A cell type and state specific gene regulation network inference method for immune regulatory analysis

作者：Xiong Li, K. Murali Krishna Rao, Chuang Chen, Yuejin Zhang, Juan Zhou, Meng Xu, Yi Hua, Jie Li, Hao Chen · 发表于：npj Systems Biology and Applications · 年份：2025 · DOI：10.1038/s41540-025-00564-4 · 被引用次数：5 · 研究领域：Single-cell and spatial transcriptomics、Gene Regulatory Network Analysis、Bioinformatics and Genomic Networks

The gene regulatory network inference method based on bulk sequencing data not only confuses different types of cells, but also ignores the phenomenon of network dynamic changes with cell state. Single cell transcriptome sequencing technology provides data support for constructing cell type and state specific gene regulatory networks. This study proposes a method for inferring cell type and state specific gene regulatory networks based on scRNA-seq data, called inferCSN. Firstly, inferCSN infers pseudo temporal information from scRNA-seq data and reorders cells based on this information. Because of the uneven distribution of cells in pseudo temporal information, the regulatory relationship tends to lean towards the high-density areas of cells. Therefore, based on the cell state, we divide the cells into different windows to eliminate the temporal information differences caused by cell density. Then, a sparse regression model, combined with reference network information, is used to construct a cell type-specific regulatory network (CSN) for each window. The experimental results on both simulated and real scRNA-seq datasets show that inferCSN outperforms other methods in multiple performance metrics. In addition, experimental results on datasets of different types (such as steady-state and linear datasets) and scales (different cell and gene numbers) show that inferCSN is robust. To further demonstrate the effectiveness and application prospects of inferCSN, we analyzed the gen...