From: Unsupervised spatially embedded deep representation of spatial transcriptomics
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 | √ |