Bird’s eye view

Background

Data lineage tracks data’s journey, detailing its origins, transformations, and interactions to trace biological insights, verify experimental outcomes, meet regulatory standards, and increase the robustness of research. While tracking data lineage is easier when it is governed by deterministic pipelines, it becomes hard when its governed by interactive human-driven analyses.

Here, we’ll backtrace file transformations through notebooks, pipelines & app uploads in a research project based on Schmidt22 which conducted genome-wide CRISPR activation and interference screens in primary human T cells to identify gene networks controlling IL-2 and IFN-γ production.

Setup

We need an instance:

!lamin init --storage ./mydata

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💡 creating schemas: core==0.46.1 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-28 13:54:04)
✅ saved: Storage(id='yTQlccVf', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-08-28 13:54:04, 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 0.51.0)

We simulate the raw data processing of Schmidt22 with toy data in a real world setting with multiple collaborators (here testuser1 and testuser2):

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()

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✅ saved: Transform(id='2Gwjq1pTD7eMBP', name='Chromium 10x upload', type='pipeline', updated_at=2023-08-28 13:54:05, created_by_id='DzTjkKse')

✅ saved: Run(id='2JNx4mvl5KxkA1uo4bgb', run_at=2023-08-28 13:54:05, transform_id='2Gwjq1pTD7eMBP', 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'

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.

ln.setup.login("testuser2")

✅ 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=2023-08-28 13:54:06)

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	2023-08-28 13:54:04
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-28 13:54:06

transform

Transform(id='gWd973slbGqaIt', name='Cell Ranger', version='7.2.0', type='pipeline', created_by_id='bKeW4T6E')

ln.track(transform)

✅ saved: Transform(id='gWd973slbGqaIt', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-08-28 13:54:06, created_by_id='bKeW4T6E')

✅ saved: Run(id='WnefvAB77JsFt9MPhySh', run_at=2023-08-28 13:54:06, transform_id='gWd973slbGqaIt', 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 Gf4QsqxPkIiCGTTrrc59 as input for run WnefvAB77JsFt9MPhySh, adding parent transform 2Gwjq1pTD7eMBP

💡 adding file CP0gGchaMIwFIegysGRX as input for run WnefvAB77JsFt9MPhySh, adding parent transform 2Gwjq1pTD7eMBP

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/

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='AAoRtj4w0X3egT', name='Preprocess Cell Ranger outputs', version='2.0', type='pipeline', updated_at=2023-08-28 13:54:06, created_by_id='bKeW4T6E')

✅ saved: Run(id='AQtkG4OkUIrLPYqR4Qn5', run_at=2023-08-28 13:54:06, transform_id='AAoRtj4w0X3egT', created_by_id='bKeW4T6E')

💡 adding file lP28OYTqv8Prf8s1BPln as input for run AQtkG4OkUIrLPYqR4Qn5, adding parent transform gWd973slbGqaIt

💡 adding file NqQ90aex3CQvdYDoPmsn as input for run AQtkG4OkUIrLPYqR4Qn5, adding parent transform gWd973slbGqaIt

💡 adding file jgWqeGfFwVAamkN82yfG as input for run AQtkG4OkUIrLPYqR4Qn5, adding parent transform gWd973slbGqaIt

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

💡 data is AnnDataLike, consider using .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

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# app upload
ln.setup.login("testuser1")
transform = ln.Transform(name="Upload GWS CRISPRa result", type="app")
ln.track(transform)
filepath = ln.dev.datasets.schmidt22_crispra_gws_IFNG(ln.settings.storage)
file = ln.File(filepath, description="Raw data of schmidt22 crispra GWS")
file.save()

# upload and analyze the GWS data
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='lapgANZ3brHG4w', name='Upload GWS CRISPRa result', type='app', updated_at=2023-08-28 13:54:07, created_by_id='DzTjkKse')

✅ saved: Run(id='f0dBYuVnm9xJIrHixYBX', run_at=2023-08-28 13:54:07, transform_id='lapgANZ3brHG4w', 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='0qgOkJBvG7rwWX', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-08-28 13:54:08, created_by_id='bKeW4T6E')

✅ saved: Run(id='Lr6JWp9k7WAEJZ0neBQa', run_at=2023-08-28 13:54:08, transform_id='0qgOkJBvG7rwWX', created_by_id='bKeW4T6E')

💡 adding file q4w1Lv1b9fAu4IaSxxHB as input for run Lr6JWp9k7WAEJZ0neBQa, adding parent transform lapgANZ3brHG4w

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

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

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

Let’s see what 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:

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# 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='lduDnaLYJ3GJQ1', name='Perform single cell analysis, integrating with CRISPRa screen', type='notebook', updated_at=2023-08-28 13:54:08, created_by_id='bKeW4T6E')

✅ saved: Run(id='Zo7kda45OR5MhnixhyZp', run_at=2023-08-28 13:54:08, transform_id='lduDnaLYJ3GJQ1', created_by_id='bKeW4T6E')

💡 adding file s2nQ8TJv4m72cZjmpgzR as input for run Zo7kda45OR5MhnixhyZp, adding parent transform AAoRtj4w0X3egT

💡 adding file pGVuvtCsZVuBGKRWmqhD as input for run Zo7kda45OR5MhnixhyZp, adding parent transform 0qgOkJBvG7rwWX

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 'tGtZ2JTQ6aZvcba0Uo37' 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 '2JUxm5pRwuudVUPSR1mp' 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: ipython==8.14.0 lamindb==0.51.0 scanpy==1.9.4

✅ saved: Transform(id='1LCd8kco9lZUz8', name='Bird's eye view', short_name='birds-eye', version='0', type=notebook, updated_at=2023-08-28 13:54:10, created_by_id='bKeW4T6E')

✅ saved: Run(id='QSvArTZTnyV5hukpCn6D', run_at=2023-08-28 13:54:10, 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()

from IPython.display import Image, display

file.stage()
display(Image(filename=file.path))

💡 adding file 2JUxm5pRwuudVUPSR1mp as input for run QSvArTZTnyV5hukpCn6D, adding parent transform lduDnaLYJ3GJQ1

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	version	initial_version_id	type	reference	updated_at	created_by_id
id
lduDnaLYJ3GJQ1	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 13:54:10	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
pGVuvtCsZVuBGKRWmqhD	yTQlccVf	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	0qgOkJBvG7rwWX	Lr6JWp9k7WAEJZ0neBQa	2023-08-28 13:54:08	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='2Gwjq1pTD7eMBP', name='Chromium 10x upload', type='pipeline', updated_at=2023-08-28 13:54:05, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-28 13:54:07)

Which transforms were created by a given user?

users = ln.User.lookup()

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

	name	short_name	version	initial_version_id	type	reference	updated_at	created_by_id
id
gWd973slbGqaIt	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-28 13:54:06	bKeW4T6E
AAoRtj4w0X3egT	Preprocess Cell Ranger outputs	None	2.0	None	pipeline	None	2023-08-28 13:54:06	bKeW4T6E
0qgOkJBvG7rwWX	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 13:54:08	bKeW4T6E
lduDnaLYJ3GJQ1	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 13:54:10	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	0	None	notebook	None	2023-08-28 13:54:10	bKeW4T6E

Which notebooks were created by a given user?

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

	name	short_name	version	initial_version_id	type	reference	updated_at	created_by_id
id
0qgOkJBvG7rwWX	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 13:54:08	bKeW4T6E
lduDnaLYJ3GJQ1	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 13:54:10	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	0	None	notebook	None	2023-08-28 13:54:10	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
2JUxm5pRwuudVUPSR1mp	yTQlccVf	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	lduDnaLYJ3GJQ1	Zo7kda45OR5MhnixhyZp	2023-08-28 13:54:10	bKeW4T6E
tGtZ2JTQ6aZvcba0Uo37	yTQlccVf	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	lduDnaLYJ3GJQ1	Zo7kda45OR5MhnixhyZp	2023-08-28 13:54:09	bKeW4T6E
pGVuvtCsZVuBGKRWmqhD	yTQlccVf	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	0qgOkJBvG7rwWX	Lr6JWp9k7WAEJZ0neBQa	2023-08-28 13:54:08	bKeW4T6E
q4w1Lv1b9fAu4IaSxxHB	yTQlccVf	schmidt22-crispra-gws-IFNG.csv	.csv	None	Raw data of schmidt22 crispra GWS	None	None	1729685	cUSH0oQ2w-WccO8_ViKRAQ	md5	lapgANZ3brHG4w	f0dBYuVnm9xJIrHixYBX	2023-08-28 13:54:07	DzTjkKse
s2nQ8TJv4m72cZjmpgzR	yTQlccVf	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	AAoRtj4w0X3egT	AQtkG4OkUIrLPYqR4Qn5	2023-08-28 13:54:06	bKeW4T6E
NqQ90aex3CQvdYDoPmsn	yTQlccVf	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	None	6	pH0SHVJ0gPf50hxbBAKrcw	md5	gWd973slbGqaIt	WnefvAB77JsFt9MPhySh	2023-08-28 13:54:06	bKeW4T6E
jgWqeGfFwVAamkN82yfG	yTQlccVf	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	None	6	kxY-SEHvkRUQfytaIFUd8A	md5	gWd973slbGqaIt	WnefvAB77JsFt9MPhySh	2023-08-28 13:54:06	bKeW4T6E

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
2JNx4mvl5KxkA1uo4bgb	2Gwjq1pTD7eMBP	2023-08-28 13:54:05	DzTjkKse	None	None
WnefvAB77JsFt9MPhySh	gWd973slbGqaIt	2023-08-28 13:54:06	bKeW4T6E	None	None
AQtkG4OkUIrLPYqR4Qn5	AAoRtj4w0X3egT	2023-08-28 13:54:06	bKeW4T6E	None	None
f0dBYuVnm9xJIrHixYBX	lapgANZ3brHG4w	2023-08-28 13:54:07	DzTjkKse	None	None
Lr6JWp9k7WAEJZ0neBQa	0qgOkJBvG7rwWX	2023-08-28 13:54:08	bKeW4T6E	None	None
Zo7kda45OR5MhnixhyZp	lduDnaLYJ3GJQ1	2023-08-28 13:54:08	bKeW4T6E	None	None
QSvArTZTnyV5hukpCn6D	1LCd8kco9lZUz8	2023-08-28 13:54:10	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
yTQlccVf	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-08-28 13:54:04	DzTjkKse

Transform

	name	short_name	version	initial_version_id	type	reference	updated_at	created_by_id
id
1LCd8kco9lZUz8	Bird's eye view	birds-eye	0	None	notebook	None	2023-08-28 13:54:10	bKeW4T6E
lduDnaLYJ3GJQ1	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 13:54:10	bKeW4T6E
0qgOkJBvG7rwWX	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 13:54:08	bKeW4T6E
lapgANZ3brHG4w	Upload GWS CRISPRa result	None	None	None	app	None	2023-08-28 13:54:07	DzTjkKse
AAoRtj4w0X3egT	Preprocess Cell Ranger outputs	None	2.0	None	pipeline	None	2023-08-28 13:54:06	bKeW4T6E
gWd973slbGqaIt	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-28 13:54:06	bKeW4T6E
2Gwjq1pTD7eMBP	Chromium 10x upload	None	None	None	pipeline	None	2023-08-28 13:54:05	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-28 13:54:08
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-28 13:54:07

Show code cell content Hide code cell content

!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