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 18:27:58)
✅ saved: Storage(id='lV7yGE5C', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-08-28 18:27:58, 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='esPe9ey7UOWkUs', name='Chromium 10x upload', type='pipeline', updated_at=2023-08-28 18:27:59, created_by_id='DzTjkKse')

✅ saved: Run(id='vYYOHgAJkgeRJIuStm1D', run_at=2023-08-28 18:27:59, transform_id='esPe9ey7UOWkUs', 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 18:28:00)

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 18:27:58
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-28 18:28:00

transform

Transform(id='JXkdP1eEWX0T9R', name='Cell Ranger', version='7.2.0', type='pipeline', created_by_id='bKeW4T6E')

ln.track(transform)

✅ saved: Transform(id='JXkdP1eEWX0T9R', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-08-28 18:28:00, created_by_id='bKeW4T6E')

✅ saved: Run(id='gXXVcK1otXqE6UHukFqd', run_at=2023-08-28 18:28:00, transform_id='JXkdP1eEWX0T9R', 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 yyzQ5nQh9AADZSJHeO94 as input for run gXXVcK1otXqE6UHukFqd, adding parent transform esPe9ey7UOWkUs

💡 adding file T2qbXLg7u11pNkJ9aoWC as input for run gXXVcK1otXqE6UHukFqd, adding parent transform esPe9ey7UOWkUs

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='AcGXPfCKv0pRbb', name='Preprocess Cell Ranger outputs', version='2.0', type='pipeline', updated_at=2023-08-28 18:28:00, created_by_id='bKeW4T6E')

✅ saved: Run(id='CcwewUVHIwRRCLgERSdY', run_at=2023-08-28 18:28:00, transform_id='AcGXPfCKv0pRbb', created_by_id='bKeW4T6E')

💡 adding file g7iYzIUri8qUnH1gWnCb as input for run CcwewUVHIwRRCLgERSdY, adding parent transform JXkdP1eEWX0T9R

💡 adding file vSKTfjcMvRjK3s4ap0k1 as input for run CcwewUVHIwRRCLgERSdY, adding parent transform JXkdP1eEWX0T9R

💡 adding file OgvKbN8gXUfplSnstlMO as input for run CcwewUVHIwRRCLgERSdY, adding parent transform JXkdP1eEWX0T9R

💡 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='o0s9dpKHxvP14n', name='Upload GWS CRISPRa result', type='app', updated_at=2023-08-28 18:28:03, created_by_id='DzTjkKse')

✅ saved: Run(id='1Xv2nS3IhOJPAM0ufjHx', run_at=2023-08-28 18:28:03, transform_id='o0s9dpKHxvP14n', 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='sZ0jO6lAxrrgfa', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-08-28 18:28:05, created_by_id='bKeW4T6E')

✅ saved: Run(id='7ZY75b0sn8jLMo36S2oP', run_at=2023-08-28 18:28:05, transform_id='sZ0jO6lAxrrgfa', created_by_id='bKeW4T6E')

💡 adding file UfHSSYBWcvwIuYcoP6JD as input for run 7ZY75b0sn8jLMo36S2oP, adding parent transform o0s9dpKHxvP14n

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/4CXgRN4MkUHFT2hQzBHi.parquet')

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

✅ storing file '4CXgRN4MkUHFT2hQzBHi' at '.lamindb/4CXgRN4MkUHFT2hQzBHi.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='68JJSlWzF0zi5h', name='Perform single cell analysis, integrating with CRISPRa screen', type='notebook', updated_at=2023-08-28 18:28:05, created_by_id='bKeW4T6E')

✅ saved: Run(id='BSZSq23Y2fMYljD3TtTo', run_at=2023-08-28 18:28:05, transform_id='68JJSlWzF0zi5h', created_by_id='bKeW4T6E')

💡 adding file nj6Sg7LVgZyrA2RB7l0K as input for run BSZSq23Y2fMYljD3TtTo, adding parent transform AcGXPfCKv0pRbb

💡 adding file 4CXgRN4MkUHFT2hQzBHi as input for run BSZSq23Y2fMYljD3TtTo, adding parent transform sZ0jO6lAxrrgfa

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 '22s3gj55XIKJUj9IE0NR' 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 'nEnEGke53hs6Cynn7yBw' 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 18:28:07, created_by_id='bKeW4T6E')

✅ saved: Run(id='WZYNFjoMvznI2RFVMXCc', run_at=2023-08-28 18:28:07, 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 nEnEGke53hs6Cynn7yBw as input for run WZYNFjoMvznI2RFVMXCc, adding parent transform 68JJSlWzF0zi5h

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
68JJSlWzF0zi5h	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 18:28:07	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
4CXgRN4MkUHFT2hQzBHi	lV7yGE5C	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	sZ0jO6lAxrrgfa	7ZY75b0sn8jLMo36S2oP	2023-08-28 18:28:05	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='esPe9ey7UOWkUs', name='Chromium 10x upload', type='pipeline', updated_at=2023-08-28 18:27:59, 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 18:28:03)

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
JXkdP1eEWX0T9R	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-28 18:28:00	bKeW4T6E
AcGXPfCKv0pRbb	Preprocess Cell Ranger outputs	None	2.0	None	pipeline	None	2023-08-28 18:28:02	bKeW4T6E
sZ0jO6lAxrrgfa	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 18:28:05	bKeW4T6E
68JJSlWzF0zi5h	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 18:28:07	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	0	None	notebook	None	2023-08-28 18:28:07	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
sZ0jO6lAxrrgfa	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 18:28:05	bKeW4T6E
68JJSlWzF0zi5h	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 18:28:07	bKeW4T6E
1LCd8kco9lZUz8	Bird's eye view	birds-eye	0	None	notebook	None	2023-08-28 18:28:07	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
nEnEGke53hs6Cynn7yBw	lV7yGE5C	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	68JJSlWzF0zi5h	BSZSq23Y2fMYljD3TtTo	2023-08-28 18:28:07	bKeW4T6E
22s3gj55XIKJUj9IE0NR	lV7yGE5C	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	68JJSlWzF0zi5h	BSZSq23Y2fMYljD3TtTo	2023-08-28 18:28:07	bKeW4T6E
4CXgRN4MkUHFT2hQzBHi	lV7yGE5C	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	sZ0jO6lAxrrgfa	7ZY75b0sn8jLMo36S2oP	2023-08-28 18:28:05	bKeW4T6E
UfHSSYBWcvwIuYcoP6JD	lV7yGE5C	schmidt22-crispra-gws-IFNG.csv	.csv	None	Raw data of schmidt22 crispra GWS	None	None	1729685	cUSH0oQ2w-WccO8_ViKRAQ	md5	o0s9dpKHxvP14n	1Xv2nS3IhOJPAM0ufjHx	2023-08-28 18:28:04	DzTjkKse
nj6Sg7LVgZyrA2RB7l0K	lV7yGE5C	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	AcGXPfCKv0pRbb	CcwewUVHIwRRCLgERSdY	2023-08-28 18:28:02	bKeW4T6E
vSKTfjcMvRjK3s4ap0k1	lV7yGE5C	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	None	6	kp6xn-SIwkaFI-xDC7Njhw	md5	JXkdP1eEWX0T9R	gXXVcK1otXqE6UHukFqd	2023-08-28 18:28:00	bKeW4T6E
OgvKbN8gXUfplSnstlMO	lV7yGE5C	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	None	6	bAkxpeZNXSoBfxLt1VXeZA	md5	JXkdP1eEWX0T9R	gXXVcK1otXqE6UHukFqd	2023-08-28 18:28:00	bKeW4T6E

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
vYYOHgAJkgeRJIuStm1D	esPe9ey7UOWkUs	2023-08-28 18:27:59	DzTjkKse	None	None
gXXVcK1otXqE6UHukFqd	JXkdP1eEWX0T9R	2023-08-28 18:28:00	bKeW4T6E	None	None
CcwewUVHIwRRCLgERSdY	AcGXPfCKv0pRbb	2023-08-28 18:28:00	bKeW4T6E	None	None
1Xv2nS3IhOJPAM0ufjHx	o0s9dpKHxvP14n	2023-08-28 18:28:03	DzTjkKse	None	None
7ZY75b0sn8jLMo36S2oP	sZ0jO6lAxrrgfa	2023-08-28 18:28:05	bKeW4T6E	None	None
BSZSq23Y2fMYljD3TtTo	68JJSlWzF0zi5h	2023-08-28 18:28:05	bKeW4T6E	None	None
WZYNFjoMvznI2RFVMXCc	1LCd8kco9lZUz8	2023-08-28 18:28:07	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
lV7yGE5C	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-08-28 18:27:58	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 18:28:07	bKeW4T6E
68JJSlWzF0zi5h	Perform single cell analysis, integrating with...	None	None	None	notebook	None	2023-08-28 18:28:07	bKeW4T6E
sZ0jO6lAxrrgfa	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-28 18:28:05	bKeW4T6E
o0s9dpKHxvP14n	Upload GWS CRISPRa result	None	None	None	app	None	2023-08-28 18:28:04	DzTjkKse
AcGXPfCKv0pRbb	Preprocess Cell Ranger outputs	None	2.0	None	pipeline	None	2023-08-28 18:28:02	bKeW4T6E
JXkdP1eEWX0T9R	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-28 18:28:00	bKeW4T6E
esPe9ey7UOWkUs	Chromium 10x upload	None	None	None	pipeline	None	2023-08-28 18:27:59	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-28 18:28:05
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-28 18:28:03

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