A Manifold Learning Based Approach to Reveal the Functional Linkages across Multiple Gene Networks.

The coordination of functional genomics is a critical and complex process in biological systems, especially across different phenotypes or organism states (e.g., time, disease, organism). Understanding how the interactions of various genomic functions relate to these states remains a challenge. To address this, we have developed a machine learning method, ManiNetCluster, which integrates and simultaneously clusters multiple gene networks to identify cross-phenotype functional gene modules, revealing the genomic functional linkages. Particularly, this method extended the manifold learning to match local and nonlinear structures among networks for maximizing the functional connectivities. For example, we showed that ManiNetCluster significantly better aligns orthologous genes from cross-species gene expression datasets than the linear state-of-art methods. As a demonstration, we have applied our method to temporal gene co-expression networks of an algal day/night cycling transcriptome. This demonstration confirmed i) the validity of our clustering method, and ii) revealed the day-night linkages of photosynthetic functions, providing novel insights of temporal genomic functional coordination in bioproduction.