[{"_is_consecutive":true,"description":null,"params":null,"finished_at":"2026-01-20T19:53:12.198072+00:00","entrypoint":null,"reference":null,"reference_type":null,"updated_at":"2026-01-20T19:52:33.734794+00:00","created_at":"2026-01-20T19:52:33.734794+00:00","is_locked":false,"started_at":"2026-01-20T19:52:33.641260+00:00","_aux":null,"uid":"Sdx38K8s7feNcNdI","name":null,"_status_code":0,"id":1932,"cli_args":null,"transform":{"id":750,"key":"ingest_habib17.ipynb","uid":"HuFKHbJ5DxKt0000","_aux":null,"hash":"go9-TC0abJ_J6CDsN6S_UA","kind":"notebook","is_latest":true,"is_locked":false,"reference":null,"created_at":"2026-01-20T19:52:33.50346+00:00","updated_at":"2026-01-20T19:52:33.50346+00:00","description":"Ingest Habib et al. 2017 dataset","source_code":"# %% [markdown]\n#\n\n# %%\nfrom pathlib import Path\nfrom urllib.request import urlretrieve\nimport json\n\nimport lamindb as ln\nfrom anndata import read_h5ad\nfrom vitessce import data_utils as vitdu\n\nln.track()\n\n# %% [markdown]\n# ## Ingest the H5AD dataset\n\n# %% [markdown]\n# Here, we download an example H5AD file (approximately 22 MB). This is a dataset from the COVID-19 Cell Atlas.\n\n# %%\nh5ad_raw_filepath = \"./habib17.raw.h5ad\"\nh5ad_processed_filepath = \"./habib17.processed.h5ad\"\nif not Path(h5ad_raw_filepath).exists():\n urlretrieve(\n \"https://covid19.cog.sanger.ac.uk/habib17.processed.h5ad\", h5ad_raw_filepath\n )\n\n# %% [markdown]\n# This AnnData file was not saved recently and `read_h5ad` will warn us about this. By reading and subsequently re-writing the file using an up-to-date `anndata` package version, we ensure that the dataset is saved using the latest AnnData H5AD format.\n\n# %%\nadata = read_h5ad(h5ad_raw_filepath)\n\n# %% [markdown]\n# Optionally, we can modify the AnnData object, for example to subset it to the highly variable genes (which will affect the amount of data that is displayed in the heatmap visualization).\n\n# %%\nadata = adata[:, adata.var[\"highly_variable\"]].copy()\n\n# %% [markdown]\n# Write and save the `.h5ad` file as a LaminDB Artifact.\n\n# %%\nadata.write_h5ad(h5ad_processed_filepath)\n\n# %%\nh5ad_artifact = ln.Artifact(\n h5ad_processed_filepath,\n key=\"vitessce_examples/habib17.h5ad\",\n description=\"Habib et al., 2017 Nature Methods, h5ad\",\n kind=\"dataset\",\n).save()\n\n# %% [markdown]\n# ## Construct Reference Specification\n\n# %% [markdown]\n# When using .h5ad files, we construct a Reference Specification which enables interoperability with the Zarr interface.\n\n# %%\nref_spec = vitdu.generate_h5ad_ref_spec(h5ad_processed_filepath)\n\n# %% [markdown]\n# We next need to save the corresponding Reference Specification to a JSON file and upload it to LaminDB as an Artifact.\n\n# %%\nref_path = \"./habib17.processed.reference.json\"\nwith open(ref_path, \"w\") as file:\n json.dump(ref_spec, file)\nref_artifact = ln.Artifact(\n ref_path,\n key=\"vitessce_examples/habib17.reference.json\",\n description=\"Reference JSON for H5AD file, Habib et al., 2017 Nature Methods\",\n).save()\n\n\n# %% [markdown]\n# ## Ingest the zarr dataset\n\n# %% [markdown]\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to Zarr stores using AnnData’s `write_zarr` method. Often, Zarr data is stored on-disk using a [DirectoryStore](https://zarr.readthedocs.io/en/v2.2.0/api/storage.html). By convention, these directories are named using a `.zarr` suffix.\n\n# %%\nzarr_processed_filepath = \"./habib17.adata.zarr\"\n\n# %%\nadata = read_h5ad(h5ad_raw_filepath)\n\n# %% [markdown]\n# Just like in the above section, we may want to modify the AnnData object prior to saving it as a LaminDB Artifact that we will use for visualization.\n\n# %%\nadata = adata[:, adata.var[\"highly_variable\"]].copy()\n\n# %% [markdown]\n# Note the usage of `.write_zarr` below, as opposed to `.write_h5ad`.\n\n# %%\nadata.write_zarr(zarr_processed_filepath)\n\n# %% [markdown]\n# Save the `.zarr` directory as a LaminDB Artifact.\n\n# %%\nadata_zarr_artifact = ln.Artifact(\n zarr_processed_filepath,\n key=\"vitessce_examples/habib17.adata.zarr\",\n description=\"Habib et al., 2017 Nature Methods, zarr\",\n kind=\"dataset\",\n).save()\n\n# %%\nln.finish()\n","version_tag":null,"reference_type":null},"report":{"id":12856,"key":null,"uid":"NDvhXeHeKFG4PpwR0000","_aux":null,"hash":"nSQ-InK9h5hb32Cc1CD4Aw","kind":"__lamindb_run__","size":293829,"otype":null,"suffix":".html","n_files":null,"_real_key":null,"is_latest":true,"is_locked":false,"_hash_type":"md5","created_at":"2026-01-20T19:53:12.526111+00:00","updated_at":"2026-01-20T19:53:12.526111+00:00","description":"Report of run Sdx38K8s7feNcNdI","version_tag":null,"n_observations":null,"_key_is_virtual":true,"_overwrite_versions":false,"storage":{"id":2,"uid":"D9BilDV2","_aux":null,"root":"s3://lamindata","type":"s3","region":"us-east-1","is_locked":false,"created_at":"2023-04-22T05:50:06.537267+00:00","updated_at":"2024-09-24T15:51:49.408949+00:00","description":null,"instance_uid":"4XIuR0tvaiXM"}},"environment":{"id":12855,"key":null,"uid":"3IyL1nOYb45s6yg60000","_aux":null,"hash":"Pk18uUQAJlNPsYv0Ei-K3w","kind":"__lamindb_run__","size":20623,"otype":null,"suffix":".txt","n_files":null,"_real_key":null,"is_latest":true,"is_locked":false,"_hash_type":"md5","created_at":"2026-01-20T19:53:11.699125+00:00","updated_at":"2026-01-20T19:53:11.699125+00:00","description":"requirements.txt","version_tag":null,"n_observations":null,"_key_is_virtual":true,"_overwrite_versions":false,"storage":{"id":2,"uid":"D9BilDV2","_aux":null,"root":"s3://lamindata","type":"s3","region":"us-east-1","is_locked":false,"created_at":"2023-04-22T05:50:06.537267+00:00","updated_at":"2024-09-24T15:51:49.408949+00:00","description":null,"instance_uid":"4XIuR0tvaiXM"}},"json_values":[],"branch":{"id":1,"uid":"mmmmmmmmmmmm","_aux":null,"name":"main","created_at":"2025-06-01T17:45:24.268563+00:00","description":"The main & default branch of the instance.","_status_code":0},"input_collections":[],"output_collections":[],"input_artifacts":[],"input_records":[],"records":[],"output_artifacts":[{"id":12852,"key":"vitessce_examples/habib17.h5ad","uid":"NNUzTpWz3a0PU2aT0000","_aux":null,"hash":"nBetKKNFs4mjJ04X_f4uaA","kind":"dataset","size":22586523,"otype":"AnnData","suffix":".h5ad","n_files":null,"_real_key":null,"is_latest":true,"is_locked":false,"_hash_type":"md5","created_at":"2026-01-20T19:52:45.252392+00:00","updated_at":"2026-01-20T19:52:45.252392+00:00","description":"Habib et al., 2017 Nature Methods, h5ad","version_tag":null,"n_observations":null,"_key_is_virtual":true,"_overwrite_versions":false,"input_of_runs":[{"id":1935,"uid":"8gyjeMQy9L70utEJ","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:28:39.175122+00:00","entrypoint":null,"started_at":"2026-01-20T20:28:39.175122+00:00","updated_at":"2026-01-20T20:28:39.175122+00:00","description":null,"finished_at":"2026-01-20T20:28:40.05353+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1934,"uid":"5KO1Hx0D4FWTdkNj","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:28:36.836286+00:00","entrypoint":null,"started_at":"2026-01-20T20:28:36.832075+00:00","updated_at":"2026-01-20T20:28:36.836286+00:00","description":null,"finished_at":"2026-01-20T20:28:45.42825+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":752,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90001","_aux":null,"hash":"XxyXmkwBhT7NBvcFoilC6A","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:28:36.804057+00:00","updated_at":"2026-01-20T20:28:36.804057+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored in LaminDB artifacts. \n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset in the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n# Note that when using an `.h5ad`-based Artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# This will render an interactive Vitessce viewer:\n\n# %% [markdown]\n#

\n\n# %% tags=[\"hide-cell\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% tags=[\"hide-output\"]\nln.finish()\n","version_tag":null,"reference_type":null}},{"id":1942,"uid":"cg242z0he40WPOxU","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:45:12.34382+00:00","entrypoint":null,"started_at":"2026-01-20T20:45:12.34382+00:00","updated_at":"2026-01-20T20:45:12.34382+00:00","description":null,"finished_at":"2026-01-20T20:45:13.17717+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1941,"uid":"WsYzMmzlvawZDsac","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:45:09.770544+00:00","entrypoint":null,"started_at":"2026-01-20T20:45:09.766486+00:00","updated_at":"2026-01-20T20:45:09.770544+00:00","description":null,"finished_at":"2026-01-20T20:45:18.194868+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":752,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90001","_aux":null,"hash":"XxyXmkwBhT7NBvcFoilC6A","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-20T20:28:36.804057+00:00","updated_at":"2026-01-20T20:28:36.804057+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored in LaminDB artifacts. \n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset in the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n# Note that when using an `.h5ad`-based Artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# This will render an interactive Vitessce viewer:\n\n# %% [markdown]\n#

\n\n# %% tags=[\"hide-cell\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% tags=[\"hide-output\"]\nln.finish()\n","version_tag":null,"reference_type":null}},{"id":1984,"uid":"x7g1770nd18JFD5f","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:56.049786+00:00","entrypoint":null,"started_at":"2026-01-22T23:34:56.049786+00:00","updated_at":"2026-01-22T23:34:56.049786+00:00","description":null,"finished_at":"2026-01-22T23:34:56.779702+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1983,"uid":"bYCZyXqbTkbhxUZ9","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:53.906622+00:00","entrypoint":null,"started_at":"2026-01-22T23:34:53.905206+00:00","updated_at":"2026-01-22T23:34:53.906622+00:00","description":null,"finished_at":"2026-01-22T23:35:01.22438+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":759,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90002","_aux":null,"hash":"937p0EPHWO3jcsctDlCQEQ","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:53.884025+00:00","updated_at":"2026-01-22T23:34:53.884025+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored as LaminDB artifacts. \n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# After saving, a Vitessce button appears on the [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based artifact) or [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) (`.h5ad`-based artifact) page in the web interface. Clicking it launches an interactive viewer:\n\n# %% [markdown]\n#

\n\n# %% tags=[\"hide-cell\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% tags=[\"hide-output\"]\nln.finish()\n","version_tag":null,"reference_type":null}},{"id":1993,"uid":"6O3Vu5fbnbxJtQM1","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:31.397827+00:00","entrypoint":null,"started_at":"2026-01-22T23:39:31.397827+00:00","updated_at":"2026-01-22T23:39:31.397827+00:00","description":null,"finished_at":"2026-01-22T23:39:32.192034+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1992,"uid":"t4CFZeHrI89USKn2","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:29.18968+00:00","entrypoint":null,"started_at":"2026-01-22T23:39:29.184293+00:00","updated_at":"2026-01-22T23:39:29.18968+00:00","description":null,"finished_at":"2026-01-22T23:39:36.781075+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":761,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90003","_aux":null,"hash":"HfinZDiR3AyNhWm_gEIr7Q","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:29.156974+00:00","updated_at":"2026-01-22T23:39:29.156974+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored as LaminDB artifacts. It requires a remote LaminDB instance with cloud storage to enable the Vitessce button in the web interface.\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# After saving, a Vitessce button appears on the [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based artifact) or [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) (`.h5ad`-based artifact) page in the web interface. Clicking it launches an interactive viewer:\n\n# %% [markdown]\n#

\n\n# %% [markdown]\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n\n# %% [markdown]\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n\n# %% [markdown]\n#

\n\n# %% [markdown]\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown]\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n\n# %% [markdown]\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n\n# %% [markdown]\n#

\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% tags=[\"hide-output\"]\n# pip install 'vitessce[all]>=3.5.0' lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown]\n# ## Visualize an AnnData object (H5AD format)\n#\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes.\n# The dataset was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown]\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface.\n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above.\n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a collection that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a collection.\n#\n# :::\n#\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n#\n# ## Visualize an AnnData object (Zarr format)\n#\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n#\n# Just like in the above section, we use a Zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n#\n# Clicking the Vitessce button for this `.zarr`-based [artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) or `.h5ad`-based [collection](#visualize-an-anndata-object-h5ad-format) launches the same interactive viewer, as both formats represent the same dataset:\n#\n#

\n\n# %% [markdown]\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown]\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n\n# %% [markdown]\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n\n# %% [markdown]\n#

\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ## Visualize an AnnData object (H5AD format)\n#\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes.\n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface.\n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above.\n#\n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ### Save a VitessceConfig object\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Save the `VitessceConfig` object.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n#\n\n# %% [markdown]\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ## Visualize an AnnData object (Zarr format)\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n#\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ### Save a VitessceConfig object\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Save the `VitessceConfig` object.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n#\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n#\n#

\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown]\n# ## Visualize an AnnData object (H5AD format)\n#\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes.\n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown]\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface.\n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above.\n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n#\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n#\n# ## Visualize an AnnData object (Zarr format)\n#\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n#\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n#\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n#\n#

\n\n# %% tags=[\"hide-cell\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% tags=[\"hide-output\"]\nln.finish()\n","version_tag":null,"reference_type":null}},{"id":1984,"uid":"x7g1770nd18JFD5f","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:56.049786+00:00","entrypoint":null,"started_at":"2026-01-22T23:34:56.049786+00:00","updated_at":"2026-01-22T23:34:56.049786+00:00","description":null,"finished_at":"2026-01-22T23:34:56.779702+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1983,"uid":"bYCZyXqbTkbhxUZ9","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:53.906622+00:00","entrypoint":null,"started_at":"2026-01-22T23:34:53.905206+00:00","updated_at":"2026-01-22T23:34:53.906622+00:00","description":null,"finished_at":"2026-01-22T23:35:01.22438+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":759,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90002","_aux":null,"hash":"937p0EPHWO3jcsctDlCQEQ","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:34:53.884025+00:00","updated_at":"2026-01-22T23:34:53.884025+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored as LaminDB artifacts. \n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# After saving, a Vitessce button appears on the [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based artifact) or [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) (`.h5ad`-based artifact) page in the web interface. Clicking it launches an interactive viewer:\n\n# %% [markdown]\n#

\n\n# %% tags=[\"hide-cell\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% tags=[\"hide-output\"]\nln.finish()\n","version_tag":null,"reference_type":null}},{"id":1993,"uid":"6O3Vu5fbnbxJtQM1","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:31.397827+00:00","entrypoint":null,"started_at":"2026-01-22T23:39:31.397827+00:00","updated_at":"2026-01-22T23:39:31.397827+00:00","description":null,"finished_at":"2026-01-22T23:39:32.192034+00:00","_status_code":-3,"reference_type":null,"_is_consecutive":null,"transform":{"id":411,"key":"save_vitessce_config","uid":"kup03MJBsIVa0002","_aux":null,"hash":null,"kind":"function","is_latest":true,"is_locked":false,"reference":null,"created_at":"2025-04-21T13:09:01.525876+00:00","updated_at":"2025-04-21T13:09:01.525876+00:00","description":"save_vitessce_config","source_code":null,"version_tag":"3","reference_type":null}},{"id":1992,"uid":"t4CFZeHrI89USKn2","_aux":null,"name":null,"params":null,"cli_args":null,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:29.18968+00:00","entrypoint":null,"started_at":"2026-01-22T23:39:29.184293+00:00","updated_at":"2026-01-22T23:39:29.18968+00:00","description":null,"finished_at":"2026-01-22T23:39:36.781075+00:00","_status_code":0,"reference_type":null,"_is_consecutive":true,"transform":{"id":761,"key":"vitessce.ipynb","uid":"ZOf7HJEqB4y90003","_aux":null,"hash":"HfinZDiR3AyNhWm_gEIr7Q","kind":"notebook","is_latest":false,"is_locked":false,"reference":null,"created_at":"2026-01-22T23:39:29.156974+00:00","updated_at":"2026-01-22T23:39:29.156974+00:00","description":"Vitessce: AnnData","source_code":"# %% [markdown]\n# [![](https://img.shields.io/badge/Source%20on%20GitHub-orange)](https://github.com/laminlabs/lamin-spatial/blob/main/docs/vitessce.ipynb) [![hub](https://img.shields.io/badge/View%20on%20lamin.ai-mediumseagreen)](https://lamin.ai/vitessce/examples/transform/BZhZQ6uIbkWv)\n\n# %% [markdown] nbsphinx=\"hidden\"\n#\n\n# %% [markdown]\n# This tutorial demonstrates how to use Vitessce to create interactive visualizations for data stored as LaminDB artifacts. It requires a remote LaminDB instance with cloud storage to enable the Vitessce button in the web interface.\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# After saving, a Vitessce button appears on the [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based artifact) or [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) (`.h5ad`-based artifact) page in the web interface. Clicking it launches an interactive viewer:\n\n# %% [markdown]\n#

\n\n# %% [markdown]\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (H5AD format)\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes. \n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface. \n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above. \n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n\n# %% [markdown]\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ## Visualize an AnnData object (Zarr format)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# ### Save a VitessceConfig object\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n# Save the `VitessceConfig` object.\n\n# %% editable=true slideshow={\"slide_type\": \"\"} tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n\n# %% [markdown]\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n\n# %% [markdown]\n#

\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ## Visualize an AnnData object (H5AD format)\n#\n\n# %% [markdown]\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes.\n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface.\n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above.\n#\n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ### Save a VitessceConfig object\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Save the `VitessceConfig` object.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n#\n\n# %% [markdown]\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ## Visualize an AnnData object (Zarr format)\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n#\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# ### Save a VitessceConfig object\n#\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown] editable=true slideshow={\"slide_type\": \"\"}\n#\n# Save the `VitessceConfig` object.\n#\n\n# %% editable=true slideshow={\"slide_type\": \"\"}\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n#\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n#\n#

\n#\n# It has been adapted from the data preparation examples in [the Vitessce documentation](https://vitessce.github.io/vitessce-python).\n#\n# In this part, we'll visualize an AnnData object stored in both H5AD and Zarr formats.\n\n# %% tags=[\"hide-output\"]\n# pip install \"vitessce[all]>=3.5.0\" \"generate-tiff-offsets>=0.1.9\" lamindb\n# !lamin connect laminlabs/lamindata # <-- replace with your remote instance\n\n# %% tags=[\"hide-output\"]\nimport vitessce as vit\nimport lamindb as ln\n\nln.track()\n\n# %% [markdown]\n# ## Visualize an AnnData object (H5AD format)\n#\n# Here we use the Habib et al. 2017 dataset from the [COVID-19 Cell Atlas](https://www.covid19cellatlas.org/index.healthy.html#habib17) that has been previously subset to highly variable genes.\n# It was ingested into the public [laminlabs/lamindata](https://lamin.ai/laminlabs/lamindata) instance in this [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt).\n\n# %% tags=[\"hide-output\"]\nh5ad_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.h5ad\")\n\n# %% [markdown]\n# When using `.h5ad` files, we construct a [Reference Specification](https://fsspec.github.io/kerchunk/spec.html) which enables interoperability with the [Zarr](https://zarrita.dev/packages/storage.html#referencestore) interface.\n# The Reference Specification JSON was also generated in the [transform](https://lamin.ai/laminlabs/lamindata/transform/HuFKHbJ5DxKt) above.\n\n# %% tags=[\"hide-output\"]\nref_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.reference.json\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n#\n# You can pass artifacts to the `AnnDataWrapper` class using the `adata_artifact` and `ref_artifact` [parameters](https://python-docs.vitessce.io/api_data.html#vitessce.wrappers.AnnDataWrapper).\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=h5ad_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=h5ad_artifact,\n ref_artifact=ref_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.ViewType.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.ViewType.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.ViewType.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.ViewType.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.ViewType.FEATURE_LIST, dataset=dataset)\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nh5ad_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 (h5ad) in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# Note that when using an `.h5ad`-based artifact, the presence of the corresponding `.reference.json` file will result in the creation of a Collection.\n#\n# :::\n#\n# The Vitessce button for this dataset is available on the [Collection](https://lamin.ai/laminlabs/lamindata/collection/H1AlT19wFq7HdHqZ) page.\n#\n# ## Visualize an AnnData object (Zarr format)\n#\n# AnnData objects can be saved on-disk to not only `.h5ad` files, but also to [Zarr stores](https://zarr.readthedocs.io/en/stable/tutorial.html#storage-alternatives) using AnnData's [write_zarr](https://anndata.readthedocs.io/en/latest/generated/anndata.AnnData.write_zarr.html) method.\n#\n# Just like in the above section, we use a zarr storage that has been previously written with `write_zarr()` and subset to highly variable genes and ingested into the `vitessce/examples` instance.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_artifact = ln.Artifact.get(key=\"vitessce_examples/habib17.adata.zarr\")\n\n# %% [markdown]\n# ### Save a VitessceConfig object\n#\n# You can create a dashboard for one or several datasets by using Vitessce's component API.\n# Here, we configure the visualization the same way as above in the `.h5ad`-based example, with the exception of the `ref_artifact` parameter, as `.zarr`-based AnnData objects do not require a Reference Specification for Zarr interoperability.\n\n# %% tags=[\"hide-output\"]\nvc = vit.VitessceConfig(\n schema_version=\"1.0.18\",\n description=adata_zarr_artifact.description,\n)\n\ndataset = vc.add_dataset(name=\"Habib 2017\").add_object(\n vit.AnnDataWrapper(\n adata_artifact=adata_zarr_artifact,\n obs_feature_matrix_path=\"X\",\n obs_embedding_paths=[\"obsm/X_umap\"],\n obs_embedding_names=[\"UMAP\"],\n obs_set_paths=[\"obs/CellType\"],\n obs_set_names=[\"Cell Type\"],\n )\n)\n\nobs_sets = vc.add_view(vit.Component.OBS_SETS, dataset=dataset)\nobs_sets_sizes = vc.add_view(vit.Component.OBS_SET_SIZES, dataset=dataset)\nscatterplot = vc.add_view(vit.Component.SCATTERPLOT, dataset=dataset, mapping=\"UMAP\")\nheatmap = vc.add_view(vit.Component.HEATMAP, dataset=dataset)\ngenes = vc.add_view(vit.Component.FEATURE_LIST, dataset=dataset)\n\nvc.link_views([scatterplot, heatmap], [\"featureValueColormapRange\"], [[0.0, 0.1]])\nvc.layout(((scatterplot | obs_sets) / heatmap) | (obs_sets_sizes / genes))\n\n# %% [markdown]\n# Save the `VitessceConfig` object.\n\n# %% tags=[\"hide-output\"]\nadata_zarr_vc_artifact = ln.integrations.save_vitessce_config(\n vc,\n description=\"View Habib17 in Vitessce\",\n)\n\n# %% [markdown]\n# :::{note}\n#\n# After running `save_vitessce_config`, a Vitessce button will appear next to the dataset on the [Artifacts](https://lamin.ai/laminlabs/lamindata/artifacts) or [Collections](https://lamin.ai/laminlabs/lamindata/collections) page of the web interface.\n#\n# If your `VitessceConfig` object references data from multiple artifacts, the Vitessce button will appear next to a `Collection` that groups these artifacts (on the [Collections](https://lamin.ai/laminlabs/lamindata/collections) tab of the Artifacts page).\n#\n# :::\n#\n# Clicking the Vitessce button for this [Artifact](https://lamin.ai/laminlabs/lamindata/artifact/Ljx9cSOxELkGisVt) (`.zarr`-based) or [Collection](#visualize-an-anndata-object-h5ad-format) (`.h5ad`-based) launches the same interactive viewer, as both formats represent the same dataset here:\n#\n#

\n\n# %% tags=[\"hide-output\"]\n# compare the generated vitessce config to the public one on vitessce/examples (H5AD)\ndb = ln.DB(\"vitessce/examples\")\npublic_vc_json = db.Artifact.get(\"ffUKrGJGNHL3TDhG0000\").load()\nh5ad_vc_json = h5ad_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == h5ad_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == h5ad_vc_json[\"coordinationSpace\"]\n\n# compare the generated vitessce config to the public one on vitessce/examples (Zarr)\npublic_vc_json = db.Artifact.get(\"J4tMB6qAeHvsgEsp0000\").load()\nadata_zarr_vc_json = adata_zarr_vc_artifact.load()\n\nassert public_vc_json[\"layout\"] == adata_zarr_vc_json[\"layout\"]\nassert public_vc_json[\"coordinationSpace\"] == adata_zarr_vc_json[\"coordinationSpace\"]\n\n# %% 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Input	Output
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