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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
.... Order determined by a coin flip. Corresponding author: braphael@princeton.eduAbstractSpatially resolved transcriptomics (SRT) technologies measure gene expression across thousands of spatial locations within a tissue slice. Multiple SRT technologies are currently available and others are in active...
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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
...). Therefore, there is a need to develop computational tools50 to address the limitations of different platforms and improve the reliability of the51 interpretation of spatial transcriptomics.52 Efforts have been made to address the limitations of these two platforms. For53 imaging-based technologies...
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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
.... We evaluate the performance of geneCover across various scRNA-seq and spatial transcriptomics data sets, comparing it to other label-free algorithms to highlight its utility and potential in diverse biological contexts.The identification of marker genes plays a critical role in advancing our...
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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
...RNA-seq transcriptomics data (Gan et al. 2018; Cui et al. 2021). However, to the best of our knowledge, consensus clustering frameworks crafted for spatial transcriptomics (ST) data remain scarce. Systematic assessments of clustering accuracy and robustness across various consensus frameworks are lacking.To address...
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Method: High-resolution spatial transcriptomics in fixed tissue using a cost-effective PCL-seq workflow
- Xue Dong,
- Mengzhu Hu,
- Xiaonan Cui,
- Wenjian Zhou,
- Jingtao Cai,
- Guangyao Mao,
- and Weiyang Shi
Genome Res. September 2025 35: 2052-2063; Published in Advance August 5, 2025, doi:10.1101/gr.279906.124
.... 2023; Shiau et al. 2023).Existing spatial transcriptomics approaches can be broadly classified into two categories. The first involves analyzing gene expression across entire tissue sections using either gene-specific in situ hybridization (ISH) probe pools (Rahman and Zenklusen 2013; Chen et al. 2015...
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Method: Harnessing agent-based frameworks in CellAgentChat to unravel cell–cell interactions from single-cell and spatial transcriptomics
- Vishvak Raghavan,
- Yumin Zheng,
- Yue Li,
- and Jun Ding
Genome Res. July 2025 35: 1646-1663; Published in Advance May 2, 2025, doi:10.1101/gr.279771.124
...), COMMOT (Cang et al. 2023), Scriabin (Wilk et al. 2024), Connectome (Raredon et al. 2022), SingleCellSignalR (Cabello-Aguilar et al. 2020), NATMI (Hou et al. 2020), and scSeqComm (Baruzzo et al. 2022). We evaluated the performance of all these methods across five different spatial transcriptomics data...
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Method: A unified analysis of atlas single-cell data
- Hao Chen,
- Nam D. Nguyen,
- Matthew Ruffalo,
- and Ziv Bar-Joseph
Genome Res. May 2025 35: 1219-1233; Published in Advance February 18, 2025, doi:10.1101/gr.279631.124
...and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells...
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Method: Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains
- Hang Zhang,
- Yi Zhang,
- Kai Ming Ting,
- Jie Zhang,
- and Qiuran Zhao
Genome Res. February 2025 35: 355-367; Published in Advance February 5, 2025, doi:10.1101/gr.278983.124
...to this work. Corresponding authors: tingkm@nju.edu.cn, zhangj_ai@nju.edu.cnAbstractSpatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data...
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Method: Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data
- Lulu Yan,
- Jinyu Cheng,
- Qing Nie,
- and Xiaoqiang Sun
Genome Res. June 2025 35: 1400-1414; Published in Advance April 22, 2025, doi:10.1101/gr.279857.124
...Dissecting multilayer cell–cell communications with signaling feedback loops from spatial transcriptomics data Lulu Yan1,4, Jinyu Cheng2,4, Qing Nie3 and Xiaoqiang Sun1 1School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China; 2Zhongshan School of Medicine, Sun Yat-sen University...
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Method: Characterizing tandem repeat complexities across long-read sequencing platforms with TREAT and otter
- Niccoló Tesi,
- Alex Salazar,
- Yaran Zhang,
- Sven van der Lee,
- Marc Hulsman,
- Lydian Knoop,
- Sanduni Wijesekera,
- Jana Krizova,
- Anne-Fleur Schneider,
- Maartje Pennings,
- Kristel Sleegers,
- Erik-Jan Kamsteeg,
- Marcel Reinders,
- and Henne Holstege
Genome Res. November 2024 34: 1942-1953; Published in Advance October 15, 2024, doi:10.1101/gr.279351.124
...of TRs; however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterization, visualization...
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