Project flow

LaminDB allows tracking data flow on the entire project level.

Here, we walk through exemplified app uploads, pipelines & notebooks following Schmidt et al., 2022.

A CRISPR screen reading out a phenotypic endpoint on T cells is paired with scRNA-seq to generate insights into IFN-γ production.

These insights get linked back to the original data through the steps taken in the project to provide context for interpretation & future decision making.

More specifically: Why should I care about data flow?

Data flow tracks data sources & transformations to trace biological insights, verify experimental outcomes, meet regulatory standards, increase the robustness of research and optimize the feedback loop of team-wide learning iterations.

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

LaminDB interfaces workflow mangers for the former and embraces the latter.

Setup

Init a test instance:

!lamin init --storage ./mydata

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✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-23 14:27:41)
✅ saved: Storage(id='IwvWjBM0', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-09-23 14:27:41, 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
from IPython.display import Image, display

💡 loaded instance: testuser1/mydata (lamindb 0.54.1)

Steps

In the following, we walk through exemplified steps covering different types of transforms (Transform).

Note

The full notebooks are in this repository.

App upload of phenotypic data

Register data through app upload from wetlab by testuser1:

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

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💡 Transform(id='xcXA0JbsNKNdaW', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-23 14:27:44, created_by_id='DzTjkKse')

💡 Run(id='31OlUiLQ4OpvBQv30PcX', run_at=2023-09-23 14:27:44, transform_id='xcXA0JbsNKNdaW', created_by_id='DzTjkKse')

Hit identification in notebook

Access, transform & register data in drylab by testuser2:

ln.setup.login("testuser2")
transform = ln.Transform(name="GWS CRIPSRa analysis", type="notebook")
ln.track(transform)
# access
input_file = ln.File.filter(key="schmidt22-crispra-gws-IFNG.csv").one()
# identify hits
input_df = input_file.load().set_index("id")
output_df = input_df[input_df["pos|fdr"] < 0.01].copy()
# register hits in output file
ln.File(output_df, description="hits from schmidt22 crispra GWS").save()

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💡 Transform(id='NjGHw1LGbqmBpl', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-09-23 14:27:46, created_by_id='bKeW4T6E')

💡 Run(id='HnTzKCYaHYhkDAdWIGSD', run_at=2023-09-23 14:27:46, transform_id='NjGHw1LGbqmBpl', created_by_id='bKeW4T6E')

Inspect data flow:

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

Sequencer upload

Upload files from sequencer:

ln.setup.login("testuser1")
ln.track(ln.Transform(name="Chromium 10x upload", type="pipeline"))
# register output files of upload
upload_dir = ln.dev.datasets.dir_scrnaseq_cellranger(
    "perturbseq", basedir=ln.settings.storage, output_only=False
)
ln.File(upload_dir.parent / "fastq/perturbseq_R1_001.fastq.gz").save()
ln.File(upload_dir.parent / "fastq/perturbseq_R2_001.fastq.gz").save()
ln.setup.login("testuser2")

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💡 Transform(id='UH9HETpmnjSCKY', name='Chromium 10x upload', type='pipeline', updated_at=2023-09-23 14:27:48, created_by_id='DzTjkKse')

💡 Run(id='YVahujDgCTMvyYTQbND6', run_at=2023-09-23 14:27:48, transform_id='UH9HETpmnjSCKY', created_by_id='DzTjkKse')

❗ file has more than one suffix (path.suffixes), inferring: '.fastq.gz'

❗ file has more than one suffix (path.suffixes), inferring: '.fastq.gz'

scRNA-seq bioinformatics pipeline

Process uploaded files using a script or workflow manager: Pipelines and obtain 3 output files in a directory filtered_feature_bc_matrix/:

transform = ln.Transform(name="Cell Ranger", version="7.2.0", type="pipeline")
ln.track(transform)
# access uploaded files as inputs for the pipeline
input_files = ln.File.filter(key__startswith="fastq/perturbseq").all()
input_paths = [file.stage() for file in input_files]
# register output files
output_files = ln.File.from_dir("./mydata/perturbseq/filtered_feature_bc_matrix/")
ln.save(output_files)

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💡 Transform(id='oJLLmXE2Jtg1bG', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-09-23 14:27:49, created_by_id='bKeW4T6E')

💡 Run(id='cJDIbhZOG6r07MjCUL19', run_at=2023-09-23 14:27:49, transform_id='oJLLmXE2Jtg1bG', created_by_id='bKeW4T6E')

❗ file has more than one suffix (path.suffixes), inferring: '.mtx.gz'

❗ file has more than one suffix (path.suffixes), inferring: '.tsv.gz'

❗ file has more than one suffix (path.suffixes), inferring: '.tsv.gz'

Post-process these 3 files:

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

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

	id	__ratio__
name
Cell Ranger	oJLLmXE2Jtg1bG	90.0

💡 Transform(id='LlfL4pmZ5bQcoO', name='Postprocess Cell Ranger', version='2.0', type='pipeline', updated_at=2023-09-23 14:27:49, created_by_id='bKeW4T6E')

💡 Run(id='rgX01MVqHbAN9G4XiZHp', run_at=2023-09-23 14:27:49, transform_id='LlfL4pmZ5bQcoO', created_by_id='bKeW4T6E')

Inspect data flow:

output_files[0].view_flow()

Integrate scRNA-seq & phenotypic data

Integrate data in a notebook:

transform = ln.Transform(
    name="Perform single cell analysis, integrate with CRISPRa screen",
    type="notebook",
)
ln.track(transform)

file_ps = ln.File.filter(description__icontains="perturbseq").one()
adata = file_ps.load()
file_hits = ln.File.filter(description="hits from schmidt22 crispra GWS").one()
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()

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💡 Transform(id='WcixBFVicaVLMc', name='Perform single cell analysis, integrate with CRISPRa screen', type='notebook', updated_at=2023-09-23 14:27:50, created_by_id='bKeW4T6E')

💡 Run(id='faAYs2TpPBvwOWoAAOqD', run_at=2023-09-23 14:27:50, transform_id='WcixBFVicaVLMc', created_by_id='bKeW4T6E')

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

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

Review results

Let’s load one of the plots:

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

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💡 notebook imports: ipython==8.15.0 lamindb==0.54.1 scanpy==1.9.5

💡 Transform(id='1LCd8kco9lZUz8', name='Project flow', short_name='project-flow', version='0', type=notebook, updated_at=2023-09-23 14:27:53, created_by_id='bKeW4T6E')

💡 Run(id='wG3c4OsvOF3vA2oxNJaI', run_at=2023-09-23 14:27:53, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

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

display(Image(filename=file.path))

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_flow()

Alternatively, we can also look at the sequence of transforms:

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

	name	short_name	version	type	reference	reference_type	initial_version_id	updated_at	created_by_id
id
LlfL4pmZ5bQcoO	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-23 14:27:50	bKeW4T6E
NjGHw1LGbqmBpl	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-23 14:27:46	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	size	hash	hash_type	transform_id	run_id	initial_version_id	updated_at	created_by_id
id
7qLehn6mxnmpklCEvpog	IwvWjBM0	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	NjGHw1LGbqmBpl	HnTzKCYaHYhkDAdWIGSD	None	2023-09-23 14:27:46	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='xcXA0JbsNKNdaW', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-23 14:27:45, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-23 14:27:48)

Which transforms were created by a given user?

users = ln.User.lookup()

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

	name	short_name	version	type	reference	reference_type	initial_version_id	updated_at	created_by_id
id
NjGHw1LGbqmBpl	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-23 14:27:46	bKeW4T6E
oJLLmXE2Jtg1bG	Cell Ranger	None	7.2.0	pipeline	None	None	None	2023-09-23 14:27:49	bKeW4T6E
LlfL4pmZ5bQcoO	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-23 14:27:50	bKeW4T6E
WcixBFVicaVLMc	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-23 14:27:52	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	None	2023-09-23 14:27:53	bKeW4T6E

Which notebooks were created by a given user?

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

	name	short_name	version	type	reference	reference_type	initial_version_id	updated_at	created_by_id
id
NjGHw1LGbqmBpl	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-23 14:27:46	bKeW4T6E
WcixBFVicaVLMc	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-23 14:27:52	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	None	2023-09-23 14:27:53	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	size	hash	hash_type	transform_id	run_id	initial_version_id	updated_at	created_by_id
id
Wkj75ba8vnRrP2lZhtBa	IwvWjBM0	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	28814	H0Pxpa-fZOvigo74eXHZsQ	md5	WcixBFVicaVLMc	faAYs2TpPBvwOWoAAOqD	None	2023-09-23 14:27:52	bKeW4T6E
C9mX8eZueHCZtCUxwBJ6	IwvWjBM0	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	118999	1-WtAvRL1d_SSjZvMMOMkg	md5	WcixBFVicaVLMc	faAYs2TpPBvwOWoAAOqD	None	2023-09-23 14:27:52	bKeW4T6E
L5QaCwZ5D61b8YLpPTxf	IwvWjBM0	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	LlfL4pmZ5bQcoO	rgX01MVqHbAN9G4XiZHp	None	2023-09-23 14:27:50	bKeW4T6E
gkBnxC3YjVGndCE9wcDz	IwvWjBM0	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	6	9q4dgXKjj7I_tOgeGXcb-A	md5	oJLLmXE2Jtg1bG	cJDIbhZOG6r07MjCUL19	None	2023-09-23 14:27:49	bKeW4T6E
rXgjNNi7SidW3KF7eeNj	IwvWjBM0	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	6	SvlRdD2Na3gO-v0N-MWMJg	md5	oJLLmXE2Jtg1bG	cJDIbhZOG6r07MjCUL19	None	2023-09-23 14:27:49	bKeW4T6E
9pxGlZfJ63IcX6hDpdi2	IwvWjBM0	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	6	wieNc83bWkkWz7Cora8WeQ	md5	oJLLmXE2Jtg1bG	cJDIbhZOG6r07MjCUL19	None	2023-09-23 14:27:49	bKeW4T6E
VFhfWfwbxMmmcuUWXluG	IwvWjBM0	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	6	0TvkoQDwTSMVEA1Z0rkuaQ	md5	UH9HETpmnjSCKY	YVahujDgCTMvyYTQbND6	None	2023-09-23 14:27:48	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
31OlUiLQ4OpvBQv30PcX	xcXA0JbsNKNdaW	2023-09-23 14:27:44	DzTjkKse	None	None
HnTzKCYaHYhkDAdWIGSD	NjGHw1LGbqmBpl	2023-09-23 14:27:46	bKeW4T6E	None	None
YVahujDgCTMvyYTQbND6	UH9HETpmnjSCKY	2023-09-23 14:27:48	DzTjkKse	None	None
cJDIbhZOG6r07MjCUL19	oJLLmXE2Jtg1bG	2023-09-23 14:27:49	bKeW4T6E	None	None
rgX01MVqHbAN9G4XiZHp	LlfL4pmZ5bQcoO	2023-09-23 14:27:49	bKeW4T6E	None	None
faAYs2TpPBvwOWoAAOqD	WcixBFVicaVLMc	2023-09-23 14:27:50	bKeW4T6E	None	None
wG3c4OsvOF3vA2oxNJaI	1LCd8kco9lZUz8	2023-09-23 14:27:53	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
IwvWjBM0	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-09-23 14:27:41	DzTjkKse

Transform

	name	short_name	version	type	reference	reference_type	initial_version_id	updated_at	created_by_id
id
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	None	2023-09-23 14:27:53	bKeW4T6E
WcixBFVicaVLMc	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-23 14:27:52	bKeW4T6E
LlfL4pmZ5bQcoO	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-23 14:27:50	bKeW4T6E
oJLLmXE2Jtg1bG	Cell Ranger	None	7.2.0	pipeline	None	None	None	2023-09-23 14:27:49	bKeW4T6E
UH9HETpmnjSCKY	Chromium 10x upload	None	None	pipeline	None	None	None	2023-09-23 14:27:48	DzTjkKse
NjGHw1LGbqmBpl	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-23 14:27:46	bKeW4T6E
xcXA0JbsNKNdaW	Upload GWS CRISPRa result	None	None	app	None	None	None	2023-09-23 14:27:45	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-09-23 14:27:49
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-09-23 14:27:48

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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