Project flow¶
LaminDB allows tracking data lineage 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.
# !pip install 'lamindb[jupyter,bionty,aws]'
!lamin init --storage ./mydata
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→ initialized lamindb: testuser1/mydata
Import lamindb:
import lamindb as ln
from IPython.display import Image, display
→ connected lamindb: testuser1/mydata
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:
# This function mimics the upload of artifacts via the UI
# In reality, you simply drag and drop files into the UI
def mock_upload_crispra_result_app():
ln.setup.login("testuser1")
transform = ln.Transform(name="Upload GWS CRISPRa result", type="upload")
ln.track(transform=transform)
output_path = ln.core.datasets.schmidt22_crispra_gws_IFNG(ln.settings.storage.root)
output_file = ln.Artifact(
output_path, description="Raw data of schmidt22 crispra GWS"
)
output_file.save()
mock_upload_crispra_result_app()
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/tmp/ipykernel_3628/3297532022.py:5: FutureWarning: `name` will be removed soon, please pass 'Upload GWS CRISPRa result' to `key` instead
transform = ln.Transform(name="Upload GWS CRISPRa result", type="upload")
→ created Transform('oQZtEID6mfMs0000'), started new Run('M4xPcmu0...') at 2025-06-03 16:36:47 UTC
• recommendation: to identify the script across renames, pass the uid: ln.track("oQZtEID6mfMs")
Hit identification in notebook 
¶
Access, transform & register data in drylab by testuser2 in notebook hit-identification.
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# the following mimics the integrated analysis notebook
# In reality, you would execute inside the notebook
import nbproject_test
from pathlib import Path
cwd = Path.cwd()
nbproject_test.execute_notebooks(
cwd / "project-flow-scripts/hit-identification.ipynb", write=True
)
Inspect data flow:
artifact = ln.Artifact.get(description="hits from schmidt22 crispra GWS")
artifact.view_lineage()
Sequencer upload 
¶
Upload files from sequencer via script chromium_10x_upload.py:
!python project-flow-scripts/chromium_10x_upload.py
scRNA-seq bioinformatics pipeline 
¶
Process uploaded files using a script or workflow manager: Pipelines – workflow managers and obtain 3 output files in a directory filtered_feature_bc_matrix/:
!python project-flow-scripts/cellranger.py
!python project-flow-scripts/postprocess_cellranger.py
Inspect data flow:
output_file = ln.Artifact.get(description="perturbseq counts")
output_file.view_lineage()
Integrate scRNA-seq & phenotypic data ¶
Integrate data in notebook integrated-analysis.
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# the following mimics the integrated analysis notebook
# In reality, you would execute inside the notebook
nbproject_test.execute_notebooks(
cwd / "project-flow-scripts/integrated-analysis.ipynb", write=True
)
Review results¶
Let’s load one of the plots:
# track the current notebook as transform
ln.track("1LCd8kco9lZU0000")
artifact = ln.Artifact.get(key__contains="figures/matrixplot")
artifact.cache()
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PosixUPath('/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/SwBDAJb5Q2N80MvS0000.png')
display(Image(filename=artifact.path))
We see that the image artifact is tracked as an input of the current notebook. The input is highlighted, the notebook follows at the bottom:
artifact.view_lineage()
Alternatively, we can also look at the sequence of transforms:
transform = ln.Transform.search("Project flow").first()
transform.predecessors.df()
| uid | id | key | description | type | source_code | hash | reference | reference_type | space_id | _template_id | version | is_latest | created_at | created_by_id | _aux | branch_id |
|---|
transform.view_lineage()
Understand runs¶
We tracked pipeline and notebook runs through track(), which stores a Transform and a Run record within a global context.
Artifact 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 an artifact:
run = ln.Run(transform=transform)
ln.Artifact(filepath, run=run)
When does an artifact appear as a run input?
When accessing an artifact via cache(), load() or open(), two things happen:
The current run gets added to
artifact.input_ofThe transform of that artifact 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:
artifact.load(is_run_input=True)
Query by provenance¶
We can query or search for the notebook that created the artifact:
transform = ln.Transform.search("GWS CRIPSRa analysis").first()
And then find all the artifacts created by that notebook:
ln.Artifact.filter(transform=transform).df()
| uid | key | description | suffix | kind | otype | size | hash | n_files | n_observations | _hash_type | _key_is_virtual | _overwrite_versions | space_id | storage_id | schema_id | version | is_latest | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||||||
| 2 | 7mPlKcSQUy8vOC610000 | None | hits from schmidt22 crispra GWS | .parquet | dataset | DataFrame | 17075 | 7CBkfBTtG6o46AtrcgAL-w | None | 123 | md5 | True | False | 1 | 1 | None | None | True | 2 | 2025-06-03 16:36:53.331000+00:00 | 2 | None | 1 |
Which transform ingested a given artifact?
artifact = ln.Artifact.filter().first()
artifact.transform
Transform(uid='oQZtEID6mfMs0000', is_latest=True, key='Upload GWS CRISPRa result', type='upload', branch_id=1, space_id=1, created_by_id=1, created_at=2025-06-03 16:36:47 UTC)
And which user?
artifact.created_by
<User: User object (1)>
Which transforms were created by a given user?
users = ln.User.lookup()
ln.Transform.filter(created_by=users.testuser1).df()
| uid | key | description | type | source_code | hash | reference | reference_type | space_id | _template_id | version | is_latest | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||
| 1 | oQZtEID6mfMs0000 | Upload GWS CRISPRa result | None | upload | None | None | None | None | 1 | None | None | True | 2025-06-03 16:36:47.850000+00:00 | 1 | None | 1 |
| 3 | qCJPkOuZAi9q0000 | chromium_10x_upload.py | chromium_10x_upload.py | script | import lamindb as ln\n\nln.setup.login("testus... | nXWdh475QhVKuoAfToWZTw | None | None | 1 | None | None | True | 2025-06-03 16:36:56.341000+00:00 | 1 | None | 1 |
| 7 | 1LCd8kco9lZU0000 | project-flow.ipynb | Project flow | notebook | None | None | None | None | 1 | None | None | True | 2025-06-03 16:37:10.122000+00:00 | 1 | None | 1 |
Which notebooks were created by a given user?
ln.Transform.filter(created_by=users.testuser1, type="notebook").df()
| uid | key | description | type | source_code | hash | reference | reference_type | space_id | _template_id | version | is_latest | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||
| 7 | 1LCd8kco9lZU0000 | project-flow.ipynb | Project flow | notebook | None | None | None | None | 1 | None | None | True | 2025-06-03 16:37:10.122000+00:00 | 1 | None | 1 |
We can also view all recent additions to the entire database:
ln.view()
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| uid | key | description | suffix | kind | otype | size | hash | n_files | n_observations | _hash_type | _key_is_virtual | _overwrite_versions | space_id | storage_id | schema_id | version | is_latest | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||||||||||||||||
| 12 | SwBDAJb5Q2N80MvS0000 | figures/matrixplot_fig2_score-wgs-hits-per-clu... | None | .png | None | None | 28815 | DF_kMhhFqw-Lad8zK9u4Sw | None | None | md5 | True | False | 1 | 1 | None | None | True | 6.0 | 2025-06-03 16:37:08.537000+00:00 | 2 | None | 1 |
| 11 | l7BIiqdmadfgIx7c0000 | figures/umap_fig1_score-wgs-hits.png | None | .png | None | None | 119000 | BXPM4Ap5dGUESVHXgs-7Xg | None | None | md5 | True | False | 1 | 1 | None | None | True | 6.0 | 2025-06-03 16:37:08.365000+00:00 | 2 | None | 1 |
| 10 | eZdPv7PDZt6ntkG10000 | schmidt22_perturbseq.h5ad | perturbseq counts | .h5ad | None | AnnData | 20659936 | la7EvqEUMDlug9-rpw-udA | None | None | md5 | False | False | 1 | 1 | None | None | True | 5.0 | 2025-06-03 16:37:02.964000+00:00 | 2 | None | 1 |
| 8 | 1MnxlXA87Gb5LVMv0000 | perturbseq/filtered_feature_bc_matrix/features... | None | .tsv.gz | None | None | 6 | Uhpp6pAuqXVnf07gj7l_yw | None | None | md5 | False | False | 1 | 1 | None | None | True | 4.0 | 2025-06-03 16:36:59.975000+00:00 | 2 | None | 1 |
| 9 | 8d68EvVO60Xo4jBz0000 | perturbseq/filtered_feature_bc_matrix/matrix.m... | None | .mtx.gz | None | None | 6 | 1_Ku_SDDEFM9mDAb0V7vYw | None | None | md5 | False | False | 1 | 1 | None | None | True | 4.0 | 2025-06-03 16:36:59.975000+00:00 | 2 | None | 1 |
| 7 | VohqCTvE9R3Bvm7z0000 | perturbseq/filtered_feature_bc_matrix/barcodes... | None | .tsv.gz | None | None | 6 | KExK4Jy0wLhYkTc4aln4pA | None | None | md5 | False | False | 1 | 1 | None | None | True | 4.0 | 2025-06-03 16:36:59.974000+00:00 | 2 | None | 1 |
| 6 | JyD4S6zxno7TEiqn0000 | None | log streams of run hUqBdMNbR3WTmEUG | .txt | __lamindb_run__ | None | 0 | 1B2M2Y8AsgTpgAmY7PhCfg | None | None | md5 | True | False | 1 | 1 | None | None | True | NaN | 2025-06-03 16:36:56.886000+00:00 | 1 | None | 1 |
| uid | name | started_at | finished_at | reference | reference_type | _is_consecutive | _status_code | space_id | transform_id | report_id | _logfile_id | environment_id | initiated_by_run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 1 | M4xPcmu0zWwnT0SB | None | 2025-06-03 16:36:47.852079+00:00 | NaT | None | None | None | 0 | 1 | 1 | NaN | None | NaN | None | 2025-06-03 16:36:47.852000+00:00 | 1 | None | 1 |
| 2 | KUJochUBhqFowKeK | None | 2025-06-03 16:36:52.892235+00:00 | NaT | None | None | None | 0 | 1 | 2 | NaN | None | NaN | None | 2025-06-03 16:36:52.892000+00:00 | 2 | None | 1 |
| 3 | hUqBdMNbR3WTmEUG | None | 2025-06-03 16:36:56.343433+00:00 | 2025-06-03 16:36:56.884115+00:00 | None | None | True | 0 | 1 | 3 | 6.0 | None | 5.0 | None | 2025-06-03 16:36:56.344000+00:00 | 1 | None | 1 |
| 4 | rRn96BRymZKXuo2U | None | 2025-06-03 16:36:59.575358+00:00 | NaT | None | None | None | 0 | 1 | 4 | NaN | None | NaN | None | 2025-06-03 16:36:59.575000+00:00 | 2 | None | 1 |
| 5 | tpxXFehTeuQgTvIH | None | 2025-06-03 16:37:02.110148+00:00 | NaT | None | None | None | 0 | 1 | 5 | NaN | None | NaN | None | 2025-06-03 16:37:02.110000+00:00 | 2 | None | 1 |
| 6 | lL0So69XGPbKD658 | None | 2025-06-03 16:37:07.513645+00:00 | NaT | None | None | None | 0 | 1 | 6 | NaN | None | NaN | None | 2025-06-03 16:37:07.514000+00:00 | 2 | None | 1 |
| 7 | 2sbVsAvcC4aC4VwB | None | 2025-06-03 16:37:10.125612+00:00 | NaT | None | None | None | 0 | 1 | 7 | NaN | None | NaN | None | 2025-06-03 16:37:10.125000+00:00 | 1 | None | 1 |
| uid | root | description | type | region | instance_uid | space_id | run_id | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 1 | 1jzldE2w58q6 | /home/runner/work/lamin-usecases/lamin-usecase... | None | local | None | 54ZGqgkROOFf | 1 | None | 2025-06-03 16:36:45.052000+00:00 | 1 | None | 1 |
| uid | key | description | type | source_code | hash | reference | reference_type | space_id | _template_id | version | is_latest | created_at | created_by_id | _aux | branch_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||
| 7 | 1LCd8kco9lZU0000 | project-flow.ipynb | Project flow | notebook | None | None | None | None | 1 | None | None | True | 2025-06-03 16:37:10.122000+00:00 | 1 | None | 1 |
| 6 | lB3IyPLQSmvt0000 | integrated-analysis.ipynb | Perform single cell analysis, integrate with C... | notebook | None | None | None | None | 1 | None | None | True | 2025-06-03 16:37:07.509000+00:00 | 2 | None | 1 |
| 5 | YqmbO6oMXjRj0000 | postprocess_cellranger.py | postprocess_cellranger.py | script | None | None | None | None | 1 | None | None | True | 2025-06-03 16:37:02.108000+00:00 | 2 | None | 1 |
| 4 | TJnnjuFG8ebU0000 | Cell Ranger | None | pipeline | None | None | https://www.10xgenomics.com/support/software/c... | None | 1 | None | 7.2.0 | True | 2025-06-03 16:36:59.573000+00:00 | 2 | None | 1 |
| 3 | qCJPkOuZAi9q0000 | chromium_10x_upload.py | chromium_10x_upload.py | script | import lamindb as ln\n\nln.setup.login("testus... | nXWdh475QhVKuoAfToWZTw | None | None | 1 | None | None | True | 2025-06-03 16:36:56.341000+00:00 | 1 | None | 1 |
| 2 | T0T28btuB0PG0000 | hit-identification.ipynb | GWS CRIPSRa analysis | notebook | None | None | None | None | 1 | None | None | True | 2025-06-03 16:36:52.889000+00:00 | 2 | None | 1 |
| 1 | oQZtEID6mfMs0000 | Upload GWS CRISPRa result | None | upload | None | None | None | None | 1 | None | None | True | 2025-06-03 16:36:47.850000+00:00 | 1 | None | 1 |
Show code cell content
!lamin login testuser1
!rm -r ./mydata
!lamin delete --force mydata