Integrate scRNA-seq datasets#
scRNA-seq data integration is the process of analyzing data from several scRNA sequencing experiments to uncover common or distinct biological insights and patterns.
Here, we’ll demonstrate how to fetch two scRNA-seq datasets by registered metadata such as cell types to finally integrate them.
Setup#
!lamin load test-scrna
Show code cell output
💡 found cached instance metadata: /home/runner/.lamin/instance--testuser1--test-scrna.env
✅ loaded instance: testuser1/test-scrna
import lamindb as ln
import lnschema_bionty as lb
import anndata as ad
✅ loaded instance: testuser1/test-scrna (lamindb 0.52.2)
ln.track()
💡 notebook imports: anndata==0.9.2 lamindb==0.52.2 lnschema_bionty==0.30.3
✅ saved: Transform(id='agayZTonayqAz8', name='Integrate scRNA-seq datasets', short_name='scrna2', version='0', type=notebook, updated_at=2023-09-05 09:41:02, created_by_id='DzTjkKse')
✅ saved: Run(id='j3Xy8YZURND5iMcgD4Ya', run_at=2023-09-05 09:41:02, transform_id='agayZTonayqAz8', created_by_id='DzTjkKse')
Access 
#
Query files by provenance metadata#
users = ln.User.lookup()
ln.Transform.filter(created_by=users.testuser1).search("register scrna")
| id | __ratio__ | |
|---|---|---|
| name | ||
| Validate & register scRNA-seq datasets | Nv48yAceNSh8z8 | 90.0 |
| Integrate scRNA-seq datasets | agayZTonayqAz8 | 85.5 |
transform = ln.Transform.filter(id="Nv48yAceNSh8z8").one()
ln.File.filter(transform=transform).df()
| storage_id | key | suffix | accessor | description | version | size | hash | hash_type | transform_id | run_id | initial_version_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| AOjGsKUpbFkH3ZSRuYhH | hk9n0b2X | None | .h5ad | AnnData | Conde22 | None | 28049505 | WEFcMZxJNmMiUOFrcSTaig | md5 | Nv48yAceNSh8z8 | wIbg2kKvYAENHU5gdJJ4 | None | 2023-09-05 09:40:21 | DzTjkKse |
| 72QGaC65m8nXGlyUB8i5 | hk9n0b2X | None | .h5ad | AnnData | 10x reference pbmc68k | None | 660792 | GU-hbSJqGkENOxVKFLmvbA | md5 | Nv48yAceNSh8z8 | wIbg2kKvYAENHU5gdJJ4 | None | 2023-09-05 09:40:54 | DzTjkKse |
Query files based on biological metadata#
assays = lb.ExperimentalFactor.lookup()
species = lb.Species.lookup()
cell_types = lb.CellType.lookup()
query = ln.File.filter(
experimental_factors=assays.single_cell_rna_sequencing,
species=species.human,
cell_types=cell_types.cd8_positive_alpha_beta_memory_t_cell,
)
query.df()
| storage_id | key | suffix | accessor | description | version | size | hash | hash_type | transform_id | run_id | initial_version_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| 72QGaC65m8nXGlyUB8i5 | hk9n0b2X | None | .h5ad | AnnData | 10x reference pbmc68k | None | 660792 | GU-hbSJqGkENOxVKFLmvbA | md5 | Nv48yAceNSh8z8 | wIbg2kKvYAENHU5gdJJ4 | None | 2023-09-05 09:40:54 | DzTjkKse |
| AOjGsKUpbFkH3ZSRuYhH | hk9n0b2X | None | .h5ad | AnnData | Conde22 | None | 28049505 | WEFcMZxJNmMiUOFrcSTaig | md5 | Nv48yAceNSh8z8 | wIbg2kKvYAENHU5gdJJ4 | None | 2023-09-05 09:40:21 | DzTjkKse |
Transform 
#
Compare gene sets#
Get file objects:
file1, file2 = query.list()
file1.describe()
💡 File(id='72QGaC65m8nXGlyUB8i5', suffix='.h5ad', accessor='AnnData', description='10x reference pbmc68k', size=660792, hash='GU-hbSJqGkENOxVKFLmvbA', hash_type='md5', updated_at=2023-09-05 09:40:54)
Provenance:
🗃️ storage: Storage(id='hk9n0b2X', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-05 09:41:00, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-09-05 09:40:53, created_by_id='DzTjkKse')
👣 run: Run(id='wIbg2kKvYAENHU5gdJJ4', run_at=2023-09-05 09:39:27, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-05 09:41:00)
Features:
var: FeatureSet(id='a10V9SMgIbejcxgA3KDr', n=754, type='number', registry='bionty.Gene', hash='WMDxN7253SdzGwmznV5d', updated_at=2023-09-05 09:40:53, created_by_id='DzTjkKse')
'EXOG', 'ANXA11', 'IGBP1', 'MRPL9', 'HIGD2A', 'HHEX', 'SNHG32', 'CALM3', 'FLT3LG', 'TRAM1'
obs: FeatureSet(id='ktPByYwmTWgKCc07FcMk', n=1, registry='core.Feature', hash='11kHqIZyz1H0rGDwWxMR', updated_at=2023-09-05 09:40:54, modality_id='Jnvu0FDE', created_by_id='DzTjkKse')
🔗 cell_type (9, bionty.CellType): 'CD8-positive, alpha-beta memory T cell', 'B cell, CD19-positive', 'dendritic cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD16-positive, CD56-dim natural killer cell, human', 'Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', 'mature T cell', 'monocyte'
external: FeatureSet(id='xN3fq4CRlsrKo2DAlE10', n=2, registry='core.Feature', hash='Uz5nCBOqzmhRUlMkSczx', updated_at=2023-09-05 09:40:54, modality_id='Jnvu0FDE', created_by_id='DzTjkKse')
🔗 assay (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'
🔗 species (1, bionty.Species): 'human'
Labels:
🏷️ species (1, bionty.Species): 'human'
🏷️ cell_types (9, bionty.CellType): 'CD8-positive, alpha-beta memory T cell', 'B cell, CD19-positive', 'dendritic cell', 'central memory CD8-positive, alpha-beta T cell', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'CD16-positive, CD56-dim natural killer cell, human', 'Cd4-negative, CD8_alpha-negative, CD11b-positive dendritic cell', 'mature T cell', 'monocyte'
🏷️ experimental_factors (1, bionty.ExperimentalFactor): 'single-cell RNA sequencing'
file1.view_flow()
file2.describe()
💡 File(id='AOjGsKUpbFkH3ZSRuYhH', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-09-05 09:40:21)
Provenance:
🗃️ storage: Storage(id='hk9n0b2X', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-09-05 09:41:00, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-09-05 09:40:53, created_by_id='DzTjkKse')
👣 run: Run(id='wIbg2kKvYAENHU5gdJJ4', run_at=2023-09-05 09:39:27, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-05 09:41:00)
Features:
var: FeatureSet(id='pFIyiRnRih1KcRTTFKRD', n=36503, type='number', registry='bionty.Gene', hash='dnRexHCtxtmOU81_EpoJ', updated_at=2023-09-05 09:40:16, modality_id='eSUtd5SO', created_by_id='DzTjkKse')
'LINC01836', 'KCNMB4', 'None', 'MRPS18B', 'PON1', 'None', 'LINC01230', 'FBXO25', 'OSER1', 'None'
obs: FeatureSet(id='MbKTzhjVNDUga8ygXVJb', n=4, registry='core.Feature', hash='mXfg7YGGRtcU1GYRlsTY', updated_at=2023-09-05 09:40:21, modality_id='Jnvu0FDE', created_by_id='DzTjkKse')
🔗 donor (12, core.Label): 'D503', '640C', '621B', '582C', 'A35', 'A37', 'A29', '637C', 'D496', 'A31', ...
🔗 assay (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v1', '10x 5' v2'
🔗 tissue (17, bionty.Tissue): 'transverse colon', 'ileum', 'jejunal epithelium', 'mesenteric lymph node', 'liver', 'bone marrow', 'duodenum', 'caecum', 'blood', 'sigmoid colon', ...
🔗 cell_type (32, bionty.CellType): 'gamma-delta T cell', 'animal cell', 'alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'CD16-positive, CD56-dim natural killer cell, human', 'dendritic cell, human', 'CD4-positive helper T cell', 'memory B cell', 'mast cell', 'lymphocyte', ...
Labels:
🏷️ species (1, bionty.Species): 'human'
🏷️ tissues (17, bionty.Tissue): 'transverse colon', 'ileum', 'jejunal epithelium', 'mesenteric lymph node', 'liver', 'bone marrow', 'duodenum', 'caecum', 'blood', 'sigmoid colon', ...
🏷️ cell_types (32, bionty.CellType): 'gamma-delta T cell', 'animal cell', 'alpha-beta T cell', 'effector memory CD8-positive, alpha-beta T cell, terminally differentiated', 'CD16-positive, CD56-dim natural killer cell, human', 'dendritic cell, human', 'CD4-positive helper T cell', 'memory B cell', 'mast cell', 'lymphocyte', ...
🏷️ experimental_factors (4, bionty.ExperimentalFactor): 'single-cell RNA sequencing', '10x 3' v3', '10x 5' v1', '10x 5' v2'
🏷️ labels (12, core.Label): 'D503', '640C', '621B', '582C', 'A35', 'A37', 'A29', '637C', 'D496', 'A31', ...
file2.view_flow()
Load files into memory:
file1_adata = file1.load()
file2_adata = file2.load()
💡 adding file 72QGaC65m8nXGlyUB8i5 as input for run j3Xy8YZURND5iMcgD4Ya, adding parent transform Nv48yAceNSh8z8
💡 adding file AOjGsKUpbFkH3ZSRuYhH as input for run j3Xy8YZURND5iMcgD4Ya, adding parent transform Nv48yAceNSh8z8
Here we compute shared genes without loading files:
file1_genes = file1.features["var"]
file2_genes = file2.features["var"]
shared_genes = file1_genes & file2_genes
len(shared_genes)
749
shared_genes.list("symbol")[:10]
['EXOG',
'ANXA11',
'IGBP1',
'MRPL9',
'HIGD2A',
'HHEX',
'SNHG32',
'CALM3',
'FLT3LG',
'TRAM1']
Compare cell types#
file1_celltypes = file1.cell_types.all()
file2_celltypes = file2.cell_types.all()
shared_celltypes = file1_celltypes & file2_celltypes
shared_celltypes_names = shared_celltypes.list("name")
shared_celltypes_names
['CD8-positive, alpha-beta memory T cell',
'CD16-positive, CD56-dim natural killer cell, human']
We can now subset the two datasets by shared cell types:
file1_adata_subset = file1_adata[
file1_adata.obs["cell_type"].isin(shared_celltypes_names)
]
file2_adata_subset = file2_adata[
file2_adata.obs["cell_type"].isin(shared_celltypes_names)
]
Concatenate subsetted datasets:
adata_concat = ad.concat(
[file1_adata_subset, file2_adata_subset],
label="file",
keys=[file1.description, file2.description],
)
adata_concat
AnnData object with n_obs × n_vars = 244 × 749
obs: 'cell_type', 'file'
obsm: 'X_umap'
adata_concat.obs.value_counts()
cell_type file
CD8-positive, alpha-beta memory T cell Conde22 120
CD16-positive, CD56-dim natural killer cell, human Conde22 114
CD8-positive, alpha-beta memory T cell 10x reference pbmc68k 7
CD16-positive, CD56-dim natural killer cell, human 10x reference pbmc68k 3
dtype: int64
# clean up test instance
!lamin delete --force test-scrna
!rm -r ./test-scrna
Show code cell output
💡 deleting instance testuser1/test-scrna
✅ deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-scrna.env
✅ instance cache deleted
✅ deleted '.lndb' sqlite file
❗ consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna