The comparative analysis of gene regulatory networks (GRNs) across various biological conditions reveals crucial shifts in regulatory mechanisms, shedding light on how genetic and environmental signals influence gene function. Recent advances in highresolution technologies have provided support and made it possible to profile gene expression at the single-cell level, thereby enabling more precise studies of transcriptional regulation. We have developed the scTenifoldNet and scTenifoldKnk R packages, which offer streamlined workflows for constructing single-cell gene regulatory networks (scGRNs) and facilitating comparisons across different samples or between pre- and post-gene perturbation states. Both packages employ a “tensor decomposition + manifold alignment” approach to achieve robust and effective comparisons.

References

• Faber, N. K. M., Bro, R., & Hopke, P. K. (2003). Recent developments in CANDECOMP/PARAFAC algorithms: A critical review. Chemometrics and Intelligent Laboratory Systems, 65(1), 119–137. https://doi.org/10.1016/S0169-7439(02)00089-8
• Jolliffe, I. T. (1982). A note on the use of principal components in regression. Journal of the Royal Statistical Society Series C: Applied Statistics, 31(3), 300–303.
• Osorio, D., Zhong, Y., Li, G., Huang, J. Z., & Cai, J. J. (2020). scTenifoldNet: A machine learning workflow for constructing and comparing transcriptome-wide gene regulatory networks from single-cell data. Patterns, 1(9), Article 100139. https://doi.org/10.1016/j.patter.2020.100139
• Osorio, D., Zhong, Y., Li, G., Xu, Q., Yang, Y., Tian, Y., Chapkin, R. S., Huang, J. Z., & Cai, J. J. (2022). scTenifoldKnk: An efficient virtual knockout tool for gene function predictions via single-cell gene regulatory network perturbation. Patterns, 3(3), Article 100434. https://doi.org/10.1016/j.patter.2022.100434

To cite this article: Yan Zhong, Daniel Osorio, Guanxun Li, Qian Xu, Yongjian Yang, Jianhua Z. Huang & James J. Cai (29 Sep 2025): scTenifoldNet and scTenifoldKnk: A package suite for single-cell gene regulatory network construction, comparison, and perturbation analysis, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2025.2557719

To link to this article: https://doi.org/10.1080/24754269.2025.2557719