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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 Lê Cao
Genome Res. April 2026 36: 785-801; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
..., Canberra 2601, Australia; 3Bioinformatics and Cellular Genomics, St Vincent’s Institute, Fitzroy, Victoria 3065, Australia ↵4 These authors contributed equally to this work. Corresponding author: kimanh.lecao@unimelb.edu.auAbstractGene networks provide a fundamental framework for understanding...
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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 transcriptomic data 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. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
...that using embeddings from Enformer gave us the best cell-type resolution, whereas other embeddings such as text-based and random-based did not help much. In our comparison for the choices of encoder, we found that modeling the spatial information with graph neural network did not directly contribute...
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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: 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: 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: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
- Quanyou Cai,
- Lihui Lin,
- Xin Liu,
- and Jiekai Chen
Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
.... Corresponding authors: chen_jiekai@gibh.ac.cn, lin_lihui@gibh.ac.cnAbstractIntegration of single-cell and spatial transcriptomes represents a fundamental strategy to enhance spatial data quality. However, existing methods for mapping single-cell data to spatial coordinates struggle with large-scale data sets...
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