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 pandas as pd
import anndata as ad
✅ loaded instance: testuser1/test-scrna (lamindb 0.51.2)
ln.track()
💡 notebook imports: anndata==0.9.2 lamindb==0.51.2 lnschema_bionty==0.30.2 pandas==1.5.3
✅ saved: Transform(id='agayZTonayqAz8', name='Integrate scRNA-seq datasets', short_name='scrna2', version='0', type=notebook, updated_at=2023-08-30 13:54:57, created_by_id='DzTjkKse')
✅ saved: Run(id='k2meR2P70UbFN4MRQ6kS', run_at=2023-08-30 13:54:57, 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 | ||||||||||||||
| Oy6qOAHJ70JYO9KboACv | JcsjX8ry | None | .h5ad | AnnData | Conde22 | None | None | 28049505 | WEFcMZxJNmMiUOFrcSTaig | md5 | Nv48yAceNSh8z8 | CjznAQYFpOChcImqi5Rh | 2023-08-30 13:54:40 | DzTjkKse |
| VypFdfi6dxmi5ehxnkJO | JcsjX8ry | None | .h5ad | AnnData | 10x reference pbmc68k | None | None | 589484 | eKVXV5okt5YRYjySMTKGEw | md5 | Nv48yAceNSh8z8 | CjznAQYFpOChcImqi5Rh | 2023-08-30 13:54:51 | DzTjkKse |
Intersect measured genes between two datasets#
# get file objects
file1, file2 = query.list()
file1.describe()
💡 File(id='Oy6qOAHJ70JYO9KboACv', suffix='.h5ad', accessor='AnnData', description='Conde22', size=28049505, hash='WEFcMZxJNmMiUOFrcSTaig', hash_type='md5', updated_at=2023-08-30 13:54:40)
Provenance:
🗃️ storage: Storage(id='JcsjX8ry', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-30 13:54:56, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-08-30 13:54:51, created_by_id='DzTjkKse')
👣 run: Run(id='CjznAQYFpOChcImqi5Rh', run_at=2023-08-30 13:54:07, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:54:56)
Features:
var (X):
🔗 index (36503, bionty.Gene.id): ['ejRJTjuMLw8u', 'EqxkMcG5CXg0', '0CmxX38x3U7t', 'li3IhJ0B0cGx', 'mwK1HAypNvsN'...]
obs (metadata):
🔗 cell_type (32, bionty.CellType): ['naive B cell', 'macrophage', 'lymphocyte', 'naive thymus-derived CD4-positive, alpha-beta T cell', 'memory B cell']
🔗 assay (4, bionty.ExperimentalFactor): ["10x 5' v1", "10x 5' v2", "10x 3' v3", 'single-cell RNA sequencing']
🔗 tissue (17, bionty.Tissue): ['jejunal epithelium', 'skeletal muscle tissue', 'bone marrow', 'sigmoid colon', 'thymus']
🔗 donor (12, core.Label): ['582C', 'D496', 'A36', 'D503', 'A52']
file1.view_flow()
file2.describe()
💡 File(id='VypFdfi6dxmi5ehxnkJO', suffix='.h5ad', accessor='AnnData', description='10x reference pbmc68k', size=589484, hash='eKVXV5okt5YRYjySMTKGEw', hash_type='md5', updated_at=2023-08-30 13:54:51)
Provenance:
🗃️ storage: Storage(id='JcsjX8ry', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-scrna', type='local', updated_at=2023-08-30 13:54:56, created_by_id='DzTjkKse')
📔 transform: Transform(id='Nv48yAceNSh8z8', name='Validate & register scRNA-seq datasets', short_name='scrna', version='0', type='notebook', updated_at=2023-08-30 13:54:51, created_by_id='DzTjkKse')
👣 run: Run(id='CjznAQYFpOChcImqi5Rh', run_at=2023-08-30 13:54:07, transform_id='Nv48yAceNSh8z8', created_by_id='DzTjkKse')
👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:54:56)
Features:
var (X):
🔗 index (695, bionty.Gene.id): ['rk0wvy6H0nfb', 'vdWoHAHsKucN', 'Jf9vlAMND2f0', 'gcWdcha1esvb', 'T3vZYmBaajgO'...]
external:
🔗 assay (1, bionty.ExperimentalFactor): ['single-cell RNA sequencing']
🔗 species (1, bionty.Species): ['human']
obs (metadata):
🔗 cell_type (9, bionty.CellType): ['dendritic cell', 'CD14-positive, CD16-negative classical monocyte', 'CD8-positive, CD25-positive, alpha-beta regulatory T cell', 'conventional dendritic cell', 'CD38-negative naive B cell']
file2.view_flow()
Load files into memory:
file1_adata = file1.load()
file2_adata = file2.load()
💡 adding file Oy6qOAHJ70JYO9KboACv as input for run k2meR2P70UbFN4MRQ6kS, adding parent transform Nv48yAceNSh8z8
💡 adding file VypFdfi6dxmi5ehxnkJO as input for run k2meR2P70UbFN4MRQ6kS, 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]
['NPM1',
'SUMO2',
'SNHG7',
'PRF1',
'ARPC5L',
'FGFBP2',
'PLD4',
'PPM1G',
'FCGR3A',
'SSR3']
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
ENSG00000181163 NPM1
ENSG00000188612 SUMO2
ENSG00000233016 SNHG7
ENSG00000180644 PRF1
ENSG00000136950 ARPC5L
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
['conventional dendritic 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 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 × 0
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