The from_object shortcut

1. Import dependencies

Import the functions and classes that we will be using.

import os
from os.path import join
from urllib.request import urlretrieve
from anndata import read_h5ad
import scanpy as sc

from vitessce import (
    VitessceConfig,
    Component as cm,
    CoordinationType as ct,
    AnnDataWrapper,
)

2. Download the data

For this example, we need to download a dataset from the COVID-19 Cell Atlas https://www.covid19cellatlas.org/index.healthy.html#habib17.

os.makedirs("data", exist_ok=True)
adata_filepath = join("data", "habib17.processed.h5ad")
urlretrieve('https://covid19.cog.sanger.ac.uk/habib17.processed.h5ad', adata_filepath)

3. Load the data

adata = read_h5ad(join("data", "habib17.processed.h5ad"))

3.1. Preprocess the Data For Visualization

top_dispersion = adata.var["dispersions_norm"][
    sorted(
        range(len(adata.var["dispersions_norm"])),
        key=lambda k: adata.var["dispersions_norm"][k],
    )[-51:][0]
]
adata.var["top_highly_variable"] = (
    adata.var["dispersions_norm"] > top_dispersion
)

With one line of code, you may create a Vitessce widget based on an automatically inferred configuration.

vw = VitessceConfig.from_object(AnnDataWrapper(
        adata,
        obs_embedding_paths=["obsm/X_umap"],
        obs_embedding_names=["UMAP"],
        obs_set_paths=["obs/CellType"],
        obs_set_names=["Cell Type"],
        obs_feature_matrix_path="X",
        feature_filter_path="var/top_highly_variable"
), schema_version="1.0.15").widget(height=800)
vw