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 17:07:13)
✅ saved: Storage(id='Udj9u0Sg', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-09-06 17:07:13, 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='9zlBz3Xgc2IvZn', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-06 17:07:16, created_by_id='DzTjkKse')

✅ saved: Run(id='Mh7ZIOGLh4Na8EtEQZ1J', run_at=2023-09-06 17:07:16, transform_id='9zlBz3Xgc2IvZn', 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 17:07:18)

✅ saved: Transform(id='UGgTnujdMjmqf6', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-09-06 17:07:18, created_by_id='bKeW4T6E')

✅ saved: Run(id='E2t4mz0kkJfRinWQrscv', run_at=2023-09-06 17:07:18, transform_id='UGgTnujdMjmqf6', created_by_id='bKeW4T6E')

💡 adding file gsUgigboT5ZeRSTCNsE6 as input for run E2t4mz0kkJfRinWQrscv, adding parent transform 9zlBz3Xgc2IvZn

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

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

✅ storing file 'OzkiIMBLbKgZXDrREzEg' at '.lamindb/OzkiIMBLbKgZXDrREzEg.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='ltkGKFRg4sbMte', name='Chromium 10x upload', type='pipeline', updated_at=2023-09-06 17:07:20, created_by_id='DzTjkKse')

✅ saved: Run(id='vAm0C5eKHe74FlbUAqC1', run_at=2023-09-06 17:07:20, transform_id='ltkGKFRg4sbMte', 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='4XIBS8xJGi8sFs', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-09-06 17:07:22, created_by_id='bKeW4T6E')

✅ saved: Run(id='Ssq3hkHqB73ZwZ7zcETE', run_at=2023-09-06 17:07:22, transform_id='4XIBS8xJGi8sFs', created_by_id='bKeW4T6E')

💡 adding file ATw96RJQxoswHdScqLnL as input for run Ssq3hkHqB73ZwZ7zcETE, adding parent transform ltkGKFRg4sbMte

💡 adding file zwYFmU5OADqTlYaKtVlu as input for run Ssq3hkHqB73ZwZ7zcETE, adding parent transform ltkGKFRg4sbMte

❗ 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	4XIBS8xJGi8sFs	90.0

✅ saved: Transform(id='h7U5HRgHwlEtGy', name='Postprocess Cell Ranger', version='2.0', type='pipeline', updated_at=2023-09-06 17:07:22, created_by_id='bKeW4T6E')

✅ saved: Run(id='amikacjTRYhZzVYxi0uk', run_at=2023-09-06 17:07:22, transform_id='h7U5HRgHwlEtGy', created_by_id='bKeW4T6E')

💡 adding file cZiu9ejcDmgQEtlXBoxS as input for run amikacjTRYhZzVYxi0uk, adding parent transform 4XIBS8xJGi8sFs

💡 adding file bMxW8PX5OdIESwgN8g8g as input for run amikacjTRYhZzVYxi0uk, adding parent transform 4XIBS8xJGi8sFs

💡 adding file kxEtq1lqJ2WvKTDCu8Kj as input for run amikacjTRYhZzVYxi0uk, adding parent transform 4XIBS8xJGi8sFs

💡 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	4XIBS8xJGi8sFs	85.5
GWS CRIPSRa analysis	UGgTnujdMjmqf6	85.5
Postprocess Cell Ranger	h7U5HRgHwlEtGy	85.5
Upload GWS CRISPRa result	9zlBz3Xgc2IvZn	85.5

✅ saved: Transform(id='dxHUsksyyvEsyS', name='Perform single cell analysis, integrate with CRISPRa screen', type='notebook', updated_at=2023-09-06 17:07:24, created_by_id='bKeW4T6E')

✅ saved: Run(id='CDJe5lOSCtuq4PfCgeGo', run_at=2023-09-06 17:07:24, transform_id='dxHUsksyyvEsyS', created_by_id='bKeW4T6E')

💡 adding file B1AJFQ7IeoUgiRt3dHnY as input for run CDJe5lOSCtuq4PfCgeGo, adding parent transform h7U5HRgHwlEtGy

💡 adding file OzkiIMBLbKgZXDrREzEg as input for run CDJe5lOSCtuq4PfCgeGo, adding parent transform UGgTnujdMjmqf6

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 'azpJZMuHZ4ZGQK6aPdwe' 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 'rpVLIkwI2z6NBXeOKyN1' 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 17:07:26, created_by_id='bKeW4T6E')

✅ saved: Run(id='XT60q0CRDS8sL7isxBC0', run_at=2023-09-06 17:07:26, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

💡 adding file rpVLIkwI2z6NBXeOKyN1 as input for run XT60q0CRDS8sL7isxBC0, adding parent transform dxHUsksyyvEsyS

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
UGgTnujdMjmqf6	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:07:19	bKeW4T6E
h7U5HRgHwlEtGy	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:07:23	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
OzkiIMBLbKgZXDrREzEg	Udj9u0Sg	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	UGgTnujdMjmqf6	E2t4mz0kkJfRinWQrscv	None	2023-09-06 17:07:19	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='9zlBz3Xgc2IvZn', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-06 17:07:17, 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 17:07:20)

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
UGgTnujdMjmqf6	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:07:19	bKeW4T6E
4XIBS8xJGi8sFs	Cell Ranger	None	7.2.0	pipeline	None	None	2023-09-06 17:07:22	bKeW4T6E
h7U5HRgHwlEtGy	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:07:23	bKeW4T6E
dxHUsksyyvEsyS	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:07:25	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	2023-09-06 17:07:26	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
UGgTnujdMjmqf6	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:07:19	bKeW4T6E
dxHUsksyyvEsyS	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:07:25	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	2023-09-06 17:07:26	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
rpVLIkwI2z6NBXeOKyN1	Udj9u0Sg	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	dxHUsksyyvEsyS	CDJe5lOSCtuq4PfCgeGo	None	2023-09-06 17:07:25	bKeW4T6E
azpJZMuHZ4ZGQK6aPdwe	Udj9u0Sg	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	dxHUsksyyvEsyS	CDJe5lOSCtuq4PfCgeGo	None	2023-09-06 17:07:25	bKeW4T6E
B1AJFQ7IeoUgiRt3dHnY	Udj9u0Sg	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	h7U5HRgHwlEtGy	amikacjTRYhZzVYxi0uk	None	2023-09-06 17:07:23	bKeW4T6E
cZiu9ejcDmgQEtlXBoxS	Udj9u0Sg	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	6	97nWvv88hWxE99rke-sxXw	md5	4XIBS8xJGi8sFs	Ssq3hkHqB73ZwZ7zcETE	None	2023-09-06 17:07:22	bKeW4T6E
kxEtq1lqJ2WvKTDCu8Kj	Udj9u0Sg	perturbseq/filtered_feature_bc_matrix/matrix.m...	.gz	None	None	None	6	MOcYvUKNZwWZyhtlrWmB4Q	md5	4XIBS8xJGi8sFs	Ssq3hkHqB73ZwZ7zcETE	None	2023-09-06 17:07:22	bKeW4T6E
bMxW8PX5OdIESwgN8g8g	Udj9u0Sg	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	6	uy-TIga_67kTxT9eKK_bzg	md5	4XIBS8xJGi8sFs	Ssq3hkHqB73ZwZ7zcETE	None	2023-09-06 17:07:22	bKeW4T6E
zwYFmU5OADqTlYaKtVlu	Udj9u0Sg	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	6	XJher6k6hT_6I9hPGitzgQ	md5	ltkGKFRg4sbMte	vAm0C5eKHe74FlbUAqC1	None	2023-09-06 17:07:20	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
Mh7ZIOGLh4Na8EtEQZ1J	9zlBz3Xgc2IvZn	2023-09-06 17:07:16	DzTjkKse	None	None
E2t4mz0kkJfRinWQrscv	UGgTnujdMjmqf6	2023-09-06 17:07:18	bKeW4T6E	None	None
vAm0C5eKHe74FlbUAqC1	ltkGKFRg4sbMte	2023-09-06 17:07:20	DzTjkKse	None	None
Ssq3hkHqB73ZwZ7zcETE	4XIBS8xJGi8sFs	2023-09-06 17:07:22	bKeW4T6E	None	None
amikacjTRYhZzVYxi0uk	h7U5HRgHwlEtGy	2023-09-06 17:07:22	bKeW4T6E	None	None
CDJe5lOSCtuq4PfCgeGo	dxHUsksyyvEsyS	2023-09-06 17:07:24	bKeW4T6E	None	None
XT60q0CRDS8sL7isxBC0	1LCd8kco9lZUz8	2023-09-06 17:07:26	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
Udj9u0Sg	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-09-06 17:07:13	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 17:07:26	bKeW4T6E
dxHUsksyyvEsyS	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	2023-09-06 17:07:25	bKeW4T6E
h7U5HRgHwlEtGy	Postprocess Cell Ranger	None	2.0	pipeline	None	None	2023-09-06 17:07:23	bKeW4T6E
4XIBS8xJGi8sFs	Cell Ranger	None	7.2.0	pipeline	None	None	2023-09-06 17:07:22	bKeW4T6E
ltkGKFRg4sbMte	Chromium 10x upload	None	None	pipeline	None	None	2023-09-06 17:07:20	DzTjkKse
UGgTnujdMjmqf6	GWS CRIPSRa analysis	None	None	notebook	None	None	2023-09-06 17:07:19	bKeW4T6E
9zlBz3Xgc2IvZn	Upload GWS CRISPRa result	None	None	app	None	None	2023-09-06 17:07:17	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-09-06 17:07:22
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-09-06 17:07:20

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