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. 2024 Jan 12;16:12. doi: 10.1186/s13073-024-01283-x

Table 1.

Summary of features of the methods for detecting spatial domains. Compared to other methods, SEDR allows the implementation of more types of data and provides more information for downstream analyses, including latent representation and de-noised feature values. In addition, it uses GPU to accelerate calculations

Methods Model Resolution Latent representation De-noising Batch integration Programming GPU
Seurat Principal component analysis Spot or single cell  ×  R  × 
SpatialLDA Latent Dirichlet allocation Single cell  ×   ×  Python  × 
Giotto (HMRF) Hidden Markov random field Spot or single cell  ×   ×   ×  R  × 
stLearn Spaital morphological gene expression normalization Spot or single cell  ×  Python
SpaGene Spatial network (KNN) Single cell  ×   ×  R  × 
SpaGCN Graph convolutional network Spot or single cell  ×   ×  Python  × 
BayesSpace Bayesian model with a Markov random field Spot  ×   ×   ×  R  × 
DeepST Variational graph autoencoder Spot or single cell  ×  Python
STAGATE Graph attention autoencoder Spot or single cell  ×  Python
UTAG Graph + clustering Single cell  ×   ×  Python  × 
SEDR Variational graph autoencoder + masked self-supervised Spot or single cell Python