Pathway Analysis Through Mutual Information

Pathway analysis comes in many forms. Most are seeking to establish a connection between the activity of a certain biological pathway and a difference in phenotype, often relying on an upstream differential expression analysis to establish the difference between case and control. This process usually models this relationship using many assumptions, often of a linear nature, and may also involve statistical tests where the calculation of false discovery rates is not trivial. Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles, and therefore is absent of a model for the nature of the association between pathway activity and phenotype, resulting on a very minimal set of assumptions. For this, we construct a different graph of samples for each pathway and score the association between the structure of this graph and any phenotype variable using Mutual Information, while adjusting for the effects of random chance in each score. Our experiments show that this method produces robust and reproducible scores that successfully result in a high rank for target pathways on single cell datasets, outperforming established methods for pathway analysis on these same conditions.