Identifying emerging phenomenon in long temporal phenotyping experiments

Jiajie Peng, Junya Lu, Donghee Hoh, Ayesha S. Dina, Xuequn Shang, David M. Kramer, Jin Chen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The rapid improvement of phenotyping capability, accuracy and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. Results: We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust and biologically significant.

Original languageEnglish
Pages (from-to)568-577
Number of pages10
Issue number2
StatePublished - Jan 15 2020

Bibliographical note

Funding Information:
This work was supported by US NSF ABI [grant numbers 1458556, 1716340], US DOE BES [grant number DEFG0291ER20021] and NSFC [grant numbers 61702421, U1811262, 61772426]. China Postdoctoral Science Foundation (No. 2017M610651), Fundamental Research Funds for the Central Universities (No. 3102018zy033), Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University.

Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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