Cell marker¶
lamindb provides access to the following public cell marker ontologies through bionty:
Here we show how to access and search cell marker ontologies to standardize new data.
import bionty as bt
import pandas as pd
💡 connected lamindb: testuser1/test-public-ontologies
PublicOntology objects¶
Let us create a public ontology accessor with public()
, which chooses a default public ontology source from PublicSource
. It’s a PublicOntology object, which you can think about as a public registry:
public = bt.CellMarker.public(organism="human")
public
PublicOntology
Entity: CellMarker
Organism: human
Source: cellmarker, 2.0
#terms: 15466
As for registries, you can export the ontology as a DataFrame
:
df = public.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 public.ontology
.
Look up terms¶
As for registries, terms can be looked up with auto-complete:
lookup = public.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:
public.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
):
public.search("CD4", field=public.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:
public.inspect(markers.index, public.name);
✅ 6 terms (42.90%) are validated for 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 = public.standardize(markers.index, return_mapper=True)
synonyms_mapper
💡 standardized 10/14 terms
{'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:
public.inspect(markers.index, public.name);
✅ 10 terms (71.40%) are validated for 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 = public.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:
public.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:
public.validate(curated_df.index, public.name);
✅ 11 terms (78.60%) are validated
❗ 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:
bt.PublicSource.filter(entity="CellMarker").df()
uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | run_id | created_by_id | updated_at | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | |||||||||||||
26 | 5nkB | CellMarker | human | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/human_cellmarker_2.0_CellMa... | d565d4a542a5c7e7a06255975358e4f4 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | None | 1 | 2024-05-29 09:56:16.485913+00:00 |
27 | 6AFz | CellMarker | mouse | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/mouse_cellmarker_2.0_CellMa... | 189586732c63be949e40dfa6a3636105 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | None | 1 | 2024-05-29 09:56:16.486005+00:00 |
When instantiating a Bionty object, we can choose a source or version:
public_source = bt.PublicSource.filter(
source="cellmarker", version="2.0", organism="human"
).one()
public = bt.CellMarker.public(public_source=public_source)
public
PublicOntology
Entity: CellMarker
Organism: human
Source: cellmarker, 2.0
#terms: 15466
The currently used ontologies can be displayed using:
bt.PublicSource.filter(currently_used=True).df()
Show code cell output
uid | entity | organism | currently_used | source | source_name | version | url | md5 | source_website | run_id | created_by_id | updated_at | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
id | |||||||||||||
1 | 5Dlc | Organism | vertebrates | True | ensembl | Ensembl | release-112 | https://ftp.ensembl.org/pub/release-112/specie... | 0ec37e77f4bc2d0b0b47c6c62b9f122d | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.483555+00:00 |
6 | 2Jzh | Organism | bacteria | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/bacte... | ee28510ed5586ea7ab4495717c96efc8 | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484046+00:00 |
7 | 1kdI | Organism | fungi | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/fungi/releas... | dbcde58f4396ab8b2480f7fe9f83df8a | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484138+00:00 |
8 | 2mIM | Organism | metazoa | True | ensembl | Ensembl | release-57 | http://ftp.ensemblgenomes.org/pub/metazoa/rele... | 424636a574fec078a61cbdddb05f9132 | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484233+00:00 |
9 | 2XQ6 | Organism | plants | True | ensembl | Ensembl | release-57 | https://ftp.ensemblgenomes.ebi.ac.uk/pub/plant... | eadaa1f3e527e4c3940c90c7fa5c8bf4 | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484333+00:00 |
10 | 1Vzs | Organism | all | True | ncbitaxon | NCBItaxon Ontology | 2023-06-20 | s3://bionty-assets/df_all__ncbitaxon__2023-06-... | 00d97ba65627f1cd65636d2df22ea76c | https://github.com/obophenotype/ncbitaxon | None | 1 | 2024-05-29 09:56:16.484429+00:00 |
11 | 1hx4 | Gene | human | True | ensembl | Ensembl | release-112 | s3://bionty-assets/df_human__ensembl__release-... | 4ccda4d88720a326737376c534e8446b | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484524+00:00 |
15 | 76FX | Gene | mouse | True | ensembl | Ensembl | release-112 | s3://bionty-assets/df_mouse__ensembl__release-... | 519cf7b8acc3c948274f66f3155a3210 | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.484895+00:00 |
19 | 7LW6 | Gene | saccharomyces cerevisiae | True | ensembl | Ensembl | release-112 | s3://bionty-assets/df_saccharomyces cerevisiae... | 11775126b101233525a0a9e2dd64edae | https://www.ensembl.org | None | 1 | 2024-05-29 09:56:16.485271+00:00 |
22 | 7llW | Protein | human | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_human__uniprot__2023-03_... | 1c46e85c6faf5eff3de5b4e1e4edc4d3 | https://www.uniprot.org | None | 1 | 2024-05-29 09:56:16.485547+00:00 |
24 | 5U7J | Protein | mouse | True | uniprot | Uniprot | 2023-03 | s3://bionty-assets/df_mouse__uniprot__2023-03_... | 9d5e9a8225011d3218e10f9bbb96a46c | https://www.uniprot.org | None | 1 | 2024-05-29 09:56:16.485730+00:00 |
26 | 5nkB | CellMarker | human | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/human_cellmarker_2.0_CellMa... | d565d4a542a5c7e7a06255975358e4f4 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | None | 1 | 2024-05-29 09:56:16.485913+00:00 |
27 | 6AFz | CellMarker | mouse | True | cellmarker | CellMarker | 2.0 | s3://bionty-assets/mouse_cellmarker_2.0_CellMa... | 189586732c63be949e40dfa6a3636105 | http://bio-bigdata.hrbmu.edu.cn/CellMarker | None | 1 | 2024-05-29 09:56:16.486005+00:00 |
28 | 6cbC | CellLine | all | True | clo | Cell Line Ontology | 2022-03-21 | https://data.bioontology.org/ontologies/CLO/su... | ea58a1010b7e745702a8397a526b3a33 | https://bioportal.bioontology.org/ontologies/CLO | None | 1 | 2024-05-29 09:56:16.486097+00:00 |
29 | 3DeN | CellType | all | True | cl | Cell Ontology | 2024-02-13 | http://purl.obolibrary.org/obo/cl/releases/202... | https://obophenotype.github.io/cell-ontology | None | 1 | 2024-05-29 09:56:16.486189+00:00 | |
34 | 1AyH | Tissue | all | True | uberon | Uberon multi-species anatomy ontology | 2024-02-20 | http://purl.obolibrary.org/obo/uberon/releases... | 2048667b5fdf93192384bdf53cafba18 | http://obophenotype.github.io/uberon | None | 1 | 2024-05-29 09:56:16.486674+00:00 |
39 | LoCG | Disease | all | True | mondo | Mondo Disease Ontology | 2024-02-06 | http://purl.obolibrary.org/obo/mondo/releases/... | 78914fa236773c5ea6605f7570df6245 | https://mondo.monarchinitiative.org | None | 1 | 2024-05-29 09:56:16.487135+00:00 |
44 | 2mou | Disease | human | True | doid | Human Disease Ontology | 2024-01-31 | http://purl.obolibrary.org/obo/doid/releases/2... | b36c15a4610757094f8db64b78ae2693 | https://disease-ontology.org | None | 1 | 2024-05-29 09:56:16.487603+00:00 |
51 | 4usY | ExperimentalFactor | all | True | efo | The Experimental Factor Ontology | 3.63.0 | http://www.ebi.ac.uk/efo/releases/v3.63.0/efo.owl | 603e6f6981d53d501c5921aa3940b095 | https://bioportal.bioontology.org/ontologies/EFO | None | 1 | 2024-05-29 09:56:16.488253+00:00 |
54 | 2WLc | Phenotype | human | True | hp | Human Phenotype Ontology | 2024-03-06 | https://github.com/obophenotype/human-phenotyp... | 36b0d00c24a68edb9131707bc146a4c7 | https://hpo.jax.org | None | 1 | 2024-05-29 09:56:16.488529+00:00 |
58 | 6zE1 | Phenotype | mammalian | True | mp | Mammalian Phenotype Ontology | 2024-02-07 | https://github.com/mgijax/mammalian-phenotype-... | 31c27ed2c7d5774f8b20a77e4e1fd278 | https://github.com/mgijax/mammalian-phenotype-... | None | 1 | 2024-05-29 09:56:16.488896+00:00 |
60 | 7EnA | Phenotype | zebrafish | True | zp | Zebrafish Phenotype Ontology | 2024-01-22 | https://github.com/obophenotype/zebrafish-phen... | 01600a5d392419b27fc567362d4cfff8 | https://github.com/obophenotype/zebrafish-phen... | None | 1 | 2024-05-29 09:56:16.489080+00:00 |
63 | 55lY | 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 | None | 1 | 2024-05-29 09:56:16.489354+00:00 |
64 | 48aa | Pathway | all | True | go | Gene Ontology | 2023-05-10 | https://data.bioontology.org/ontologies/GO/sub... | e9845499eadaef2418f464cd7e9ac92e | http://geneontology.org | None | 1 | 2024-05-29 09:56:16.489445+00:00 |
67 | 3rm9 | BFXPipeline | all | True | lamin | Bioinformatics Pipeline | 1.0.0 | s3://bionty-assets/bfxpipelines.json | a7eff57a256994692fba46e0199ffc94 | https://lamin.ai | None | 1 | 2024-05-29 09:56:16.489724+00:00 |
68 | 5alK | Drug | all | True | dron | Drug Ontology | 2024-03-02 | https://data.bioontology.org/ontologies/DRON/s... | 84138459de4f65034e979f4e46783747 | https://bioportal.bioontology.org/ontologies/DRON | None | 1 | 2024-05-29 09:56:16.489824+00:00 |
70 | 7CRn | DevelopmentalStage | human | True | hsapdv | Human Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/HSAPDV/11... | 52181d59df84578ed69214a5cb614036 | https://github.com/obophenotype/developmental-... | None | 1 | 2024-05-29 09:56:16.490012+00:00 |
71 | 16tR | DevelopmentalStage | mouse | True | mmusdv | Mouse Developmental Stages | 2020-03-10 | http://aber-owl.net/media/ontologies/MMUSDV/9/... | 5bef72395d853c7f65450e6c2a1fc653 | https://github.com/obophenotype/developmental-... | None | 1 | 2024-05-29 09:56:16.490106+00:00 |
72 | 3Tlc | Ethnicity | human | True | hancestro | Human Ancestry Ontology | 3.0 | https://github.com/EBISPOT/hancestro/raw/3.0/h... | 76dd9efda9c2abd4bc32fc57c0b755dd | https://github.com/EBISPOT/hancestro | None | 1 | 2024-05-29 09:56:16.492428+00:00 |
73 | 5JnV | 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... | None | 1 | 2024-05-29 09:56:16.492535+00:00 |