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© 2023 Oxford University Press. All rights reserved. Although many single-cell computational methods proposed use gene expression as input, recent studies show that replacing 'unstable' gene expression with 'stable' gene-gene associations can greatly improve the performance of downstream analysis. To obtain accurate gene-gene associations, conditional cell-specific network method (c-CSN) filters out the indirect associations of cell-specific network method (CSN) based on the conditional independence of statistics. However, when there are strong connections in networks, the c-CSN suffers from false negative problem in network construction. To overcome this problem, a new partial cell-specific network method (p-CSN) based on the partial independence of statistics is proposed in this paper, which eliminates the singularity of the c-CSN by implicitly including direct associations among estimated variables. Based on the p-CSN, single-cell network entropy (scNEntropy) is further proposed to quantify cell state. The superiorities of our method are verified on several datasets. (i) Compared with traditional gene regulatory network construction methods, the p-CSN constructs partial cell-specific networks, namely, one cell to one network. (ii) When there are strong connections in networks, the p-CSN reduces the false negative probability of the c-CSN. (iii) The input of more accurate gene-gene associations further optimizes the performance of downstream analyses. (iv) The scNEntropy effectively quantifies cell state and reconstructs cell pseudo-time.
Author(s): Wang Y, Xuan C, Wu H, Zhang B, Ding T, Gao J
Publication type: Article
Publication status: Published
Journal: Briefings in Bioinformatics
Year: 2023
Volume: 24
Issue: 3
Online publication date: 10/05/2023
Acceptance date: 19/04/2023
ISSN (print): 1467-5463
ISSN (electronic): 1477-4054
Publisher: Oxford University Press
URL: https://doi.org/10.1093/bib/bbad180
DOI: 10.1093/bib/bbad180
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