scGraphVerse: A Modular Workflow for Single-Cell Gene Network Inference

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ID: 322602
2026
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Abstract
Abstract Motivation Inferring gene networks from single-cell RNA sequencing data is challenging due to high sparsity, dimensionality, and technical noise. Current pipelines lack the multi-dataset integration and comprehensive post-processing analysis. Results scGraphVerse is an R package that integrates multiple algorithms (GENIE3, GRNBoost2, ZILGM, PCzinb, and JRF) with extensive evaluation and visualization tools. Its modular workflow supports early, late, and joint integration strategies for multi-dataset analysis, providing standardized input/output interfaces and biological interpretation tools, including community detection, pathway enrichment, and literature mining. Benchmarking on simulated data showed model-based methods (PCzinb and ZILGM) perform well with limited sample sizes, while JRF performs best as the network size and dataset numbers increase. A PBMC case study demonstrates JRF’s ability to identify literature-supported regulatory communities across donors. Availability and implementation The package is available in Bioconductor 3.22 at https://bioconductor.org/packages/release/bioc/html/scGraphVerse.html. Code and examples: https://github.com/ngsFC/scGV_analysis.
Reference Key
openalex_W7171292454 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Francesco Cecere, Daniela De Canditiis, Annamaria Carissimo, Claudia Angelini
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Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag208
URL
https://doi.org/10.1093/bioadv/vbag208
Keywords Keywords not found

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