scGraphVerse: a modular workflow for single-cell gene network inference
Francesco Cecere, Daniela De Canditiis, Annamaria Carissimo, Claudia Angelini, Anaïs Baudot · 2026
Read scGraphVerse: a modular workflow for single-cell gene network inference by Francesco Cecere, Daniela De Canditiis, Annamaria Carissimo, Claudia Angelini, Anaïs Baudot (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13524424.txt.
Description
Francesco Cecere and others present, in a 2026 methods paper, scGraphVerse, a software workflow for mapping gene networks in single-cell RNA sequencing data. Such data are sparse, wide, and noisy, making network reconstruction hard. Tools rarely combine several data sets or support thorough follow-up.
The R package joins five algorithms with shared inputs and outputs, plus tools for group detection, pathway enrichment, and mining of past papers. It supports early, late, and joint ways for merging data sets. Benchmarks on test data showed model-based methods doing well with small samples, while a joint forest method did best as networks and data sets grew. A blood cell case study found regulatory groups backed by past papers across donors. The package is shared through Bioconductor.
Catalog record
| CREATOR | Francesco Cecere, Daniela De Canditiis, Annamaria Carissimo, Claudia Angelini, Anaïs Baudot |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 52186 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |