Concatenate datasets to a single array store¶

In the previous notebooks, we’ve seen how to incrementally create a collection of scRNA-seq datasets and train models on it.

Sometimes we want to concatenate all datasets into one big array to speed up ad-hoc queries for slices for arbitrary metadata.

This is also what CELLxGENE does to create Census: a number of .h5ad files are concatenated to give rise to a single tiledbsoma array store (CELLxGENE: scRNA-seq).

import lamindb as ln
import pandas as pd
import scanpy as sc
import tiledbsoma.io
from functools import reduce

ln.context.uid = "oJN8WmVrxI8m0000"
ln.context.track()

Query the collection of h5ad files that we’d like to concatenate into a single array.

collection = ln.Collection.get(
    name="My versioned scRNA-seq collection", version="2"
)
collection.describe()

Prepare the AnnData objects¶

To concatenate the AnnData objects into a single tiledbsoma.Experiment, they need to have the same .var and .obs columns.

# load a number of AnnData objects that's small enough to fit into memory
adatas = [artifact.load() for artifact in collection.ordered_artifacts]

# compute the intersection of columns for these objects
var_columns = reduce(pd.Index.intersection, [adata.var.columns for adata in adatas])  # this only affects metadata columns of features (say, gene annotations)
var_raw_columns = reduce(pd.Index.intersection, [adata.raw.var.columns for adata in adatas])
obs_columns = reduce(pd.Index.intersection, [adata.obs.columns for adata in adatas])  # this actually subsets features (dataset dimensions)

Prepare the AnnData objects for concatenation. Prepare id fields, sanitize index names, intersect columns, drop .obsp, .uns and columns that aren’t part of the intersection.

for i, adata in enumerate(adatas):
    del adata.obsp  # not supported by tiledbsoma
    del adata.uns   # not supported by tiledbsoma
    
    adata.obs = adata.obs.filter(obs_columns)  # filter columns to intersection
    adata.obs["obs_id"] = adata.obs.index  # prepare a column for tiledbsoma to use as an index
    adata.obs["dataset"] = i
    adata.obs.index.name = None
    
    adata.var = adata.var.filter(var_columns)  # filter columns to intersection
    adata.var["var_id"] = adata.var.index
    adata.var.index.name = None
    
    drop_raw_var_columns = adata.raw.var.columns.difference(var_raw_columns)
    adata.raw.var.drop(columns=drop_raw_var_columns, inplace=True)
    adata.raw.var["var_id"] = adata.raw.var.index
    adata.raw.var.index.name = None

Create the array store¶

Save the AnnData objects in one array store referenced by an Artifact.

soma_artifact = ln.integrations.save_tiledbsoma_experiment(
    adatas,
    description="tiledbsoma experiment",
    measurement_name="RNA",
    obs_id_name="obs_id",
    var_id_name="var_id",
    append_obsm_varm=True
)

Note

Provenance is tracked by writing the current run.uid to tiledbsoma.Experiment.obs as lamin_run_uid.

If you know tiledbsoma API, then note that save_tiledbsoma_experiment() abstracts over both tiledbsoma.io.register_anndatas and tiledbsoma.io.from_anndata.

Query the array store¶

Here we query the obs from the array store.

with soma_artifact.open() as soma_store:
    obs = soma_store["obs"]
    var = soma_store["ms"]["RNA"]["var"]
    
    obs_columns_store = obs.schema.names
    var_columns_store = var.schema.names
    
    obs_store_df = obs.read().concat().to_pandas()
    
    display(obs_store_df)

Show code cell output Hide code cell output

	soma_joinid	cell_type	obs_id	dataset	lamin_run_uid
0	0	dendritic cell	GCAGGGCTGGATTC-1	0	zM7dziY3NiUSPb6qtloC
1	1	B cell, CD19-positive	CTTTAGTGGTTACG-6	0	zM7dziY3NiUSPb6qtloC
2	2	dendritic cell	TGACTGGAACCATG-7	0	zM7dziY3NiUSPb6qtloC
3	3	B cell, CD19-positive	TCAATCACCCTTCG-8	0	zM7dziY3NiUSPb6qtloC
4	4	effector memory CD4-positive, alpha-beta T cel...	CGTTATACAGTACC-8	0	zM7dziY3NiUSPb6qtloC
...	...	...	...	...	...
1713	1713	naive thymus-derived CD4-positive, alpha-beta ...	Pan_T7991594_CTCACACTCCAGGGCT	1	zM7dziY3NiUSPb6qtloC
1714	1714	naive thymus-derived CD4-positive, alpha-beta ...	Pan_T7980358_CGAGCACAGAAGATTC	1	zM7dziY3NiUSPb6qtloC
1715	1715	naive thymus-derived CD4-positive, alpha-beta ...	CZINY-0064_AGACCATCACGCTGCA	1	zM7dziY3NiUSPb6qtloC
1716	1716	CD8-positive, alpha-beta memory T cell	CZINY-0050_TCGATTTAGATGTTGA	1	zM7dziY3NiUSPb6qtloC
1717	1717	naive thymus-derived CD4-positive, alpha-beta ...	CZINY-0064_AGTGTTGTCCGAGCTG	1	zM7dziY3NiUSPb6qtloC

1718 rows × 5 columns

Append to the array store¶

Prepare a new AnnData object to be appended to the store.

adata = ln.core.datasets.anndata_with_obs()

adata.obs_names_make_unique()
adata.var_names_make_unique()

adata.obs["obs_id"] = adata.obs.index
adata.var["var_id"] = adata.var.index

adata.obs["dataset"] = obs_store_df["dataset"].max()

obs_columns_same = [obs_col for obs_col in adata.obs.columns if obs_col in obs_columns_store]
adata.obs = adata.obs[obs_columns_same]

var_columns_same = [var_col for var_col in adata.var.columns if var_col in var_columns_store]
adata.var = adata.var[var_columns_same]

adata.write_h5ad("adata_to_append.h5ad")

Append the AnnData object from disk by revising soma_artifact.

soma_artifact = ln.integrations.save_tiledbsoma_experiment(
    ["adata_to_append.h5ad"],
    revises=soma_artifact,
    measurement_name="RNA",
    obs_id_name="obs_id",
    var_id_name="var_id"
)

Update the array store¶

Add a new embedding to the existing array store.

# read the data matrix
with soma_artifact.open() as soma_store:
    ms_rna = soma_store["ms"]["RNA"]
    n_obs = len(soma_store["obs"])
    n_var = len(ms_rna["var"])
    X = ms_rna["X"]["data"].read().coos((n_obs, n_var)).concat().to_scipy()

# calculate PCA embedding from the queried `X`
pca_array = sc.pp.pca(X, n_comps=2)

Open the array store in write mode and add PCA.

with soma_artifact.open(mode="w") as soma_store:
    tiledbsoma.io.add_matrix_to_collection(
        exp=soma_store,
        measurement_name="RNA",
        collection_name="obsm",
        matrix_name="pca",
        matrix_data=pca_array
    )

See array store mutations¶

During the append-to and update operations, the data in the array store was changed. LaminDB automatically tracks these revisions recording the number of objects, hashes, and provenance.

soma_artifact.versions.df()

	uid	version	is_latest	description	key	suffix	type	size	hash	n_objects	n_observations	_hash_type	_accessor	visibility	_key_is_virtual	storage_id	transform_id	run_id	created_by_id	updated_at
id
4	l77Km7tzLdmRY4aV0000	None	False	tiledbsoma experiment	None	.tiledbsoma	None	15056695	fpnwUeC0IEqNsCDyNGjSFQ	149	None	md5-d	None	1	True	1	6	6	1	2024-09-06 08:29:20.016732+00:00
5	l77Km7tzLdmRY4aV0001	None	False	tiledbsoma experiment	None	.tiledbsoma	None	15068505	Q6C4av2xWDd8_1qV8Tx7Lw	173	None	md5-d	None	1	True	1	6	6	1	2024-09-06 08:29:20.899713+00:00
6	l77Km7tzLdmRY4aV0002	None	True	tiledbsoma experiment	None	.tiledbsoma	None	15089309	sAZo1GGTucf__qxNi1_1QQ	182	None	md5-d	None	1	True	1	6	6	1	2024-09-06 08:29:20.900671+00:00

Note

For the underlying API, see the tiledbsoma documentation.