Multi-modal¶
Here, we’ll showcase how to curate and register ECCITE-seq data from Papalexi21 in the form of MuData objects.
ECCITE-seq is designed to enable interrogation of single-cell transcriptomes together with surface protein markers in the context of CRISPR screens.
MuData objects build on top of AnnData objects to store multimodal data.
# !pip install 'lamindb[jupyter,bionty]'
!lamin init --storage ./test-multimodal --modules bionty
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→ initialized lamindb: testuser1/test-multimodal
import lamindb as ln
import bionty as bt
ln.track()
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→ connected lamindb: testuser1/test-multimodal
→ created Transform('WAb1KqU0uRGz0000'), started new Run('cMZ8ounV...') at 2025-05-29 10:25:59 UTC
→ notebook imports: bionty==1.4a1 lamindb==1.6a1
• recommendation: to identify the notebook across renames, pass the uid: ln.track("WAb1KqU0uRGz")
Creating MuData Artifacts¶
lamindb provides a from_mudata() method to create Artifact from MuData objects.
mdata = ln.core.datasets.mudata_papalexi21_subset()
mdata
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MuData object with n_obs × n_vars = 200 × 300
obs: 'perturbation', 'replicate'
var: 'name'
4 modalities
rna: 200 x 173
obs: 'nCount_RNA', 'nFeature_RNA', 'percent.mito'
var: 'name'
adt: 200 x 4
obs: 'nCount_ADT', 'nFeature_ADT'
var: 'name'
hto: 200 x 12
obs: 'nCount_HTO', 'nFeature_HTO', 'technique'
var: 'name'
gdo: 200 x 111
obs: 'nCount_GDO'
var: 'name'mdata_af = ln.Artifact.from_mudata(mdata, key="papalexi.h5mu")
mdata_af
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Artifact(uid='Lfyr0sX88iFbhU1K0000', is_latest=True, key='papalexi.h5mu', suffix='.h5mu', kind='dataset', otype='MuData', size=549984, hash='DVP0v1Ox99dbtx__KLuYPg', n_observations=200, branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7f2dc8f0e0c0>)
# MuData Artifacts have the corresponding otype
mdata_af.otype
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'MuData'
# MuData Artifacts can easily be loaded back into memory
papalexi_in_memory = mdata_af.load()
papalexi_in_memory
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MuData object with n_obs × n_vars = 200 × 300
obs: 'perturbation', 'replicate'
var: 'name'
4 modalities
rna: 200 x 173
obs: 'nCount_RNA', 'nFeature_RNA', 'percent.mito'
var: 'name'
adt: 200 x 4
obs: 'nCount_ADT', 'nFeature_ADT'
var: 'name'
hto: 200 x 12
obs: 'nCount_HTO', 'nFeature_HTO', 'technique'
var: 'name'
gdo: 200 x 111
obs: 'nCount_GDO'
var: 'name'Schema¶
# define labels
perturbation = ln.ULabel(name="Perturbation", is_type=True).save()
ln.ULabel(name="Perturbed", type=perturbation).save()
ln.ULabel(name="NT", type=perturbation).save()
replicate = ln.ULabel(name="Replicate", is_type=True).save()
ln.ULabel(name="rep1", type=replicate).save()
ln.ULabel(name="rep2", type=replicate).save()
ln.ULabel(name="rep3", type=replicate).save()
# define obs schema
obs_schema = ln.Schema(
name="mudata_papalexi21_subset_obs_schema",
features=[
ln.Feature(name="perturbation", dtype="cat[ULabel[Perturbation]]").save(),
ln.Feature(name="replicate", dtype="cat[ULabel[Replicate]]").save(),
],
).save()
obs_schema_rna = ln.Schema(
name="mudata_papalexi21_subset_rna_obs_schema",
features=[
ln.Feature(name="nCount_RNA", dtype=int).save(),
ln.Feature(name="nFeature_RNA", dtype=int).save(),
ln.Feature(name="percent.mito", dtype=float).save(),
],
coerce_dtype=True,
).save()
obs_schema_hto = ln.Schema(
name="mudata_papalexi21_subset_hto_obs_schema",
features=[
ln.Feature(name="nCount_HTO", dtype=float).save(),
ln.Feature(name="nFeature_HTO", dtype=int).save(),
ln.Feature(name="technique", dtype=bt.ExperimentalFactor).save(),
],
coerce_dtype=True,
).save()
var_schema_rna = ln.Schema(
name="mudata_papalexi21_subset_rna_var_schema",
itype=bt.Gene.symbol,
dtype=float,
).save()
# define composite schema
mudata_schema = ln.Schema(
name="mudata_papalexi21_subset_mudata_schema",
otype="MuData",
slots={
"obs": obs_schema,
"rna:obs": obs_schema_rna,
"hto:obs": obs_schema_hto,
"rna:var": var_schema_rna,
},
).save()
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! record with similar name exists! did you mean to load it?
| uid | name | is_type | description | reference | reference_type | space_id | type_id | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||
| 1 | rxGnwfq6 | Perturbation | True | None | None | None | 1 | None | 1 | 2025-05-29 10:26:00.654000+00:00 | 1 | None | 1 |
! record with similar name exists! did you mean to load it?
| uid | name | dtype | is_type | unit | description | array_rank | array_size | array_shape | proxy_dtype | synonyms | _expect_many | _curation | space_id | type_id | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||||
| 4 | olm2XRVml2XF | nFeature_RNA | int | None | None | None | 0 | 0 | None | None | None | None | None | 1 | None | 1 | 2025-05-29 10:26:00.713000+00:00 | 1 | {'af': {'0': None, '1': True, '2': False}} | 1 |
mudata_schema
Schema(uid='jDQdRolUMw3Sd5dd', name='mudata_papalexi21_subset_mudata_schema', n=-1, is_type=False, itype='Composite', otype='MuData', dtype='num', hash='V-OLTVLXmArG3M8gyxJt7g', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-29 10:26:00 UTC)
Validate MuData annotations¶
curator = ln.curators.MuDataCurator(mdata, mudata_schema)
! auto-transposed `var` for backward compat, please indicate transposition in the schema definition by calling out `.T`: slots={'var.T': itype=bt.Gene.ensembl_gene_id}
try:
curator.validate()
except ln.errors.ValidationError:
pass
! using default organism = human
! using default organism = human
! using default organism = human
! 96 terms not validated in feature 'columns': 'RP5-827C21.6', 'XX-CR54.1', 'RP11-379B18.5', 'RP11-778D9.12', 'RP11-703G6.1', 'AC005150.1', 'RP11-717H13.1', 'CTC-498J12.1', 'CTC-467M3.1', 'HIST1H4K', 'RP11-524H19.2', 'AC006042.7', 'AC002066.1', 'AC073934.6', 'RP11-268G12.1', 'U52111.14', 'RP11-235C23.5', 'RP11-12J10.3', 'CASC1', 'RP11-324E6.9', ...
12 synonyms found: "CTC-467M3.1" → "MEF2C-AS2", "HIST1H4K" → "H4C12", "CASC1" → "DNAI7", "LARGE" → "LARGE1", "NBPF16" → "NBPF15", "C1orf65" → "CCDC185", "IBA57-AS1" → "IBA57-DT", "KIAA1239" → "NWD2", "TMEM75" → "LINC02912", "AP003419.16" → "RPS6KB2-AS1", "FAM65C" → "RIPOR3", "C14orf177" → "LINC02914"
→ curate synonyms via: .standardize("columns")
for remaining terms:
→ fix typos, remove non-existent values, or save terms via: curator.cat.add_new_from('columns')
curator.slots["rna:var"].cat.standardize("columns")
curator.slots["rna:var"].cat.add_new_from("columns")
curator.validate()
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! using default organism = human
! using default organism = human
! using default organism = human
Register curated Artifact¶
artifact = curator.save_artifact(key="mudata_papalexi21_subset.h5mu")
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→ returning existing schema with same hash: Schema(uid='S4SmSX75ZBF0cDV4', name='mudata_papalexi21_subset_obs_schema', n=2, is_type=False, itype='Feature', hash='ErgN8zZBgCs5DcabT-S_4g', minimal_set=True, ordered_set=False, maximal_set=False, branch_id=1, space_id=1, created_by_id=1, run_id=1, created_at=2025-05-29 10:26:00 UTC)
artifact.describe()
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Artifact .h5mu/MuData ├── General │ ├── .uid = 'PI5Fj3NQ0Le8Oann0000' │ ├── .key = 'mudata_papalexi21_subset.h5mu' │ ├── .size = 549984 │ ├── .hash = 'DVP0v1Ox99dbtx__KLuYPg' │ ├── .n_observations = 200 │ ├── .path = │ │ /home/runner/work/lamin-usecases/lamin-usecases/docs/test-multimodal/.lamindb/PI5Fj3NQ0Le8Oann0000.h5mu │ ├── .created_by = testuser1 (Test User1) │ ├── .created_at = 2025-05-29 10:26:05 │ └── .transform = 'Multi-modal' ├── Dataset features │ ├── obs • 2 [Feature] │ │ perturbation cat[ULabel[Perturbation]] NT, Perturbed │ │ replicate cat[ULabel[Replicate]] rep1, rep2, rep3 │ ├── rna:obs • 3 [Feature] │ │ nCount_RNA int │ │ nFeature_RNA int │ │ percent.mito float │ ├── hto:obs • 3 [Feature] │ │ technique cat[bionty.ExperimentalF… cell hashing │ │ nCount_HTO float │ │ nFeature_HTO int │ └── rna:var • 184 [bionty.Gene.symbol] │ SH2D6 num │ MEF2C-AS2 num │ ARHGAP26-AS1 num │ GABRA1 num │ H4C12 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ HLA-DQB1-AS1 num │ SPACA1 num │ VNN1 num │ CTAGE15 num │ CTAGE15 num │ PFKFB1 num │ TRPC5 num │ RBPMS-AS1 num │ CA8 num └── Labels └── .experimental_factors bionty.ExperimentalFactor cell hashing .ulabels ULabel Perturbed, NT, rep1, rep2, rep3
ln.finish()
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! cells [(15, 17)] were not run consecutively
→ finished Run('cMZ8ounV') after 7s at 2025-05-29 10:26:06 UTC
# clean up test instance
!rm -r test-multimodal
!lamin delete --force test-multimodal
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• deleting instance testuser1/test-multimodal