Query arrays¶
We saw how LaminDB allows to query & search across artifacts & collections using registries: Query & search registries.
Let us now look at the following case:
# get a lookup for labels
ulabels = ln.ULabel.lookup()
# query a parquet file matching an "setosa"
df = ln.Artifact.filter(ulabels=ulabels.setosa, suffix=".suffix").first().load()
# query all observations in the DataFrame matching "setosa"
df_setosa = df.loc[:, df.iris_organism_name == ulabels.setosa.name]
Because the artifact was validated, querying the DataFrame is guaranteed to succeed!
Such within-collection queries are also possible for cloud-backed collections using DuckDB, TileDB, zarr, HDF5, parquet, and other storage backends.
For a use case with TileDB, see: CELLxGENE: scRNA-seq
For a use case with DuckDB, see: RxRx: cell imaging
In this notebook, we show how to subset an AnnData and generic HDF5 and zarr collections accessed in the cloud.
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!lamin login testuser1
!lamin init --storage s3://lamindb-ci/test-array-notebook --name test-array-notebook
✓ logged in with email [email protected] (uid: DzTjkKse)
→ go to: https://lamin.ai/testuser1/test-array-notebook
! updating cloud SQLite 's3://lamindb-ci/test-array-notebook/58eab9b6d7965975a7dc17a4bcbc5306.lndb' of instance 'testuser1/test-array-notebook'
→ connected lamindb: testuser1/test-array-notebook
! locked instance (to unlock and push changes to the cloud SQLite file, call: lamin disconnect)
import lamindb as ln
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→ connected lamindb: testuser1/test-array-notebook
ln.settings.verbosity = "info"
We’ll need some test data:
ln.Artifact("s3://lamindb-ci/lndb-storage/pbmc68k.h5ad").save()
ln.Artifact("s3://lamindb-ci/lndb-storage/testfile.hdf5").save()
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! no run & transform got linked, call `ln.track()` & re-run
! record with similar root exists! did you mean to load it?
| uid | root | description | type | region | instance_uid | run_id | created_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||
| 1 | lNCh0JvhUNR1 | s3://lamindb-ci/test-array-notebook | None | s3 | us-west-1 | 6BlTiS2HOWwo | None | 2024-11-21 05:38:41.534271+00:00 | 1 |
! no run & transform got linked, call `ln.track()` & re-run
Artifact(uid='78GmMfGLSyGW3n5g0000', is_latest=True, key='lndb-storage/testfile.hdf5', suffix='.hdf5', size=1400, hash='UCWPjJkhzBjO97rtuo_8Yg', _hash_type='md5', visibility=1, _key_is_virtual=False, storage_id=2, created_by_id=1, created_at=2024-11-21 05:38:52 UTC)
Note that it is also possible to register Hugging Face paths. For this huggingface_hub package should be installed.
ln.Artifact("hf://datasets/Koncopd/lamindb-test/sharded_parquet").save()
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/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
! no run & transform got linked, call `ln.track()` & re-run
! will manage storage location hf://datasets/Koncopd/lamindb-test with instance testuser1/test-array-notebook
→ due to lack of write access, LaminDB won't manage storage location: hf://datasets/Koncopd/lamindb-test
→ deleted storage record on hub e82908a3045a5fecadfe01b36107a2e4
Artifact(uid='nUTzItKUPXonT1mn0000', is_latest=True, key='sharded_parquet', suffix='', size=42767, hash='oj6I3nNKj_eiX2I1q26qaw', n_objects=11, _hash_type='md5-d', visibility=1, _key_is_virtual=False, storage_id=3, created_by_id=1, created_at=2024-11-21 05:38:56 UTC)
AnnData¶
An h5ad artifact stored on s3:
artifact = ln.Artifact.get(key="lndb-storage/pbmc68k.h5ad")
artifact.path
S3Path('s3://lamindb-ci/lndb-storage/pbmc68k.h5ad')
adata = artifact.open()
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! run input wasn't tracked, call `ln.track()` and re-run
This object is an AnnDataAccessor object, an AnnData object backed in the cloud:
adata
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AnnDataAccessor object with n_obs × n_vars = 70 × 765
constructed for the AnnData object pbmc68k.h5ad
obs: ['cell_type', 'index', 'louvain', 'n_genes', 'percent_mito']
obsm: ['X_pca', 'X_umap']
obsp: ['connectivities', 'distances']
uns: ['louvain', 'louvain_colors', 'neighbors', 'pca']
var: ['highly_variable', 'index', 'n_counts']
varm: ['PCs']
Without subsetting, the AnnDataAccessor object references underlying lazy h5 or zarr arrays:
adata.X
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<HDF5 dataset "X": shape (70, 765), type "<f4">
You can subset it like a normal AnnData object:
obs_idx = adata.obs.cell_type.isin(["Dendritic cells", "CD14+ Monocytes"]) & (
adata.obs.percent_mito <= 0.05
)
adata_subset = adata[obs_idx]
adata_subset
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AnnDataAccessorSubset object with n_obs × n_vars = 35 × 765
obs: ['cell_type', 'index', 'louvain', 'n_genes', 'percent_mito']
obsm: ['X_pca', 'X_umap']
obsp: ['connectivities', 'distances']
uns: ['louvain', 'louvain_colors', 'neighbors', 'pca']
var: ['highly_variable', 'index', 'n_counts']
varm: ['PCs']
Subsets load arrays into memory upon direct access:
adata_subset.X
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array([[-0.326, -0.191, 0.499, ..., -0.21 , -0.636, -0.49 ],
[ 0.811, -0.191, -0.728, ..., -0.21 , 0.604, -0.49 ],
[-0.326, -0.191, 0.643, ..., -0.21 , 2.303, -0.49 ],
...,
[-0.326, -0.191, -0.728, ..., -0.21 , 0.626, -0.49 ],
[-0.326, -0.191, -0.728, ..., -0.21 , -0.636, -0.49 ],
[-0.326, -0.191, -0.728, ..., -0.21 , -0.636, -0.49 ]],
dtype=float32)
To load the entire subset into memory as an actual AnnData object, use to_memory():
adata_subset.to_memory()
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AnnData object with n_obs × n_vars = 35 × 765
obs: 'cell_type', 'n_genes', 'percent_mito', 'louvain'
var: 'n_counts', 'highly_variable'
uns: 'louvain', 'louvain_colors', 'neighbors', 'pca'
obsm: 'X_pca', 'X_umap'
varm: 'PCs'
obsp: 'connectivities', 'distances'
Generic HDF5¶
Let us query a generic HDF5 artifact:
artifact = ln.Artifact.get(key="lndb-storage/testfile.hdf5")
And get a backed accessor:
backed = artifact.open()
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! run input wasn't tracked, call `ln.track()` and re-run
The returned object contains the .connection and h5py.File or zarr.Group in .storage
backed
BackedAccessor(connection=<File-like object S3FileSystem, lamindb-ci/lndb-storage/testfile.hdf5>, storage=<HDF5 file "testfile.hdf5>" (mode r)>)
backed.storage
<HDF5 file "testfile.hdf5>" (mode r)>
Parquet¶
A dataframe stored as sharded parquet.
artifact = ln.Artifact.get(key="sharded_parquet")
artifact.path.view_tree()
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11 sub-directories & 11 files with suffixes '.parquet'
hf://datasets/Koncopd/lamindb-test/sharded_parquet
├── louvain=0/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=1/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=10/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=2/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=3/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=4/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=5/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=6/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=7/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
├── louvain=8/
│ └── 947eee0b064440c9b9910ca2eb89e608-0.parquet
└── louvain=9/
└── 947eee0b064440c9b9910ca2eb89e608-0.parquet
backed = artifact.open()
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! run input wasn't tracked, call `ln.track()` and re-run
This returns a pyarrow dataset.
backed
<pyarrow._dataset.FileSystemDataset at 0x7f8a3d4bee00>
backed.head(5).to_pandas()
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| cell_type | n_genes | percent_mito | |
|---|---|---|---|
| index | |||
| CGTTATACAGTACC-8 | CD4+/CD45RO+ Memory | 1034 | 0.010163 |
| AGATATTGACCACA-1 | CD4+/CD45RO+ Memory | 1078 | 0.012831 |
| GCAGGGCTGTATGC-8 | CD8+/CD45RA+ Naive Cytotoxic | 1055 | 0.012287 |
| TTATGGCTGGCAAG-2 | CD4+/CD25 T Reg | 1236 | 0.023963 |
| CACGACCTGGGAGT-7 | CD4+/CD25 T Reg | 1010 | 0.016620 |
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# clean up test instance
!lamin delete --force test-array-notebook
• deleting instance testuser1/test-array-notebook
→ deleted storage record on hub e0641645e20f57989a1a3e3364b9e548
→ deleted instance record on hub 58eab9b6d7965975a7dc17a4bcbc5306