgical analysis core from neurommsig_topology(). Defaults to 1.0.

Return type

float

Returns

The NeuroMMSig composite score