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‎docs/notebooks/spatial_data_visium_hd.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"nbsphinx": "hidden"
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},
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"source": [
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"# Vitessce Widget Tutorial"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Visualization of a SpatialData object"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Import dependencies\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from os.path import join, isfile, isdir\n",
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"from urllib.request import urlretrieve\n",
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"import zipfile\n",
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"\n",
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"from vitessce import (\n",
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" VitessceConfig,\n",
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" ViewType as vt,\n",
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" CoordinationType as ct,\n",
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" CoordinationLevel as CL,\n",
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" SpatialDataWrapper,\n",
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" get_initial_coordination_scope_prefix\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_dir = \"data\"\n",
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"zip_filepath = join(data_dir, \"visium_hd_3.0.0_io.zip\")\n",
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"spatialdata_filepath = join(data_dir, \"visium_hd_3.0.0.spatialdata.zarr\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if not isdir(spatialdata_filepath):\n",
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" if not isfile(zip_filepath):\n",
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" os.makedirs(data_dir, exist_ok=True)\n",
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" urlretrieve('https://s3.embl.de/spatialdata/spatialdata-sandbox/visium_hd_3.0.0_io.zip', zip_filepath)\n",
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" with zipfile.ZipFile(zip_filepath,\"r\") as zip_ref:\n",
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" zip_ref.extractall(data_dir)\n",
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" os.rename(join(data_dir, \"data.zarr\"), spatialdata_filepath)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Rasterize bins\n",
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"Reference: https://spatialdata.scverse.org/en/stable/tutorials/notebooks/notebooks/examples/technology_visium_hd.html#performant-on-the-fly-data-rasterization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from spatialdata import (\n",
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" read_zarr,\n",
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" rasterize_bins,\n",
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" rasterize_bins_link_table_to_labels\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sdata = read_zarr(spatialdata_filepath)\n",
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"sdata"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for bin_size in [\"016\", \"008\", \"002\"]:\n",
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" # rasterize_bins() requires a compresed sparse column (csc) matrix\n",
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" sdata.tables[f\"square_{bin_size}um\"].X = sdata.tables[f\"square_{bin_size}um\"].X.tocsc()\n",
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" rasterized = rasterize_bins(\n",
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" sdata,\n",
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" f\"Visium_HD_Mouse_Small_Intestine_square_{bin_size}um\",\n",
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" f\"square_{bin_size}um\",\n",
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" \"array_col\",\n",
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" \"array_row\",\n",
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" # We want to rasterize to a Labels element, rather than an Image element.\n",
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" return_region_as_labels=True\n",
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" )\n",
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" sdata[f\"rasterized_{bin_size}um\"] = rasterized\n",
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" rasterize_bins_link_table_to_labels(\n",
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" sdata,\n",
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" table_name=f\"square_{bin_size}um\",\n",
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" rasterized_labels_name=f\"rasterized_{bin_size}um\",\n",
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" )\n",
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" sdata.write_element(f\"rasterized_{bin_size}um\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sdata"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Test with spatialdata-plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import spatialdata_plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sdata.pl.render_images(\"Visium_HD_Mouse_Small_Intestine_full_image\").pl.render_labels(\"rasterized_016um\").pl.show(\"Visium_HD_Mouse_Small_Intestine\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Configure Vitessce\n",
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"\n",
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"Vitessce needs to know which pieces of data we are interested in visualizing, the visualization types we would like to use, and how we want to coordinate (or link) the views."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"vc = VitessceConfig(\n",
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" schema_version=\"1.0.18\",\n",
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" name='Visium HD SpatialData Demo',\n",
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")\n",
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"# Add data to the configuration:\n",
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"wrapper = SpatialDataWrapper(\n",
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" sdata_path=spatialdata_filepath,\n",
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" # The following paths are relative to the root of the SpatialData zarr store on-disk.\n",
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" image_path=\"images/Visium_HD_Mouse_Small_Intestine_full_image\",\n",
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" table_path=\"tables/square_016um\",\n",
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" obs_feature_matrix_path=\"tables/square_016um/X\",\n",
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" obs_segmentations_path=\"labels/rasterized_016um\",\n",
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" #region=\"CytAssist_FFPE_Human_Breast_Cancer\",\n",
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" coordinate_system=\"Visium_HD_Mouse_Small_Intestine\",\n",
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" coordination_values={\n",
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" # The following tells Vitessce to consider each observation as a \"bin\"\n",
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" \"obsType\": \"bin\",\n",
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" }\n",
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")\n",
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"dataset = vc.add_dataset(name='Visium HD').add_object(wrapper)\n",
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"\n",
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"# Add views (visualizations) to the configuration:\n",
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"spatial = vc.add_view(\"spatialBeta\", dataset=dataset)\n",
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"feature_list = vc.add_view(\"featureList\", dataset=dataset)\n",
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"layer_controller = vc.add_view(\"layerControllerBeta\", dataset=dataset)\n",
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"vc.link_views_by_dict([spatial, layer_controller], {\n",
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" 'imageLayer': CL([{\n",
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" 'photometricInterpretation': 'RGB',\n",
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" }]),\n",
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"}, scope_prefix=get_initial_coordination_scope_prefix(\"A\", \"image\"))\n",
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"obs_sets = vc.add_view(vt.OBS_SETS, dataset=dataset)\n",
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"vc.link_views([spatial, layer_controller, feature_list, obs_sets], ['obsType'], [wrapper.obs_type_label])\n",
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"\n",
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"# Layout the views\n",
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"vc.layout(spatial | (feature_list / layer_controller / obs_sets));"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Render the widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"vw = vc.widget()\n",
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"vw"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"print(json.dumps(vc.to_dict(base_url=f\"http://localhost:{vw.port}\")))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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