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Updated: Feb 23, 2026
Reusable Single Cell for Iterative Epigenomic Analyses
Published on: February 11, 2022
HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington's epigenetic
1Biomedical Informatics Laboratory, School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
Motivation:
The interpretation of transcriptional dynamics in single-cell data, especially pseudotime estimation, could help understand the transition of gene expression profiles. The recovery of pseudotime increases the temporal resolution of single-cell transcriptional data, but is challenging due to the high variability in gene expression between individual cells. Here, we introduce HopLand, a pseudotime recovery method using continuous Hopfield network to map cells to a Waddington's epigenetic landscape. It reveals from the single-cell data the combinatorial regulatory interactions among genes that control the dynamic progression through successive cell states.
Results:
We applied HopLand to different types of single-cell transcriptomic data. It achieved high accuracies of pseudotime prediction compared with existing methods. Moreover, a kinetic model can be extracted from each dataset. Through the analysis of such a model, we identified key genes and regulatory interactions driving the transition of cell states. Therefore, our method has the potential to generate fundamental insights into cell fate regulation.
Availability And Implementation:
The MATLAB implementation of HopLand is available at https://github.com/NetLand-NTU/HopLand .
Contact:
zhengjie@ntu.edu.sg.
