Bird’s eye view

Here, you’ll backtrace file transformations through notebooks, pipelines & app uploads in a research project based on Schmidt22.

It’s a mix of a guide & a demo usecase.

Why should I care about data lineage?

Data lineage enables to trace the origin of biological insights, verify experimental outcomes, meet regulatory standards, and increase the reproducibility & reliability research.

While tracking data lineage is easier when it’s governed by deterministic pipelines, it becomes hard when its governed by interactive human-driven analyses.

This is where LaminDB fills a gap in the tools space.

Setup

We need an instance:

!lamin init --storage ./mydata

Show code cell output Hide code cell output

💡 creating schemas: core==0.45.5 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-17 14:15:31)
✅ saved: Storage(id='WsXwNHjM', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-08-17 14:15:31, created_by_id='DzTjkKse')
✅ loaded instance: testuser1/mydata
💡 did not register local instance on hub (if you want, call `lamin register`)

Import lamindb:

import lamindb as ln

✅ loaded instance: testuser1/mydata (lamindb 0.50.7)

We’ll need toy data:

assert ln.setup.settings.user.handle == "testuser1"
bfx_run_output = ln.dev.datasets.dir_scrnaseq_cellranger(
    "perturbseq", basedir=ln.settings.storage, output_only=False
)
ln.track(ln.Transform(name="Chromium 10x upload", type="pipeline"))
ln.File(bfx_run_output.parent / "fastq/perturbseq_R1_001.fastq.gz").save()
ln.File(bfx_run_output.parent / "fastq/perturbseq_R2_001.fastq.gz").save()
ln.setup.login("testuser2")

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✅ saved: Transform(id='flpCitEbdeV1z8', name='Chromium 10x upload', stem_id='flpCitEbdeV1', version='0', type='pipeline', updated_at=2023-08-17 14:15:32, created_by_id='DzTjkKse')

✅ saved: Run(id='fuyKmlN7vKcdx6fVFa2M', run_at=2023-08-17 14:15:32, transform_id='flpCitEbdeV1z8', created_by_id='DzTjkKse')

💡 file in storage 'mydata' with key 'fastq/perturbseq_R1_001.fastq.gz'

💡 file in storage 'mydata' with key 'fastq/perturbseq_R2_001.fastq.gz'

✅ logged in with email testuser2@lamin.ai and id bKeW4T6E

❗ record with similar name exist! did you mean to load it?

	id	__ratio__
name
Test User1	DzTjkKse	90.0

✅ saved: User(id='bKeW4T6E', handle='testuser2', email='testuser2@lamin.ai', name='Test User2', updated_at=2023-08-17 14:15:34)

Track a bioinformatics pipeline

When working with a pipeline, we’ll register it before running it.

This only happens once and could be done by anyone on your team.

transform = ln.Transform(name="Cell Ranger", version="7.2.0", type="pipeline")

ln.User.filter().df()

	handle	email	name	updated_at
id
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-17 14:15:31
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-17 14:15:34

transform

Transform(id='Z8KOETA6N5UusM', name='Cell Ranger', stem_id='Z8KOETA6N5Uu', version='7.2.0', type='pipeline', created_by_id='bKeW4T6E')

ln.track(transform)

✅ saved: Transform(id='Z8KOETA6N5UusM', name='Cell Ranger', stem_id='Z8KOETA6N5Uu', version='7.2.0', type='pipeline', updated_at=2023-08-17 14:15:34, created_by_id='bKeW4T6E')

✅ saved: Run(id='0zC7OlBjBfeCGrpMjb3w', run_at=2023-08-17 14:15:34, transform_id='Z8KOETA6N5UusM', created_by_id='bKeW4T6E')

Now, let’s stage a few files from an instrument upload:

files = ln.File.filter(key__startswith="fastq/perturbseq").all()
filepaths = [file.stage() for file in files]

💡 adding file d3GLVFuOjJry2qxPbGML as input for run 0zC7OlBjBfeCGrpMjb3w, adding parent transform flpCitEbdeV1z8

💡 adding file bvUrX5brP3YDvFa9pdhk as input for run 0zC7OlBjBfeCGrpMjb3w, adding parent transform flpCitEbdeV1z8

Assume we processed them and obtained 3 output files in a folder 'filtered_feature_bc_matrix':

output_files = ln.File.from_dir("./mydata/perturbseq/filtered_feature_bc_matrix/")
ln.save(output_files)

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✅ created 3 files from directory using storage /home/runner/work/lamin-usecases/lamin-usecases/docs/mydata and key = perturbseq/filtered_feature_bc_matrix/

✅ storing file 'quahVY09cfhvjoPljUTQ' at 'perturbseq/filtered_feature_bc_matrix/matrix.mtx.gz'

✅ storing file 'nGghdA1OlZAaWN1JruWR' at 'perturbseq/filtered_feature_bc_matrix/barcodes.tsv.gz'

✅ storing file 'pAuqr2PEkFDSYxbS15WB' at 'perturbseq/filtered_feature_bc_matrix/features.tsv.gz'

Let’s look at the data lineage at this stage:

output_files[0].view_lineage()

And let’s keep running the Cell Ranger pipeline in the background.

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transform = ln.Transform(
    name="Preprocess Cell Ranger outputs", version="2.0", type="pipeline"
)
ln.track(transform)
[f.stage() for f in output_files]
filepath = ln.dev.datasets.schmidt22_perturbseq(basedir=ln.settings.storage)
file = ln.File(filepath, description="perturbseq counts")
file.save()

✅ saved: Transform(id='qhtHmM1xAOcg0b', name='Preprocess Cell Ranger outputs', stem_id='qhtHmM1xAOcg', version='2.0', type='pipeline', updated_at=2023-08-17 14:15:34, created_by_id='bKeW4T6E')

✅ saved: Run(id='XAEgfjgCCEDNLsfJM2bj', run_at=2023-08-17 14:15:34, transform_id='qhtHmM1xAOcg0b', created_by_id='bKeW4T6E')

💡 adding file nGghdA1OlZAaWN1JruWR as input for run XAEgfjgCCEDNLsfJM2bj, adding parent transform Z8KOETA6N5UusM

💡 adding file pAuqr2PEkFDSYxbS15WB as input for run XAEgfjgCCEDNLsfJM2bj, adding parent transform Z8KOETA6N5UusM

💡 adding file quahVY09cfhvjoPljUTQ as input for run XAEgfjgCCEDNLsfJM2bj, adding parent transform Z8KOETA6N5UusM

💡 file in storage 'mydata' with key 'schmidt22_perturbseq.h5ad'

💡 file is AnnDataLike, consider using File.from_anndata() to link var_names and obs.columns as features

Track app upload & analytics

The hidden cell below simulates additional analytic steps including:

• uploading phenotypic screen data

• scRNA-seq analysis

• analyses of the integrated datasets

Show code cell content Hide code cell content

# app upload
ln.setup.login("testuser1")
transform = ln.Transform(name="Upload GWS CRISPRa result", type="app")
ln.track(transform)

# upload and analyze the GWS data
filepath = ln.dev.datasets.schmidt22_crispra_gws_IFNG(ln.settings.storage)
file = ln.File(filepath, description="Raw data of schmidt22 crispra GWS")
file.save()
ln.setup.login("testuser2")
transform = ln.Transform(name="GWS CRIPSRa analysis", type="notebook")
ln.track(transform)

file_wgs = ln.File.filter(key="schmidt22-crispra-gws-IFNG.csv").one()
df = file_wgs.load().set_index("id")
hits_df = df[df["pos|fdr"] < 0.01].copy()
file_hits = ln.File(hits_df, description="hits from schmidt22 crispra GWS")
file_hits.save()

✅ logged in with email testuser1@lamin.ai and id DzTjkKse

✅ saved: Transform(id='8GC0s5UrRMwwz8', name='Upload GWS CRISPRa result', stem_id='8GC0s5UrRMww', version='0', type='app', updated_at=2023-08-17 14:15:37, created_by_id='DzTjkKse')

✅ saved: Run(id='Kggz0ibxQPDLQjte0Rw1', run_at=2023-08-17 14:15:37, transform_id='8GC0s5UrRMwwz8', created_by_id='DzTjkKse')

💡 file in storage 'mydata' with key 'schmidt22-crispra-gws-IFNG.csv'

✅ logged in with email testuser2@lamin.ai and id bKeW4T6E

✅ saved: Transform(id='HWWYy67og9YKz8', name='GWS CRIPSRa analysis', stem_id='HWWYy67og9YK', version='0', type='notebook', updated_at=2023-08-17 14:15:40, created_by_id='bKeW4T6E')

✅ saved: Run(id='8yI6SOkXsbgHDVeIay3O', run_at=2023-08-17 14:15:40, transform_id='HWWYy67og9YKz8', created_by_id='bKeW4T6E')

💡 adding file G5giHcBkFpiZhiPUQmhZ as input for run 8yI6SOkXsbgHDVeIay3O, adding parent transform 8GC0s5UrRMwwz8

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/X6GsDqJn5qInAxfk6mzk.parquet')

💡 file is a dataframe, consider using File.from_df() to link column names as features

✅ storing file 'X6GsDqJn5qInAxfk6mzk' at '.lamindb/X6GsDqJn5qInAxfk6mzk.parquet'

Let’s see how the data lineage of this looks:

file = ln.File.filter(description="hits from schmidt22 crispra GWS").one()
file.view_lineage()

In the backgound, somebody integrated and analyzed the outputs of the app upload and the Cell Ranger pipeline:

Show code cell content Hide code cell content

# Let us add analytics on top of the cell ranger pipeline and the phenotypic screening
transform = ln.Transform(
    name="Perform single cell analysis, integrating with CRISPRa screen",
    type="notebook",
)
ln.track(transform)

file_ps = ln.File.filter(description__icontains="perturbseq").one()
adata = file_ps.load()
screen_hits = file_hits.load()
import scanpy as sc

sc.tl.score_genes(adata, adata.var_names.intersection(screen_hits.index).tolist())
filesuffix = "_fig1_score-wgs-hits.png"
sc.pl.umap(adata, color="score", show=False, save=filesuffix)
filepath = f"figures/umap{filesuffix}"
file = ln.File(filepath, key=filepath)
file.save()
filesuffix = "fig2_score-wgs-hits-per-cluster.png"
sc.pl.matrixplot(
    adata, groupby="cluster_name", var_names=["score"], show=False, save=filesuffix
)
filepath = f"figures/matrixplot_{filesuffix}"
file = ln.File(filepath, key=filepath)
file.save()

✅ saved: Transform(id='W1Rlzo9sQzclz8', name='Perform single cell analysis, integrating with CRISPRa screen', stem_id='W1Rlzo9sQzcl', version='0', type='notebook', updated_at=2023-08-17 14:15:40, created_by_id='bKeW4T6E')

✅ saved: Run(id='DfUBUMZ1j3o7gQaT74X8', run_at=2023-08-17 14:15:40, transform_id='W1Rlzo9sQzclz8', created_by_id='bKeW4T6E')

💡 adding file bcY6VQnhaWDaooJuaGVb as input for run DfUBUMZ1j3o7gQaT74X8, adding parent transform qhtHmM1xAOcg0b

💡 adding file X6GsDqJn5qInAxfk6mzk as input for run DfUBUMZ1j3o7gQaT74X8, adding parent transform HWWYy67og9YKz8

WARNING: saving figure to file figures/umap_fig1_score-wgs-hits.png

💡 file will be copied to default storage upon `save()` with key 'figures/umap_fig1_score-wgs-hits.png'

✅ storing file 'xYyPgkoUrBovipr5g52X' at 'figures/umap_fig1_score-wgs-hits.png'

WARNING: saving figure to file figures/matrixplot_fig2_score-wgs-hits-per-cluster.png

💡 file will be copied to default storage upon `save()` with key 'figures/matrixplot_fig2_score-wgs-hits-per-cluster.png'

✅ storing file 'mo53EMhYHr0d6ngGvyq7' at 'figures/matrixplot_fig2_score-wgs-hits-per-cluster.png'

The outcome of it are a few figures stored as image files. Let’s query one of them and look at the data lineage:

Track notebooks

We’d now like to track the current Jupyter notebook to continue the work:

ln.track()

💡 notebook imports: lamindb==0.50.7 scanpy==1.9.3

✅ saved: Transform(id='1LCd8kco9lZUz8', name='Bird's eye view', short_name='birds-eye', stem_id='1LCd8kco9lZU', version='0', type=notebook, updated_at=2023-08-17 14:15:43, created_by_id='bKeW4T6E')

✅ saved: Run(id='Lt6hY37YBVlxOwVTaNyh', run_at=2023-08-17 14:15:43, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

Visualize data lineage

Let’s load one of the plots:

file = ln.File.filter(key__contains="figures/matrixplot").one()
file.stage()

💡 adding file mo53EMhYHr0d6ngGvyq7 as input for run Lt6hY37YBVlxOwVTaNyh, adding parent transform W1Rlzo9sQzclz8

PosixPath('/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/figures/matrixplot_fig2_score-wgs-hits-per-cluster.png')

We see that the image file is tracked as an input of the current notebook. The input is highlighted, the notebook follows at the bottom:

file.view_lineage()

Alternatively, we can also purely look at the sequence of transforms and ignore the files:

transform = ln.Transform.search("Bird's eye view", return_queryset=True).first()

transform.parents.df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
W1Rlzo9sQzclz8	Perform single cell analysis, integrating with...	None	W1Rlzo9sQzcl	0	notebook	None	2023-08-17 14:15:42	bKeW4T6E

transform.view_parents()

Understand runs

We tracked pipeline and notebook runs through run_context, which stores a Transform and a Run record as a global context.

File objects are the inputs and outputs of runs.

What if I don’t want a global context?

Sometimes, we don’t want to create a global run context but manually pass a run when creating a file:

run = ln.Run(transform=transform)
ln.File(filepath, run=run)

When does a file appear as a run input?

When accessing a file via stage(), load() or backed(), two things happen:

1. The current run gets added to file.input_of

2. The transform of that file gets added as a parent of the current transform

You can then switch off auto-tracking of run inputs if you set ln.settings.track_run_inputs = False: Can I disable tracking run inputs?

You can also track run inputs on a case by case basis via is_run_input=True, e.g., here:

file.load(is_run_input=True)

Query by provenance

We can query or search for the notebook that created the file:

transform = ln.Transform.search("GWS CRIPSRa analysis", return_queryset=True).first()

And then find all the files created by that notebook:

ln.File.filter(transform=transform).df()

	storage_id	key	suffix	accessor	description	version	initial_version_id	size	hash	hash_type	transform_id	run_id	updated_at	created_by_id
id
X6GsDqJn5qInAxfk6mzk	WsXwNHjM	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	yw5f-kMLJhaNhdEF-lhxOQ	md5	HWWYy67og9YKz8	8yI6SOkXsbgHDVeIay3O	2023-08-17 14:15:40	bKeW4T6E

Which transform ingested a given file?

file = ln.File.filter().first()
file.transform

Transform(id='flpCitEbdeV1z8', name='Chromium 10x upload', stem_id='flpCitEbdeV1', version='0', type='pipeline', updated_at=2023-08-17 14:15:32, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-17 14:15:37)

Which transforms were created by a given user?

users = ln.User.lookup()

ln.Transform.filter(created_by=users.testuser2).df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
Z8KOETA6N5UusM	Cell Ranger	None	Z8KOETA6N5Uu	7.2.0	pipeline	None	2023-08-17 14:15:34	bKeW4T6E
qhtHmM1xAOcg0b	Preprocess Cell Ranger outputs	None	qhtHmM1xAOcg	2.0	pipeline	None	2023-08-17 14:15:36	bKeW4T6E
HWWYy67og9YKz8	GWS CRIPSRa analysis	None	HWWYy67og9YK	0	notebook	None	2023-08-17 14:15:40	bKeW4T6E
W1Rlzo9sQzclz8	Perform single cell analysis, integrating with...	None	W1Rlzo9sQzcl	0	notebook	None	2023-08-17 14:15:42	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-17 14:15:43	bKeW4T6E

Which notebooks were created by a given user?

ln.Transform.filter(created_by=users.testuser2, type="notebook").df()

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
HWWYy67og9YKz8	GWS CRIPSRa analysis	None	HWWYy67og9YK	0	notebook	None	2023-08-17 14:15:40	bKeW4T6E
W1Rlzo9sQzclz8	Perform single cell analysis, integrating with...	None	W1Rlzo9sQzcl	0	notebook	None	2023-08-17 14:15:42	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-17 14:15:43	bKeW4T6E

We can also view all recent additions to the entire database:

ln.view()

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File

	storage_id	key	suffix	accessor	description	version	initial_version_id	size	hash	hash_type	transform_id	run_id	updated_at	created_by_id
id
mo53EMhYHr0d6ngGvyq7	WsXwNHjM	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	W1Rlzo9sQzclz8	DfUBUMZ1j3o7gQaT74X8	2023-08-17 14:15:42	bKeW4T6E
xYyPgkoUrBovipr5g52X	WsXwNHjM	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	W1Rlzo9sQzclz8	DfUBUMZ1j3o7gQaT74X8	2023-08-17 14:15:42	bKeW4T6E
X6GsDqJn5qInAxfk6mzk	WsXwNHjM	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	yw5f-kMLJhaNhdEF-lhxOQ	md5	HWWYy67og9YKz8	8yI6SOkXsbgHDVeIay3O	2023-08-17 14:15:40	bKeW4T6E
G5giHcBkFpiZhiPUQmhZ	WsXwNHjM	schmidt22-crispra-gws-IFNG.csv	.csv	None	Raw data of schmidt22 crispra GWS	None	None	1729685	cUSH0oQ2w-WccO8_ViKRAQ	md5	8GC0s5UrRMwwz8	Kggz0ibxQPDLQjte0Rw1	2023-08-17 14:15:38	DzTjkKse
bcY6VQnhaWDaooJuaGVb	WsXwNHjM	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	qhtHmM1xAOcg0b	XAEgfjgCCEDNLsfJM2bj	2023-08-17 14:15:36	bKeW4T6E
pAuqr2PEkFDSYxbS15WB	WsXwNHjM	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	None	6	sEVrI6rE96RQ-BoOcwFFNw	md5	Z8KOETA6N5UusM	0zC7OlBjBfeCGrpMjb3w	2023-08-17 14:15:34	bKeW4T6E
nGghdA1OlZAaWN1JruWR	WsXwNHjM	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	None	6	JqXKNc0vDBAFYYljBr9v_w	md5	Z8KOETA6N5UusM	0zC7OlBjBfeCGrpMjb3w	2023-08-17 14:15:34	bKeW4T6E
quahVY09cfhvjoPljUTQ	WsXwNHjM	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	None	6	OzFyK080iOXuSo2arSD-qw	md5	Z8KOETA6N5UusM	0zC7OlBjBfeCGrpMjb3w	2023-08-17 14:15:34	bKeW4T6E
bvUrX5brP3YDvFa9pdhk	WsXwNHjM	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	None	6	4ptHFNYZgPppTEVJfPCYJw	md5	flpCitEbdeV1z8	fuyKmlN7vKcdx6fVFa2M	2023-08-17 14:15:32	DzTjkKse
d3GLVFuOjJry2qxPbGML	WsXwNHjM	fastq/perturbseq_R1_001.fastq.gz	.fastq.gz	None	None	None	None	6	dDAq_6_ymUnpFMwgLuEucQ	md5	flpCitEbdeV1z8	fuyKmlN7vKcdx6fVFa2M	2023-08-17 14:15:32	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
fuyKmlN7vKcdx6fVFa2M	flpCitEbdeV1z8	2023-08-17 14:15:32	DzTjkKse	None	None
0zC7OlBjBfeCGrpMjb3w	Z8KOETA6N5UusM	2023-08-17 14:15:34	bKeW4T6E	None	None
XAEgfjgCCEDNLsfJM2bj	qhtHmM1xAOcg0b	2023-08-17 14:15:34	bKeW4T6E	None	None
Kggz0ibxQPDLQjte0Rw1	8GC0s5UrRMwwz8	2023-08-17 14:15:37	DzTjkKse	None	None
8yI6SOkXsbgHDVeIay3O	HWWYy67og9YKz8	2023-08-17 14:15:40	bKeW4T6E	None	None
DfUBUMZ1j3o7gQaT74X8	W1Rlzo9sQzclz8	2023-08-17 14:15:40	bKeW4T6E	None	None
Lt6hY37YBVlxOwVTaNyh	1LCd8kco9lZUz8	2023-08-17 14:15:43	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
WsXwNHjM	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-08-17 14:15:31	DzTjkKse

Transform

	name	short_name	stem_id	version	type	reference	updated_at	created_by_id
id
1LCd8kco9lZUz8	Bird's eye view	birds-eye	1LCd8kco9lZU	0	notebook	None	2023-08-17 14:15:43	bKeW4T6E
W1Rlzo9sQzclz8	Perform single cell analysis, integrating with...	None	W1Rlzo9sQzcl	0	notebook	None	2023-08-17 14:15:42	bKeW4T6E
HWWYy67og9YKz8	GWS CRIPSRa analysis	None	HWWYy67og9YK	0	notebook	None	2023-08-17 14:15:40	bKeW4T6E
8GC0s5UrRMwwz8	Upload GWS CRISPRa result	None	8GC0s5UrRMww	0	app	None	2023-08-17 14:15:38	DzTjkKse
qhtHmM1xAOcg0b	Preprocess Cell Ranger outputs	None	qhtHmM1xAOcg	2.0	pipeline	None	2023-08-17 14:15:36	bKeW4T6E
Z8KOETA6N5UusM	Cell Ranger	None	Z8KOETA6N5Uu	7.2.0	pipeline	None	2023-08-17 14:15:34	bKeW4T6E
flpCitEbdeV1z8	Chromium 10x upload	None	flpCitEbdeV1	0	pipeline	None	2023-08-17 14:15:32	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-17 14:15:40
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-17 14:15:37

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!lamin login testuser1
!lamin delete --force mydata
!rm -r ./mydata

✅ logged in with email testuser1@lamin.ai and id DzTjkKse

💡 deleting instance testuser1/mydata

✅     deleted instance settings file: /home/runner/.lamin/instance--testuser1--mydata.env

✅     instance cache deleted
✅     deleted '.lndb' sqlite file
❗     consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/mydata