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Identification and prioritization of differentially expressed genes for time-series gene expression data

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Abstract

Identification of differentially expressed genes (DEGs) in time course studies is very useful for understanding gene function, and can help determine key genes during specific stages of plant development. A few existing methods focus on the detection of DEGs within a single biological group, enabling to study temporal changes in gene expression. To utilize a rapidly increasing amount of single-group time-series expression data, we propose a two-step method that integrates the temporal characteristics of time-series data to obtain a B-spline curve fit. Firstly, a flat gene filter based on the Ljung–Box test is used to filter out flat genes. Then, a B-spline model is used to identify DEGs. For use in biological experiments, these DEGs should be screened, to determine their biological importance. To identify high-confidence promising DEGs for specific biological processes, we propose a novel gene prioritization approach based on the partner evaluation principle. This novel gene prioritization approach utilizes existing co-expression information to rank DEGs that are likely to be involved in a specific biological process/condition. The proposed method is validated on the Arabidopsis thaliana seed germination dataset and on the rice anther development expression dataset.

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Acknowledgments

This paper was supported by the National Natural Science Foundation of China (Grant Nos. 61271346, 61571163, 61532014, 91335112, 61671189 and 61402132).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Linlin Xing, Maozu Guo, Xiaoyan Liu & Chunyu Wang

Authors
  1. Linlin Xing
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  2. Maozu Guo
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  3. Xiaoyan Liu
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  4. Chunyu Wang
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Corresponding author

Correspondence to Maozu Guo.

Additional information

Linlin Xing received his MS degree in computer science form Harbin Institute of Technology (HIT), China in 2012. He is currently a PhD candidate under the supervision of Professor Maozu Guo in the School of Computer Science and Technology, HIT. His research interests include gene expression data analysis and biological network construction.

Maozu Guo received his BS and MS degrees from Harbin Engineering University, China in 1988 and 1991 respectively, and PhD degree from Harbin Institute of Technology (HIT), China in 1998, all in computer science. He is currently a professor in the School of Computer Science and Technology, HIT. His research interests include Bioinformatics and machine learning.

Xiaoyan Liu received her BS and MS degrees in computer science from Harbin Engineering University, China, and PhD degree in Engineering Mechanics from Harbin Institute of Technology (HIT), China. She is currently an associate professor in School of Computer Science and Technology at HIT. Her research interests include Bioinformatics and knowledge-based systems.

Chunyu Wang received his BS, MS and PhD degrees in computer science from Harbin Institute of Technology (HIT), China. Now he is an associate professor in computer science and technology at HIT. His research interests include bioinformatics and machine learning.

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Xing, L., Guo, M., Liu, X. et al. Identification and prioritization of differentially expressed genes for time-series gene expression data. Front. Comput. Sci. 12, 813–823 (2018). https://doi.org/10.1007/s11704-016-6287-7

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  • DOI: https://doi.org/10.1007/s11704-016-6287-7

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