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
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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