Brown, S.; Xiao, B.; Oros Klein, K.; Dupuis, J.; Zhang, Q.; Greenwood, C. M. T.

Multiomic datasets contain complex nonlinear relationships that are often missed by conventional analysis methods. Topological data analysis (TDA) can detect some such patterns (i.e., loops), and here we extend previous TDA approaches with MOLA (MultiOmic Loop Analysis), a framework for multiomic loop visualization, filtering, association with sample-level characteristics and functional characterization. In an Influenza A virus dataset, MOLA better captured anticipated characteristic-dependent epigenetic alterations compared to standard approaches. In a breast cancer cohort, higher loop participation in five genes was associated with increased hazard of progression. MOLA provides an interpretable framework for discovering biologically meaningful multiomic patterns and potential biomarkers.