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Bioinformatics

Cellular deconvolution with continuous transitions

Nature Computational Science volume 3, pages 582–583 (2023)Cite this article

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A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.

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Fig. 1: The overview of various cellular deconvolution methods.

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Authors and Affiliations

  1. State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian, China

    Zheyang Zhang & Jialiang Huang

  2. National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China

    Zheyang Zhang & Jialiang Huang

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  1. Zheyang Zhang
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  2. Jialiang Huang
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Correspondence to Jialiang Huang.

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The authors declare no competing interests.

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Zhang, Z., Huang, J. Cellular deconvolution with continuous transitions. Nat Comput Sci 3, 582–583 (2023). https://doi.org/10.1038/s43588-023-00489-0

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