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

Show code cell output Hide code cell output

💡 creating schemas: core==0.46.3 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:57:45)
✅ saved: Storage(id='DE9gh7kk', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata', type='local', updated_at=2023-08-30 13:57:45, 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 0.51.2)

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()

Show code cell output Hide code cell output

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

✅ saved: Transform(id='LZUpRpX9PLFrh9', name='Upload GWS CRISPRa result', type='app', updated_at=2023-08-30 13:57:47, created_by_id='DzTjkKse')

✅ saved: Run(id='RMn2HPsjcq6Awy60e7RQ', run_at=2023-08-30 13:57:47, transform_id='LZUpRpX9PLFrh9', 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()

Show code cell output Hide code cell output

✅ 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=2023-08-30 13:57:48)

✅ saved: Transform(id='HTTA2IE76s4v07', name='GWS CRIPSRa analysis', type='notebook', updated_at=2023-08-30 13:57:48, created_by_id='bKeW4T6E')

✅ saved: Run(id='YtfoF1jsmmtkHtTQJndL', run_at=2023-08-30 13:57:48, transform_id='HTTA2IE76s4v07', created_by_id='bKeW4T6E')

💡 adding file zZScBF1Mror14Bv3vlh6 as input for run YtfoF1jsmmtkHtTQJndL, adding parent transform LZUpRpX9PLFrh9

💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/xBMB2SjQ6MgUAS2WDt41.parquet')

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

✅ storing file 'xBMB2SjQ6MgUAS2WDt41' at '.lamindb/xBMB2SjQ6MgUAS2WDt41.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")

Show code cell output Hide code cell output

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

✅ saved: Transform(id='slb7MJddMVlVwZ', name='Chromium 10x upload', type='pipeline', updated_at=2023-08-30 13:57:49, created_by_id='DzTjkKse')

✅ saved: Run(id='eSKXarzaWRTI3b62CIRZ', run_at=2023-08-30 13:57:49, transform_id='slb7MJddMVlVwZ', 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'

✅ 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)

Show code cell output Hide code cell output

✅ saved: Transform(id='HqzU93mdqCWuym', name='Cell Ranger', version='7.2.0', type='pipeline', updated_at=2023-08-30 13:57:50, created_by_id='bKeW4T6E')

✅ saved: Run(id='907lAHK5Dg0zmP8YMq30', run_at=2023-08-30 13:57:50, transform_id='HqzU93mdqCWuym', created_by_id='bKeW4T6E')

💡 adding file G5FuAsmwnwAMyjNhuxTm as input for run 907lAHK5Dg0zmP8YMq30, adding parent transform slb7MJddMVlVwZ

💡 adding file Z3xMcsSD1oa4yq20exGR as input for run 907lAHK5Dg0zmP8YMq30, adding parent transform slb7MJddMVlVwZ

✅ 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()

Show code cell output Hide code cell output

✅ saved: Transform(id='0z1LmQPIOdrDQn', name='Postprocess Cell Ranger', version='2.0', type='pipeline', updated_at=2023-08-30 13:57:50, created_by_id='bKeW4T6E')

✅ saved: Run(id='smB1j0pzgTkPYetsHONB', run_at=2023-08-30 13:57:50, transform_id='0z1LmQPIOdrDQn', created_by_id='bKeW4T6E')

💡 adding file wQFUAdqfjxBz5NcGRlEG as input for run smB1j0pzgTkPYetsHONB, adding parent transform HqzU93mdqCWuym

💡 adding file mbVl8C2325PJpXsem2Ph as input for run smB1j0pzgTkPYetsHONB, adding parent transform HqzU93mdqCWuym

💡 adding file I9BuQds9Cw3qdBk0NoIe as input for run smB1j0pzgTkPYetsHONB, adding parent transform HqzU93mdqCWuym

💡 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()

Show code cell output Hide code cell output

✅ saved: Transform(id='a4R8rGPdfhuLJc', name='Perform single cell analysis, integrate with CRISPRa screen', type='notebook', updated_at=2023-08-30 13:57:50, created_by_id='bKeW4T6E')

✅ saved: Run(id='IMmyJ0Sl7VpPVVC11Adx', run_at=2023-08-30 13:57:50, transform_id='a4R8rGPdfhuLJc', created_by_id='bKeW4T6E')

💡 adding file ptzKAoPD1mLCxhStG2wV as input for run IMmyJ0Sl7VpPVVC11Adx, adding parent transform 0z1LmQPIOdrDQn

💡 adding file xBMB2SjQ6MgUAS2WDt41 as input for run IMmyJ0Sl7VpPVVC11Adx, adding parent transform HTTA2IE76s4v07

/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/anndata/_core/anndata.py:1113: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(df_full[k]):

/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/anndata/_core/anndata.py:1113: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(df_full[k]):

/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scanpy/plotting/_tools/scatterplots.py:1207: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(values):

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 '5PJMSsgZGypIy13DAXiW' at 'figures/umap_fig1_score-wgs-hits.png'

/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/scanpy/plotting/_matrixplot.py:143: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  values_df = self.obs_tidy.groupby(level=0).mean()

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 'PS5jELNGpsaK0wuCqKW8' 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()

Show code cell output Hide code cell output

💡 notebook imports: ipython==8.14.0 lamindb==0.51.2 scanpy==1.9.4

✅ saved: Transform(id='1LCd8kco9lZUz8', name='Project flow', short_name='project-flow', version='0', type=notebook, updated_at=2023-08-30 13:57:52, created_by_id='bKeW4T6E')

✅ saved: Run(id='6uQCrfx3WtZxVayelsDw', run_at=2023-08-30 13:57:52, transform_id='1LCd8kco9lZUz8', created_by_id='bKeW4T6E')

💡 adding file PS5jELNGpsaK0wuCqKW8 as input for run 6uQCrfx3WtZxVayelsDw, adding parent transform a4R8rGPdfhuLJc

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	initial_version_id	type	reference	updated_at	created_by_id
id
HqzU93mdqCWuym	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-30 13:57:50	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
xBMB2SjQ6MgUAS2WDt41	DE9gh7kk	None	.parquet	DataFrame	hits from schmidt22 crispra GWS	None	None	18368	yFC1asXuK86w1NOBD_4dgw	md5	HTTA2IE76s4v07	YtfoF1jsmmtkHtTQJndL	2023-08-30 13:57:48	bKeW4T6E

Which transform ingested a given file?

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

Transform(id='LZUpRpX9PLFrh9', name='Upload GWS CRISPRa result', type='app', updated_at=2023-08-30 13:57:47, created_by_id='DzTjkKse')

And which user?

file.created_by

User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-08-30 13:57:49)

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
HTTA2IE76s4v07	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-30 13:57:48	bKeW4T6E
HqzU93mdqCWuym	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-30 13:57:50	bKeW4T6E
0z1LmQPIOdrDQn	Postprocess Cell Ranger	None	2.0	None	pipeline	None	2023-08-30 13:57:50	bKeW4T6E
a4R8rGPdfhuLJc	Perform single cell analysis, integrate with C...	None	None	None	notebook	None	2023-08-30 13:57:52	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	None	notebook	None	2023-08-30 13:57:52	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
HTTA2IE76s4v07	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-30 13:57:48	bKeW4T6E
a4R8rGPdfhuLJc	Perform single cell analysis, integrate with C...	None	None	None	notebook	None	2023-08-30 13:57:52	bKeW4T6E
1LCd8kco9lZUz8	Project flow	project-flow	0	None	notebook	None	2023-08-30 13:57:52	bKeW4T6E

We can also view all recent additions to the entire database:

ln.view()

Show code cell output Hide code cell output

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
PS5jELNGpsaK0wuCqKW8	DE9gh7kk	figures/matrixplot_fig2_score-wgs-hits-per-clu...	.png	None	None	None	None	28814	JYIPcat0YWYVCX3RVd3mww	md5	a4R8rGPdfhuLJc	IMmyJ0Sl7VpPVVC11Adx	2023-08-30 13:57:52	bKeW4T6E
5PJMSsgZGypIy13DAXiW	DE9gh7kk	figures/umap_fig1_score-wgs-hits.png	.png	None	None	None	None	118999	laQjVk4gh70YFzaUyzbUNg	md5	a4R8rGPdfhuLJc	IMmyJ0Sl7VpPVVC11Adx	2023-08-30 13:57:52	bKeW4T6E
ptzKAoPD1mLCxhStG2wV	DE9gh7kk	schmidt22_perturbseq.h5ad	.h5ad	AnnData	perturbseq counts	None	None	20659936	la7EvqEUMDlug9-rpw-udA	md5	0z1LmQPIOdrDQn	smB1j0pzgTkPYetsHONB	2023-08-30 13:57:50	bKeW4T6E
wQFUAdqfjxBz5NcGRlEG	DE9gh7kk	perturbseq/filtered_feature_bc_matrix/barcodes...	.tsv.gz	None	None	None	None	6	S2SCt9zOGtAW2oEIcaSzXQ	md5	HqzU93mdqCWuym	907lAHK5Dg0zmP8YMq30	2023-08-30 13:57:50	bKeW4T6E
mbVl8C2325PJpXsem2Ph	DE9gh7kk	perturbseq/filtered_feature_bc_matrix/features...	.tsv.gz	None	None	None	None	6	mZ9squsQ93O8vJ3sAmV5IA	md5	HqzU93mdqCWuym	907lAHK5Dg0zmP8YMq30	2023-08-30 13:57:50	bKeW4T6E
I9BuQds9Cw3qdBk0NoIe	DE9gh7kk	perturbseq/filtered_feature_bc_matrix/matrix.m...	.mtx.gz	None	None	None	None	6	NpqTJ41DRA1m1jZ_2uFydg	md5	HqzU93mdqCWuym	907lAHK5Dg0zmP8YMq30	2023-08-30 13:57:50	bKeW4T6E
Z3xMcsSD1oa4yq20exGR	DE9gh7kk	fastq/perturbseq_R2_001.fastq.gz	.fastq.gz	None	None	None	None	6	s_mXu4q0usZRMCdNXwfN6A	md5	slb7MJddMVlVwZ	eSKXarzaWRTI3b62CIRZ	2023-08-30 13:57:49	DzTjkKse

Run

	transform_id	run_at	created_by_id	reference	reference_type
id
RMn2HPsjcq6Awy60e7RQ	LZUpRpX9PLFrh9	2023-08-30 13:57:47	DzTjkKse	None	None
YtfoF1jsmmtkHtTQJndL	HTTA2IE76s4v07	2023-08-30 13:57:48	bKeW4T6E	None	None
eSKXarzaWRTI3b62CIRZ	slb7MJddMVlVwZ	2023-08-30 13:57:49	DzTjkKse	None	None
907lAHK5Dg0zmP8YMq30	HqzU93mdqCWuym	2023-08-30 13:57:50	bKeW4T6E	None	None
smB1j0pzgTkPYetsHONB	0z1LmQPIOdrDQn	2023-08-30 13:57:50	bKeW4T6E	None	None
IMmyJ0Sl7VpPVVC11Adx	a4R8rGPdfhuLJc	2023-08-30 13:57:50	bKeW4T6E	None	None
6uQCrfx3WtZxVayelsDw	1LCd8kco9lZUz8	2023-08-30 13:57:52	bKeW4T6E	None	None

Storage

	root	type	region	updated_at	created_by_id
id
DE9gh7kk	/home/runner/work/lamin-usecases/lamin-usecase...	local	None	2023-08-30 13:57:45	DzTjkKse

Transform

	name	short_name	version	initial_version_id	type	reference	updated_at	created_by_id
id
1LCd8kco9lZUz8	Project flow	project-flow	0	None	notebook	None	2023-08-30 13:57:52	bKeW4T6E
a4R8rGPdfhuLJc	Perform single cell analysis, integrate with C...	None	None	None	notebook	None	2023-08-30 13:57:52	bKeW4T6E
0z1LmQPIOdrDQn	Postprocess Cell Ranger	None	2.0	None	pipeline	None	2023-08-30 13:57:50	bKeW4T6E
HqzU93mdqCWuym	Cell Ranger	None	7.2.0	None	pipeline	None	2023-08-30 13:57:50	bKeW4T6E
slb7MJddMVlVwZ	Chromium 10x upload	None	None	None	pipeline	None	2023-08-30 13:57:49	DzTjkKse
HTTA2IE76s4v07	GWS CRIPSRa analysis	None	None	None	notebook	None	2023-08-30 13:57:48	bKeW4T6E
LZUpRpX9PLFrh9	Upload GWS CRISPRa result	None	None	None	app	None	2023-08-30 13:57:47	DzTjkKse

User

	handle	email	name	updated_at
id
bKeW4T6E	testuser2	testuser2@lamin.ai	Test User2	2023-08-30 13:57:50
DzTjkKse	testuser1	testuser1@lamin.ai	Test User1	2023-08-30 13:57:49

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