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Add notebook. Upgrade js pkg version. Add support for obsFeatureColumns in anndata wrapper
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‎docs/notebooks/widget_brain_with_quality_metric.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": "markdown",
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"metadata": {},
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"source": [
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"## 1. Import dependencies\n",
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"\n",
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"We need to import the classes and functions that we will be using from the corresponding packages."
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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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"\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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" optimize_adata,\n",
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" VAR_CHUNK_SIZE,\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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"## 2. Download the data\n",
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"\n",
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"For this example, we need to download a dataset from the COVID-19 Cell Atlas https://www.covid19cellatlas.org/index.healthy.html#habib17."
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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\", \"habib17.processed.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('https://covid19.cog.sanger.ac.uk/habib17.processed.h5ad', 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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"## 3. Load the data\n",
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"\n",
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"Note: this function may print a `FutureWarning`"
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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 = read_h5ad(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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"tags": []
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},
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"source": [
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"## 3.1. Preprocess the Data For Visualization\n",
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"\n",
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"This dataset contains 25,587 genes. We prepare to visualize the top 50 highly variable genes for the heatmap as ranked by dispersion norm, although one may use any boolean array filter for the heatmap."
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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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"top_dispersion = adata.var[\"dispersions_norm\"][\n",
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" sorted(\n",
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" range(len(adata.var[\"dispersions_norm\"])),\n",
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" key=lambda k: adata.var[\"dispersions_norm\"][k],\n",
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" )[-51:][0]\n",
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"]\n",
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"adata.var[\"top_highly_variable\"] = (\n",
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" adata.var[\"dispersions_norm\"] > top_dispersion\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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"## 3.2 Save the Data to Zarr store\n",
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"\n",
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"We want to convert the original `h5ad` file to a [Zarr](https://zarr.readthedocs.io/en/stable/) store, which Vitessce is able to load. We can use the `optimize_adata` function to ensure that all arrays and dataframe columns that we intend to use in our visualization are in the optimal format to be loaded by Vitessce. This function will cast arrays to numerical data types that take up less space (as long as the values allow). Note: unused arrays and columns (i.e., not specified in any of the parameters to `optimize_adata`) will not be copied into the new AnnData object."
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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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"zarr_filepath = join(\"data\", \"habib17.h5ad.zarr\")\n",
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"if not isdir(zarr_filepath):\n",
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" adata.write_zarr(zarr_filepath, chunks=[adata.shape[0], VAR_CHUNK_SIZE])"
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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=\"1.0.17\",\n",
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" name='Habib et al',\n",
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" description='COVID-19 Healthy Donor Brain'\n",
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")\n",
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"\n",
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"# Add data.\n",
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"dataset = vc.add_dataset(name='Brain').add_object(AnnDataWrapper(\n",
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" adata_path=zarr_filepath,\n",
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" obs_embedding_paths=[\"obsm/X_umap\"],\n",
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" obs_embedding_names=[\"UMAP\"],\n",
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" obs_set_paths=[\"obs/CellType\"],\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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" initial_feature_filter_path=\"var/top_highly_variable\",\n",
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" coordination_values={\n",
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" \"obsType\": 'cell',\n",
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" \"featureType\": 'gene',\n",
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" \"featureValueType\": 'expression',\n",
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" },\n",
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")).add_object(AnnDataWrapper(\n",
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" adata_path=zarr_filepath,\n",
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" obs_feature_column_paths=[\"obs/percent_mito\"],\n",
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" coordination_values={\n",
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" \"obsType\": 'cell',\n",
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" \"featureType\": 'qualityMetric',\n",
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" \"featureValueType\": 'value',\n",
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" }\n",
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"))\n",
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"\n",
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"# Add views.\n",
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"scatterplot = vc.add_view(cm.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\n",
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"scatterplot_2 = 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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"genes = vc.add_view(cm.FEATURE_LIST, dataset=dataset)\n",
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"histogram = vc.add_view(cm.FEATURE_VALUE_HISTOGRAM, dataset=dataset)\n",
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"\n",
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"# Link views.\n",
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"\n",
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"# Color one of the two scatterplots by the percent_mito quality metric.\n",
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"# Also use this quality metric for the histogram values.\n",
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"vc.link_views_by_dict([histogram, scatterplot_2], {\n",
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" \"obsType\": 'cell',\n",
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" \"featureType\": 'qualityMetric',\n",
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" \"featureValueType\": 'value',\n",
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" \"featureSelection\": [\"percent_mito\"],\n",
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" \"obsColorEncoding\": \"geneSelection\",\n",
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"}, meta=False)\n",
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"\n",
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"# Synchronize the zooming and panning of the two scatterplots\n",
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"vc.link_views_by_dict([scatterplot, scatterplot_2], {\n",
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" \"embeddingZoom\": None,\n",
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" \"embeddingTargetX\": None,\n",
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" \"embeddingTargetY\": None,\n",
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"}, meta=False)\n",
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"\n",
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"# Define the layout.\n",
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"vc.layout((scatterplot | (cell_sets / genes)) / (scatterplot_2 | histogram));"
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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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"## 5. Create the widget\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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"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": "3.10.14"
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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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}

‎src/vitessce/file_def_utils.py‎

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@@ -100,6 +100,16 @@ def gen_obs_labels_schema(options: dict, paths: Optional[list[str]] = None, name
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options["obsLabels"] = obs_labels
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return options
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def gen_obs_feature_columns_schema(options: dict, obs_feature_column_paths: Optional[list[str]] = None):
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if obs_feature_column_paths is not None:
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options["obsFeatureColumns"] = [
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{
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"path": col_path
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}
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for col_path in obs_feature_column_paths
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]
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return options
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def gen_path_schema(key: str, path: Optional[str], options: dict):
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if path is not None:

‎src/vitessce/widget.py‎

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next_port = DEFAULT_PORT
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js_package_version = Unicode('3.6.3').tag(sync=True)
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js_package_version = Unicode('3.6.4').tag(sync=True)
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js_dev_mode = Bool(False).tag(sync=True)
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custom_js_url = Unicode('').tag(sync=True)
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plugin_esm = List(trait=Unicode(''), default_value=[]).tag(sync=True)
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store_urls = List(trait=Unicode(''), default_value=[]).tag(sync=True)
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def __init__(self, config, height=600, theme='auto', uid=None, port=None, proxy=False, js_package_version='3.6.3', js_dev_mode=False, custom_js_url='', plugins=None, remount_on_uid_change=True, prefer_local=True, invoke_timeout=300000, invoke_batched=True, page_mode=False, page_esm=None, prevent_scroll=True):
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def __init__(self, config, height=600, theme='auto', uid=None, port=None, proxy=False, js_package_version='3.6.4', js_dev_mode=False, custom_js_url='', plugins=None, remount_on_uid_change=True, prefer_local=True, invoke_timeout=300000, invoke_batched=True, page_mode=False, page_esm=None, prevent_scroll=True):
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"""
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Construct a new Vitessce widget.
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# Launch Vitessce using plain HTML representation (no ipywidgets)
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def ipython_display(config, height=600, theme='auto', base_url=None, host_name=None, uid=None, port=None, proxy=False, js_package_version='3.6.3', js_dev_mode=False, custom_js_url='', plugins=None, remount_on_uid_change=True, page_mode=False, page_esm=None):
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def ipython_display(config, height=600, theme='auto', base_url=None, host_name=None, uid=None, port=None, proxy=False, js_package_version='3.6.4', js_dev_mode=False, custom_js_url='', plugins=None, remount_on_uid_change=True, page_mode=False, page_esm=None):
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from IPython.display import display, HTML
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uid_str = "vitessce" + get_uid_str(uid)
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‎src/vitessce/wrappers.py‎

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gen_sdata_obs_spots_schema,
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gen_sdata_obs_sets_schema,
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gen_sdata_obs_feature_matrix_schema,
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gen_obs_feature_columns_schema,
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)
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from .constants import (
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class AnnDataWrapper(AbstractWrapper):
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def __init__(self, adata_path=None, adata_url=None, adata_store=None, adata_artifact=None, ref_path=None, ref_url=None, ref_artifact=None, obs_feature_matrix_path=None, feature_filter_path=None, initial_feature_filter_path=None, obs_set_paths=None, obs_set_names=None, obs_locations_path=None, obs_segmentations_path=None, obs_embedding_paths=None, obs_embedding_names=None, obs_embedding_dims=None, obs_spots_path=None, obs_points_path=None, feature_labels_path=None, obs_labels_path=None, convert_to_dense=True, coordination_values=None, obs_labels_paths=None, obs_labels_names=None, is_zip=None, **kwargs):
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def __init__(self, adata_path=None, adata_url=None, adata_store=None, adata_artifact=None, ref_path=None, ref_url=None, ref_artifact=None, obs_feature_matrix_path=None, obs_feature_column_paths=None, feature_filter_path=None, initial_feature_filter_path=None, obs_set_paths=None, obs_set_names=None, obs_locations_path=None, obs_segmentations_path=None, obs_embedding_paths=None, obs_embedding_names=None, obs_embedding_dims=None, obs_spots_path=None, obs_points_path=None, feature_labels_path=None, obs_labels_path=None, convert_to_dense=True, coordination_values=None, obs_labels_paths=None, obs_labels_names=None, is_zip=None, **kwargs):
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"""
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Wrap an AnnData object by creating an instance of the ``AnnDataWrapper`` class.
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:param str obs_labels_path: (DEPRECATED) The name of a column containing observation labels (e.g., alternate cell IDs), instead of the default index in `obs` of the AnnData store. Use `obs_labels_paths` and `obs_labels_names` instead. This arg will be removed in a future release.
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:param list[str] obs_labels_paths: The names of columns containing observation labels (e.g., alternate cell IDs), instead of the default index in `obs` of the AnnData store.
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:param list[str] obs_labels_names: The optional display names of columns containing observation labels (e.g., alternate cell IDs), instead of the default index in `obs` of the AnnData store.
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:param list[str] obs_feature_column_paths: The paths to columns (typically in `obs`) that contain numerical values per observation (e.g., cell size, quality control metrics, etc.) which are not part of the main expression matrix.
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:param bool convert_to_dense: Whether or not to convert `X` to dense the zarr store (dense is faster but takes more disk space).
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:param coordination_values: Coordination values for the file definition.
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:param is_zip: Boolean indicating whether the Zarr store is in a zipped format.
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self._spatial_spots_obsm = obs_spots_path
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self._spatial_points_obsm = obs_points_path
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self._feature_labels = feature_labels_path
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self._obs_feature_column_paths = obs_feature_column_paths
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# Support legacy provision of single obs labels path
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if (obs_labels_path is not None):
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warnings.warn("`obs_labels_path` will be deprecated in a future release.", DeprecationWarning)
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options = gen_obs_feature_matrix_schema(options, self._expression_matrix, self._gene_var_filter, self._matrix_gene_var_filter)
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options = gen_feature_labels_schema(self._feature_labels, options)
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options = gen_obs_labels_schema(options, self._obs_labels_elems, self._obs_labels_names)
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options = gen_obs_feature_columns_schema(options, self._obs_feature_column_paths)
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if len(options.keys()) > 0:
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if self.is_h5ad:

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