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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💡 creating schemas: core==0.47.5 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:23:47)
✅ saved: Storage(id='oJ5MWMxM', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-09-06 17:23:47, 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 0.52.2)

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

✅ saved: Transform(id='s4LPhVnykuaLx9', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-06 17:23:49, created_by_id='DzTjkKse')

✅ saved: Run(id='6LXMF159xiEAS1QYPPOa', run_at=2023-09-06 17:23:49, transform_id='s4LPhVnykuaLx9', created_by_id='DzTjkKse')

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

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

✅ saved: User(id='bKeW4T6E', handle='testuser2', email='testuser2@lamin.ai', name='Test User2', updated_at=2023-09-06 17:23:50)

✅ saved: Transform(id='qBCjaQHpKfIsZm', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-09-06 17:23:50, created_by_id='bKeW4T6E')

✅ saved: Run(id='9M7Rfmgc4qIdKRV05ZgP', run_at=2023-09-06 17:23:50, transform_id='qBCjaQHpKfIsZm', created_by_id='bKeW4T6E')

💡 adding file bKqc6tmJiDpzOXfR6YRN as input for run 9M7Rfmgc4qIdKRV05ZgP, adding parent transform s4LPhVnykuaLx9

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/MxMRMbv8t97Khanw8PKR.parquet')

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

✅ storing file 'MxMRMbv8t97Khanw8PKR' at '.lamindb/MxMRMbv8t97Khanw8PKR.parquet'

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

✅ saved: Transform(id='lub9rJoMCxWO48', name='Chromium 10x upload', type='pipeline', updated_at=2023-09-06 17:23:51, created_by_id='DzTjkKse')

✅ saved: Run(id='OnitYPqw53030yZqlpzU', run_at=2023-09-06 17:23:51, transform_id='lub9rJoMCxWO48', created_by_id='DzTjkKse')

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

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

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

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

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

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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✅ saved: Transform(id='1lyCPM7UktPARA', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-09-06 17:23:52, created_by_id='bKeW4T6E')

✅ saved: Run(id='uKwtoPBWgIhKRvfAmDij', run_at=2023-09-06 17:23:52, transform_id='1lyCPM7UktPARA', created_by_id='bKeW4T6E')

💡 adding file Chr1ygMwadeQ4cSHA0z9 as input for run uKwtoPBWgIhKRvfAmDij, adding parent transform lub9rJoMCxWO48

💡 adding file pRPxgofrlsa3MD1qjlMe as input for run uKwtoPBWgIhKRvfAmDij, adding parent transform lub9rJoMCxWO48

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

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

❗ file has more than one suffix (path.suffixes), using only last suffix: '.gz'

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

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	1lyCPM7UktPARA	90.0

✅ saved: Transform(id='UT497nlSqyvPFT', name='Postprocess Cell Ranger', version='2.0', type='pipeline', updated_at=2023-09-06 17:23:52, created_by_id='bKeW4T6E')

✅ saved: Run(id='TvpDLGOg6Z3Ot8dpKvDS', run_at=2023-09-06 17:23:52, transform_id='UT497nlSqyvPFT', created_by_id='bKeW4T6E')

💡 adding file 5HO0bEpM57EmnbQf2F3D as input for run TvpDLGOg6Z3Ot8dpKvDS, adding parent transform 1lyCPM7UktPARA

💡 adding file ydBpTjW2Lf8JhuVDaHJ2 as input for run TvpDLGOg6Z3Ot8dpKvDS, adding parent transform 1lyCPM7UktPARA

💡 adding file vyyA5ryK7KhCh8Pi2BYw as input for run TvpDLGOg6Z3Ot8dpKvDS, adding parent transform 1lyCPM7UktPARA

💡 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

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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❗ records with similar names exist! did you mean to load one of them?

	id	__ratio__
name
Cell Ranger	1lyCPM7UktPARA	85.5
GWS CRIPSRa analysis	qBCjaQHpKfIsZm	85.5
Postprocess Cell Ranger	UT497nlSqyvPFT	85.5
Upload GWS CRISPRa result	s4LPhVnykuaLx9	85.5

✅ saved: Transform(id='2bBmrL0v57PBFb', name='Perform single cell analysis, integrate with CRISPRa screen', type='notebook', updated_at=2023-09-06 17:23:53, created_by_id='bKeW4T6E')

✅ saved: Run(id='eKt2iXkTOzJ4HAA7JakD', run_at=2023-09-06 17:23:53, transform_id='2bBmrL0v57PBFb', created_by_id='bKeW4T6E')

💡 adding file YmPfE8kMIMPQXWLqMN9Z as input for run eKt2iXkTOzJ4HAA7JakD, adding parent transform UT497nlSqyvPFT

💡 adding file MxMRMbv8t97Khanw8PKR as input for run eKt2iXkTOzJ4HAA7JakD, adding parent transform qBCjaQHpKfIsZm

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 'kSzJeYmAodKRpSqAEj3T' 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 'txEpioqEcozvQkhUL8GL' at '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==0.52.2 scanpy==1.9.4

✅ saved: Transform(id='1LCd8kco9lZUz8', name='Project flow', short_name='project-flow', version='0', type=notebook, updated_at=2023-09-06 17:23:55, created_by_id='bKeW4T6E')

✅ saved: Run(id='prxo762xPZEM71uV1Wvn', run_at=2023-09-06 17:23:55, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

💡 adding file txEpioqEcozvQkhUL8GL as input for run prxo762xPZEM71uV1Wvn, adding parent transform 2bBmrL0v57PBFb

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	initial_version_id	updated_at	created_by_id
id
UT497nlSqyvPFT	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:23:53	bKeW4T6E
qBCjaQHpKfIsZm	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:23:51	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
MxMRMbv8t97Khanw8PKR	oJ5MWMxM	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	qBCjaQHpKfIsZm	9M7Rfmgc4qIdKRV05ZgP	None	2023-09-06 17:23:51	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='s4LPhVnykuaLx9', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-06 17:23:50, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:23:51)

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	initial_version_id	updated_at	created_by_id
id
qBCjaQHpKfIsZm	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:23:51	bKeW4T6E
1lyCPM7UktPARA	Cell Ranger	None	7.2.0	pipeline	None	None	2023-09-06 17:23:52	bKeW4T6E
UT497nlSqyvPFT	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:23:53	bKeW4T6E
2bBmrL0v57PBFb	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:23:54	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	2023-09-06 17:23:55	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	initial_version_id	updated_at	created_by_id
id
qBCjaQHpKfIsZm	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:23:51	bKeW4T6E
2bBmrL0v57PBFb	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:23:54	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	2023-09-06 17:23:55	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
txEpioqEcozvQkhUL8GL	oJ5MWMxM	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	2bBmrL0v57PBFb	eKt2iXkTOzJ4HAA7JakD	None	2023-09-06 17:23:54	bKeW4T6E
kSzJeYmAodKRpSqAEj3T	oJ5MWMxM	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	2bBmrL0v57PBFb	eKt2iXkTOzJ4HAA7JakD	None	2023-09-06 17:23:54	bKeW4T6E
YmPfE8kMIMPQXWLqMN9Z	oJ5MWMxM	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	UT497nlSqyvPFT	TvpDLGOg6Z3Ot8dpKvDS	None	2023-09-06 17:23:53	bKeW4T6E
5HO0bEpM57EmnbQf2F3D	oJ5MWMxM	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	6	PfjRN-UDwuNfacHdQaSC7g	md5	1lyCPM7UktPARA	uKwtoPBWgIhKRvfAmDij	None	2023-09-06 17:23:52	bKeW4T6E
vyyA5ryK7KhCh8Pi2BYw	oJ5MWMxM	perturbseq/filtered_feature_bc_matrix/matrix.m...	.gz	None	None	None	6	U4a9JRZUHyTNgVwJdGVVfQ	md5	1lyCPM7UktPARA	uKwtoPBWgIhKRvfAmDij	None	2023-09-06 17:23:52	bKeW4T6E
ydBpTjW2Lf8JhuVDaHJ2	oJ5MWMxM	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	6	mdqJdubapHvpAkj1QMXLZA	md5	1lyCPM7UktPARA	uKwtoPBWgIhKRvfAmDij	None	2023-09-06 17:23:52	bKeW4T6E
pRPxgofrlsa3MD1qjlMe	oJ5MWMxM	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	6	pK66bpMxisWYu5TayY0gBA	md5	lub9rJoMCxWO48	OnitYPqw53030yZqlpzU	None	2023-09-06 17:23:51	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
6LXMF159xiEAS1QYPPOa	s4LPhVnykuaLx9	2023-09-06 17:23:49	DzTjkKse	None	None
9M7Rfmgc4qIdKRV05ZgP	qBCjaQHpKfIsZm	2023-09-06 17:23:50	bKeW4T6E	None	None
OnitYPqw53030yZqlpzU	lub9rJoMCxWO48	2023-09-06 17:23:51	DzTjkKse	None	None
uKwtoPBWgIhKRvfAmDij	1lyCPM7UktPARA	2023-09-06 17:23:52	bKeW4T6E	None	None
TvpDLGOg6Z3Ot8dpKvDS	UT497nlSqyvPFT	2023-09-06 17:23:52	bKeW4T6E	None	None
eKt2iXkTOzJ4HAA7JakD	2bBmrL0v57PBFb	2023-09-06 17:23:53	bKeW4T6E	None	None
prxo762xPZEM71uV1Wvn	1LCd8kco9lZUz8	2023-09-06 17:23:55	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
oJ5MWMxM	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-09-06 17:23:47	DzTjkKse

Transform

	name	short_name	version	type	reference	initial_version_id	updated_at	created_by_id
id
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	2023-09-06 17:23:55	bKeW4T6E
2bBmrL0v57PBFb	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:23:54	bKeW4T6E
UT497nlSqyvPFT	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:23:53	bKeW4T6E
1lyCPM7UktPARA	Cell Ranger	None	7.2.0	pipeline	None	None	2023-09-06 17:23:52	bKeW4T6E
lub9rJoMCxWO48	Chromium 10x upload	None	None	pipeline	None	None	2023-09-06 17:23:51	DzTjkKse
qBCjaQHpKfIsZm	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:23:51	bKeW4T6E
s4LPhVnykuaLx9	Upload GWS CRISPRa result	None	None	app	None	None	2023-09-06 17:23:50	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-09-06 17:23:52
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-09-06 17:23:51

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