Benchmarking clustering, alignment, and integration methods for spatial transcriptomics

• Published in: Genome Biology Vol. 25; no. 1; p. 212
• Main Authors: Hu, Yunfei, Xie, Manfei, Li, Yikang, Rao, Mingxing, Shen, Wenjun, Luo, Can, Qin, Haoran, Baek, Jihoon, Zhou, Xin Maizie
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.08.2024; Springer Nature B.V; BMC
• Subjects: Algorithms; Alignment; Animal Genetics and Genomics; Animals; Batch correction; Benchmarking; Bioinformatics; Biomedical and Life Sciences; Cluster Analysis; Clustering; data quality; Datasets; Deep learning; Evolutionary Biology; Gene expression; Gene Expression Profiling - methods; genome; Histology; Human Genetics; Humans; Integration; Life Sciences; Microbial Genetics and Genomics; Neural networks; Performance evaluation; Plant Genetics and Genomics; reproduction; Sequence Alignment - methods; Software; spatial data; Spatial transcriptomics; species; Transcriptome; Transcriptomics; Alignment; Integration; 3D reconstruction; Spatial transcriptomics; Batch correction; Benchmarking; Clustering
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-024-03361-0

Background Spatial 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. Results In 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. Conclusions Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.