Abstract Number:
3812
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Hao Xue (1), Sofie Delbare (1), Martin Wells (1), Sumanta Basu (1), Andrew G. Clark (1)
Institutions:
(1) Cornell University, United States
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Multi-omics|Edgington's method|Time-course|Translational regulation| |
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
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