ArticleNature communications2025
scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- FuTCM-PDD: a multi-level information fusion framework integrating KAN modeling and AutoML for phenotype-based drug discovery from Traditional Chinese Medicine.Chinese medicine · 2026Article
- Single-cell multi-omic profiling of heterogeneous PROCRiScience · 2026Article
- Benchmarking methods for inferring single-cell transcription factor activity using large-scale perturbation sequencing data.Briefings in bioinformatics · 2026Article
- Single-cell insights into plant growth, adaptation, and evolution.Journal of integrative plant biology · 2026Review
- Persistent homology of blood gene co-expression networks reveals reduced cycle structure in autism spectrum disorder: a multi-cohort analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- scHashFormer: a hash-driven graph transformer for scalable scRNA-seq clustering.Briefings in bioinformatics · 2026Article
- Single-cell gain-of-function mapping reveals latent regulatory programs governing CD8Research square · 2026Article
- Targeting the astrocyte-microglia EFEMP1-GALNT10 axis: a spatially programmable therapeutic strategy for hippocampal vulnerability in Alzheimer's disease.Journal of translational medicine · 2026Article
- Cell type-resolved proteomics reveals intra- and intercellular signaling in Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Latent Regulatory Programs Generate Synthetic T Cell States with Enhanced Therapeutic Potential.bioRxiv : the preprint server for biology · 2026Article
- lncAPNet enables the deciphering of lncRNA-mRNA connections in patient transcriptomic data.Bioinformatics advances · 2026Article
- TEAD switches interacting partners along neural progenitor lineage progression to execute distinct functions.Genes & development · 2025Article
- scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data.Nature communications · 2025Article
- Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.Cancer genomics & proteomicsReview
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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.
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