scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering

Zhenhui He and Dong Kangning

PLOS Computational Biology, 2026, vol. 22, issue 6, 1-22

Abstract: Resolving fine-grained cell subtypes in single-cell RNA sequencing (scRNA-seq) data remains challenging, as their subtle transcriptional differences are often obscured by technical noise and data sparsity. Here, we present scMagnifier, a consensus clustering framework that leverages gene regulatory network (GRN)-informed in silico perturbations to amplify subtle transcriptional differences and uncover latent cell subpopulations. scMagnifier perturbs candidate transcription factors (TFs), propagates perturbation effects through cluster-specific GRNs to simulate post-perturbation expression profiles, and integrates clustering results across multiple perturbations into stable subtype assignments. Additionally, scMagnifier introduces regulatory perturbation consensus UMAP (rpcUMAP), a perturbation-aware visualization that provides clearer separation between cell subtypes and guides the selection of the optimal number of clusters. In both single-batch and multi-batch benchmarks, scMagnifier consistently improves the resolution and accuracy of fine-grained cell type identification. Notably, when integrated with spatial clustering methods such as STAGATE, scMagnifier is compatible with spatial transcriptomics workflows and effectively reveals tumor cell subtypes and their spatial organization in ovarian cancer.Author summary: Understanding the diversity of cells in tissues is key to studying health and disease, but identifying subtle differences between closely related cell types is often challenging. Small molecular variations and technical noise can hide rare immune cells, transitional states, or tumor subtypes. We developed scMagnifier, a computational tool that amplifies these subtle differences by simulating genetic perturbations, allowing cells to be grouped into fine-grained subtypes more accurately. We also created a visualization method to clearly separate cell groups and guide the identification of meaningful cell types. When tested on single-cell, multi-batch, and spatial transcriptomics data, scMagnifier consistently outperformed existing methods, revealing rare cell populations and mapping tumor subgroups in ovarian cancer. This approach provides researchers with an accessible and reliable way to uncover hidden cellular diversity, with potential applications in immunology, cancer biology, and studies of disease-related cell states.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1014167

DOI: 10.1371/journal.pcbi.1014167

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