Update URL links

README.md

@@ -12,7 +12,7 @@
 
 </div>
 
-`DECIPHER` aims to learn cells’ disentangled embeddings from intracellular state and extracellular contexts view based on single-cell spatial omics data.
+`DECIPHER` aims to learn cells’ disentangled embeddings from intracellular state and extracellular context views based on spatial omics data.
 
 ![DECIPHER](./docs/_static/Model.png)
 
@@ -48,35 +48,43 @@ Here is a minimal example for quick start:
 ```python
 import scanpy as sc
 from decipher import DECIPHER
+from decipher.utils import scanpy_viz
 
 # Init model
 model = DECIPHER(work_dir='/path/to/work_dir')
 
-# Register data
+# Register data (adata.X is raw counts, adata.obsm['spatial'] is spatial coordinates)
 adata = sc.read_h5ad('/path/to/adata.h5ad')
 model.register_data(adata)
 
-# Fit model
+# Fit DECIPHER model
 model.fit_omics()
 
-# Get disentangled omics and spatial embeddings
-omics_emb = model.center_emb
-spatial_emb = model.nbr_emb
+# Clustering disentangled embeddings
+adata.obsm['X_center'] = model.center_emb  # intracellular embedding
+adata.obsm['X_nbr'] =  model.nbr_emb  # context embedding
+adata = scanpy_viz(adata, ['center', 'nbr'], rapids=False)
+
+# Plot
+adata.obsm['X_umap'] = adata.obsm['X_umap_center'].copy()
+sc.pl.umap(adata, color=['cell_type'])
+adata.obsm['X_umap'] = adata.obsm['X_umap_nbr'].copy()
+sc.pl.umap(adata, color=['region'])
 ```
 
 ### Tutorials
-> Please check [**documentation**](https://slat.readthedocs.io/en/latest/) for all tutorials.
+<!-- > Please check [**documentation**](https://slat.readthedocs.io/en/latest/) for all tutorials. -->
 
 | Name                                    | Description                                                  | Colab                                                        |
 | --------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
-| [Basic Model Tutorial]()                | Tutorial on how to use DECIPHER                            | [![Open In Colab](https://img.shields.io/badge/Colab-PyTorch-blue?logo=googlecolab)](https://colab.research.google.com/drive/14PEtrgqlf-KbLOTfBLc9gbx0YvY6mi0S?usp=sharing) |
-| [Multi-slices with Batch Effects]()     | Tutorial on how to apply DECIPHER to multiple slices with batch effects | [![Open In Colab](https://img.shields.io/badge/Colab-PyTorch-blue?logo=googlecolab)](https://colab.research.google.com/drive/1eLJeRDZFq2tlDUWpETlSxVUdzRv9CeSD?usp=sharing) |
-| [Identify Localization-related LRs]() | Tutorial on how to identify ligand-receptors which related wtih cells’ localization based on DECIPHER embeddings | Insufficient resources |
-| [Multi-GPUs Training]()                        | Tutorial on how to use DECIPHER with multi-GPUs on spatial atlas | Insufficient resources |
+| [Basic Model Tutorial](./docs/tutorials/1-train_model.ipynb)                | Tutorial on how to use DECIPHER                            | [![Open In Colab](https://img.shields.io/badge/Colab-PyTorch-blue?logo=googlecolab)](https://colab.research.google.com/drive/14PEtrgqlf-KbLOTfBLc9gbx0YvY6mi0S?usp=sharing) |
+| [Multi-slices with Batch Effects](./docs/tutorials/2-remove_batch.ipynb)     | Tutorial on how to apply DECIPHER to multiple slices with batch effects | [![Open In Colab](https://img.shields.io/badge/Colab-PyTorch-blue?logo=googlecolab)](https://colab.research.google.com/drive/1eLJeRDZFq2tlDUWpETlSxVUdzRv9CeSD?usp=sharing) |
+| [Identify Localization-related LRs](./docs/tutorials/3-select_LRs.ipynb) | Tutorial on how to identify ligand-receptors which related wtih cells’ localization based on DECIPHER embeddings | Insufficient resources |
+| [Multi-GPUs Training](./docs/tutorials.md#multi-gpu-training)                        | Tutorial on how to use DECIPHER with multi-GPUs on spatial atlas | Insufficient resources |
 
 
 ## Citation
-TBD
+In preparation.
 
 > If you want to repeat our benchmarks and case studies, please check the [**benchmark**](./benchmark/README.md) and [**experiments**](./experiments/README.md) folder.
 
@@ -85,6 +93,9 @@ TBD
 
 We observed `model.train_gene_select()` function in [Identify Localization-related LRs]() tutorial  (700,000 cells and 1,000 genes) uses ~40G GPU memory. If your GPU do not have enough memory, you still can train model on GPU but disabling GPU in `train_gene_select()` function.
 
+2. Visium or ST data
+
+DECIPHER is designed for single cell resolution data. As for Visium or ST, DECIPHER still can be applied after obtaining single-cell resolution through deconvolution or spatial mapping strategies.
 
 ## Acknowledgement
 We thank following great open-source projects for their help or inspiration: