Jupyter Notebook

Validate & register flow cytometry data#

!lamin init --storage ./test-flow --schema bionty
Hide code cell output 
πŸ’‘ creating schemas: core==0.46.1 bionty==0.30.0 
βœ… saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-28 13:52:12)
βœ… saved: Storage(id='H7Pxu11w', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-flow', type='local', updated_at=2023-08-28 13:52:12, created_by_id='DzTjkKse')

βœ… loaded instance: testuser1/test-flow
πŸ’‘ did not register local instance on hub (if you want, call `lamin register`)


import lamindb as ln
import lnschema_bionty as lb
import readfcs

lb.settings.species = "human"  # globally set species

βœ… loaded instance: testuser1/test-flow (lamindb 0.51.0)

βœ… set species: Species(id='uHJU', name='human', taxon_id=9606, scientific_name='homo_sapiens', updated_at=2023-08-28 13:52:14, bionty_source_id='7Wa0', created_by_id='DzTjkKse')

ln.track()

πŸ’‘ notebook imports: lamindb==0.51.0 lnschema_bionty==0.30.0 readfcs==1.1.6

βœ… saved: Transform(id='OWuTtS4SAponz8', name='Validate & register flow cytometry data', short_name='flow', version='0', type=notebook, updated_at=2023-08-28 13:52:14, created_by_id='DzTjkKse')

βœ… saved: Run(id='SswraaPvvTqVHpKZo6iA', run_at=2023-08-28 13:52:14, transform_id='OWuTtS4SAponz8', created_by_id='DzTjkKse')

Transform #

(Here we skip steps of data transformations, which often includes filtering, normalizing, or formatting data.)

We start with a flow cytometry file from Alpert19:

ln.dev.datasets.file_fcs_alpert19(
    populate_registries=True,  # pre-populate registries to simulate an used instance
)





PosixPath('Alpert19.fcs')









Use readfcs to read the fcs file into memory:






adata = readfcs.read("Alpert19.fcs")
adata









AnnData object with n_obs Γ— n_vars = 166537 Γ— 40
    var: 'n', 'channel', 'marker', '$PnB', '$PnE', '$PnR'
    uns: 'meta'













Validate #


First, let’s validate the features in .var.


We’ll use the CellMarker reference to link features:






lb.CellMarker.validate(adata.var.index, "name");









πŸ’‘ using global setting species = human





βœ… 27 terms (67.50%) are validated for name





❗ 13 terms (32.50%) are not validated for name: Time, Cell_length, Dead, (Ba138)Dd, Bead, CD19, CD4, IgD, CD11b, CD14, CCR6, CCR7, PD-1









We see that many features aren’t validated. Let’s standardize the identifiers:






adata.var.index = lb.CellMarker.standardize(adata.var.index)









πŸ’‘ using global setting species = human





πŸ’‘ standardized 35/40 terms









Now things look much better, but we still have 5 CellMaker records that seem more like metadata.






validated = lb.CellMarker.validate(adata.var.index, "name")









πŸ’‘ using global setting species = human





βœ… 35 terms (87.50%) are validated for name





❗ 5 terms (12.50%) are not validated for name: Time, Cell_length, Dead, (Ba138)Dd, Bead









Hence, let’s curate the AnnData a bit more.


Let’s move metadata (non-validated cell markers) into adata.obs:






adata.obs = adata[:, ~validated].to_df()
adata = adata[:, validated].copy()









Now we have a clean panel of 35 cell markers:






lb.CellMarker.validate(adata.var.index, "name");









πŸ’‘ using global setting species = human





βœ… 35 terms (100.00%) are validated for name









Next, let’s register the metadata features we moved to .obs:






features = ln.Feature.from_df(
    adata.obs
)  # Feature.from_df create feature records with type auto-populated













ln.add(features)









In addition, We’d also like to link this file with external features:






ln.Feature.validate("assay", "name")
lb.ExperimentalFactor.validate("FACS", "name");









βœ… 1 term (100.00%) is validated for name





❗ 1 term (100.00%) is not validated for name: FACS









Since we never validated the term β€œFACS”, let’s search for it’s ontology and register it:






lb.ExperimentalFactor.bionty().search("FACS").head(2)













  
    

      
      ontology_id
      definition
      synonyms
      parents
      molecule
      instrument
      measurement
      __ratio__
    

    

      name
      
      
      
      
      
      
      
      
    

  
  
    

      fluorescence-activated cell sorting
      EFO:0009108
      A Flow Cytometry Assay That Provides A Method ...
      FACS|FAC sorting
      []
      None
      None
      None
      100.000000
    

    

      acute chest syndrome
      EFO:0007129
      A Vaso-Occlusive Crisis Of The Pulmonary Vascu...
      ACS|Acute Chest Syndrome|acute chest syndrome|...
      [EFO:0003818]
      None
      None
      None
      85.714286
    

  











facs = lb.ExperimentalFactor.from_bionty(ontology_id="EFO:0009108")
facs.save()









βœ… created 1 ExperimentalFactor record from Bionty matching ontology_id: EFO:0009108









Adding a new modality:






modality = ln.Modality(name="protein", description="readouts of protein abundance")
modality.save()













Register #






file = ln.File.from_anndata(adata, description="Alpert19", var_ref=lb.CellMarker.name)









πŸ’‘ file will be copied to default storage upon `save()` with key `None` ('.lamindb/LhWKPCF6x4twXk0dR1x9.h5ad')





πŸ’‘ parsing feature names of X stored in slot 'var'





πŸ’‘    using global setting species = human





βœ…    35 terms (100.00%) are validated for name





πŸ’‘    using global setting species = human





βœ…    linked: FeatureSet(id='om9elFwRNETTAVa7DnUX', n=35, type='float', registry='bionty.CellMarker', hash='ldY9_GmptHLCcT7Nrpgo', created_by_id='DzTjkKse')





πŸ’‘ parsing feature names of slot 'obs'





βœ…    5 terms (100.00%) are validated for name





βœ…    linked: FeatureSet(id='1xPw4lpxAKxmsIynZ7XY', n=5, registry='core.Feature', hash='Vg9TGnGYfiWe_xegabUX', modality_id='woWqSfKt', created_by_id='DzTjkKse')













file.save()









βœ… saved 2 feature sets for slots: 'var','obs'





βœ… storing file 'LhWKPCF6x4twXk0dR1x9' at '.lamindb/LhWKPCF6x4twXk0dR1x9.h5ad'













file.add_labels(facs, "assay")
file.add_labels(lb.settings.species, "species")









βœ… linked new feature 'assay' together with new feature set FeatureSet(id='OsHMgu4GieqPEzY535pG', n=1, registry='core.Feature', hash='ylt2e3mmGjTejCKrQxza', updated_at=2023-08-28 13:52:19, modality_id='woWqSfKt', created_by_id='DzTjkKse')





πŸ’‘ no file links to it anymore, deleting feature set FeatureSet(id='OsHMgu4GieqPEzY535pG', n=1, registry='core.Feature', hash='ylt2e3mmGjTejCKrQxza', updated_at=2023-08-28 13:52:19, modality_id='woWqSfKt', created_by_id='DzTjkKse')





βœ… linked new feature 'species' together with new feature set FeatureSet(id='U5vIJj85XYwRU1gkMKFl', n=2, registry='core.Feature', hash='h2fOf0lfmzWz0gCeP9OP', updated_at=2023-08-28 13:52:19, modality_id='woWqSfKt', created_by_id='DzTjkKse')













var_feature_set = file.features.get_feature_set("var")
var_feature_set.modality = modality
var_feature_set.save()













file.features









'var': FeatureSet(id='om9elFwRNETTAVa7DnUX', n=35, type='float', registry='bionty.CellMarker', hash='ldY9_GmptHLCcT7Nrpgo', updated_at=2023-08-28 13:52:19, modality_id='OX1girsy', created_by_id='DzTjkKse')
'obs': FeatureSet(id='1xPw4lpxAKxmsIynZ7XY', n=5, registry='core.Feature', hash='Vg9TGnGYfiWe_xegabUX', updated_at=2023-08-28 13:52:18, modality_id='woWqSfKt', created_by_id='DzTjkKse')
'external': FeatureSet(id='U5vIJj85XYwRU1gkMKFl', n=2, registry='core.Feature', hash='h2fOf0lfmzWz0gCeP9OP', updated_at=2023-08-28 13:52:19, modality_id='woWqSfKt', created_by_id='DzTjkKse')









Check a few validated cell markers in .var:






file.features["var"].df().head(10)













  
    

      
      name
      synonyms
      gene_symbol
      ncbi_gene_id
      uniprotkb_id
      species_id
      bionty_source_id
      updated_at
      created_by_id
    

    

      id
      
      
      
      
      
      
      
      
      
    

  
  
    

      cFJEI6e6wml3
      CD20
      
      MS4A1
      931
      A0A024R507
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      2VeZenLi2dj5
      PD1
      PID1|PD-1|PD 1
      PDCD1
      5133
      A0A0M3M0G7
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      lRZYuH929QDw
      CD85j
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      CR7DAHxybgyi
      CD38
      
      CD38
      952
      B4E006
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      fpPkjlGv15C9
      Ccr6
      
      CCR6
      1235
      P51684
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      4EojtgN0CjBH
      CD161
      
      KLRB1
      3820
      Q12918
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      roEbL8zuLC5k
      Cd14
      
      CD14
      4695
      O43678
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      yCyTIVxZkIUz
      DNA2
      
      DNA2
      1763
      P51530
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      uThe3c0V3d4i
      CD27
      
      CD27
      939
      P26842
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      sYcK7uoWCtco
      Ccr7
      
      CCR7
      1236
      P32248
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

  











Validate and register a second dataset#


Let’s validate and register another flow file:






adata2 = readfcs.read(ln.dev.datasets.file_fcs())











We’d like to track all features in .var, so we register them:






adata2.var.index = lb.CellMarker.bionty().standardize(adata2.var.index)









πŸ’‘ using global setting species = human





πŸ’‘ standardized 14/16 terms













markers = lb.CellMarker.from_values(adata2.var.index, "name")
ln.save(markers)









πŸ’‘ using global setting species = human





βœ… loaded 10 CellMarker records matching name: CD3, CD28, CD8, Cd4, CD57, Cd14, Cd19, CD27, Ccr7, CD127





βœ… created 4 CellMarker records from Bionty matching name: CCR5, CD45RO, Ki67, SSC-A





❗ did not create CellMarker records for 2 non-validated names: FSC-A, FSC-H









Standardize synonyms so that all features pass validation:






adata2.var.index = lb.CellMarker.standardize(adata2.var.index)









πŸ’‘ using global setting species = human





πŸ’‘ standardized 14/16 terms













lb.CellMarker.validate(adata2.var.index, "name");









πŸ’‘ using global setting species = human





βœ… 14 terms (87.50%) are validated for name





❗ 2 terms (12.50%) are not validated for name: FSC-A, FSC-H















file2 = ln.File.from_anndata(
    adata2, description="My fcs file", var_ref=lb.CellMarker.name
)









πŸ’‘ file will be copied to default storage upon `save()` with key `None` ('.lamindb/4p3ouvLoPTJXfcq6dEA5.h5ad')





πŸ’‘ parsing feature names of X stored in slot 'var'





πŸ’‘    using global setting species = human





βœ…    14 terms (87.50%) are validated for name





❗    2 terms (12.50%) are not validated for name: FSC-A, FSC-H





πŸ’‘    using global setting species = human





βœ…    linked: FeatureSet(id='FA0HDOn6RahAVIjs50iH', n=14, type='float', registry='bionty.CellMarker', hash='npy5P7AYbjKLInpXlNvb', created_by_id='DzTjkKse')













file2.save()









βœ… saved 1 feature set for slot: 'var'





βœ… storing file '4p3ouvLoPTJXfcq6dEA5' at '.lamindb/4p3ouvLoPTJXfcq6dEA5.h5ad'













file2.add_labels(facs, "assay")
file2.add_labels(lb.settings.species, "species")









βœ… linked new feature 'assay' together with new feature set FeatureSet(id='BC2pHSmrRsm3Dr1i1KAM', n=1, registry='core.Feature', hash='ylt2e3mmGjTejCKrQxza', updated_at=2023-08-28 13:52:21, modality_id='woWqSfKt', created_by_id='DzTjkKse')





βœ… loaded: FeatureSet(id='U5vIJj85XYwRU1gkMKFl', n=2, registry='core.Feature', hash='h2fOf0lfmzWz0gCeP9OP', updated_at=2023-08-28 13:52:19, modality_id='woWqSfKt', created_by_id='DzTjkKse')





βœ… linked new feature 'species' together with new feature set FeatureSet(id='U5vIJj85XYwRU1gkMKFl', n=2, registry='core.Feature', hash='h2fOf0lfmzWz0gCeP9OP', updated_at=2023-08-28 13:52:21, modality_id='woWqSfKt', created_by_id='DzTjkKse')













var_feature_set = file2.features.get_feature_set("var")
var_feature_set.modality = modality
var_feature_set.save()













file2.features









'var': FeatureSet(id='FA0HDOn6RahAVIjs50iH', n=14, type='float', registry='bionty.CellMarker', hash='npy5P7AYbjKLInpXlNvb', updated_at=2023-08-28 13:52:21, modality_id='OX1girsy', created_by_id='DzTjkKse')
'external': FeatureSet(id='U5vIJj85XYwRU1gkMKFl', n=2, registry='core.Feature', hash='h2fOf0lfmzWz0gCeP9OP', updated_at=2023-08-28 13:52:21, modality_id='woWqSfKt', created_by_id='DzTjkKse')













file2.view_lineage()








https://d33wubrfki0l68.cloudfront.net/449786ec05c70e6ce4bb0bf0cf827666b8624872/1f87d/_images/3c4e90337c6f12c9cd5d5916e798da3ed2772e899b80168d14d6b56842e249b3.svg








Query by cell markers#


Which datasets have CD14 in the flow panel:






cell_markers = lb.CellMarker.lookup()













cell_markers.cd14









CellMarker(id='roEbL8zuLC5k', name='Cd14', synonyms='', gene_symbol='CD14', ncbi_gene_id='4695', uniprotkb_id='O43678', updated_at=2023-08-28 13:52:16, species_id='uHJU', bionty_source_id='i6sZ', created_by_id='DzTjkKse')













panels_with_cd14 = ln.FeatureSet.filter(cell_markers=cell_markers.cd14).all()













ln.File.filter(feature_sets__in=panels_with_cd14).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
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      4p3ouvLoPTJXfcq6dEA5
      H7Pxu11w
      None
      .h5ad
      AnnData
      My fcs file
      None
      None
      6876232
      Cf4Fhfw_RDMtKd5amM6Gtw
      md5
      OWuTtS4SAponz8
      SswraaPvvTqVHpKZo6iA
      2023-08-28 13:52:21
      DzTjkKse
    

    

      LhWKPCF6x4twXk0dR1x9
      H7Pxu11w
      None
      .h5ad
      AnnData
      Alpert19
      None
      None
      33367624
      14w5ElNsR_MqdiJtvnS1aw
      md5
      OWuTtS4SAponz8
      SswraaPvvTqVHpKZo6iA
      2023-08-28 13:52:18
      DzTjkKse
    

  







Shared cell markers between two files:






files = ln.File.filter(feature_sets__in=panels_with_cd14, species__name="human").list()
file1, file2 = files[0], files[1]













file1_markers = file1.features["var"]
file2_markers = file2.features["var"]

shared_markers = file1_markers & file2_markers
shared_markers.list("name")









['CD8', 'CD57', 'CD127', 'Cd19', 'Cd4', 'CD28', 'CD3', 'Cd14', 'CD27', 'Ccr7']













Flow marker registry#


Check out your CellMarker registry:






lb.CellMarker.filter().df()













  
    

      
      name
      synonyms
      gene_symbol
      ncbi_gene_id
      uniprotkb_id
      species_id
      bionty_source_id
      updated_at
      created_by_id
    

    

      id
      
      
      
      
      
      
      
      
      
    

  
  
    

      cFJEI6e6wml3
      CD20
      
      MS4A1
      931
      A0A024R507
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      2VeZenLi2dj5
      PD1
      PID1|PD-1|PD 1
      PDCD1
      5133
      A0A0M3M0G7
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      lRZYuH929QDw
      CD85j
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      CR7DAHxybgyi
      CD38
      
      CD38
      952
      B4E006
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      fpPkjlGv15C9
      Ccr6
      
      CCR6
      1235
      P51684
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      4EojtgN0CjBH
      CD161
      
      KLRB1
      3820
      Q12918
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      roEbL8zuLC5k
      Cd14
      
      CD14
      4695
      O43678
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      yCyTIVxZkIUz
      DNA2
      
      DNA2
      1763
      P51530
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      uThe3c0V3d4i
      CD27
      
      CD27
      939
      P26842
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      sYcK7uoWCtco
      Ccr7
      
      CCR7
      1236
      P32248
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      k0zGbSgZEX3q
      HLADR
      HLA‐DR|HLA-DR|HLA DR
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      L0m6f7FPiDeg
      CD86
      
      CD86
      942
      A8K632
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      YA5Ezh6SAy10
      DNA1
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      ttBc0Fs01sYk
      CD8
      
      CD8A
      925
      P01732
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      0qCmUijBeByY
      CD94
      
      KLRD1
      3824
      Q13241
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      h4rkCALR5WfU
      CD56
      
      NCAM1
      4684
      P13591
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      bspnQ0igku6c
      CD16
      
      FCGR3A
      2215
      O75015
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      hVNEgxlcDV10
      CD127
      
      IL7R
      3575
      P16871
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      a624IeIqbchl
      CD45RA
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      8OhpfB7wwV32
      Cd19
      
      CD19
      930
      P15391
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      n40112OuX7Cq
      CD123
      
      IL3RA
      3563
      P26951
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      a4hvNp34IYP0
      CD3
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      0evamYEdmaoY
      Igd
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      CLFUvJpioHoA
      CD28
      
      CD28
      940
      B4E0L1
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      4uiPHmCPV5i1
      CXCR5
      
      CXCR5
      643
      A0N0R2
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      ljp5UfCF9HCi
      TCRgd
      TCRGAMMADELTA|TCRΞ³Ξ΄
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      N2F6Qv9CxJch
      CD11B
      
      ITGAM
      3684
      P11215
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      HEK41hvaIazP
      Cd4
      
      CD4
      920
      B4DT49
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      agQD0dEzuoNA
      CXCR3
      
      CXCR3
      2833
      P49682
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      50v4SaR2m5zQ
      CD25
      
      IL2RA
      3559
      P01589
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      gEfe8qTsIHl0
      CD24
      
      CD24
      100133941
      B6EC88
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      Nb2sscq9cBcB
      CD57
      
      B3GAT1
      27087
      Q9P2W7
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      L0WKZ3fufq0J
      CD11c
      
      ITGAX
      3687
      P20702
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      0vAls2cmLKWq
      ICOS
      
      ICOS
      29851
      Q53QY6
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      c3dZKHFOdllB
      CD33
      
      CD33
      945
      P20138
      uHJU
      i6sZ
      2023-08-28 13:52:16
      DzTjkKse
    

    

      XvpJ6oL3SG7w
      CD45RO
      
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:20
      DzTjkKse
    

    

      UMsp5g0fgMwY
      CCR5
      
      CCR5
      1234
      P51681
      uHJU
      i6sZ
      2023-08-28 13:52:20
      DzTjkKse
    

    

      VZBURNy04vBi
      SSC-A
      SSC A|SSCA
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:20
      DzTjkKse
    

    

      Qa4ozz9tyesQ
      Ki67
      Ki-67|KI 67
      None
      None
      None
      uHJU
      i6sZ
      2023-08-28 13:52:20
      DzTjkKse
    

  












Hide code cell content




# a few tests
assert set(shared_markers.list("name")) == set(
    [
        "Ccr7",
        "CD3",
        "Cd14",
        "Cd19",
        "CD127",
        "CD27",
        "CD28",
        "CD8",
        "Cd4",
        "CD57",
    ]
)
ln.File.filter(feature_sets__in=panels_with_cd14).exists()









True
















Hide code cell content




# clean up test instance
!lamin delete --force test-flow
!rm -r test-flow









πŸ’‘ deleting instance testuser1/test-flow
βœ…     deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-flow.env





βœ…     instance cache deleted
βœ…     deleted '.lndb' sqlite file
❗     consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-flow