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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1038%2Fs43588-023-00489-0/MediaObjects/43588_2023_489_Fig1_HTML.png)
References
Newman, A. M. et al. Nat. Biotechnol. 37, 773–782 (2019).
Jin, H. & Liu, Z. Genome Biol. 22, 102 (2021).
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P. & De Preter, K. Nat. Commun. 11, 5650 (2020).
Song, L., Sun, X., Qi, T. & Yang, J. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00487-2 (2023).
Wagner, A., Regev, A. & Yosef, N. Nat. Biotechnol. 34, 1145–1160 (2016).
Trapnell, C. et al. Nat. Biotechnol. 32, 381–386 (2014).
Frishberg, A. et al. Nat. Methods 16, 327–332 (2019).
Allen, M. P. Understanding Regression Analysis 176–180 (Springer, 1997).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00489-0
- Springer Nature America, Inc.