CellTypist¶
Cell types classify cells based on public and private knowledge from studying transcription, morphology, function & other properties. Established cell types have well-characterized markers and properties; however, cell subtypes and states are continuously being discovered, refined and better understood.
In this notebook, we register the immune cell type vocabulary from CellTypist, a computational tool used for cell type classification in scRNA-seq data.
In the following Standardize metadata on-the-fly notebook, we’ll demonstrate how to annotate datasets analyzed with CellTypist enrichment analysis and track the dataset with LaminDB.
Setup¶
!lamin load use-cases-registries
Show code cell output
💡 connected lamindb: testuser1/use-cases-registries
Show code cell content
# filter warnings from celltypist
import warnings
warnings.filterwarnings("ignore", message=".*The 'nopython' keyword.*")
import lamindb as ln
import bionty as bt
💡 connected lamindb: testuser1/use-cases-registries
Access CellTypist records 
¶
As a first step we will read in CellTypist’s immune cell encyclopedia
import pandas as pd
description = "CellTypist Pan Immune Atlas v2: basic cell type information"
celltypist_source_v2_url = "https://github.com/Teichlab/celltypist_wiki/raw/main/atlases/Pan_Immune_CellTypist/v2/tables/Basic_celltype_information.xlsx"
celltypist_df = pd.read_excel(celltypist_source_v2_url)
It provides an ontology_id of the public Cell Ontology for the majority of records.
celltypist_df.head()
| High-hierarchy cell types | Low-hierarchy cell types | Description | Cell Ontology ID | Curated markers | |
|---|---|---|---|---|---|
| 0 | B cells | B cells | B lymphocytes with diverse cell surface immuno... | CL:0000236 | CD79A, MS4A1, CD19 |
| 1 | B cells | Follicular B cells | resting mature B lymphocytes found in the prim... | CL:0000843 | CXCR5, TNFRSF13B, CD22 |
| 2 | B cells | Proliferative germinal center B cells | proliferating germinal center B cells | CL:0000844 | MKI67, SUGCT, AICDA |
| 3 | B cells | Germinal center B cells | proliferating mature B cells that undergo soma... | CL:0000844 | POU2AF1, CD40, SUGCT |
| 4 | B cells | Memory B cells | long-lived mature B lymphocytes which are form... | CL:0000787 | CR2, CD27, MS4A1 |
The “Cell Ontology ID” is associated with multiple “Low-hierarchy cell types”:
celltypist_df.set_index(["Cell Ontology ID", "Low-hierarchy cell types"]).head(10)
| High-hierarchy cell types | Description | Curated markers | ||
|---|---|---|---|---|
| Cell Ontology ID | Low-hierarchy cell types | |||
| CL:0000236 | B cells | B cells | B lymphocytes with diverse cell surface immuno... | CD79A, MS4A1, CD19 |
| CL:0000843 | Follicular B cells | B cells | resting mature B lymphocytes found in the prim... | CXCR5, TNFRSF13B, CD22 |
| CL:0000844 | Proliferative germinal center B cells | B cells | proliferating germinal center B cells | MKI67, SUGCT, AICDA |
| Germinal center B cells | B cells | proliferating mature B cells that undergo soma... | POU2AF1, CD40, SUGCT | |
| CL:0000787 | Memory B cells | B cells | long-lived mature B lymphocytes which are form... | CR2, CD27, MS4A1 |
| Age-associated B cells | B cells | CD11c+ T-bet+ memory B cells associated with a... | FCRL2, ITGAX, TBX21 | |
| CL:0000788 | Naive B cells | B cells | mature B lymphocytes which express cell-surfac... | IGHM, IGHD, TCL1A |
| CL:0000818 | Transitional B cells | B cells | immature B cell precursors in the bone marrow ... | CD24, MYO1C, MS4A1 |
| CL:0000817 | Large pre-B cells | B-cell lineage | proliferative B lymphocyte precursors derived ... | MME, CD24, MKI67 |
| Small pre-B cells | B-cell lineage | non-proliferative B lymphocyte precursors deri... | MME, CD24, IGLL5 |
Validate CellTypist records 
¶
For any cell type record that can be validated against the public Cell Ontology, we’d like to ensure that it’s actually validated.
This will avoid that we’ll refer to the same cell type with different identifiers.
We need a Bionty object for this:
bionty = bt.CellType.public()
bionty
PublicOntology
Entity: CellType
Organism: all
Source: cl, 2024-02-13
#terms: 2918
We can now validate the "Cell Ontology ID" column:
bionty.inspect(celltypist_df["Cell Ontology ID"], bionty.ontology_id);
This looks good!
But when inspecting the names, most of them don’t validate:
bionty.inspect(celltypist_df["Low-hierarchy cell types"], bionty.name);
❗ 97 terms (99.00%) are not validated for name: B cells, Follicular B cells, Proliferative germinal center B cells, Germinal center B cells, Memory B cells, Age-associated B cells, Naive B cells, Transitional B cells, Large pre-B cells, Small pre-B cells, Pre-pro-B cells, Pro-B cells, Cycling B cells, Cycling DCs, Cycling gamma-delta T cells, Cycling monocytes, Cycling NK cells, Cycling T cells, DC, DC1, ...
detected 6 terms with synonyms: DC1, DC2, ETP, ILC2, ILC3, pDC
→ standardize terms via .standardize()
A search tells us that terms that are named in plural in Cell Typist occur with a name in singular in the Cell Ontology:
celltypist_df["Low-hierarchy cell types"][0]
'B cells'
bionty.search(celltypist_df["Low-hierarchy cell types"][0]).head(2)
| ontology_id | definition | synonyms | parents | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| B cell | CL:0000236 | A Lymphocyte Of B Lineage That Is Capable Of B... | B-cell|B-lymphocyte|B lymphocyte | [CL:0000945] | b cell | 92.307692 |
| B-2 B cell | CL:0000822 | A Conventional B Cell Subject To Antigenic Sti... | B2 B-cell|B-2 B-cell|B2 B cell|B2 B-lymphocyte... | [CL:0000785] | b-2 b cell | 85.714286 |
Let’s try to strip "s" and inspect if more names are now validated. Yes, there are!
bionty.inspect(
[i.rstrip("s") for i in celltypist_df["Low-hierarchy cell types"]],
bionty.name,
);
❗ 93 terms (94.90%) are not validated for name: Follicular B cell, Proliferative germinal center B cell, Germinal center B cell, Memory B cell, Age-associated B cell, Naive B cell, Transitional B cell, Large pre-B cell, Small pre-B cell, Pre-pro-B cell, Pro-B cell, Cycling B cell, Cycling DC, Cycling gamma-delta T cell, Cycling monocyte, Cycling NK cell, Cycling T cell, DC, DC1, DC2, ...
detected 31 terms with inconsistent casing/synonyms: Follicular B cell, Germinal center B cell, Memory B cell, Naive B cell, Transitional B cell, Small pre-B cell, Pro-B cell, DC1, DC2, Endothelial cell, Epithelial cell, Erythrocyte, ETP, Fibroblast, Granulocyte, Neutrophil, ILC2, ILC3, NK cell, Alveolar macrophage, ...
→ standardize terms via .standardize()
Every “low-hierarchy cell type” has an ontology id and most “high-hierarchy cell types” also appear as “low-hierarchy cell types” in the Cell Typist table. Four, however, don’t, and therefore don’t have an ontology ID.
high_terms = celltypist_df["High-hierarchy cell types"].unique()
low_terms = celltypist_df["Low-hierarchy cell types"].unique()
high_terms_nonval = set(high_terms).difference(low_terms)
high_terms_nonval
{'B-cell lineage', 'Cycling cells', 'Erythroid', 'T cells'}
Register CellTypist records 
¶
Let’s first add the “High-hierarchy cell types” as a column "parent".
This enables LaminDB to populate the parents and children fields, which will enable you to query for hierarchical relationships.
celltypist_df["parent"] = celltypist_df.pop("High-hierarchy cell types")
# if high and low terms are the same, no parents
celltypist_df.loc[
(celltypist_df["parent"] == celltypist_df["Low-hierarchy cell types"]), "parent"
] = None
# rename columns, drop markers
celltypist_df.drop(columns=["Curated markers"], inplace=True)
celltypist_df.rename(
columns={"Low-hierarchy cell types": "ct_name", "Cell Ontology ID": "ontology_id"},
inplace=True,
)
celltypist_df.columns = celltypist_df.columns.str.lower()
# add standardize names for each ontology_id
celltypist_df["name"] = bionty.df().loc[celltypist_df["ontology_id"]].name.values
celltypist_df.head(2)
| ct_name | description | ontology_id | parent | name | |
|---|---|---|---|---|---|
| 0 | B cells | B lymphocytes with diverse cell surface immuno... | CL:0000236 | None | B cell |
| 1 | Follicular B cells | resting mature B lymphocytes found in the prim... | CL:0000843 | B cells | follicular B cell |
Now, let’s create records from the public ontology:
public_records = bt.CellType.from_values(
celltypist_df.ontology_id, bt.CellType.ontology_id
)
ln.save(public_records)
❗ now recursing through parents: this only happens once, but is much slower than bulk saving
Let’s now amend public ontology records so that they maintain additional annotations that Cell Typist might have.
public_records_dict = {r.ontology_id: r for r in public_records}
for _, row in celltypist_df.iterrows():
record = public_records_dict[row["ontology_id"]]
try:
record.add_synonym(row["ct_name"])
except SystemExit:
pass
Show code cell output
❌ input synonyms ['DC2'] already associated with the following records:
| created_at | created_by_id | run_id | updated_at | id | uid | name | ontology_id | abbr | synonyms | description | public_source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-05-29 09:57:49.685059+00:00 | 1 | None | 2024-05-29 09:57:49.685119+00:00 | 126 | 3JO0EdVd | plasmacytoid dendritic cell | CL:0000784 | None | pDC|type 2 DC|plasmacytoid T cell|T-associated... | A Dendritic Cell Type Of Distinct Morphology, ... | 29 |
❌ input synonyms ['ILC2'] already associated with the following records:
| created_at | created_by_id | run_id | updated_at | id | uid | name | ontology_id | abbr | synonyms | description | public_source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-05-29 09:57:42.986610+00:00 | 1 | None | 2024-05-29 09:57:42.986691+00:00 | 112 | 4ny4oBnr | group 2 innate lymphoid cell | CL:0001069 | None | natural helper cell|ILC2|nuocyte | An Innate Lymphoid Cell That Is Capable Of Pro... | 29 |
❌ input synonyms ['ILC3'] already associated with the following records:
| created_at | created_by_id | run_id | updated_at | id | uid | name | ontology_id | abbr | synonyms | description | public_source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-05-29 09:57:43.612245+00:00 | 1 | None | 2024-05-29 09:57:43.612305+00:00 | 113 | 3tILnbqv | group 3 innate lymphoid cell | CL:0001071 | None | ILC3 | An Innate Lymphoid Cell That Constituitively E... | 29 |
❌ input synonyms ['pDC'] already associated with the following records:
| created_at | created_by_id | run_id | updated_at | id | uid | name | ontology_id | abbr | synonyms | description | public_source_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-05-29 09:57:49.685059+00:00 | 1 | None | 2024-05-29 09:57:49.685119+00:00 | 126 | 3JO0EdVd | plasmacytoid dendritic cell | CL:0000784 | None | pDC|type 2 DC|plasmacytoid T cell|T-associated... | A Dendritic Cell Type Of Distinct Morphology, ... | 29 |
Add parent-child relationship of the records from Celltypist¶
We still need to add the renaming 4 High hierarchy terms:
list(high_terms_nonval)
['Cycling cells', 'B-cell lineage', 'Erythroid', 'T cells']
Let’s get the top hits from a search:
for term in list(high_terms_nonval):
print(f"Term: {term}")
display(bionty.search(term).head(2))
Term: Cycling cells
| ontology_id | definition | synonyms | parents | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| circulating cell | CL:0000080 | A Cell Which Moves Among Different Tissues Of ... | None | [CL:0000000] | circulating cell | 75.862069 |
| lining cell | CL:0000213 | A Cell Within An Epithelial Cell Sheet Whose M... | boundary cell | [CL:0000215] | lining cell | 75.000000 |
Term: B-cell lineage
| ontology_id | definition | synonyms | parents | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| obsolete cell by lineage | CL:0000220 | None | None | [] | obsolete cell by lineage | 73.684211 |
| obsolete cell line cell | CL:0007014 | Obsolete: A Cultured Cell That Has Been Passag... | passaged cultured cell | [] | obsolete cell line cell | 64.864865 |
Term: Erythroid
| ontology_id | definition | synonyms | parents | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| erythroid progenitor cell | CL:0000038 | A Progenitor Cell Committed To The Erythroid L... | None | [CL:0000764, CL:0000839] | erythroid progenitor cell | 90.0 |
| megakaryocyte-erythroid progenitor cell | CL:0000050 | A Progenitor Cell Committed To The Megakaryocy... | colony forming unit erythroid megakaryocyte|CF... | [CL:0002032, CL:0011026, CL:0000763] | megakaryocyte-erythroid progenitor cell | 90.0 |
Term: T cells
| ontology_id | definition | synonyms | parents | __agg__ | __ratio__ | |
|---|---|---|---|---|---|---|
| name | ||||||
| T cell | CL:0000084 | A Type Of Lymphocyte Whose Defining Characteri... | T-lymphocyte|T lymphocyte|T-cell | [CL:0000542] | t cell | 92.307692 |
| exhausted T cell | CL:0011025 | None | An effector T cell that displays impaired effe... | [CL:0000911] | exhausted t cell | 80.000000 |
So we decide to:
Add the “T cells” to the synonyms of the public “T cell” record
Create the remaining 3 terms only using their names (we think “B cell flow” shouldn’t be identified with “B cell”)
for name in high_terms_nonval:
if name == "T cells":
record = bt.CellType.from_public(name="T cell")
record.add_synonym(name)
record.save()
elif name == "Erythroid":
record = bt.CellType.from_public(name="erythroid lineage cell")
record.add_synonym(name)
record.save()
else:
record = bt.CellType(name=name)
record.save()
❗ records with similar names exist! did you mean to load one of them?
| uid | name | ontology_id | abbr | synonyms | description | public_source_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 1 | ryEtgi1y | B cell | CL:0000236 | None | B lymphocyte|B-cell|B cells|B-lymphocyte|Cycli... | A Lymphocyte Of B Lineage That Is Capable Of B... | 29 | None | 1 | 2024-05-29 09:57:56.491573+00:00 |
| 2 | 2EhFTUoZ | follicular B cell | CL:0000843 | None | Follicular B cells|follicular B-lymphocyte|Fo ... | A Resting Mature B Cell That Has The Phenotype... | 29 | None | 1 | 2024-05-29 09:57:56.272333+00:00 |
| 3 | 4IowPafD | germinal center B cell | CL:0000844 | None | GC B lymphocyte|germinal center B lymphocyte|P... | A Rapidly Cycling Mature B Cell That Has Disti... | 29 | None | 1 | 2024-05-29 09:57:56.311170+00:00 |
| 4 | 2cUPBtY8 | memory B cell | CL:0000787 | None | memory B lymphocyte|Age-associated B cells|mem... | A Memory B Cell Is A Mature B Cell That Is Lon... | 29 | None | 1 | 2024-05-29 09:57:56.351173+00:00 |
| 5 | 3jdCg7zi | naive B cell | CL:0000788 | None | Naive B cells|naive B-cell|naive B lymphocyte|... | A Naive B Cell Is A Mature B Cell That Has The... | 29 | None | 1 | 2024-05-29 09:57:56.370589+00:00 |
❗ records with similar names exist! did you mean to load one of them?
| uid | name | ontology_id | abbr | synonyms | description | public_source_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 1 | ryEtgi1y | B cell | CL:0000236 | None | B lymphocyte|B-cell|B cells|B-lymphocyte|Cycli... | A Lymphocyte Of B Lineage That Is Capable Of B... | 29 | None | 1 | 2024-05-29 09:57:56.491573+00:00 |
| 2 | 2EhFTUoZ | follicular B cell | CL:0000843 | None | Follicular B cells|follicular B-lymphocyte|Fo ... | A Resting Mature B Cell That Has The Phenotype... | 29 | None | 1 | 2024-05-29 09:57:56.272333+00:00 |
| 3 | 4IowPafD | germinal center B cell | CL:0000844 | None | GC B lymphocyte|germinal center B lymphocyte|P... | A Rapidly Cycling Mature B Cell That Has Disti... | 29 | None | 1 | 2024-05-29 09:57:56.311170+00:00 |
| 4 | 2cUPBtY8 | memory B cell | CL:0000787 | None | memory B lymphocyte|Age-associated B cells|mem... | A Memory B Cell Is A Mature B Cell That Is Lon... | 29 | None | 1 | 2024-05-29 09:57:56.351173+00:00 |
| 5 | 3jdCg7zi | naive B cell | CL:0000788 | None | Naive B cells|naive B-cell|naive B lymphocyte|... | A Naive B Cell Is A Mature B Cell That Has The... | 29 | None | 1 | 2024-05-29 09:57:56.370589+00:00 |
high_terms_nonval
{'B-cell lineage', 'Cycling cells', 'Erythroid', 'T cells'}
bt.CellType(name="B-cell lineage").save()
💡 returning existing CellType record with same name: 'B-cell lineage'
Now let’s add the parent records:
celltypist_df["parent"] = bt.CellType.standardize(celltypist_df["parent"])
for _, row in celltypist_df.iterrows():
record = public_records_dict[row["ontology_id"]]
if row["parent"] is not None:
parent_record = bt.CellType.filter(name=row["parent"]).one()
record.parents.add(parent_record)
Access the registry¶
The previously added CellTypist ontology registry is now available in LaminDB.
To retrieve the full ontology table as a Pandas DataFrame we can use .filter:
bt.CellType.df()
| uid | name | ontology_id | abbr | synonyms | description | public_source_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 140 | 5gxL2SWr | B-cell lineage | None | None | None | None | NaN | None | 1 | 2024-05-29 09:57:58.580454+00:00 |
| 14 | 22LvKd01 | T cell | CL:0000084 | None | T lymphocyte|T-cell|T cells|CD8a/a|Cycling T c... | A Type Of Lymphocyte Whose Defining Characteri... | 29.0 | None | 1 | 2024-05-29 09:57:58.560910+00:00 |
| 24 | 2KfvYuU7 | erythroid lineage cell | CL:0000764 | None | Late erythroid|Mid erythroid|Erythroid|Early e... | A Immature Or Mature Cell In The Lineage Leadi... | 29.0 | None | 1 | 2024-05-29 09:57:58.543658+00:00 |
| 139 | 5jshKSVL | Cycling cells | None | None | None | None | NaN | None | 1 | 2024-05-29 09:57:58.506985+00:00 |
| 68 | 7j3YpGzu | T-helper 17 cell | CL:0000899 | None | T helper cells type 17|Th17 T-cell|Type 17 hel... | Cd4-Positive, Alpha-Beta T Cell With The Pheno... | 29.0 | None | 1 | 2024-05-29 09:57:58.232932+00:00 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 71 | 2Jgr5Xx4 | mononuclear cell | CL:0000842 | None | mononuclear leukocyte | A Leukocyte With A Single Non-Segmented Nucleu... | 29.0 | None | 1 | 2024-05-29 09:57:24.766510+00:00 |
| 70 | X6c7osZ5 | lymphocyte | CL:0000542 | None | None | A Lymphocyte Is A Leukocyte Commonly Found In ... | 29.0 | None | 1 | 2024-05-29 09:57:24.143325+00:00 |
| 69 | 7GpphKmr | lymphocyte of B lineage | CL:0000945 | None | None | A Lymphocyte Of B Lineage With The Commitment ... | 29.0 | None | 1 | 2024-05-29 09:57:23.518914+00:00 |
| 38 | 1aLpWgJc | group 3 innate lymphoid cell, human | CL:0001078 | None | ILC3, human | A Group 3 Innate Lymphoid Cell In The Human Wi... | 29.0 | None | 1 | 2024-05-29 09:57:23.024582+00:00 |
| 37 | 6NmzCwsn | group 2 innate lymphoid cell, human | CL:0001081 | None | ILC2, human | A Group 2 Innate Lymphoid Cell In The Human Wi... | 29.0 | None | 1 | 2024-05-29 09:57:23.024493+00:00 |
140 rows × 10 columns
This enables us to look for cell types by creating a lookup object from our new CellType registry.
db_lookup = bt.CellType.lookup()
db_lookup.memory_b_cell
CellType(uid='2cUPBtY8', name='memory B cell', ontology_id='CL:0000787', synonyms='memory B lymphocyte|Age-associated B cells|memory B-cell|Memory B cells|memory B-lymphocyte', description='A Memory B Cell Is A Mature B Cell That Is Long-Lived, Readily Activated Upon Re-Encounter Of Its Antigenic Determinant, And Has Been Selected For Expression Of Higher Affinity Immunoglobulin. This Cell Type Has The Phenotype Cd19-Positive, Cd20-Positive, Mhc Class Ii-Positive, And Cd138-Negative.', created_by_id=1, public_source_id=29, updated_at='2024-05-29 09:57:56 UTC')
See cell type hierarchy:
db_lookup.memory_b_cell.view_parents()
Access parents of a record:
db_lookup.memory_b_cell.parents.list()
[CellType(uid='ryEtgi1y', name='B cell', ontology_id='CL:0000236', synonyms='B lymphocyte|B-cell|B cells|B-lymphocyte|Cycling B cells', description='A Lymphocyte Of B Lineage That Is Capable Of B Cell Mediated Immunity.', created_by_id=1, public_source_id=29, updated_at='2024-05-29 09:57:56 UTC'),
CellType(uid='71xItrKo', name='mature B cell', ontology_id='CL:0000785', synonyms='mature B lymphocyte|mature B-cell|mature B-lymphocyte', description='A B Cell That Is Mature, Having Left The Bone Marrow. Initially, These Cells Are Igm-Positive And Igd-Positive, And They Can Be Activated By Antigen.', created_by_id=1, public_source_id=29, updated_at='2024-05-29 09:57:27 UTC')]
Move on to the next registry: GO pathways