scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data

Qingfei Pan, Liang Ding, Siarhei Hladyshau, Xiangyu Yao, Jiayu Zhou, Lei Yan, Yogesh Dhungana, Hao Shi, Chenxi Qian, Xinran Dong, Chad Burdyshaw, Joao Pedro Veloso, Alireza Khatamian, Zhen Xie, Isabel Risch, Xu Yang, Jiyuan Yang, Xin Huang, Jason Fang, Anuj Jain, Arihant Jain, Michael Rusch, Michael Brewer, Junmin Peng, Koon-Kiu Yan, Hongbo Chi and Jiyang Yu ()
Additional contact information
Qingfei Pan: St. Jude Children’s Research Hospital
Liang Ding: St. Jude Children’s Research Hospital
Siarhei Hladyshau: St. Jude Children’s Research Hospital
Xiangyu Yao: St. Jude Children’s Research Hospital
Jiayu Zhou: St. Jude Children’s Research Hospital
Lei Yan: St. Jude Children’s Research Hospital
Yogesh Dhungana: St. Jude Children’s Research Hospital
Hao Shi: St. Jude Children’s Research Hospital
Chenxi Qian: St. Jude Children’s Research Hospital
Xinran Dong: Children’s Hospital of Fudan University
Chad Burdyshaw: St. Jude Children’s Research Hospital
Joao Pedro Veloso: St. Jude Children’s Research Hospital
Alireza Khatamian: St. Jude Children’s Research Hospital
Zhen Xie: St. Jude Children’s Research Hospital
Isabel Risch: St. Jude Children’s Research Hospital
Xu Yang: St. Jude Children’s Research Hospital
Jiyuan Yang: St. Jude Children’s Research Hospital
Xin Huang: St. Jude Children’s Research Hospital
Jason Fang: St. Jude Children’s Research Hospital
Anuj Jain: St. Jude Children’s Research Hospital
Arihant Jain: St. Jude Children’s Research Hospital
Michael Rusch: St. Jude Children’s Research Hospital
Michael Brewer: St. Jude Children’s Research Hospital
Junmin Peng: St. Jude Children’s Research Hospital
Koon-Kiu Yan: St. Jude Children’s Research Hospital
Hongbo Chi: St. Jude Children’s Research Hospital
Jiyang Yu: St. Jude Children’s Research Hospital

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results.

Date: 2025
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DOI: 10.1038/s41467-025-59620-6

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