update

README.md

@@ -21,7 +21,7 @@ The current version of CI-FM has 138M parameters and is trained on around 23M ce
 The detailed usage of the model can be found in the [tutorial](https://huggingface.co/ynyou/CIFM/blob/main/test.ipynb).
 Before running the tutorial, please set up an environment following the [environment instruction](https://huggingface.co/ynyou/CIFM#environment).
 
-More information about the model can be found in the [preprint]().
+More information about the model can be found in the [preprint](https://www.biorxiv.org/content/10.1101/2025.01.25.634867v1).
 
 ![](./figures/cifm.png)
 ![](./figures/autoregressive.gif)
@@ -57,5 +57,12 @@ pip install torch-scatter torch-sparse torch-cluster torch-geometric -f https://
 ## Citation
 If you use this code for you research, please cite our paper.
 ```
-TBD
+@misc{you2025cifm,
+      title={Building Foundation Models to Characterize Cellular Interactions via Geometric Self-Supervised Learning on Spatial Genomics}, 
+      author={You, Yuning and Wang, Zitong and Fleisher, Kevin and Liu, Rex and Thomson, Matt},
+      year={2025},
+	  elocation-id = {2025.01.25.634867},
+      archivePrefix={bioRxiv},
+      url={https://www.biorxiv.org/content/early/2025/01/27/2025.01.25.634867}, 
+}
 ```

test.ipynb

@@ -257,6 +257,17 @@
     "    expressions = model.predict_cells_at_locations(adata, target_locs)\n",
     "expressions, expressions.shape"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you can convert it into normalize counts\n",
+    "counts_normalized = np.exp(expressions) - 1\n",
+    "counts_normalized = counts_normalized / counts_normalized.sum(axis=1, keepdims=True)"
+   ]
   }
  ],
  "metadata": {