Peer-Reviewed Publication
Interdiscip Sci2026February 6, 2026Journal Article
Multi-Network Co-expression Analysis Enhances Biological Insights from Single-Cell Gene Expression.
Alicia Gómez-Pascual1, Araks Martirosyan2, Katja Hebestreit3, Andrew Kottick3, Michelle Mighdoll3, Victor Hanson-Smith3, José Luis Mellina-Andreu1, Alejandro Cisterna1, Matthew G Holt2, Grant Belgard4, Sebastian Guelfi3, Juan A Botía5,6
1Information and Communications Engineering Department, University of Murcia, 30100, Murcia, Spain.
2VIB Center for Brain & Disease Research, KU Leuven, 30001, Leuven, Belgium.
3Verge Genomics, South San Francisco, 94080, USA.
4The Bioinformatics CRO, Orlando, 32771, USA.
5Information and Communications Engineering Department, University of Murcia, 30100, Murcia, Spain. juanbot@um.es.
6Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, WC1N 3BG, London, United Kingdom. juanbot@um.es.
Abstract
With the advent of single-cell and single-nucleus RNA sequencing (sc/snRNA-seq), we can build cell-type-specific gene co-expression networks (GCNs). However, the high sparsity of scRNA-seq data limits GCN construction. We present scCoExpNets, a framework to create and annotate single-cell GCNs. For each cell-type cluster, scCoExpNets generates an initial GCN (T0) from its expression matrix. To red…
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