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Method: Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies
- Jianing Yao,
- Jinglun Yu,
- Brian Caffo,
- Stephanie C. Page,
- Keri Martinowich,
- and Stephanie C. Hicks
Genome Res. July 2025 35: 1621-1632; Published in Advance May 20, 2025, doi:10.1101/gr.279380.124
...of tools aims to leverage more than one omic data modality, while also leveraging the spatial coordinates. SpaGCN (Hu et al. 2021a) combines gene expression, spatial information, and histology image for spatial clustering using a graph convolutional neural network. However, this tool is designed to work...
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Method: Efficient integration of spatial omics data for joint domain detection, matching, and alignment with stMSA
- Han Shu,
- Jing Chen,
- Chang Xu,
- Jialu Hu,
- Yongtian Wang,
- Jiajie Peng,
- Qinghua Jiang,
- Xuequn Shang,
- and Tao Wang
Genome Res. October 2025 35: 2285-2299; Published in Advance August 14, 2025, doi:10.1101/gr.280584.125
.... Corresponding authors: qhjiang@hit.edu.cn, Shang@nwpu.edu.cn, twang@nwpu.edu.cnAbstractSpatial omics (SOs) are powerful methodologies that enable the study of genes, proteins, and other molecular features within the spatial context of tissue architecture. With the growing availability of SO data sets...
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Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
- Wenhao Zhang,
- Lan Cao,
- Xiaoxuan Gu,
- Yongyu Long,
- and Ying Wang
Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
..., and interpretable exploration of causal GRNs with prior knowledge and multi-omics data.Gene regulatory networks (GRNs), which encapsulate the complex interactions among transcription factors (TFs), target genes, and various regulatory elements, constitute the core machinery of gene regulation (Levine and Davidson...
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Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
- Yidi Deng,
- Jiadong Mao,
- Jarny Choi,
- and Kim-Anh Le Cao
Genome Res. March 5, 2026 gr.281171.125; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
...1Scalable cell-specific coexpression networks for granular regulatory 2 pattern discovery with NeighbourNet 3 Yidi Deng1,2, Jiadong Mao1,† & Jarny Choi3,† & Kim-Anh Lê Cao1,*,† 4 1Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, 5 3010, Australia 6...
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Method: Unified integration of spatial transcriptomics across platforms with LLOKI
- Ellie Haber,
- Ajinkya Deshpande,
- Jian Ma,
- and Spencer Krieger
Genome Res. December 2025 35: 2722-2733; Published in Advance November 13, 2025, doi:10.1101/gr.280803.125
..., Pittsburgh, Pennsylvania 15213, USA Corresponding authors: jianma@cs.cmu.edu, skrieger@andrew.cmu.eduAbstractSpatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene...
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Method: spRefine denoises and imputes spatial transcriptomics with a reference-free framework powered by genomic language model
- Tianyu Liu,
- Tinglin Huang,
- Wengong Jin,
- Tinyi Chu,
- Rex Ying,
- and Hongyu Zhao
Genome Res. February 3, 2026 gr.281001.125; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...ison for the choices of encoder, we found that modeling the spatial information with178 graph neural network did not directly contribute to this task. This is explainable as179 different cells and cell types might have different spatial variation. In our comparison180 for the loss function design, we...
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Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
- An Wang,
- Stephanie Hicks,
- Donald Geman,
- and Laurent Younes
Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
...understanding of cellular and spatial heterogeneity at the transcriptomic level. With the continuous expansion of scRNA-seq and spatial omics data, the ability to identify informative marker gene panels has become essential for characterizing distinct cell states and their spatial distribution within tissues...
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Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
- Congcong Hu,
- Nana Wei,
- Jiyuan Yang,
- Hua-Jun Wu,
- and Xiaoqi Zheng
Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
...in SRT data, which can be utilized for downstream analysis tasks such as clustering, trajectory inference, and batch effect correction. SpaGCN is a graph convolutional network method to integrate multimodal data, including expression, spatial location, and histology to detect spatial domains. STAGATE...
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Method: Joint imputation and deconvolution of gene expression across spatial transcriptomics platforms
- Hongyu Zheng,
- Hirak Sarkar,
- and Benjamin J. Raphael
Genome Res. December 2025 35: 2734-2743; Published in Advance November 17, 2025, doi:10.1101/gr.280555.125
..., Fernández Navarro J, Lundeberg J. 2020. Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography. Commun Biol 3: 565. doi:10.1038/s42003-020-01247-y ↵Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W, Stegle O. 2018. Multi-Omics...
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Method: Integration of high-throughput proteomic data and complementary omics layers with PriOmics
- Robin Kosch,
- Katharina Limm,
- Annette M. Staiger,
- Nadine S. Kurz,
- Nicole Seifert,
- Bence Oláh,
- Stefan Solbrig,
- Viola Poeschel,
- Gerhard Held,
- Marita Ziepert,
- Norbert Schmitz,
- Emil Chteinberg,
- Reiner Siebert,
- Rainer Spang,
- Helena U. Zacharias,
- German Ott,
- Peter J. Oefner,
- and Michael Altenbuchinger
Genome Res. January 2026 36: 197-213; Published in Advance November 18, 2025, doi:10.1101/gr.279487.124
...-translational modifications (CTMs/PTMs). Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics/phenotypic information. Graphical models are particularly suited to study molecular networks and underlying regulatory mechanisms, as they can distinguish direct from...

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