.png){kind=icon}
Return
Benchmarking alignment methods for spatial transcriptomics data
.png){kind=icon}
PRE
AI
DOI：10.1038/s43588-026-00977-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Reconstructing the three-dimensional molecular architecture of tissues from two-dimensional spatial transcriptomics slices is a central goal in spatial biology. Spatial alignment, the computational registration of multiple tissue slices using their spatial coordinates and gene expression profiles, provides the foundational framework for this integrative perspective. Although numerous alignment methods have emerged, a comprehensive benchmark to guide their application has been notably absent. Here we address this by systematically evaluating a diverse suite of leading methods. Executing 295 distinct alignment tasks across diverse datasets and technologies, our framework quantifies method accuracy, efficiency, usability and robustness, while also assessing the downstream impact of alignment quality. Crucially, our study systematically investigates performance in challenging real-world scenarios, uncovering substantial limitations in current tools. To address these bottlenecks, we propose and validate effective mitigation strategies. Finally, we provide practical guidelines to assist researchers in selecting the optimal alignment method and optimizing their analytical workflows. A benchmark to evaluate spatial alignment methods for spatial transcriptomics shows that the best approach is dataset dependent and offers actionable guidance for the future development of methods.
Keywords:
Computational models
Data acquisition
Transcriptomics
Computer Science
general
Journal
IF:
18.3
Papers:
3.1K
Citations:
4.0K
Organization
Cited Papers
ShareSaveSANTO: a coarse-to-fine alignment and stitching method for spatial omics.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveA high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brainNatureOAAI
NatureIF0
OAAIShareSaveShareSave
ShareSaveSlide-seq: A scalable technology for measuring genome-wide expression at high spatial resolutionScienceOAAI
ScienceIF0
OAAIShareSaveSingle-cell systems biology by super-resolution imaging and combinatorial labelingNATURE METHODSOAAI
NATURE METHODSIF32.1
OAAIShareSave
View more