scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data.

Area of Science:

• Computational biology
• Genomics
• Systems biology

Background:

• Single-cell omics data present challenges in identifying transcriptional and signaling drivers due to sparsity and post-translational modifications.
• Hidden drivers with low expression or RNA-protein inconsistencies are difficult to detect using conventional analysis.

Purpose of the Study:

• To develop scMINER, a mutual information-based computational framework for unsupervised clustering and cell-type specific inference of intracellular networks.
• To identify hidden drivers and analyze network rewiring from single-cell RNA-seq data.
• To capture nonlinear relationships and infer driver activities for a comprehensive understanding of cellular states.

Main Methods:

• scMINER utilizes mutual information (MI) to analyze gene-gene and cell-cell relationships.
• The framework performs unsupervised clustering and infers intracellular networks and driver activities.
• It assesses transcription and signaling driver activities for over 6,000 drivers without relying on binding motifs.

Main Results:

• scMINER demonstrates superior performance in clustering, particularly for distinguishing similar cell types, compared to existing algorithms.
• The method effectively identifies hidden transcription and signaling drivers and dissects their regulon rewiring.
• Demonstrated applications include immune cell heterogeneity, lineage differentiation, and tissue specification.

Conclusions:

• scMINER is a highly accurate, reproducible, and scalable method for inferring cellular transcriptional and signaling networks from single-cell RNA-seq data.
• The activity-based approach enables wide applicability in dissecting cellular states and regulatory mechanisms.
• The scMINER software is publicly available for broader research use.