SEAM is a scalable platform combining high resolution imaging mass spectrometry (IMS) and a series of computational algorithms, that can display multiscale/multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints.
This repo provides notebooks for tutorial and demonstration, figure regeneration, method comparison, and spatial transcriptome analysis.
Note that the revised version of SEAM can be found in another repo "SEAM_revision". We maintain this original version of SEAM to better assess the change for reviewers.
Please cite: Yuan Z, Zhou Q, Cai L, et al. SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment[J]. Nature Methods, 2021, 18(10): 1223-1232.