scTenifoldNet and scTenifoldKnk: A package suite for single-cell gene regulatory network construction, comparison, and perturbation analysis

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.

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Publication Details

Journal
Statistical Theory and Related Fields
Published
2025-09-29
DOI
https://doi.org/10.1080/24754269.2025.2557719
Primary Topic
Gene Regulatory Network Analysis
Type
article
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article

scTenifoldNet and scTenifoldKnk: A package suite for single-cell gene regulatory network construction, comparison, and perturbation analysis

Yongjian Yang, Guanxun Li, Daniel Osorio, James J. Cai et al.
Statistical Theory and Related Fields
Gene Regulatory Network Analysis
article

scTenifoldNet and scTenifoldKnk: A package suite for single-cell gene regulatory network construction, comparison, and perturbation analysis

Yongjian Yang, Guanxun Li, Daniel Osorio, James J. Cai, Qian Xu, Jianhua Z. Huang, Yan Zhong
article en

Abstract

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.

Statistical Theory and Related FieldsVol. 9(4)
Beijing Normal University (CN), Mitchell Institute (US), Chinese University of Hong Kong, Shenzhen (CN), Digital Insight (Norway) (NO), East China Normal University (CN), Texas A&M University (US)
Openalex Percentile: Top 76%
Gene Regulatory Network Analysis
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