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

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):

Sofie Delbare  
Cornell University
Martin Wells  
Cornell University
Sumanta Basu  
Cornell University
Andrew G. Clark  
Cornell University

First Author:

Hao Xue  
Cornell University

Presenting Author:

Hao Xue  
N/A

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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