Markov Chain Ontology Analysis (MCOA)

Figure 1 – MCOA mapping between ontology, ontology extension and Markov chain (A) Simple ontology and extension. (B) Markov chain representing simple ontology and extension according to MCOA method (C) Adjusted transition probability matrix for Markov chain according to MCOA method (D) Information rank values generated from adjusted transition probability matrix using α = 0.15 and ω = 0.01.


Markov Chain Ontology Analysis (MCOA) supports the analysis of hierarchical models relative to collections of domain data. This methodology was developed to enable the analysis of more complex ontological structures, against a wider range of data distributions, than has been possible using existing methods. MCOA specifically targets the challenges of class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data.

MCOA models the classes in one more ontologies, the instances from one or more datasets and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector-based metrics for each ontology class and dataset instance. Depending on the direction and weight of state transitions, these eigenvector-based metrics can be used to quantify the importance of classes relative to the dataset, the importance of instances relative to classes, or the importance of one set of classes relative to another set of classes based on annotations of a common dataset.

Gene Ontology (GO) Enrichment Analysis using MCOA

We have developed an MCOA enrichment analysis approach and implemented it as a plugin to the Ontologizer framework. For implementation details, see the MCOA paper referenced below.

To use:


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