Integrate scRNA-seq datasets#
scRNA-seq data integration is the process of combining and 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
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💡 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 pandas as pd
import anndata as ad
✅ loaded instance: testuser1/test-scrna (lamindb 0.51.0)
ln.track()
💡 notebook imports: anndata==0.9.2 lamindb==0.51.0 lnschema_bionty==0.30.0 pandas==1.5.3
✅ saved: Transform(id='agayZTonayqAz8', name='Integrate scRNA-seq datasets', short_name='scrna2', version='0', type=notebook, updated_at=2023-08-28 17:16:04, created_by_id='DzTjkKse')
✅ saved: Run(id='WT8mTT9zT0UYmQZ6CKJ8', run_at=2023-08-28 17:16:04, transform_id='agayZTonayqAz8', created_by_id='DzTjkKse')
Query files based on metadata#
assays = lb.ExperimentalFactor.lookup()
species = lb.Species.lookup()
query = ln.File.filter(
experimental_factors=assays.single_cell_rna_sequencing, # scRNA-seq
species=species.human, # human
cell_types__name__contains="monocyte", # monocyte
).distinct()
query.df()
| storage_id | key | suffix | accessor | description | version | initial_version_id | size | hash | hash_type | transform_id | run_id | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||
| JAEIQvXk3kQzN49qyaZ1 | 1cLCSSMz | None | .h5ad | AnnData | 10x reference pbmc68k | None | None | 589484 | eKVXV5okt5YRYjySMTKGEw | md5 | Nv48yAceNSh8z8 | TCWhch5Bg3elg5AbiosQ | 2023-08-28 17:15:56 | DzTjkKse |
| VZkiaYQKBvvUpf3n3su6 | 1cLCSSMz | None | .h5ad | AnnData | Conde22 | None | None | 28049505 | WEFcMZxJNmMiUOFrcSTaig | md5 | Nv48yAceNSh8z8 | TCWhch5Bg3elg5AbiosQ | 2023-08-28 17:15:37 | DzTjkKse |
Intersect measured genes between two datasets#
# get file objects
file1, file2 = query.list()
file1.describe()
💡 File(id='JAEIQvXk3kQzN49qyaZ1', key=None, suffix='.h5ad', accessor='AnnData', description='10x reference pbmc68k', version=None, size=589484, hash='eKVXV5okt5YRYjySMTKGEw', hash_type='md5', created_at=2023-08-28 17:15:56, updated_at=2023-08-28 17:15:56)
Provenance:
🗃️ storage: Storage(id='1cLCSSMz', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-28 17:16:02, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-08-28 17:15:56, created_by_id='DzTjkKse')
👣 run: Run(id='TCWhch5Bg3elg5AbiosQ', run_at=2023-08-28 17:14:50, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-28 17:16:02)
Features:
var (X):
🔗 index (695, bionty.Gene.id): ['a3QmOo0IvnYe', 'c5hmPGeRki1o', 'o2QhTjfss9tH', 'ptpxec1TDoY2', 'Glq61Ynh7mUc'...]
external:
🔗 assay (1, bionty.ExperimentalFactor): ['single-cell RNA sequencing']
🔗 species (1, bionty.Species): ['human']
obs (metadata):
🔗 cell_type (9, bionty.CellType): ['cytotoxic T cell', 'CD16-positive, CD56-dim natural killer cell, human', 'B cell, CD19-positive', 'CD14-positive, CD16-negative classical monocyte', 'dendritic cell']
file1.view_lineage()
file2.describe()
💡 File(id='VZkiaYQKBvvUpf3n3su6', key=None, suffix='.h5ad', accessor='AnnData', description='Conde22', version=None, size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', created_at=2023-08-28 17:15:37, updated_at=2023-08-28 17:15:37)
Provenance:
🗃️ storage: Storage(id='1cLCSSMz', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-28 17:16:02, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-08-28 17:15:56, created_by_id='DzTjkKse')
👣 run: Run(id='TCWhch5Bg3elg5AbiosQ', run_at=2023-08-28 17:14:50, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-28 17:16:02)
Features:
var (X):
🔗 index (36503, bionty.Gene.id): ['0lN2hkvVbaC7', '5FZLIl9c0fbk', '5Gb7vNmjnyMe', '264EsVtODOKG', 'fuWx1qdWpSrz'...]
obs (metadata):
🔗 cell_type (32, bionty.CellType): ['classical monocyte', 'CD16-positive, CD56-dim natural killer cell, human', 'alpha-beta T cell', 'mucosal invariant T cell', 'germinal center B cell']
🔗 assay (4, bionty.ExperimentalFactor): ["10x 3' v3", "10x 5' v2", "10x 5' v1", 'single-cell RNA sequencing']
🔗 tissue (17, bionty.Tissue): ['liver', 'omentum', 'thymus', 'transverse colon', 'duodenum']
🔗 donor (12, core.Label): ['A31', '640C', 'D496', '582C', '637C']
file2.view_lineage()
Load files into memory:
file1_adata = file1.load()
file2_adata = file2.load()
💡 adding file JAEIQvXk3kQzN49qyaZ1 as input for run WT8mTT9zT0UYmQZ6CKJ8, adding parent transform Nv48yAceNSh8z8
💡 adding file VZkiaYQKBvvUpf3n3su6 as input for run WT8mTT9zT0UYmQZ6CKJ8, 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)
695
shared_genes.list("symbol")[:10]
['UBE2J1',
'ANAPC16',
'EIF2AK1',
'IGBP1',
'HCST',
'IRF7',
'S1PR5',
'MRPS25',
'IRF1',
'PTPRCAP']
We also need to convert the ensembl_gene_id to symbol for file2 so that they can be concatenated:
mapper = pd.DataFrame(shared_genes.values_list("ensembl_gene_id", "symbol")).set_index(
0
)[1]
mapper.head()
0
ENSG00000198833 UBE2J1
ENSG00000166295 ANAPC16
ENSG00000086232 EIF2AK1
ENSG00000089289 IGBP1
ENSG00000126264 HCST
Name: 1, dtype: object
file2_adata.var.rename(index=mapper, inplace=True)
Intersect 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
['CD16-positive, CD56-dim natural killer cell, human',
'conventional dendritic cell']
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 subseted 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 = 126 × 695
obs: 'cell_type', 'file'
obsm: 'X_umap'
adata_concat.obs.value_counts()
cell_type file
CD16-positive, CD56-dim natural killer cell, human Conde22 114
conventional dendritic cell Conde22 7
CD16-positive, CD56-dim natural killer cell, human 10x reference pbmc68k 3
conventional dendritic cell 10x reference pbmc68k 2
dtype: int64
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# clean up test instance
!lamin delete --force test-scrna
!rm -r ./test-scrna
💡 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