Add notebook

Author: keller-mark

docs/notebooks/widget_anndata_with_image.ipynb

@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "source": [
+    "# Vitessce Widget Tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Visualization of AnnData object containing an image in `uns`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: This approach to storing images within AnnData objects is no longer recommended now that [SpatialData](https://spatialdata.scverse.org/en/stable/) has been introduced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scanpy as sc\n",
+    "import numpy as np\n",
+    "from vitessce.data_utils import rgb_img_to_ome_zarr, VAR_CHUNK_SIZE\n",
+    "from vitessce import (\n",
+    "    VitessceConfig,\n",
+    "    AnnDataWrapper,\n",
+    "    ImageOmeZarrWrapper,\n",
+    ")\n",
+    "from os.path import join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_img = join(\"data\", \"V1_Human_Lymph_Node.ome.zarr\")\n",
+    "output_adata = join(\"data\", \"V1_Human_Lymph_Node.anndata.zarr\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata = sc.datasets.visium_sge(sample_id=\"V1_Human_Lymph_Node\", include_hires_tiff=True)\n",
+    "\n",
+    "# Write img_arr to OME-Zarr.\n",
+    "# Need to convert images from interleaved to non-interleaved (color axis should be first).\n",
+    "img_hires = adata.uns['spatial']['V1_Human_Lymph_Node']['images']['hires']\n",
+    "img_arr = np.transpose(img_hires, (2, 0, 1))\n",
+    "# Convert values from [0, 1] to [0, 255].\n",
+    "img_arr *= 255.0\n",
+    "\n",
+    "# First, save the image to an OME-Zarr image format\n",
+    "rgb_img_to_ome_zarr(img_arr, output_img, axes=\"cyx\", chunks=(1, 256, 256), img_name=\"Image\")\n",
+    "# Second, save the AnnData object to Zarr format\n",
+    "adata.write_zarr(output_adata, chunks=[adata.shape[0], VAR_CHUNK_SIZE])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vc = VitessceConfig(schema_version=\"1.0.17\", name=\"AnnData with image\")\n",
+    "dataset = vc.add_dataset(\"My dataset\").add_object(\n",
+    "    AnnDataWrapper(\n",
+    "        adata_path=output_adata,\n",
+    "        \n",
+    "    )\n",
+    ").add_object(\n",
+    "    ImageOmeZarrWrapper(\n",
+    "        img_path=output_img,\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "spatial_view = vc.add_view(\"spatialBeta\", dataset=dataset)\n",
+    "lc_view = vc.add_view(\"layerControllerBeta\", dataset=dataset)\n",
+    "\n",
+    "vc.layout(spatial_view | lc_view);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vw = vc.widget()\n",
+    "vw"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

src/vitessce/data_utils/ome.py

@@ -99,7 +99,9 @@ def rgb_img_to_ome_zarr(img_arr, output_path, img_name="Image", chunks=(1, 256,
     z_root.attrs["omero"] = {
         "name": img_name,
         "version": "0.3",
-        "rdefs": {},
+        "rdefs": {
+            "model": "color",
+        },
         "channels": [
             {
                 "label": "R",
@@ -156,7 +158,9 @@ def multiplex_img_to_ome_zarr(img_arr, channel_names, output_path, img_name="Ima
     z_root.attrs["omero"] = {
         "name": img_name,
         "version": "0.3",
-        "rdefs": {},
+        "rdefs": {
+            "model": "greyscale",
+        },
         "channels": [
             {
                 "label": channel_name,