Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.

ABSTRACT:

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, spec

SUBMITTER: Hu Y 

PROVIDER: S-EPMC11312151 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature