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scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization

Jiang, Guo ; Song, Kailu ; Fonseca, Gregory J. ; Wagner, Darcy E. LU orcid ; Clark, Iain C. ; Wang, Hui and Ding, Jun (2025) In Nature Communications 16(1).
Abstract

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by... (More)

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
16
issue
1
article number
11656
publisher
Nature Publishing Group
external identifiers
  • pmid:41298467
  • scopus:105026289313
ISSN
2041-1723
DOI
10.1038/s41467-025-66644-5
language
English
LU publication?
yes
id
690326c9-fd03-4741-902e-6a4c1529bea0
date added to LUP
2026-02-11 13:07:07
date last changed
2026-05-21 03:31:38
@article{690326c9-fd03-4741-902e-6a4c1529bea0,
  abstract     = {{<p>Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.</p>}},
  author       = {{Jiang, Guo and Song, Kailu and Fonseca, Gregory J. and Wagner, Darcy E. and Clark, Iain C. and Wang, Hui and Ding, Jun}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization}},
  url          = {{http://dx.doi.org/10.1038/s41467-025-66644-5}},
  doi          = {{10.1038/s41467-025-66644-5}},
  volume       = {{16}},
  year         = {{2025}},
}