update

README.md

@@ -16,7 +16,7 @@ tags:
 This is the PyTorch implementation of the CI-FM model -- an AI model that can simulate the biological activities within a living tissue (AI virtual tissue).
 The current version of CI-FM has 138M parameters and is trained on around 23M cells of spatial genomics. The signature functions of CI-FM are:
 - **Embedding** of celllular microenvironments via ```embeddings = model.embed(adata)``` (Figure below panel D top);
-- **Inference** of cellular gene expressions within a certain microenvironment via ```expressions = model.predict_cells_at_locations(adata, rand_loc)``` (Figure below panel D bottom).
+- **Inference** of cellular gene expressions within a certain microenvironment via ```expressions = model.predict_cells_at_locations(adata, target_loc)``` (Figure below panel D bottom).
 
 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).

test.ipynb

@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -243,14 +243,15 @@
     }
    ],
    "source": [
-    "rand_loc = np.random.rand(10, 2)\n",
+    "# we here randomly generate the locations for the cells just for demonstration\n",
+    "target_loc = np.random.rand(10, 2)\n",
     "x_min, x_max = adata.obsm['spatial'][:, 0].min(), adata.obsm['spatial'][:, 0].max()\n",
     "y_min, y_max = adata.obsm['spatial'][:, 1].min(), adata.obsm['spatial'][:, 1].max()\n",
-    "rand_loc[:, 0] = rand_loc[:, 0] * (x_max - x_min) + x_min\n",
-    "rand_loc[:, 1] = rand_loc[:, 1] * (y_max - y_min) + y_min\n",
+    "target_loc[:, 0] = target_loc[:, 0] * (x_max - x_min) + x_min\n",
+    "target_loc[:, 1] = target_loc[:, 1] * (y_max - y_min) + y_min\n",
     "\n",
     "with torch.no_grad():\n",
-    "    expressions = model.predict_cells_at_locations(adata, rand_loc)\n",
+    "    expressions = model.predict_cells_at_locations(adata, target_loc)\n",
     "expressions, expressions.shape"
    ]
   }