ARIEL-data

We develop a fast, omics-driven sample integration tool called ARIEL, which enables effortless landmark detection, spatial alignment and information transfer for multi-sample spatial transcriptomic data. ARIEL can be applied in diverse situation, including cross-individual, cross-platform, cross-resolution, cross-omics, and cross-disease scenarios.
The detailed analysis procedures and results with jupyter notebook presented in this paper are available on the Read the Docs tutorial website: https://ariel-document.readthedocs.io/.
And the code repository of ARIEL is available at: https://github.com/shilab-ecnu/ARIEL.