Manage flow cytometry data#

import lamindb as ln
import lnschema_bionty as lb
import readfcs

ln.settings.verbosity = 3  # show hints
lb.settings.species = "human"  # globally set species

ln.track()

We start with a flow cytometry file from Alpert19:

filepath

Use readfcs to read the fcs file into memory:

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

Track data with cell markers#

We’ll use the CellMarker reference to link features:

file = ln.File.from_anndata(adata, description="Alpert19", var_ref=lb.CellMarker.name)

We see that many features aren’t validated. Let’s standardize the identifiers:

adata.var.index = lb.CellMarker.bionty().map_synonyms(adata.var.index)

Now things look much better, but we still have 5 CellMaker records that seem more like metadata.

file = ln.File.from_anndata(adata, description="Alpert19", var_ref=lb.CellMarker.name)

Hence, let’s curate the AnnData a bit more:

inspect_result = lb.CellMarker.bionty().inspect(adata.var.index, "name")
validated = inspect_result["mapped"]
not_validated = inspect_result["not_mapped"]

Let’s move metadata into adata.obs:

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

Now we have a clean panel of 45 CellMarkers and metadata that we don’t want to register:

file = ln.File.from_anndata(adata, description="Alpert19", var_ref=lb.CellMarker.name)

file.save()

file.features

file.features["var"].df().head(10)

Let’s register another flow file:

adata2 = readfcs.read(ln.dev.datasets.file_fcs())
file2 = ln.File.from_anndata(
    adata2, description="My fcs file", var_ref=lb.CellMarker.name
)

adata2.var.index = lb.CellMarker.bionty().map_synonyms(adata2.var.index)

file2 = ln.File.from_anndata(
    adata2, description="My fcs file", var_ref=lb.CellMarker.name
)

adata2.var.index

There are 3 columns labeled with an empty space that are unvalidated, let’s remove them:

adata2 = adata2[:, " " != adata2.var.index]

file2 = ln.File.from_anndata(
    adata2, description="My fcs file", var_ref=lb.CellMarker.name
)

file2.save()

Query by cell markers#

Which datasets have CD14 in the flow panel:

cell_markers = lb.CellMarker.lookup()

cell_markers.cd14

panels_with_cd14 = ln.FeatureSet.filter(cell_markers=cell_markers.cd14).all()

ln.File.filter(feature_sets__in=panels_with_cd14).df()

Shared cell markers between two files:

file1 = ln.File.filter(feature_sets__in=panels_with_cd14).all()[0]
file2 = ln.File.filter(feature_sets__in=panels_with_cd14).all()[1]

file1_markers = file1.features["var"]
file2_markers = file2.features["var"]

shared_markers = file1_markers & file2_markers
shared_markers.list("name")

Flow marker registry#

Check out the first 10 rows of the CellMarker table:

lb.CellMarker.filter().df()