A comprehensive comparison on clustering methods for multi-slice spatially resolved transcriptomics data analysis.

Spatial transcriptomics (ST) data, by providing spatial information, enable simultaneous analysis of gene expression distributions and their spatial patterns within tissue. Clustering or spatial domain detection represents an essential methodology for ST data, facilitating the exploration of spatial organizations with shared gene expression or histological characteristics. Traditionally, clustering algorithms for ST have focused on individual tissue sections. However, the emergence of numerous contiguous tissue sections derived from the same or similar tissue specimens within or across individuals has led to the development of multi-slice clustering methods. In this study, we assess seven single-slice and four multi-slice clustering methods on two simulated datasets and four real datasets. Additionally, we investigate the effectiveness of preprocessing techniques, including spatial coordinate alignment (e.g. PASTE) and gene expression batch effect removal (e.g. Harmony), on clustering performance. Our study provides a comprehensive comparison of clustering methods for multi-slice ST data, serving as a practical guide for method selection in various scenarios.