Integrative Analysis of Differentially Expressed Genes in Time-Course Multi-Omics Data with MINT-DE

Oregon Convention Center 

Time-course multi-omics experiments are highly informative for obtaining a comprehensive understanding of the dynamic relationships between molecules. A fundamental step in analyzing such data involves selecting a short list of gene regions ("sites''). Two important criteria are the magnitude of change and the temporal dynamic consistency. Existing methods only consider one of them. We propose MINT-DE (Multi-omics INtegration of Time-course for Differential Expression analysis) that can select sites based on summarized measures of both aspects. We apply it to analyze a Drosophila development dataset and compare the results with existing methods. The analysis reveals that MINT-DE can identify differentially expressed time-course pairs with the highest correlations. Their corresponding genes are significantly enriched in gene-gene interaction networks and Gene Ontology terms. This suggests the effectiveness of MINT-DE in selecting sites that are both differentially expressed and temporally related across assays. This highlights the potential of MINT-DE to identify important sites and provide a complementarity of sites neglected by existing methods.

Multi-omics

Edgington's method

Time-course

Translational regulation 

Section on Statistics in Genomics and Genetics