Flow cytometry

This use case walks through a sequence of steps that walks through managing a lake of .fcs files as a Flow cytometry lakehouse.

You will

1. start by ingesting a single file and seed a versioned dataset

2. append a new batch to the dataset and create a new version

3. look at an overview of ingested data

4. query the data and turn it into analytical insights to share back with the team

Setup

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

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✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-27 19:03:28)
✅ saved: Storage(id='VvPocT7z', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-facs', type='local', updated_at=2023-09-27 19:03:28, created_by_id='DzTjkKse')

💡 loaded instance: testuser1/test-facs
💡 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"

💡 loaded instance: testuser1/test-facs (lamindb 0.54.2)

ln.track()

💡 notebook imports: lamindb==0.54.2 lnschema_bionty==0.31.2 pytometry==0.1.4 readfcs==1.1.6 scanpy==1.9.5

💡 Transform(id='OWuTtS4SAponz8', name='Flow cytometry', short_name='facs', version='0', type=notebook, updated_at=2023-09-27 19:03:32, created_by_id='DzTjkKse')

💡 Run(id='6LxzHJKBOJu5s56VPocZ', run_at=2023-09-27 19:03:32, transform_id='OWuTtS4SAponz8', created_by_id='DzTjkKse')

Ingest a first file

Access

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

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

ln.dev.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'

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

2023-09-27 19:03:35,878:INFO - Failed to extract font properties from /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face (unknown file format; error code 0x2)

2023-09-27 19:03:36,045:INFO - generated new fontManager

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

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

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

Validate: cell markers

First, we validate features in .var using CellMarker:

validated = lb.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 = lb.CellMarker.standardize(adata.var.index)
validated = lb.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 = lb.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 for the dataset:

lb.ExperimentalFactor.bionty().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 ...	FACS|FAC sorting	[]	None	None	None	100.0
FACS-seq	EFO:0008735	Fluorescence-Activated Cell Sorting And Deep S...	None	[EFO:0001457]	RNA assay	None	None	90.0

# import the record from the public ontology and save it to the registry
lb.ExperimentalFactor.from_bionty(ontology_id="EFO:0009108").save()

# show the content of the registry
lb.ExperimentalFactor.filter().df()

	name	ontology_id	abbr	synonyms	description	molecule	instrument	measurement	bionty_source_id	updated_at	created_by_id
id
lh5Cxy8w	fluorescence-activated cell sorting	EFO:0009108	None	FACS|FAC sorting	A Flow Cytometry Assay That Provides A Method ...	None	None	None	QAzN	2023-09-27 19:03:40	DzTjkKse

Register: data & annotate with metadata

modalities = ln.Modality.lookup()
features = ln.Feature.lookup()
efs = lb.ExperimentalFactor.lookup()
species = lb.Species.lookup()

file = ln.File.from_anndata(
    adata, description="Alpert19", field=lb.CellMarker.name, modality=modalities.protein
)

... storing '$PnE' as categorical

... storing '$PnR' as categorical

file.save()

Inspect the registered file

Inspect features on a high level:

file.features

Features:
  var: FeatureSet(id='pKU07rLRLFb4YWdJFVmO', n=35, type='number', registry='bionty.CellMarker', hash='ldY9_GmptHLCcT7Nrpgo', updated_at=2023-09-27 19:03:41, modality_id='Pa89rywu', created_by_id='DzTjkKse')
    'CD94', 'CD16', 'CD8', 'TCRgd', 'DNA2', 'CD86', 'ICOS', 'PD1', 'CD28', 'CD45RA', ...
  obs: FeatureSet(id='AyuadzbYqTo0hOe8d82W', n=5, registry='core.Feature', hash='O72gVchjHlX8pwpZlxqO', updated_at=2023-09-27 19:03:41, modality_id='2CEhNp28', created_by_id='DzTjkKse')
    (Ba138)Dd (number)
    Cell_length (number)
    Bead (number)
    Dead (number)
    Time (number)

Inspect low-level features in .var:

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

	name	synonyms	gene_symbol	ncbi_gene_id	uniprotkb_id	species_id	bionty_source_id	updated_at	created_by_id
id
0qCmUijBeByY	CD94		KLRD1	3824	Q13241	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
bspnQ0igku6c	CD16		FCGR3A	2215	O75015	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
ttBc0Fs01sYk	CD8		CD8A	925	P01732	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
ljp5UfCF9HCi	TCRgd	TCRGAMMADELTA|TCRγδ	None	None	None	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse
yCyTIVxZkIUz	DNA2		DNA2	1763	P51530	uHJU	RlqM	2023-09-27 19:03:35	DzTjkKse

Use auto-complete for marker names:

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

import scanpy as sc

sc.pp.pca(adata)
sc.pl.pca(adata, color=markers.cd14.name)

file.view_flow()

Create a dataset from the file

dataset = ln.Dataset(file, name="My versioned FACS dataset", version="1")

dataset

Dataset(id='8RZdIbll16NTrAwo7lRL', name='My versioned FACS dataset', version='1', hash='fnzTGHE8BlkiMMIqHLDjyA', transform_id='OWuTtS4SAponz8', run_id='6LxzHJKBOJu5s56VPocZ', file_id='8RZdIbll16NTrAwo7lRL', created_by_id='DzTjkKse')

Let’s inspect the features measured in this dataset which were inherited from the file:

dataset.features

Features:
  obs: FeatureSet(id='AyuadzbYqTo0hOe8d82W', n=5, registry='core.Feature', hash='O72gVchjHlX8pwpZlxqO', updated_at=2023-09-27 19:03:41, modality_id='2CEhNp28', created_by_id='DzTjkKse')
    (Ba138)Dd (number)
    Cell_length (number)
    Bead (number)
    Dead (number)
    Time (number)

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

dataset.save()

Annotate by linking FACS & species labels:

dataset.labels.add(efs.fluorescence_activated_cell_sorting, features.assay)
dataset.labels.add(species.human, features.species)

dataset.view_flow()