.png){kind=icon}
Return
Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
.png){kind=icon}
OA
AI
DOI：10.1186/s13059-024-03361-0.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
BackgroundSpatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development.ResultsIn this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets.ConclusionsOur analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.
Keywords:
Spatial transcriptomics
Benchmarking
Clustering
Alignment
Integration
Batch correction
3D reconstruction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
G
IF:
9.4
Papers:
6.4K
Citations:
7.3W
Organization
Cited Papers
ShareSaveSPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies.png)
GENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveShareSaveShareSaveBelayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomicsCELL SYSTEMS
OAAI
CELL SYSTEMSIF7.7
OAAIShareSaveShareSave
View more