Network Methods for Diagonal Integration of Unpaired Single-Cell Multi-Omics Data: A Review

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ID: 315802
2026
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Abstract
Abstract Motivation Advances in single-cell sequencing have enabled multi-omics profiling at unprecedented resolution; however, mass spectrometry-based single-cell proteomics (scMS) remains inherently destructive, precluding simultaneous transcriptomic capture. Unlike antibody-based methods such as CITE-seq, which permit paired profiling but are restricted to targeted protein panels, scMS provides unbiased, genome-scale coverage of the intracellular proteome yet necessitates post hoc integration of unpaired datasets. This diagonal integration challenge, where transcriptomes and proteomes are measured in separate cells lacking shared anchors, remains underserved by existing reviews, which focus predominantly on vertical integration strategies enabled by non-destructive assays. Results We survey the complete computational pipeline for constructing mechanistic proteogenomic networks from unpaired single-cell data, covering: (i) unimodal network inference such as knowledge-based approaches, probabilistic graphical models, temporal directionality inference, and generative and foundation model strategies that establish the transcriptomic scaffold; (ii) cross-modal integration architectures such as network propagation, graph neural networks (scMRDR, scmFormer, scCotag), and consensus frameworks designed explicitly for the unpaired proteomics setting; and (iii) benchmarking paradigms spanning network reconstruction (BEELINE, GRETA, CausalBench) and multi-task integration evaluation (scMultiBench, SCMMIB), with guidance on metric selection under network sparsity and class imbalance. We identify three principal axes of future development: generative proteomic translation from transcriptomic precursors, inductive prior embedding in next-generation architectures, and perturbation-based causal benchmarking. Availability and Implementation This is a review article; no novel software is distributed. A curated benchmark resource table, methods starter guide, and per-method bottleneck annotations are provided in the Supplementary Material. Supplementary Information Supplementary Material is available at Bioinformatics online attached to this article.
Reference Key
openalex_W7163319735 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marcello Barylli, Joyaditya Saha, Tineke E. Buffart, Jan Köster, Kristiaan Lenos, Louis Vermeulen, Roland V Bumbuc, Vivek M. Sheraton
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag353
URL
https://doi.org/10.1093/bioinformatics/btag353
Keywords Keywords not found

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