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.
Setup¶
Init a test instance:
!lamin init --storage ./mydata
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💡 connected 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)
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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💡 saved: Transform(uid='9xzU7BsUZvEX', name='Upload GWS CRISPRa result', type='upload', created_by_id=1, updated_at='2024-05-29 10:02:02 UTC')
💡 saved: Run(uid='O3aiwS4EVpJtXjHSgsjk', transform_id=1, created_by_id=1)
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)
Executing notebooks in /home/runner/work/lamin-usecases/lamin-usecases/docs/project-flow-scripts/hit-identification.ipynb
Scheduled: ['hit-identification']
hit-identification
✓ (3.792s)
Total time: 3.793s
Inspect data flow:
artifact = ln.Artifact.filter(description="hits from schmidt22 crispra GWS").one()
artifact.view_lineage()
Sequencer upload 
¶
Upload files from sequencer via script chromium_10x_upload.py:
!python project-flow-scripts/chromium_10x_upload.py
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💡 connected lamindb: testuser1/mydata
💡 saved: Transform(uid='qCJPkOuZAi9q5zKv', version='1', name='chromium_10x_upload.py', key='chromium_10x_upload.py', type='script', created_by_id=1, updated_at='2024-05-29 10:02:08 UTC')
💡 saved: Run(uid='nwf1mgIlHQFksVjUP62m', transform_id=3, created_by_id=1)
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/:
!python project-flow-scripts/cellranger.py
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💡 connected lamindb: testuser1/mydata
💡 saved: Transform(uid='5bvTw15P9ccn', version='7.2.0', name='Cell Ranger', type='pipeline', reference='https://www.10xgenomics.com/support/software/cell-ranger/7.2', created_by_id=2, updated_at='2024-05-29 10:02:11 UTC')
💡 saved: Run(uid='jGMQ5vfgp9zU9DQi7Ivi', transform_id=4, created_by_id=2)
❗ this creates one artifact per file in the directory - you might simply call ln.Artifact(dir) to get one artifact for the entire directory
!python project-flow-scripts/postprocess_cellranger.py
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💡 connected lamindb: testuser1/mydata
💡 saved: Transform(uid='YqmbO6oMXjRj65cN', version='2', name='postprocess_cellranger.py', key='postprocess_cellranger.py', type='script', created_by_id=2, updated_at='2024-05-29 10:02:13 UTC')
💡 saved: Run(uid='cgHcmFdSBWdq3nvkU2hJ', transform_id=5, created_by_id=2)
Inspect data flow:
output_file = ln.Artifact.filter(description="perturbseq counts").one()
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)
Executing notebooks in /home/runner/work/lamin-usecases/lamin-usecases/docs/project-flow-scripts/integrated-analysis.ipynb
Scheduled: ['integrated-analysis']
integrated-analysis
✓ (4.342s)
Total time: 4.343s
Review results¶
Let’s load one of the plots:
# track the current notebook as transform
ln.settings.transform.stem_uid = "1LCd8kco9lZU"
ln.settings.transform.version = "0"
ln.track()
💡 notebook imports: ipython==8.24.0 lamindb==0.72.1 nbproject_test==0.5.1
💡 saved: Transform(uid='1LCd8kco9lZU6K79', version='0', name='Project flow', key='project-flow', type='notebook', created_by_id=1, updated_at='2024-05-29 10:02:19 UTC')
💡 saved: Run(uid='YSzZ2DJa5SWDBma4sRuN', transform_id=7, created_by_id=1)
Run(uid='YSzZ2DJa5SWDBma4sRuN', started_at='2024-05-29 10:02:19 UTC', is_consecutive=True, transform_id=7, created_by_id=1)
artifact = ln.Artifact.filter(key__contains="figures/matrixplot").one()
artifact.cache()
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PosixUPath('/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/PDSYxeehi53eQ0BDVKCm.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.parents.df()
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 6 | lB3IyPLQSmvt5zKv | 1 | Perform single cell analysis, integrate with C... | integrated-analysis | None | notebook | None | None | None | None | 2 | 2024-05-29 10:02:17.473906+00:00 |
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.
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 backed(), 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 | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 2 | Nczsr6gy790OKv9LqE7M | None | hits from schmidt22 crispra GWS | None | .parquet | DataFrame | 18368 | 5GtKh_v__shMvLryIdXkKA | md5 | None | None | 1 | True | 1 | 2 | 2 | 2 | 2024-05-29 10:02:06.293481+00:00 |
Which transform ingested a given artifact?
artifact = ln.Artifact.filter().first()
artifact.transform
Transform(uid='9xzU7BsUZvEX', name='Upload GWS CRISPRa result', type='upload', created_by_id=1, updated_at='2024-05-29 10:02:02 UTC')
And which user?
artifact.created_by
User(uid='DzTjkKse', handle='testuser1', name='Test User1', updated_at='2024-05-29 10:02:08 UTC')
Which transforms were created by a given user?
users = ln.User.lookup()
ln.Transform.filter(created_by=users.testuser1).df()
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 1 | 9xzU7BsUZvEX | None | Upload GWS CRISPRa result | None | None | upload | None | None | None | NaN | 1 | 2024-05-29 10:02:02.021328+00:00 |
| 3 | qCJPkOuZAi9q5zKv | 1 | chromium_10x_upload.py | chromium_10x_upload.py | None | script | None | None | None | 5.0 | 1 | 2024-05-29 10:02:09.019669+00:00 |
| 7 | 1LCd8kco9lZU6K79 | 0 | Project flow | project-flow | None | notebook | None | None | None | NaN | 1 | 2024-05-29 10:02:19.274689+00:00 |
Which notebooks were created by a given user?
ln.Transform.filter(created_by=users.testuser1, type="notebook").df()
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 7 | 1LCd8kco9lZU6K79 | 0 | Project flow | project-flow | None | notebook | None | None | None | None | 1 | 2024-05-29 10:02:19.274689+00:00 |
We can also view all recent additions to the entire database:
ln.view()
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Artifact
| uid | version | description | key | suffix | accessor | size | hash | hash_type | n_objects | n_observations | visibility | key_is_virtual | storage_id | transform_id | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||||||||
| 12 | PDSYxeehi53eQ0BDVKCm | None | None | figures/matrixplot_fig2_score-wgs-hits-per-clu... | .png | None | 28814 | vKSAeP8dZnBAPkdOYa_kRQ | md5 | None | None | 1 | True | 1 | 6 | 6 | 2 | 2024-05-29 10:02:18.520723+00:00 |
| 11 | QyflA3AypTDCbc6MbJ8S | None | None | figures/umap_fig1_score-wgs-hits.png | .png | None | 118999 | IlWQvuhi-VqBf1nCqWnYXQ | md5 | None | None | 1 | True | 1 | 6 | 6 | 2 | 2024-05-29 10:02:18.207280+00:00 |
| 10 | LHUOUZC15a3wtkorhhrd | None | perturbseq counts | schmidt22_perturbseq.h5ad | .h5ad | AnnData | 20659936 | la7EvqEUMDlug9-rpw-udA | md5 | None | None | 1 | False | 1 | 5 | 5 | 2 | 2024-05-29 10:02:14.303533+00:00 |
| 9 | ybWoSEl2jUDlUhfXVR7g | None | None | perturbseq/filtered_feature_bc_matrix/features... | .tsv.gz | None | 6 | GAIrvKmrRtWi6AHJJJPuug | md5 | None | None | 1 | False | 1 | 4 | 4 | 2 | 2024-05-29 10:02:11.635893+00:00 |
| 8 | vVoDOyEa6OdJKjWWT61J | None | None | perturbseq/filtered_feature_bc_matrix/barcodes... | .tsv.gz | None | 6 | jx-Oqhm26FaVw-l6j22Qmg | md5 | None | None | 1 | False | 1 | 4 | 4 | 2 | 2024-05-29 10:02:11.635303+00:00 |
| 7 | 72gUT2E2Wz5i1IxQ23HE | None | None | perturbseq/filtered_feature_bc_matrix/matrix.m... | .mtx.gz | None | 6 | tynJni1JUh7t0MMQ7cMiVg | md5 | None | None | 1 | False | 1 | 4 | 4 | 2 | 2024-05-29 10:02:11.634435+00:00 |
| 4 | yI2ogumA75jwAKnGolXv | None | None | fastq/perturbseq_R2_001.fastq.gz | .fastq.gz | None | 6 | FWpD2h005_a6vBPEN5joXw | md5 | None | None | 1 | False | 1 | 3 | 3 | 1 | 2024-05-29 10:02:09.004973+00:00 |
Run
| uid | started_at | finished_at | is_consecutive | reference | reference_type | transform_id | report_id | environment_id | created_by_id | |
|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||
| 1 | O3aiwS4EVpJtXjHSgsjk | 2024-05-29 10:02:02.024909+00:00 | NaT | True | None | None | 1 | None | NaN | 1 |
| 2 | rXvehOjJcI2AX1Tq7vSN | 2024-05-29 10:02:05.789171+00:00 | NaT | True | None | None | 2 | None | NaN | 2 |
| 3 | nwf1mgIlHQFksVjUP62m | 2024-05-29 10:02:08.550577+00:00 | 2024-05-29 10:02:09.017807+00:00 | True | None | None | 3 | None | 6.0 | 1 |
| 4 | jGMQ5vfgp9zU9DQi7Ivi | 2024-05-29 10:02:11.174814+00:00 | NaT | None | None | None | 4 | None | NaN | 2 |
| 5 | cgHcmFdSBWdq3nvkU2hJ | 2024-05-29 10:02:13.249237+00:00 | NaT | None | None | None | 5 | None | NaN | 2 |
| 6 | jHBMRcvowiZ51Ne4H81G | 2024-05-29 10:02:17.479869+00:00 | NaT | True | None | None | 6 | None | NaN | 2 |
| 7 | YSzZ2DJa5SWDBma4sRuN | 2024-05-29 10:02:19.279949+00:00 | NaT | True | None | None | 7 | None | NaN | 1 |
Storage
| uid | root | description | type | region | instance_uid | run_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|
| id | |||||||||
| 1 | p8hRjaZDM5qa | /home/runner/work/lamin-usecases/lamin-usecase... | None | local | None | 54ZGqgkROOFf | None | 1 | 2024-05-29 10:02:00.154441+00:00 |
Transform
| uid | version | name | key | description | type | reference | reference_type | latest_report_id | source_code_id | created_by_id | updated_at | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id | ||||||||||||
| 7 | 1LCd8kco9lZU6K79 | 0 | Project flow | project-flow | None | notebook | None | None | None | NaN | 1 | 2024-05-29 10:02:19.274689+00:00 |
| 6 | lB3IyPLQSmvt5zKv | 1 | Perform single cell analysis, integrate with C... | integrated-analysis | None | notebook | None | None | None | NaN | 2 | 2024-05-29 10:02:17.473906+00:00 |
| 5 | YqmbO6oMXjRj65cN | 2 | postprocess_cellranger.py | postprocess_cellranger.py | None | script | None | None | None | NaN | 2 | 2024-05-29 10:02:13.246759+00:00 |
| 4 | 5bvTw15P9ccn | 7.2.0 | Cell Ranger | None | None | pipeline | https://www.10xgenomics.com/support/software/c... | None | None | NaN | 2 | 2024-05-29 10:02:11.172162+00:00 |
| 3 | qCJPkOuZAi9q5zKv | 1 | chromium_10x_upload.py | chromium_10x_upload.py | None | script | None | None | None | 5.0 | 1 | 2024-05-29 10:02:09.019669+00:00 |
| 2 | T0T28btuB0PG5zKv | 1 | GWS CRIPSRa analysis | hit-identification | None | notebook | None | None | None | NaN | 2 | 2024-05-29 10:02:05.784330+00:00 |
| 1 | 9xzU7BsUZvEX | None | Upload GWS CRISPRa result | None | None | upload | None | None | None | NaN | 1 | 2024-05-29 10:02:02.021328+00:00 |
User
| uid | handle | name | updated_at | |
|---|---|---|---|---|
| id | ||||
| 2 | bKeW4T6E | testuser2 | Test User2 | 2024-05-29 10:02:11.163057+00:00 |
| 1 | DzTjkKse | testuser1 | Test User1 | 2024-05-29 10:02:08.418467+00:00 |
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!lamin login testuser1
!lamin delete --force mydata
!rm -r ./mydata
✅ logged in with email testuser1@lamin.ai (uid: DzTjkKse)
Traceback (most recent call last):
File "/opt/hostedtoolcache/Python/3.10.14/x64/bin/lamin", line 8, in <module>
sys.exit(main())
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rich_click/rich_command.py", line 367, in __call__
return super().__call__(*args, **kwargs)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rich_click/rich_command.py", line 152, in main
rv = self.invoke(ctx)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1688, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamin_cli/__main__.py", line 103, in delete
return delete(instance, force=force)
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamindb_setup/_delete.py", line 98, in delete
n_objects = check_storage_is_empty(
File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/lamindb_setup/core/upath.py", line 798, in check_storage_is_empty
raise InstanceNotEmpty(message)
lamindb_setup.core.upath.InstanceNotEmpty: Storage /home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb contains 5 objects ('_is_initialized' ignored) - delete them prior to deleting the instance
['/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/72zCrUmT6ooBqMM3r2P7.py', '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/MwaZXgmScJJjXvH2fjIK.txt', '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/Nczsr6gy790OKv9LqE7M.parquet', '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/PDSYxeehi53eQ0BDVKCm.png', '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/QyflA3AypTDCbc6MbJ8S.png', '/home/runner/work/lamin-usecases/lamin-usecases/docs/mydata/.lamindb/_is_initialized']