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Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model

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In this paper, we propose a statistical framework MLDA, based on the smoothed LDA to detect the differential usage of transcript isoforms for RNA-seq data. The experimental results show that our method performs competitively on the detection of DTU and obtain more accurate relative transcript abundance compared with other alternatives on both simulated and real data.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2018YFC2001600, 2018YFC2001602).

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, 211106, China

    Jing Li, Xuejun Liu & Daoqiang Zhang

  2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, China

    Jing Li, Xuejun Liu & Daoqiang Zhang

Authors
  1. Jing Li
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  2. Xuejun Liu
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  3. Daoqiang Zhang
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Correspondence to Xuejun Liu.

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Li, J., Liu, X. & Zhang, D. Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model. Front. Comput. Sci. 15, 153319 (2021). https://doi.org/10.1007/s11704-020-9348-x

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  • DOI: https://doi.org/10.1007/s11704-020-9348-x

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