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‎.coveragerc_omit‎

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omit =
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vitessce/config.py
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vitessce/export.py
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vitessce/file_def_utils.py
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vitessce/routes.py
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vitessce/widget.py
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vitessce/wrappers.py

‎.github/workflows/test.yml‎

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runs-on: ubuntu-latest
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strategy:
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matrix:
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version: ['3.8', '3.12']
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version: ['3.9', '3.12']
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steps:
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- uses: actions/checkout@v2
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- uses: actions/setup-python@v2

‎LICENSE‎

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MIT License
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Copyright (c) 2020 Gehlenborg Lab
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Copyright (c) 2020 HIDIVE Lab
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal

‎docs/conf.py‎

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# -- Project information -----------------------------------------------------
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project = 'vitessce'
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copyright = '2020, Gehlenborg Lab'
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author = 'Gehlenborg Lab'
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copyright = '2020, HIDIVE Lab'
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author = 'HIDIVE Lab'
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# -- General configuration ---------------------------------------------------

‎docs/data_examples.rst‎

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notebooks/data_export_s3
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notebooks/data_export_files
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notebooks/widget_brain_with_base_dir
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notebooks/widget_brain_with_base_dir
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notebooks/widget_brain_h5ad

‎docs/notebooks/spatial_data.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.spatialdata.zarr.zip\")\n",
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"spatialdata_filepath = join(data_dir, \"visium.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_associated_xenium_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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"## 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=\"1.0.16\",\n",
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" name='Visium SpatialData Demo (visium_associated_xenium_io)',\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/CytAssist_FFPE_Human_Breast_Cancer_full_image\",\n",
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" table_path=\"table/table\",\n",
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" obs_feature_matrix_path=\"table/table/X\",\n",
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" obs_spots_path=\"shapes/CytAssist_FFPE_Human_Breast_Cancer\",\n",
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" region=\"CytAssist_FFPE_Human_Breast_Cancer\",\n",
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" coordinate_system=\"global\",\n",
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" coordination_values={\n",
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" # The following tells Vitessce to consider each observation as a \"spot\"\n",
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" \"obsType\": \"spot\",\n",
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" }\n",
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")\n",
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"dataset = vc.add_dataset(name='Breast Cancer Visium').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(vt.FEATURE_LIST, 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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}
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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": "3.9.0"
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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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}

‎docs/notebooks/widget_brain_h5ad.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 single-cell RNA seq data"
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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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"from anndata import read_h5ad\n",
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"import scanpy as sc\n",
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"import json\n",
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"\n",
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"from vitessce import (\n",
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" VitessceConfig,\n",
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" Component as cm,\n",
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" CoordinationType as ct,\n",
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" AnnDataWrapper,\n",
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")\n",
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"from vitessce.data_utils import (\n",
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" generate_h5ad_ref_spec\n",
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")"
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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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"## 0. Download data"
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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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"h5_url = \"https://datasets.cellxgene.cziscience.com/84df8fa1-ab53-43c9-a439-95dcb9148265.h5ad\""
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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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"adata_filepath = join(\"data\", \"84df8fa1-ab53-43c9-a439-95dcb9148265.h5ad\")\n",
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"if not isfile(adata_filepath):\n",
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" os.makedirs(\"data\", exist_ok=True)\n",
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" urlretrieve(h5_url, adata_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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"## 1. Create a Reference Spec JSON file for the H5AD file\n",
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"\n",
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"In order for Vitessce to load H5AD files, we also need to provide a corresponding [Reference Spec](https://fsspec.github.io/kerchunk/spec.html) JSON file which contains mappings between AnnData object keys and the byte offsets at which those AnnData object values begin within the H5AD file binary contents."
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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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"json_filepath = join(\"data\", \"84df8fa1-ab53-43c9-a439-95dcb9148265.h5ad.reference.json\")\n",
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"if not isfile(json_filepath):\n",
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" ref_dict = generate_h5ad_ref_spec(h5_url)\n",
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" with open(json_filepath, \"w\") as f:\n",
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" json.dump(ref_dict, f)"
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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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"tags": []
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},
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"source": [
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"## 2. Create the Vitessce widget configuration\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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"vc = VitessceConfig(schema_version=\"1.0.17\", name='Nakshatri et al', description='snRNA-seq analyses of breast tissues of healthy women of diverse genetic ancestry')\n",
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"\n",
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"dataset = vc.add_dataset(name='84df8fa1').add_object(AnnDataWrapper(\n",
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" adata_path=adata_filepath,\n",
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" ref_path=json_filepath, # We specify paths to both the H5AD and JSON files\n",
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" obs_embedding_paths=[\"obsm/X_wnn.umap\"],\n",
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" obs_embedding_names=[\"UMAP\"],\n",
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" obs_set_paths=[\"obs/cell_type\"],\n",
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" obs_set_names=[\"Cell Type\"],\n",
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" obs_feature_matrix_path=\"X\",\n",
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" )\n",
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")\n",
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"\n",
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"scatterplot = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\n",
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"cell_sets = vc.add_view(cm.OBS_SETS, dataset=dataset)\n",
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"cell_set_sizes = vc.add_view(cm.OBS_SET_SIZES, dataset=dataset)\n",
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"genes = vc.add_view(cm.FEATURE_LIST, dataset=dataset)\n",
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"\n",
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"vc.layout((scatterplot | cell_sets) / (cell_set_sizes | genes));"
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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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"tags": []
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},
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"source": [
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"## 3. Create 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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}
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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": "3.9.0"
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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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