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Site Navigation

  • Guide
  • APIs
  • Integrations
  • Changelog

Overview

  • Introduction

How to

  • Install & setup
  • Query & search
    • Query & search registries
    • Slice arrays
  • Track notebooks, scripts & functions
  • Curate datasets
  • Access public ontologies
    • Gene
    • Protein
    • Organism
    • CellLine
    • CellType
    • CellMarker
    • Tissue
    • Disease
    • Phenotype
    • Pathway
    • ExperimentalFactor
    • DevelopmentalStage
    • Ethnicity
  • Manage biological registries
  • Transfer data

Use cases

  • Atlases
    • CELLxGENE: scRNA-seq
      • Curate AnnData based on the CELLxGENE schema
    • Arc Virtual Cell Atlas: scRNA-seq
    • Hubmap: scRNA-seq
    • RxRx: cell imaging
  • Data types
    • scRNA-seq
      • Standardize and append a dataset
      • Query artifacts
      • Analyze a collection in memory
      • Train a machine learning model on a collection
      • Concatenate datasets to a single array store
    • Bulk RNA-seq
    • Flow cytometry
      • Append a new dataset
      • Query & integrate data
      • Analyze the collection and save a result
    • Spatial
      • Query and analyze spatial data
      • Train a spatial ML model
      • Interactive visualization using Vitessce
    • Multi-modal
    • sc-imaging
      • Generate single-cell images
      • Featurize single-cell images
      • Identify autophagy-positive cells
    • EHR
  • Registries
    • Gene Ontology (GO)
    • CellTypist
    • Standardize metadata on-the-fly
    • RDF export & SPARQL queries
  • Data lineage
    • Project flow
      • GWS CRIPSRa analysis
      • Perform single cell analysis, integrate with CRISPRa screen
    • Analysis flow
  • Pipelines – workflow managers
    • Redun
    • Nextflow
    • Snakemake
  • MLOps
    • Weights & Biases
    • MLFlow
    • Curate MNIST
  • Visualization
    • Vitessce

Other topics

  • Design & architecture
  • Manage access
  • Security
  • FAQ
    • Pydantic & Pandera vs. LaminDB
    • Will data get duplicated upon re-running code?
    • Will data & metadata stay in sync?
    • Can I disable tracking run inputs?
    • What happens when importing lamindb and the instance is not yet setup?
    • How do I validate & annotate arbitrary data structures?
    • What happens if I import a schema module without lamindb?
    • Where to store external links and IDs?
    • When does visibility of artifacts and collections change?
    • How to delete records?
    • Keep artifacts local in a cloud instance
    • Django field validation
    • Why should I not index datasets with gene symbols?
    • How does search work?
    • Storage FAQ
  • Influences
  • Glossary

Where to store external links and IDs?¶

When registering data in LaminDB, you might want to store a reference link or ID to indicate the source of the collection.

We have reference and reference_type fields for this purpose, they are available for Collection, Transform, Run and ULabel.

# !pip install lamindb
!lamin init --storage testreference
→ initialized lamindb: testuser1/testreference
import lamindb as ln
→ connected lamindb: testuser1/testreference

Let’s say we have a few donor samples that came form Vendor X, in order to chase back the orders, I’d like to keep track the donor ids provided by the vendor:

ln.ULabel(
    name="donor 001", reference="VX984545", reference_type="Donor ID from Vendor X"
)
ULabel(uid='vy0un1Au', name='donor 001', is_type=False, reference='VX984545', reference_type='Donor ID from Vendor X', space_id=1, created_by_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7fce63b1f2b0>)
!lamin delete --force testreference
• deleting instance testuser1/testreference

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What happens if I import a schema module without lamindb?

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When does visibility of artifacts and collections change?

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