Analyze the collection and save a result

import lamindb as ln
import bionty as bt

ln.transform.stem_uid = "zzJzdgJ763Dy"
ln.transform.version = "0"
ln.track()

💡 connected lamindb: testuser1/test-facs

💡 notebook imports: bionty==0.42.4 lamindb==0.69.2 scanpy==1.10.0

💡 saved: Transform(uid='zzJzdgJ763Dy6K79', name='Analyze the collection and save a result', key='facs4', version='0', type=notebook, updated_at=2024-03-28 12:12:29 UTC, created_by_id=1)

💡 saved: Run(uid='xLSMHzcx5SwzAa8LRLOo', transform_id=4, created_by_id=1)

ln.Collection.df()

	uid	name	description	version	hash	reference	reference_type	transform_id	run_id	artifact_id	visibility	created_at	updated_at	created_by_id
id
2	TegJbNDfqQuGlYUqiUiP	My versioned cytometry collection	None	2	ZKQxIw0uAvtMtdZk8SAj	None	None	2	2	NaN	1	2024-03-28 12:12:18.797200+00:00	2024-03-28 12:12:18.797231+00:00	1
1	TegJbNDfqQuGlYUqjaqR	My versioned cytometry collection	None	1	VsTnnzHN63ovNESaJtlRUQ	None	None	1	1	1.0	1	2024-03-28 12:12:07.891526+00:00	2024-03-28 12:12:07.891546+00:00	1

collection = ln.Collection.filter(
    name="My versioned cytometry collection", version="2"
).one()

adata = collection.load(join="inner")

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/anndata/_core/anndata.py:1838: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
  utils.warn_names_duplicates("obs")

The AnnData has the reference to the individual files in the .obs annotations:

adata.obs.artifact_uid.cat.categories

Index(['biRb1um0DljinoUvTiBq', 'TegJbNDfqQuGlYUqjaqR'], dtype='object')

By default, the intersection of features is used:

adata.var.index

Index(['CD8', 'CD27', 'Ccr7', 'Cd4', 'CD45RA', 'CD3'], dtype='object')

Let us create a plot:

markers = bt.CellMarker.lookup()

import scanpy as sc

sc.pp.pca(adata)
sc.pl.pca(adata, color=markers.cd8.name, save="_cd8")

WARNING: saving figure to file figures/pca_cd8.pdf

artifact = ln.Artifact("./figures/pca_cd8.pdf", description="My result on CD8")

artifact.save()

artifact.view_lineage()

Given the image is part of the notebook, there isn’t an actual need to save it and you can also rely on the report that you’ll create when saving the notebook via the command line via:

lamin save <notebook_path>

# clean up test instance
!lamin delete --force test-facs
!rm -r test-flow

💡 deleting instance testuser1/test-facs
❗ manually delete your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-facs

rm: cannot remove 'test-flow': No such file or directory