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

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.

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Publication Details

Journal
Nature Communications
Published
2025-05-08
DOI
https://doi.org/10.1038/s41467-025-59620-6
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
4.84

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article

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

Xinran Dong, Michael Rusch, Junmin Peng, Alireza Khatamian et al.
6 citations
Nature Communications
Single-cell and spatial transcriptomics
4.84
article

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

Xinran Dong, Michael Rusch, Junmin Peng, Alireza Khatamian, Isabel Risch, Qingfei Pan, Zhen Xie, Xiangdong Yao, Chad Burdyshaw, Siarhei Hladyshau, Jiyuan Yang, Koon‐Kiu Yan, Liang Ding, Jason Fang, Yogesh Dhungana, Chenxi Qian, Michael Brewer, Hao Shi, Xin Huang, Hongbo Chi, Joao Pedro Veloso, Anuj K. Jain, Xu Yang, Lei Yan, Jiyang Yu, Arihant Jain, Jiayu Zhou
article en
6 citations

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.

Nature CommunicationsVol. 16(1)
St. Jude Children's Research Hospital (US), University of Tennessee Health Science Center (US), Shanghai Jiao Tong University (CN), Shanghai First People's Hospital (CN), Children's Hospital of Fudan University (CN)
American Lebanese Syrian Associated Charities, National Institutes of Health, National Institute on Aging, National Cancer Institute, National Institute of General Medical Sciences
Openalex Percentile: Top 5%
Single-cell and spatial transcriptomics
4.84
DOIOpen Access PDFOpenAlex
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