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=2023-09-22 18:47:13)
✅ saved: Storage(id='ubHG2JpL', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-09-22 18:47:13, 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 0.54.1)

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='8ka6v4AJB5BSZm', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-22 18:47:16, created_by_id='DzTjkKse')

💡 Run(id='lKPp26T2DDddtlwadPRH', run_at=2023-09-22 18:47:16, transform_id='8ka6v4AJB5BSZm', 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='55arWF1TKjqN2U', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-09-22 18:47:18, created_by_id='bKeW4T6E')

💡 Run(id='BPxxuuA2DTk1iZuGWdr7', run_at=2023-09-22 18:47:18, transform_id='55arWF1TKjqN2U', 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='P1EaXI6UQot4oL', name='Chromium 10x upload', type='pipeline', updated_at=2023-09-22 18:47:19, created_by_id='DzTjkKse')

💡 Run(id='rlCULI80t8vg9kxpCvmF', run_at=2023-09-22 18:47:19, transform_id='P1EaXI6UQot4oL', 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='pOZ2Is6YZ4bwbC', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-09-22 18:47:21, created_by_id='bKeW4T6E')

💡 Run(id='N9JO8kVVs6uROsZZZ4js', run_at=2023-09-22 18:47:21, transform_id='pOZ2Is6YZ4bwbC', 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	pOZ2Is6YZ4bwbC	90.0

💡 Transform(id='58lI8YgQOhfoGm', name='Postprocess Cell Ranger', version='2.0', type='pipeline', updated_at=2023-09-22 18:47:21, created_by_id='bKeW4T6E')

💡 Run(id='F2QJX7w4p0BXXSOOJdkT', run_at=2023-09-22 18:47:21, transform_id='58lI8YgQOhfoGm', 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='tViNH23fm0AQGk', name='Perform single cell analysis, integrate with CRISPRa screen', type='notebook', updated_at=2023-09-22 18:47:22, created_by_id='bKeW4T6E')

💡 Run(id='EXqn15w68yZvDotexnZb', run_at=2023-09-22 18:47:22, transform_id='tViNH23fm0AQGk', 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=2023-09-22 18:47:25, created_by_id='bKeW4T6E')

💡 Run(id='BsGFDlJyDXNw4DFY4Nvj', run_at=2023-09-22 18:47:25, 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
55arWF1TKjqN2U	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-22 18:47:18	bKeW4T6E
58lI8YgQOhfoGm	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-22 18:47:22	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
Ef74rt3zOU6MQozwEYaz	ubHG2JpL	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	18368	O2Owo0_QlM9JBS2zAZD4Lw	md5	55arWF1TKjqN2U	BPxxuuA2DTk1iZuGWdr7	None	2023-09-22 18:47:18	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='8ka6v4AJB5BSZm', name='Upload GWS CRISPRa result', type='app', updated_at=2023-09-22 18:47:17, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-22 18:47:19)

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
55arWF1TKjqN2U	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-22 18:47:18	bKeW4T6E
pOZ2Is6YZ4bwbC	Cell Ranger	None	7.2.0	pipeline	None	None	None	2023-09-22 18:47:21	bKeW4T6E
58lI8YgQOhfoGm	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-22 18:47:22	bKeW4T6E
tViNH23fm0AQGk	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-22 18:47:24	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	None	2023-09-22 18:47:25	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
55arWF1TKjqN2U	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-22 18:47:18	bKeW4T6E
tViNH23fm0AQGk	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-22 18:47:24	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	notebook	None	None	None	2023-09-22 18:47:25	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
zuoz5pAyPLtn2F6aZBTM	ubHG2JpL	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	28814	H0Pxpa-fZOvigo74eXHZsQ	md5	tViNH23fm0AQGk	EXqn15w68yZvDotexnZb	None	2023-09-22 18:47:24	bKeW4T6E
gkLcjDHJuM0vegjjfJqm	ubHG2JpL	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	118999	1-WtAvRL1d_SSjZvMMOMkg	md5	tViNH23fm0AQGk	EXqn15w68yZvDotexnZb	None	2023-09-22 18:47:24	bKeW4T6E
b0fNsPHbjnFPGbh2ZwuT	ubHG2JpL	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	58lI8YgQOhfoGm	F2QJX7w4p0BXXSOOJdkT	None	2023-09-22 18:47:22	bKeW4T6E
dJyWW1VOX6m8TAi2Oog7	ubHG2JpL	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	6	imjlTuuHoPLT36IJK3ar5Q	md5	pOZ2Is6YZ4bwbC	N9JO8kVVs6uROsZZZ4js	None	2023-09-22 18:47:21	bKeW4T6E
siSgBak5iADNi4fcWXwY	ubHG2JpL	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	6	C48SSDslcBBoYjA1U5RLmA	md5	pOZ2Is6YZ4bwbC	N9JO8kVVs6uROsZZZ4js	None	2023-09-22 18:47:21	bKeW4T6E
LCguPcLtOQsjDnvzCKva	ubHG2JpL	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	6	PYw6XfzF6X7lo6p2pdiWBA	md5	pOZ2Is6YZ4bwbC	N9JO8kVVs6uROsZZZ4js	None	2023-09-22 18:47:21	bKeW4T6E
dtDeRby7vRMtrTZh8w2y	ubHG2JpL	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	6	z2vErwVnQ0c0eIgYLduZ7Q	md5	P1EaXI6UQot4oL	rlCULI80t8vg9kxpCvmF	None	2023-09-22 18:47:19	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
lKPp26T2DDddtlwadPRH	8ka6v4AJB5BSZm	2023-09-22 18:47:16	DzTjkKse	None	None
BPxxuuA2DTk1iZuGWdr7	55arWF1TKjqN2U	2023-09-22 18:47:18	bKeW4T6E	None	None
rlCULI80t8vg9kxpCvmF	P1EaXI6UQot4oL	2023-09-22 18:47:19	DzTjkKse	None	None
N9JO8kVVs6uROsZZZ4js	pOZ2Is6YZ4bwbC	2023-09-22 18:47:21	bKeW4T6E	None	None
F2QJX7w4p0BXXSOOJdkT	58lI8YgQOhfoGm	2023-09-22 18:47:21	bKeW4T6E	None	None
EXqn15w68yZvDotexnZb	tViNH23fm0AQGk	2023-09-22 18:47:22	bKeW4T6E	None	None
BsGFDlJyDXNw4DFY4Nvj	1LCd8kco9lZUz8	2023-09-22 18:47:25	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
ubHG2JpL	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-09-22 18:47:13	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	2023-09-22 18:47:25	bKeW4T6E
tViNH23fm0AQGk	Perform single cell analysis, integrate with C...	None	None	notebook	None	None	None	2023-09-22 18:47:24	bKeW4T6E
58lI8YgQOhfoGm	Postprocess Cell Ranger	None	2.0	pipeline	None	None	None	2023-09-22 18:47:22	bKeW4T6E
pOZ2Is6YZ4bwbC	Cell Ranger	None	7.2.0	pipeline	None	None	None	2023-09-22 18:47:21	bKeW4T6E
P1EaXI6UQot4oL	Chromium 10x upload	None	None	pipeline	None	None	None	2023-09-22 18:47:19	DzTjkKse
55arWF1TKjqN2U	GWS CRIPSRa analysis	None	None	notebook	None	None	None	2023-09-22 18:47:18	bKeW4T6E
8ka6v4AJB5BSZm	Upload GWS CRISPRa result	None	None	app	None	None	None	2023-09-22 18:47:17	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-09-22 18:47:21
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-09-22 18:47:19

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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