Flow cytometry

You’ll learn how to manage a growing number of flow cytometry data shards as a single queryable collection.

Specifically, you will

1. read a single .fcs file as an AnnData and seed a versioned collection with it (, current page)

2. append a new data shard (a new .fcs file) to create a new version of the collection ()

3. query individual files and cell markers ()

4. analyze the collection and store results as plots ()

Setup

!lamin init --storage ./test-facs --schema bionty

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💡 connected lamindb: testuser1/test-facs

import lamindb as ln
import bionty as bt
import readfcs

bt.settings.organism = "human"  # globally set organism to human

💡 connected lamindb: testuser1/test-facs

ln.transform.stem_uid = "OWuTtS4SApon"
ln.transform.version = "0"
ln.track()

💡 notebook imports: bionty==0.42.4 lamindb==0.69.2 pytometry==0.1.4 readfcs==1.1.7 scanpy==1.10.0

💡 saved: Transform(uid='OWuTtS4SApon6K79', name='Flow cytometry', key='facs', version='0', type=notebook, updated_at=2024-03-28 10:26:34 UTC, created_by_id=1)

💡 saved: Run(uid='ekomQiix4oykRaAVRgHO', transform_id=1, created_by_id=1)

Ingest a first artifact

Access

We start with a flow cytometry file from Alpert et al., Nat. Med. (2019).

Calling the following function downloads the artifact and pre-populates a few relevant registries:

ln.core.datasets.file_fcs_alpert19(populate_registries=True)

PosixPath('Alpert19.fcs')

We use readfcs to read the raw fcs file into memory and create an AnnData object:

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

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

It has the following features:

adata.var.head(10)

	n	channel	marker	$PnB	$PnE	$PnR
Time	1	Time		32	0,0	2097152
Cell_length	2	Cell_length		32	0,0	128
CD57	3	(In113)Dd	CD57	32	0,0	8192
Dead	4	(In115)Dd	Dead	32	0,0	4096
(Ba138)Dd	5	(Ba138)Dd		32	0,0	4096
Bead	6	(Ce140)Dd	Bead	32	0,0	16384
CD19	7	(Nd142)Dd	CD19	32	0,0	4096
CD4	8	(Nd143)Dd	CD4	32	0,0	4096
CD8	9	(Nd144)Dd	CD8	32	0,0	4096
IgD	10	(Nd146)Dd	IgD	32	0,0	8192

Transform: normalize

In this use case, we’d like to ingest & store curated data, and hence, we split signal and normalize using the pytometry package.

import pytometry as pm

First, we’ll split the signal from heigh and area metadata:

pm.pp.split_signal(adata, var_key="channel", data_type="cytof")

'area' is not in adata.var['signal_type']. Return all.

adata

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

Normalize the collection:

pm.tl.normalize_arcsinh(adata, cofactor=150)

Note

If the collection was a flow collection, you’ll also have to compensate the data, if possible. The metadata should contain a compensation matrix, which could then be run by the pytometry compensation function. In the case here, its a cyTOF collection, which doesn’t (really) require compensation.

Validate: cell markers

First, we validate features in .var using CellMarker:

validated = bt.CellMarker.validate(adata.var.index)

❗ 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 because they’re not standardized.

Hence, let’s standardize feature names & validate again:

adata.var.index = bt.CellMarker.standardize(adata.var.index)
validated = bt.CellMarker.validate(adata.var.index)

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

The remaining non-validated features don’t appear to be cell markers but rather metadata features.

Let’s move them into adata.obs:

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

Now we have a clean panel of 35 validated cell markers:

validated = bt.CellMarker.validate(adata.var.index)
assert all(validated)  # all markers are validated

Register: metadata

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

For this, we create one feature record for each column in the .obs dataframe:

features = ln.Feature.from_df(adata.obs)
ln.save(features)

We use the Experimental Factor Ontology through Bionty to create a “FACS” label:

bt.ExperimentalFactor.public().search("FACS").head(2)  # search the public ontology

	ontology_id	definition	synonyms	parents	molecule	instrument	measurement	__ratio__
name
fluorescence-activated cell sorting	EFO:0009108	A Flow Cytometry Assay That Provides A Method ...	FAC sorting|FACS	[]	None	None	None	100.0
BALB/c	EFO:0000602	Balb/C Is A Mouse Strain Of Albion Mice.	BALBc|BALB/cJ|C	[]	None	None	None	90.0

We found one for “FACS”, let’s save it to our in-house registry:

# import the FACS record from the public ontology and save it to the registry
facs = bt.ExperimentalFactor.from_public(ontology_id="EFO:0009108")
facs.save()

We don’t find one for “CyToF”, however, so, let’s create it without importing from a public ontology but label it as a child of “is_cytometry_assay”:

cytof = bt.ExperimentalFactor(name="CyTOF")
cytof.save()
is_cytometry_assay = bt.ExperimentalFactor(name="is_cytometry_assay")
is_cytometry_assay.save()
cytof.parents.add(is_cytometry_assay)
facs.parents.add(is_cytometry_assay)

is_cytometry_assay.view_parents(with_children=True)

Let us look at the content of the registry:

bt.ExperimentalFactor.df()

	uid	name	ontology_id	abbr	synonyms	description	molecule	instrument	measurement	public_source_id	created_at	updated_at	created_by_id
id
3	21Qymj4Q	is_cytometry_assay	None	None	None	None	None	None	None	NaN	2024-03-28 10:26:41.474000+00:00	2024-03-28 10:26:41.474020+00:00	1
2	ogoPdeOk	CyTOF	None	None	None	None	None	None	None	NaN	2024-03-28 10:26:41.460697+00:00	2024-03-28 10:26:41.460720+00:00	1
1	36GhLFoE	fluorescence-activated cell sorting	EFO:0009108	None	FAC sorting|FACS	A Flow Cytometry Assay That Provides A Method ...	None	None	None	38.0	2024-03-28 10:26:41.439504+00:00	2024-03-28 10:26:41.439531+00:00	1

Register: data & annotate with metadata

features = ln.Feature.lookup()
experimental_factors = bt.ExperimentalFactor.lookup()
organisms = bt.Organism.lookup()

artifact = ln.Artifact.from_anndata(
    adata, description="Alpert19"
)

... storing '$PnE' as categorical

... storing '$PnR' as categorical

artifact.save()

artifact.features.add_from_anndata(var_field=bt.CellMarker.name)

Inspect the registered artifact

Inspect features on a high level:

artifact.features

Features:
  var: FeatureSet(uid='QudxBzxMl73zXUo9l5iY', n=35, type='number', registry='bionty.CellMarker', hash='qsFCWDkvYitNDbgVsVd5', updated_at=2024-03-28 10:26:41 UTC, created_by_id=1)
    'CD57', 'Cd19', 'Cd4', 'CD8', 'Igd', 'CD85j', 'CD11c', 'CD16', 'CD3', 'CD38', 'CD27', 'CD11B', 'Cd14', 'Ccr6', 'CD94', 'CD86', 'CXCR5', 'CXCR3', 'Ccr7', 'CD45RA', ...
  obs: FeatureSet(uid='EshIYFlw0mu7CUYg3Qxj', n=5, registry='core.Feature', hash='fn23ch8Df00zg3tBpZh_', updated_at=2024-03-28 10:26:41 UTC, created_by_id=1)
    Time (number)
    Cell_length (number)
    Dead (number)
    (Ba138)Dd (number)
    Bead (number)

Inspect low-level features in .var:

artifact.features["var"].df().head()

	uid	name	synonyms	gene_symbol	ncbi_gene_id	uniprotkb_id	organism_id	public_source_id	created_at	updated_at	created_by_id
id
1	1dPH2YeJqtGd	CD57		B3GAT1	27087	Q9P2W7	1	18	2024-03-28 10:26:37.923976+00:00	2024-03-28 10:26:37.924000+00:00	1
2	7KaN0QtWWLnk	Cd19		CD19	930	P15391	1	18	2024-03-28 10:26:37.924091+00:00	2024-03-28 10:26:37.924103+00:00	1
3	rKHBZ9JlBdU5	Cd4		CD4	920	B4DT49	1	18	2024-03-28 10:26:37.924188+00:00	2024-03-28 10:26:37.924198+00:00	1
4	5YxpB5QNiCWr	CD8		CD8A	925	P01732	1	18	2024-03-28 10:26:37.924281+00:00	2024-03-28 10:26:37.924291+00:00	1
5	7basFKNKrv4j	Igd		None	None	None	1	18	2024-03-28 10:26:37.924373+00:00	2024-03-28 10:26:37.924382+00:00	1

Use auto-complete for marker names in the var featureset:

markers = artifact.features["var"].lookup()

markers.cd14

CellMarker(uid='5JHfKNo5DC8y', name='Cd14', synonyms='', gene_symbol='CD14', ncbi_gene_id='4695', uniprotkb_id='O43678', updated_at=2024-03-28 10:26:37 UTC, organism_id=1, public_source_id=18, created_by_id=1)

In a plot, we can now easily also show gene symbol and Uniprot ID:

import scanpy as sc

sc.pp.pca(adata)
sc.pl.pca(
    adata,
    color=markers.cd14.name,
    title=(
        f"{markers.cd14.name} / {markers.cd14.gene_symbol} /"
        f" {markers.cd14.uniprotkb_id}"
    ),
)

artifact.view_lineage()

Create a collection from the artifact

collection = ln.Collection(
    artifact, name="My versioned cytometry collection", version="1"
)

collection

Collection(uid='BXlEohVyuYed492lqYvj', name='My versioned cytometry collection', version='1', hash='VsTnnzHN63ovNESaJtlRUQ', visibility=1, transform_id=1, run_id=1, artifact_id=1, created_by_id=1)

Let’s inspect the features measured in this collection which were inherited from the artifact:

collection.features

Features:
  var: FeatureSet(uid='QudxBzxMl73zXUo9l5iY', n=35, type='number', registry='bionty.CellMarker', hash='qsFCWDkvYitNDbgVsVd5', updated_at=2024-03-28 10:26:41 UTC, created_by_id=1)
    'CD57', 'Cd19', 'Cd4', 'CD8', 'Igd', 'CD85j', 'CD11c', 'CD16', 'CD3', 'CD38', 'CD27', 'CD11B', 'Cd14', 'Ccr6', 'CD94', 'CD86', 'CXCR5', 'CXCR3', 'Ccr7', 'CD45RA', ...
  obs: FeatureSet(uid='EshIYFlw0mu7CUYg3Qxj', n=5, registry='core.Feature', hash='fn23ch8Df00zg3tBpZh_', updated_at=2024-03-28 10:26:41 UTC, created_by_id=1)
    Time (number)
    Cell_length (number)
    Dead (number)
    (Ba138)Dd (number)
    Bead (number)

This looks all good, hence, let’s save it:

collection.save()

Annotate by linking cytof & organism labels:

collection.labels.add(experimental_factors.cytof, features.assay)
collection.labels.add(organisms.human, features.organism)

collection.view_lineage()