Cell marker#
lamindb provides access to the following public cell marker ontologies through lnschema-bionty:
Here we show how to access and search cell marker ontologies to standardize new data.
Setup#
!lamin init --storage ./test-cell-marker --schema bionty
✅ saved: User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at=2023-12-22 16:53:43 UTC)
✅ saved: Storage(uid='tJwb4IJ7', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-cell-marker', type='local', updated_at=2023-12-22 16:53:43 UTC, created_by_id=1)
💡 loaded instance: testuser1/test-cell-marker
💡 did not register local instance on hub
import lnschema_bionty as lb
import pandas as pd
# adds an entry "human" into an empty instance
lb.settings.organism = "human"
💡 loaded instance: testuser1/test-cell-marker
Bionty objects#
Let us create a public knowledge accessor with bionty(), which chooses a default public knowledge source from BiontySource. It’s a Bionty object, which you can think about as a less-capable registry:
cell_marker_bt = lb.CellMarker.bionty()
cell_marker_bt
CellMarker
Organism: human
Source: cellmarker, 2.0
#terms: 15466
📖 CellMarker.df(): ontology reference table
🔎 CellMarker.lookup(): autocompletion of terms
🎯 CellMarker.search(): free text search of terms
✅ CellMarker.validate(): strictly validate values
🧐 CellMarker.inspect(): full inspection of values
👽 CellMarker.standardize(): convert to standardized names
🪜 CellMarker.diff(): difference between two versions
🔗 CellMarker.ontology: Pronto.Ontology object
As for registries, you can export the ontology as a DataFrame:
df = cell_marker_bt.df()
df.head()
| name | synonyms | gene_symbol | ncbi_gene_id | uniprotkb_id | |
|---|---|---|---|---|---|
| 0 | A1BG | A1BG | 1 | P04217 | |
| 1 | A2M | A2M | 3494 | None | |
| 2 | A2ML1 | A2ML1 | 144568 | A8K2U0 | |
| 3 | A4GALT | A4GALT | 53947 | A0A0S2Z5J1 | |
| 4 | AADAC | AADAC | 13 | P22760 |
Unlike registries, you can also export it as a Pronto object via cell_marker_bt.ontology.
Look up terms#
As for registries, terms can be looked up with auto-complete:
lookup = cell_marker_bt.lookup()
The . accessor provides normalized terms (lower case, only contains alphanumeric characters and underscores):
lookup.immp1l
CellMarker(name='IMMP1L', synonyms='', gene_symbol='IMMP1L', ncbi_gene_id='196294', uniprotkb_id='Q96LU5')
To look up the exact original strings, convert the lookup object to dict and use the [] accessor:
lookup_dict = lookup.dict()
lookup_dict["IMMP1L"]
CellMarker(name='IMMP1L', synonyms='', gene_symbol='IMMP1L', ncbi_gene_id='196294', uniprotkb_id='Q96LU5')
Search terms#
Search behaves in the same way as it does for registries:
cell_marker_bt = lb.CellMarker.bionty()
cell_marker_bt.search("CD4").head(5)
| synonyms | gene_symbol | ncbi_gene_id | uniprotkb_id | __ratio__ | |
|---|---|---|---|---|---|
| name | |||||
| Cd4 | CD4 | 920 | B4DT49 | 100.0 | |
| CD4+ | None | None | None | 100.0 | |
| CD45RB | None | None | None | 90.0 | |
| CD45RO | None | None | None | 90.0 | |
| CD44R | None | None | None | 90.0 |
Search another field (default is .name):
cell_marker_bt.search("CD4", field=cell_marker_bt.gene_symbol).head(1)
| name | synonyms | ncbi_gene_id | uniprotkb_id | __ratio__ | |
|---|---|---|---|---|---|
| gene_symbol | |||||
| CD4 | Cd4 | 920 | B4DT49 | 100.0 |
Standardize cell marker identifiers#
Let us generate a DataFrame that stores a number of cell markers identifiers, some of which corrupted:
markers = pd.DataFrame(
index=[
"KI67",
"CCR7",
"CD14",
"CD8",
"CD45RA",
"CD4",
"CD3",
"CD127a",
"PD1",
"Invalid-1",
"Invalid-2",
"CD66b",
"Siglec8",
"Time",
]
)
Now let’s check which cell markers can be found in the reference:
cell_marker_bt.inspect(markers.index, cell_marker_bt.name);
❗ 8 terms (57.10%) are not validated for name: KI67, CCR7, CD14, CD4, CD127a, Invalid-1, Invalid-2, Time
detected 4 terms with inconsistent casing/synonyms: KI67, CCR7, CD14, CD4
→ standardize terms via .standardize()
Logging suggests to map synonyms:
synonyms_mapper = cell_marker_bt.standardize(markers.index, return_mapper=True)
synonyms_mapper
{'KI67': 'Ki67', 'CCR7': 'Ccr7', 'CD14': 'Cd14', 'CD4': 'Cd4'}
Let’s replace the synonyms with standardized names in the DataFrame:
markers.rename(index=synonyms_mapper, inplace=True)
The Time, Invalid-1 and Invalid-2 are non-marker channels which won’t be curated by cell marker:
cell_marker_bt.inspect(markers.index, cell_marker_bt.name);
❗ 4 terms (28.60%) are not validated for name: CD127a, Invalid-1, Invalid-2, Time
We don’t find CD127a, let’s check in the lookup with auto-completion:
lookup = cell_marker_bt.lookup()
lookup.cd127
CellMarker(name='CD127', synonyms='', gene_symbol='IL7R', ncbi_gene_id='3575', uniprotkb_id='P16871', _5='cd127')
It should be cd127, we had a typo there with cd127a:
curated_df = markers.rename(index={"CD127a": lookup.cd127.name})
Optionally, search:
cell_marker_bt.search("CD127a").head()
| synonyms | gene_symbol | ncbi_gene_id | uniprotkb_id | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| CD127 | IL7R | 3575 | P16871 | cd127 | 90.909091 | |
| CD1 | CD1A | 910 | P29016 | cd1 | 90.000000 | |
| CD172a | None | None | None | cd172a | 83.333333 | |
| CD167a | None | None | None | cd167a | 83.333333 | |
| CD121a | None | None | None | cd121a | 83.333333 |
Now we see that all cell marker candidates validate:
cell_marker_bt.validate(curated_df.index, cell_marker_bt.name);
❗ 3 terms (21.40%) are not validated: Invalid-1, Invalid-2, Time
Ontology source versions#
For any given entity, we can choose from a number of versions:
lb.BiontySource.filter(entity="CellMarker").df()
| uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 18 | vqWI | CellMarker | human | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/human_cellmarker_2.0_CellMa... | d565d4a542a5c7e7a06255975358e4f4 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 16:53:43.617544+00:00 | 1 |
| 19 | ypPK | CellMarker | mouse | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/mouse_cellmarker_2.0_CellMa... | 189586732c63be949e40dfa6a3636105 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 16:53:43.617579+00:00 | 1 |
When instantiating a Bionty object, we can choose a source or version:
bionty_source = lb.BiontySource.filter(
source="cellmarker", version="2.0", organism="human"
).one()
cell_marker_bt = lb.CellType(bionty_source=bionty_source)
cell_marker_bt
CellType(uid='6aqH4ddb', name='', bionty_source_id=18, created_by_id=1)
The currently used ontologies can be displayed using:
lb.BiontySource.filter(currently_used=True).df()
| uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | updated_at | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 1 | zvGR | Organism | vertebrates | True | ensembl | Ensembl | release-110 | https://ftp.ensembl.org/pub/release-110/specie... | f3faf95648d3a2b50fd3625456739706 | https://www.ensembl.org | 2023-12-22 16:53:43.616936+00:00 | 1 |
| 4 | TE9h | Organism | bacteria | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/bacte... | ee28510ed5586ea7ab4495717c96efc8 | https://www.ensembl.org | 2023-12-22 16:53:43.617062+00:00 | 1 |
| 5 | OZIG | Organism | fungi | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/fungi/releas... | dbcde58f4396ab8b2480f7fe9f83df8a | https://www.ensembl.org | 2023-12-22 16:53:43.617097+00:00 | 1 |
| 6 | W07m | Organism | metazoa | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/metazoa/rele... | 424636a574fec078a61cbdddb05f9132 | https://www.ensembl.org | 2023-12-22 16:53:43.617133+00:00 | 1 |
| 7 | AVh3 | Organism | plants | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/plant... | eadaa1f3e527e4c3940c90c7fa5c8bf4 | https://www.ensembl.org | 2023-12-22 16:53:43.617167+00:00 | 1 |
| 8 | MdBu | Organism | all | True | ncbitaxon | NCBItaxon Ontology | 2023-06-20 | s3://bionty-assets/df_all__ncbitaxon__2023-06-... | 00d97ba65627f1cd65636d2df22ea76c | https://github.com/obophenotype/ncbitaxon | 2023-12-22 16:53:43.617201+00:00 | 1 |
| 9 | o36k | Gene | human | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_human__ensembl__release-... | 832f3947e83664588d419608a469b528 | https://www.ensembl.org | 2023-12-22 16:53:43.617235+00:00 | 1 |
| 11 | VTEw | Gene | mouse | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_mouse__ensembl__release-... | fa4ce130f2929aefd7ac3bc8eaf0c4de | https://www.ensembl.org | 2023-12-22 16:53:43.617304+00:00 | 1 |
| 13 | Uhnp | Gene | saccharomyces cerevisiae | True | ensembl | Ensembl | release-110 | s3://bionty-assets/df_saccharomyces cerevisiae... | 2e59495a3e87ea6575e408697dd73459 | https://www.ensembl.org | 2023-12-22 16:53:43.617373+00:00 | 1 |
| 14 | 000Q | Protein | human | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_human__uniprot__2023-03_... | 1c46e85c6faf5eff3de5b4e1e4edc4d3 | https://www.uniprot.org | 2023-12-22 16:53:43.617407+00:00 | 1 |
| 16 | tD7O | Protein | mouse | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_mouse__uniprot__2023-03_... | 9d5e9a8225011d3218e10f9bbb96a46c | https://www.uniprot.org | 2023-12-22 16:53:43.617475+00:00 | 1 |
| 18 | vqWI | CellMarker | human | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/human_cellmarker_2.0_CellMa... | d565d4a542a5c7e7a06255975358e4f4 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 16:53:43.617544+00:00 | 1 |
| 19 | ypPK | CellMarker | mouse | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/mouse_cellmarker_2.0_CellMa... | 189586732c63be949e40dfa6a3636105 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | 2023-12-22 16:53:43.617579+00:00 | 1 |
| 20 | 2Zjk | CellLine | all | True | clo | Cell Line Ontology | 2022-03-21 | https://data.bioontology.org/ontologies/CLO/su... | ea58a1010b7e745702a8397a526b3a33 | https://bioportal.bioontology.org/ontologies/CLO | 2023-12-22 16:53:43.617620+00:00 | 1 |
| 21 | 4shh | CellType | all | True | cl | Cell Ontology | 2023-08-24 | http://purl.obolibrary.org/obo/cl/releases/202... | 46e7dd89421f1255cf0191eca1548f73 | https://obophenotype.github.io/cell-ontology | 2023-12-22 16:53:43.617654+00:00 | 1 |
| 25 | LmWQ | Tissue | all | True | uberon | Uberon multi-species anatomy ontology | 2023-09-05 | http://purl.obolibrary.org/obo/uberon/releases... | abcee3ede566d1311d758b853ccdf5aa | http://obophenotype.github.io/uberon | 2023-12-22 16:53:43.617790+00:00 | 1 |
| 29 | zMWv | Disease | all | True | mondo | Mondo Disease Ontology | 2023-08-02 | http://purl.obolibrary.org/obo/mondo/releases/... | 7f33767422042eec29f08b501fc851db | https://mondo.monarchinitiative.org | 2023-12-22 16:53:43.617925+00:00 | 1 |
| 33 | cxPr | Disease | human | True | doid | Human Disease Ontology | 2023-03-31 | http://purl.obolibrary.org/obo/doid/releases/2... | 64f083a1e47867c307c8eae308afc3bb | https://disease-ontology.org | 2023-12-22 16:53:43.618061+00:00 | 1 |
| 35 | 2wto | ExperimentalFactor | all | True | efo | The Experimental Factor Ontology | 3.57.0 | http://www.ebi.ac.uk/efo/releases/v3.57.0/efo.owl | 2ecafc69b3aba7bdb31ad99438505c05 | https://bioportal.bioontology.org/ontologies/EFO | 2023-12-22 16:53:43.618129+00:00 | 1 |
| 37 | 3SSF | Phenotype | human | True | hp | Human Phenotype Ontology | 2023-06-17 | https://github.com/obophenotype/human-phenotyp... | 65e8d96bc81deb893163927063b10c06 | https://hpo.jax.org | 2023-12-22 16:53:43.618197+00:00 | 1 |
| 40 | nwdt | Phenotype | mammalian | True | mp | Mammalian Phenotype Ontology | 2023-05-31 | https://github.com/mgijax/mammalian-phenotype-... | be89052cf6d9c0b6197038fe347ef293 | https://github.com/mgijax/mammalian-phenotype-... | 2023-12-22 16:53:43.618297+00:00 | 1 |
| 41 | zAfB | Phenotype | zebrafish | True | zp | Zebrafish Phenotype Ontology | 2022-12-17 | https://github.com/obophenotype/zebrafish-phen... | 03430b567bf153216c0fa4c3440b3b24 | https://github.com/obophenotype/zebrafish-phen... | 2023-12-22 16:53:43.618331+00:00 | 1 |
| 43 | p1co | Phenotype | all | True | pato | Phenotype And Trait Ontology | 2023-05-18 | http://purl.obolibrary.org/obo/pato/releases/2... | bd472f4971492109493d4ad8a779a8dd | https://github.com/pato-ontology/pato | 2023-12-22 16:53:43.618398+00:00 | 1 |
| 44 | h0rU | Pathway | all | True | go | Gene Ontology | 2023-05-10 | https://data.bioontology.org/ontologies/GO/sub... | e9845499eadaef2418f464cd7e9ac92e | http://geneontology.org | 2023-12-22 16:53:43.618432+00:00 | 1 |
| 46 | fxHJ | BFXPipeline | all | True | lamin | Bioinformatics Pipeline | 1.0.0 | s3://bionty-assets/bfxpipelines.json | a7eff57a256994692fba46e0199ffc94 | https://lamin.ai | 2023-12-22 16:53:43.618500+00:00 | 1 |
| 47 | chfO | Drug | all | True | dron | Drug Ontology | 2023-03-10 | https://data.bioontology.org/ontologies/DRON/s... | 75e86011158fae76bb46d96662a33ba3 | https://bioportal.bioontology.org/ontologies/DRON | 2023-12-22 16:53:43.618551+00:00 | 1 |
| 48 | 7JhT | DevelopmentalStage | human | True | hsapdv | Human Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/HSAPDV/11... | 52181d59df84578ed69214a5cb614036 | https://github.com/obophenotype/developmental-... | 2023-12-22 16:53:43.618588+00:00 | 1 |
| 49 | JIKv | DevelopmentalStage | mouse | True | mmusdv | Mouse Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/MMUSDV/9/... | 5bef72395d853c7f65450e6c2a1fc653 | https://github.com/obophenotype/developmental-... | 2023-12-22 16:53:43.618623+00:00 | 1 |
| 50 | clid | Ethnicity | human | True | hancestro | Human Ancestry Ontology | 3.0 | https://github.com/EBISPOT/hancestro/raw/3.0/h... | 76dd9efda9c2abd4bc32fc57c0b755dd | https://github.com/EBISPOT/hancestro | 2023-12-22 16:53:43.618658+00:00 | 1 |
| 51 | rsbG | BioSample | all | True | ncbi | NCBI BioSample attributes | 2023-09 | s3://bionty-assets/df_all__ncbi__2023-09__BioS... | 918db9bd1734b97c596c67d9654a4126 | https://www.ncbi.nlm.nih.gov/biosample/docs/at... | 2023-12-22 16:53:43.618691+00:00 | 1 |
Show code cell content
!lamin delete --force test-cell-marker
!rm -r test-cell-marker
💡 deleting instance testuser1/test-cell-marker
✅ deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-cell-marker.env
✅ instance cache deleted
✅ deleted '.lndb' sqlite file
❗ consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-cell-marker