Introduction

This vignette introduces the basic {laminr} workflow.

To learn more about LaminDB, please refer to the online documentation https://docs.lamin.ai/.

library(laminr)

Setup

Install {laminr} from CRAN:

install.packages("laminr", dependencies = TRUE)

Set up the Python environment:

laminr::install_lamindb()

Set the default LaminDB instance:

laminr::lamin_connect("<owner>/<name>")

This instance acts as the default instance for everything that follows. Any data and tracking information will be added to it.

See vignette("setup", package = "laminr") for more details.

For this vignette we use a temporary test instance.

lamin_init_temp(name = "laminr-intro", modules = c("bionty"))

This vignette requires the bionty Python package to be available.

Import

To start working with {laminr}, import the lamindb module:

ln <- import_module("lamindb")
#> → connected lamindb: testuser1/laminr-intro-20250320115956

This is equivalent to import lamindb as ln in Python.

Walkthrough

This section of the vignette reproduces the walkthrough from the LaminDB Introduction guide. The equivalent {laminr} code is included here, for the related text see the associated links.

See https://docs.lamin.ai/guide#walkthrough.

Transforms

See https://docs.lamin.ai/guide#transforms.

ln <- import_module("lamindb")
ln$track()
#> → created Transform('3bgvjtwGHfPS0000'), started new Run('q8rqesKD...') at 2025-03-20 12:00:14 UTC

ln$Transform$df()
#>                uid              key      description     type source_code hash
#> 1 3bgvjtwGHfPS0000 introduction.Rmd introduction.Rmd notebook        <NA> <NA>
#>   reference reference_type space_id _template_id version is_latest
#> 1      <NA>           <NA>        1         <NA>    <NA>      TRUE
#>            created_at created_by_id _aux _branch_code
#> 1 2025-03-20 12:00:14             1 <NA>            1

ln$Run$df()
#>                    uid name          started_at finished_at reference
#> 1 q8rqesKDvw1kNPTAMHpO <NA> 2025-03-20 12:00:14        <NA>      <NA>
#>   reference_type _is_consecutive _status_code space_id transform_id report_id
#> 1           <NA>            <NA>            0        1            1      <NA>
#>   _logfile_id environment_id initiated_by_run_id          created_at
#> 1        <NA>           <NA>                <NA> 2025-03-20 12:00:14
#>   created_by_id _aux _branch_code
#> 1             1 <NA>            1

Artifacts

Artifacts are objects that bundle data and associated metadata. An artifact can be any file or folder but is typically a dataset.

See https://docs.lamin.ai/guide#artifacts.

df <- ln$core$datasets$small_dataset1(otype = "DataFrame", with_typo = TRUE)
df
#>         ENSG00000153563 ENSG00000010610 ENSG00000170458 perturbation
#> sample1               1               3               5         DMSO
#> sample2               2               4               6         IFNJ
#> sample3               3               5               7         DMSO
#>         sample_note             cell_type_by_expert cell_type_by_model
#> sample1      was ok                          B cell             B cell
#> sample2  looks naah CD8-positive, alpha-beta T cell             T cell
#> sample3  pretty! 🤩 CD8-positive, alpha-beta T cell             T cell
#>           assay_oid concentration treatment_time_h donor
#> sample1 EFO:0008913          0.1%               24 D0001
#> sample2 EFO:0008913        200 nM               24 D0002
#> sample3 EFO:0008913          0.1%                6  <NA>

artifact <- ln$Artifact$from_df(df, key = "my_datasets/rnaseq1.parquet")$save()
artifact$describe()
#> Artifact .parquet/DataFrame
#> └── General
#>     ├── .uid = 'lG3C2TLNTSay7y4q0000'
#>     ├── .key = 'my_datasets/rnaseq1.parquet'
#>     ├── .size = 8530
#>     ├── .hash = 'hDTNFwEPy7pY1EKnOHog2A'
#>     ├── .n_observations = 3
#>     ├── .path = 
#>     │   /tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/lG3C2TLNTSay7y4q000
#>     │   0.parquet
#>     ├── .created_by = testuser1 (Test User1)
#>     ├── .created_at = 2025-03-20 12:00:15
#>     └── .transform = 'introduction.Rmd'

artifact$cache()
#> [1] "/tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/lG3C2TLNTSay7y4q0000.parquet"

dataset <- artifact$open()
as.data.frame(dataset)
#> # A tibble: 3 × 12
#>   ENSG00000153563 ENSG00000010610 ENSG00000170458 perturbation sample_note
#>             <dbl>           <dbl>           <dbl> <fct>        <chr>      
#> 1               1               3               5 DMSO         was ok     
#> 2               2               4               6 IFNJ         looks naah 
#> 3               3               5               7 DMSO         pretty! 🤩 
#> # ℹ 7 more variables: cell_type_by_expert <fct>, cell_type_by_model <fct>,
#> #   assay_oid <fct>, concentration <chr>, treatment_time_h <dbl>, donor <chr>,
#> #   `__index_level_0__` <chr>

artifact$load()
#>         ENSG00000153563 ENSG00000010610 ENSG00000170458 perturbation
#> sample1               1               3               5         DMSO
#> sample2               2               4               6         IFNJ
#> sample3               3               5               7         DMSO
#>         sample_note             cell_type_by_expert cell_type_by_model
#> sample1      was ok                          B cell             B cell
#> sample2  looks naah CD8-positive, alpha-beta T cell             T cell
#> sample3  pretty! 🤩 CD8-positive, alpha-beta T cell             T cell
#>           assay_oid concentration treatment_time_h donor
#> sample1 EFO:0008913          0.1%               24 D0001
#> sample2 EFO:0008913        200 nM               24 D0002
#> sample3 EFO:0008913          0.1%                6  <NA>

artifact$view_lineage()
#> ✖ `view_lineage()` is not yet implemented. Please view the lineage in the web interface.

df_typo <- df
levels(df$perturbation) <- c("DMSO", "IFNG")
df["sample2", "perturbation"] <- "IFNG"
artifact <- ln$Artifact$from_df(df, key = "my_datasets/rnaseq1.parquet")$save()
#> → creating new artifact version for key='my_datasets/rnaseq1.parquet' (storage: '/tmp/RtmppGs707/laminr-intro-20250320115956')
artifact$versions$df()
#>                    uid                         key description   suffix    kind
#> 1 lG3C2TLNTSay7y4q0000 my_datasets/rnaseq1.parquet        <NA> .parquet dataset
#> 2 lG3C2TLNTSay7y4q0001 my_datasets/rnaseq1.parquet        <NA> .parquet dataset
#>       otype size                   hash n_files n_observations _hash_type
#> 1 DataFrame 8530 hDTNFwEPy7pY1EKnOHog2A    <NA>              3        md5
#> 2 DataFrame 8530 RVElCjRCfyOAxbLNDEAMOg    <NA>              3        md5
#>   _key_is_virtual _overwrite_versions space_id storage_id schema_id version
#> 1            TRUE               FALSE        1          1      <NA>    <NA>
#> 2            TRUE               FALSE        1          1      <NA>    <NA>
#>   is_latest run_id          created_at created_by_id _aux _branch_code
#> 1     FALSE      1 2025-03-20 12:00:15             1 <NA>            1
#> 2      TRUE      1 2025-03-20 12:00:17             1 <NA>            1

Labels

See https://docs.lamin.ai/guide#labels.

bt <- import_module("bionty")

experiment_type <- ln$ULabel(name = "Experiment", is_type = TRUE)$save()
candidate_marker_experiment <- ln$ULabel(
  name = "Candidate marker experiment", type = experiment_type
)$save()

artifact$ulabels$add(candidate_marker_experiment)

cell_type <- bt$CellType$from_source(name = "effector T cell")$save()
artifact$cell_types$add(cell_type)

artifact$describe()
#> Artifact .parquet/DataFrame
#> ├── General
#> │   ├── .uid = 'lG3C2TLNTSay7y4q0001'
#> │   ├── .key = 'my_datasets/rnaseq1.parquet'
#> │   ├── .size = 8530
#> │   ├── .hash = 'RVElCjRCfyOAxbLNDEAMOg'
#> │   ├── .n_observations = 3
#> │   ├── .path = 
#> │   │   /tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/lG3C2TLNTSay7y4q000
#> │   │   1.parquet
#> │   ├── .created_by = testuser1 (Test User1)
#> │   ├── .created_at = 2025-03-20 12:00:17
#> │   └── .transform = 'introduction.Rmd'
#> └── Labels
#>     └── .cell_types         bionty.CellType    effector T cell                  
#>         .ulabels            ULabel             Candidate marker experiment

Registries

See https://docs.lamin.ai/guide#registries.

ln$ULabel$df()
#>        uid                        name is_type description reference
#> 2 3ZHgsoij Candidate marker experiment   FALSE        <NA>      <NA>
#> 1 5fV9ovtE                  Experiment    TRUE        <NA>      <NA>
#>   reference_type space_id type_id run_id          created_at created_by_id _aux
#> 2           <NA>        1       1      1 2025-03-20 12:00:17             1 <NA>
#> 1           <NA>        1     NaN      1 2025-03-20 12:00:17             1 <NA>
#>   _branch_code
#> 2            1
#> 1            1

ln$Artifact
#> Artifact
#>   Simple fields
#>     .uid: CharField
#>     .key: CharField
#>     .description: CharField
#>     .suffix: CharField
#>     .kind: CharField
#>     .otype: CharField
#>     .size: BigIntegerField
#>     .hash: CharField
#>     .n_files: BigIntegerField
#>     .n_observations: BigIntegerField
#>     .version: CharField
#>     .is_latest: BooleanField
#>     .created_at: DateTimeField
#>     .updated_at: DateTimeField
#>   Relational fields
#>     .space: Space
#>     .storage: Storage
#>     .run: Run
#>     .schema: Schema
#>     .created_by: User
#>     .ulabels: ULabel
#>     .input_of_runs: Run
#>     .feature_sets: Schema
#>     .collections: Collection
#>     .references: Reference
#>     .projects: Project
#>   Bionty fields
#>     .organisms: bionty.Organism
#>     .genes: bionty.Gene
#>     .proteins: bionty.Protein
#>     .cell_markers: bionty.CellMarker
#>     .tissues: bionty.Tissue
#>     .cell_types: bionty.CellType
#>     .diseases: bionty.Disease
#>     .cell_lines: bionty.CellLine
#>     .phenotypes: bionty.Phenotype
#>     .pathways: bionty.Pathway
#>     .experimental_factors: bionty.ExperimentalFactor
#>     .developmental_stages: bionty.DevelopmentalStage
#>     .ethnicities: bionty.Ethnicity
#>  signature: (*args, **kwargs)

Query & search

See https://docs.lamin.ai/guide#query-search.

transform <- ln$Transform$get(key = "introduction.Rmd")

ln$Artifact$filter(key__startswith = "my_datasets/")$df()
#>                    uid                         key description   suffix    kind
#> 1 lG3C2TLNTSay7y4q0000 my_datasets/rnaseq1.parquet        <NA> .parquet dataset
#> 2 lG3C2TLNTSay7y4q0001 my_datasets/rnaseq1.parquet        <NA> .parquet dataset
#>       otype size                   hash n_files n_observations _hash_type
#> 1 DataFrame 8530 hDTNFwEPy7pY1EKnOHog2A    <NA>              3        md5
#> 2 DataFrame 8530 RVElCjRCfyOAxbLNDEAMOg    <NA>              3        md5
#>   _key_is_virtual _overwrite_versions space_id storage_id schema_id version
#> 1            TRUE               FALSE        1          1      <NA>    <NA>
#> 2            TRUE               FALSE        1          1      <NA>    <NA>
#>   is_latest run_id          created_at created_by_id _aux _branch_code
#> 1     FALSE      1 2025-03-20 12:00:15             1 <NA>            1
#> 2      TRUE      1 2025-03-20 12:00:17             1 <NA>            1

artifacts <- ln$Artifact$filter(transform = transform)$all()

artifacts <- ln$Artifact$filter(
  transform__description__icontains = "intro", ulabels = candidate_marker_experiment
)$all()

ln$Transform$search("intro")$df()
#>                uid              key      description     type source_code hash
#> 1 3bgvjtwGHfPS0000 introduction.Rmd introduction.Rmd notebook        <NA> <NA>
#>   reference reference_type space_id _template_id version is_latest
#> 1      <NA>           <NA>        1         <NA>    <NA>      TRUE
#>            created_at created_by_id _aux _branch_code
#> 1 2025-03-20 12:00:14             1 <NA>            1
ulabels <- ln$ULabel$lookup()
cell_types <- bt$CellType$lookup()

Features

See https://docs.lamin.ai/guide#features.

ln$Feature(name = "temperature", dtype = "float")$save()
#> Feature(uid='85alvfNQSdVK', name='temperature', dtype='float', array_rank=0, array_size=0, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:19 UTC)

ln$Feature(name = "experiment", dtype = ln$ULabel)$save()
#> Feature(uid='KENxPiP7TogT', name='experiment', dtype='cat[ULabel]', array_rank=0, array_size=0, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:19 UTC)

artifact$features$add_values(
  list("temperature" = 21.6, "experiment" = "Candidate marker experiment")
)

artifact$describe()
#> Artifact .parquet/DataFrame
#> ├── General
#> │   ├── .uid = 'lG3C2TLNTSay7y4q0001'
#> │   ├── .key = 'my_datasets/rnaseq1.parquet'
#> │   ├── .size = 8530
#> │   ├── .hash = 'RVElCjRCfyOAxbLNDEAMOg'
#> │   ├── .n_observations = 3
#> │   ├── .path = 
#> │   │   /tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/lG3C2TLNTSay7y4q000
#> │   │   1.parquet
#> │   ├── .created_by = testuser1 (Test User1)
#> │   ├── .created_at = 2025-03-20 12:00:17
#> │   └── .transform = 'introduction.Rmd'
#> ├── Linked features
#> │   └── experiment          cat[ULabel]        Candidate marker experiment      
#> │       temperature         float              21.6                             
#> └── Labels
#>     └── .cell_types         bionty.CellType    effector T cell                  
#>         .ulabels            ULabel             Candidate marker experiment

ln$Artifact$features$filter(experiment__contains = "marker experiment")$df()
#>                    uid                         key description   suffix    kind
#> 2 lG3C2TLNTSay7y4q0001 my_datasets/rnaseq1.parquet        <NA> .parquet dataset
#>       otype size                   hash n_files n_observations _hash_type
#> 2 DataFrame 8530 RVElCjRCfyOAxbLNDEAMOg    <NA>              3        md5
#>   _key_is_virtual _overwrite_versions space_id storage_id schema_id version
#> 2            TRUE               FALSE        1          1      <NA>    <NA>
#>   is_latest run_id          created_at created_by_id _aux _branch_code
#> 2      TRUE      1 2025-03-20 12:00:17             1 <NA>            1

Key use cases

This section of reproduces the key use cases from the LaminDB Introduction guide.

See https://docs.lamin.ai/guide#key-use-cases.

Understand data lineage

See https://docs.lamin.ai/guide#understand-data-lineage.

artifact$view_lineage()
#> ✖ `view_lineage()` is not yet implemented. Please view the lineage in the web interface.
transform$view_lineage()
#> ✖ `view_lineage()` is not yet implemented. Please view the lineage in the web interface.

# Example only, not run
ln <- import_module("lamindb")
ln$track()
ln$finish()

# lamin load https://lamin.ai/laminlabs/lamindata/transform/13VINnFk89PE0004

Curate datasets

See https://docs.lamin.ai/introduction#curate-datasets.

perturbation_type <- ln$ULabel(name = "Perturbation", is_type = TRUE)$save()
ln$ULabel(name = "DMSO", type = perturbation_type)$save()
#> ULabel(uid='AiFevwyw', name='DMSO', is_type=False, space_id=1, created_by_id=1, run_id=1, type_id=3, created_at=2025-03-20 12:00:19 UTC)
ln$ULabel(name = "IFNG", type = perturbation_type)$save()
#> ULabel(uid='AVltt2Fe', name='IFNG', is_type=False, space_id=1, created_by_id=1, run_id=1, type_id=3, created_at=2025-03-20 12:00:19 UTC)

# Load Python built ins to get access to dtypes
py_builtins <- reticulate::import_builtins()

schema <- ln$Schema(
  name = "My DataFrame schema",
  features = list(
    # NOTE: These have dtype=int in the original guide
    ln$Feature(name = "ENSG00000153563", dtype = py_builtins$float)$save(),
    ln$Feature(name = "ENSG00000010610", dtype = py_builtins$float)$save(),
    ln$Feature(name = "ENSG00000170458", dtype = py_builtins$float)$save(),
    ln$Feature(name = "perturbation", dtype = ln$ULabel)$save()
  )
)$save()

curator <- ln$curators$DataFrameCurator(df, schema)
artifact <- curator$save_artifact(key = "my_curated_dataset.parquet")
#> ✓ "perturbation" is validated against ULabel.name
#> → returning existing artifact with same hash: Artifact(uid='lG3C2TLNTSay7y4q0001', is_latest=True, key='my_datasets/rnaseq1.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=8530, hash='RVElCjRCfyOAxbLNDEAMOg', n_observations=3, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-03-20 12:00:17 UTC); to track this artifact as an input, use: ln.Artifact.get()
#> ! key my_datasets/rnaseq1.parquet on existing artifact differs from passed key my_curated_dataset.parquet
#> ✓ 4 unique terms (36.40%) are validated for name
#> ! 7 unique terms (63.60%) are not validated for name: 'sample_note', 'cell_type_by_expert', 'cell_type_by_model', 'assay_oid', 'concentration', 'treatment_time_h', 'donor'
#> ✓ loaded 4 Feature records matching name: 'ENSG00000153563', 'ENSG00000010610', 'ENSG00000170458', 'perturbation'
#> ! did not create Feature records for 7 non-validated names: 'assay_oid', 'cell_type_by_expert', 'cell_type_by_model', 'concentration', 'donor', 'sample_note', 'treatment_time_h'
#> → returning existing schema with same hash: Schema(uid='4Spq3c0JSAbUyz0TWhEW', name='My DataFrame schema', n=4, itype='Feature', is_type=False, hash='-5KxGxur9yYPy-ItVPyC_Q', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:20 UTC)
#> ! updated otype from None to DataFrame
artifact$describe()
#> Artifact .parquet/DataFrame
#> ├── General
#> │   ├── .uid = 'lG3C2TLNTSay7y4q0001'
#> │   ├── .key = 'my_datasets/rnaseq1.parquet'
#> │   ├── .size = 8530
#> │   ├── .hash = 'RVElCjRCfyOAxbLNDEAMOg'
#> │   ├── .n_observations = 3
#> │   ├── .path = 
#> │   │   /tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/lG3C2TLNTSay7y4q000
#> │   │   1.parquet
#> │   ├── .created_by = testuser1 (Test User1)
#> │   ├── .created_at = 2025-03-20 12:00:17
#> │   └── .transform = 'introduction.Rmd'
#> ├── Dataset features/.feature_sets
#> │   └── columns • 4         [Feature]                                           
#> │       perturbation        cat[ULabel]        DMSO, IFNG                       
#> │       ENSG00000153563     float                                               
#> │       ENSG00000010610     float                                               
#> │       ENSG00000170458     float                                               
#> ├── Linked features
#> │   └── experiment          cat[ULabel]        Candidate marker experiment      
#> │       temperature         float              21.6                             
#> └── Labels
#>     └── .cell_types         bionty.CellType    effector T cell                  
#>         .ulabels            ULabel             Candidate marker experiment, DMS…
ln$Artifact$get(ulabels__name = "IFNG")
#> Artifact(uid='lG3C2TLNTSay7y4q0001', is_latest=True, key='my_datasets/rnaseq1.parquet', suffix='.parquet', kind='dataset', otype='DataFrame', size=8530, hash='RVElCjRCfyOAxbLNDEAMOg', n_observations=3, space_id=1, storage_id=1, run_id=1, schema_id=1, created_by_id=1, created_at=2025-03-20 12:00:17 UTC)

curator <- ln$curators$DataFrameCurator(df_typo, schema)
tryCatch(
  curator$validate(),
  error = function(err) {
    cat(conditionMessage(err))
  }
)
#> • mapping "perturbation" on ULabel.name
#> !   1 term is not validated: 'IFNJ'
#>     → fix typos, remove non-existent values, or save terms via .add_new_from("perturbation")
#> lamindb.errors.ValidationError: 1 term is not validated: 'IFNJ'
#>     → fix typos, remove non-existent values, or save terms via .add_new_from("perturbation")
#> Run `reticulate::py_last_error()` for details.

Manage biological registries

See https://docs.lamin.ai/introduction#manage-biological-registries.

cell_types <- bt$CellType$public()
cell_types
#> PublicOntology
#> Entity: CellType
#> Organism: all
#> Source: cl, 2024-08-16
#> #terms: 2959
cell_types$search("gamma-delta T cell") |> head(2)
#>                                  name
#> CL:0000798         gamma-delta T cell
#> CL:4033072 cycling gamma-delta T cell
#>                                                                definition
#> CL:0000798 A T Cell That Expresses A Gamma-Delta T Cell Receptor Complex.
#> CL:4033072                       A(N) Gamma-Delta T Cell That Is Cycling.
#>                                                                                          synonyms
#> CL:0000798 gamma-delta T-cell|gamma-delta T lymphocyte|gammadelta T cell|gamma-delta T-lymphocyte
#> CL:4033072                                                       proliferating gamma-delta T cell
#>                           parents
#> CL:0000798             CL:0000084
#> CL:4033072 CL:4033069, CL:0000798

var_schema <- ln$Schema(
  name = "my_var_schema",
  itype = bt$Gene$ensembl_gene_id,
  dtype = py_builtins$float
)$save()
obs_schema <- ln$Schema(
  name = "my_obs_schema",
  features = list(
    ln$Feature(name = "perturbation", dtype = ln$ULabel)$save()
  )
)$save()
#> → returning existing Feature record with same name: 'perturbation'
anndata_schema <- ln$Schema(
  name = "my_anndata_schema",
  otype = "AnnData",
  components = list("obs" = obs_schema, "var" = var_schema)
)$save()

library(anndata)
adata <- AnnData(
  df[c("ENSG00000153563", "ENSG00000010610", "ENSG00000170458")],
  obs = df[, "perturbation", drop = FALSE]
)
curator <- ln$curators$AnnDataCurator(adata, anndata_schema)
#> • saving validated records of 'columns'
#> ✓ added 3 records from public with Gene.ensembl_gene_id for "columns": 'ENSG00000170458', 'ENSG00000153563', 'ENSG00000010610'
artifact <- curator$save_artifact(description = "my RNA-seq")
#> ✓ "perturbation" is validated against ULabel.name
#> • path content will be copied to default storage upon `save()` with key `None` ('.lamindb/RK57PCOxp5dRXuQn0000.h5ad')
#> ✓ storing artifact 'RK57PCOxp5dRXuQn0000' at '/tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/RK57PCOxp5dRXuQn0000.h5ad'
#> • parsing feature names of X stored in slot 'var'
#> ✓    3 unique terms (100.00%) are validated for ensembl_gene_id
#> ✓    linked: Schema(uid='shWmUzyuKF0woYYAiG3O', n=3, dtype='float', itype='bionty.Gene', is_type=False, hash='f2UVeHefaZxXFjmUwo9Ozw', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7f75e83dfb60>)
#> • parsing feature names of slot 'obs'
#> ✓    1 unique term (100.00%) is validated for name
#> →    returning existing schema with same hash: Schema(uid='DDujl63nz7wLhd948CgJ', name='my_obs_schema', n=1, itype='Feature', is_type=False, hash='KLviCQkYTAuSlRVPd3yngA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:21 UTC)
#> !    updated otype from None to DataFrame
#> ✓    linked: Schema(uid='DDujl63nz7wLhd948CgJ', name='my_obs_schema', n=1, itype='Feature', is_type=False, otype='DataFrame', hash='KLviCQkYTAuSlRVPd3yngA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:21 UTC)
#> ✓ saved 1 feature set for slot: 'var'
artifact$describe()
#> Artifact .h5ad/AnnData
#> ├── General
#> │   ├── .uid = 'RK57PCOxp5dRXuQn0000'
#> │   ├── .size = 19240
#> │   ├── .hash = 'gO44MDqttaaKNyBLVM-zzA'
#> │   ├── .n_observations = 3
#> │   ├── .path = 
#> │   │   /tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/RK57PCOxp5dRXuQn000
#> │   │   0.h5ad
#> │   ├── .created_by = testuser1 (Test User1)
#> │   ├── .created_at = 2025-03-20 12:00:23
#> │   └── .transform = 'introduction.Rmd'
#> ├── Dataset features/.feature_sets
#> │   ├── var • 3             [bionty.Gene]                                       
#> │   │   CD14                float                                               
#> │   │   CD8A                float                                               
#> │   │   CD4                 float                                               
#> │   └── obs • 1             [Feature]                                           
#> │       perturbation        cat[ULabel]        DMSO, IFNG                       
#> └── Labels
#>     └── .ulabels            ULabel             DMSO, IFNG

genes <- bt$Gene$filter(organism__name = "human")$lookup()
feature_sets <- ln$FeatureSet$filter(genes = genes$cd8a)$all()
ln$Artifact$filter(feature_sets__in = feature_sets)$df()
#>                    uid  key description suffix    kind   otype  size
#> 3 RK57PCOxp5dRXuQn0000 <NA>  my RNA-seq  .h5ad dataset AnnData 19240
#>                     hash n_files n_observations _hash_type _key_is_virtual
#> 3 gO44MDqttaaKNyBLVM-zzA    <NA>              3        md5            TRUE
#>   _overwrite_versions space_id storage_id schema_id version is_latest run_id
#> 3               FALSE        1          1         4    <NA>      TRUE      1
#>            created_at created_by_id _aux _branch_code
#> 3 2025-03-20 12:00:23             1 <NA>            1

neuron <- bt$CellType$from_source(name = "neuron")$save()
#> ✓ created 1 CellType record from Bionty matching name: 'neuron'
#> ✓ created 3 CellType records from Bionty matching ontology_id: 'CL:0002319', 'CL:0000404', 'CL:0000393'
new_cell_state <- bt$CellType(
  name = "my neuron cell state", description = "explains X"
)$save()
new_cell_state$parents$add(neuron)
new_cell_state$view_parents(distance = 2)

Scale learning

See https://docs.lamin.ai/introduction#scale-learning.

df2 <- ln$core$datasets$small_dataset2(otype = "DataFrame")
adata <- AnnData(
  df2[c("ENSG00000153563", "ENSG00000010610", "ENSG00000004468")],
  obs = df2[, "perturbation", drop = FALSE]
)
curator <- ln$curators$AnnDataCurator(adata, anndata_schema)
#> • saving validated records of 'columns'
#> ✓ added 1 record from public with Gene.ensembl_gene_id for "columns": 'ENSG00000004468'
artifact2 <- curator$save_artifact(key = "my_datasets/my_rnaseq2.h5ad")
#> ✓ "perturbation" is validated against ULabel.name
#> • path content will be copied to default storage upon `save()` with key 'my_datasets/my_rnaseq2.h5ad'
#> ✓ storing artifact 'qte7EZLMuzOovNOb0000' at '/tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/qte7EZLMuzOovNOb0000.h5ad'
#> • parsing feature names of X stored in slot 'var'
#> ✓    3 unique terms (100.00%) are validated for ensembl_gene_id
#> ✓    linked: Schema(uid='feEtpDLKPyZB69dA9lc7', n=3, dtype='float', itype='bionty.Gene', is_type=False, hash='QW2rHuIo5-eGNZbRxHMDCw', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7f75f01fd610>)
#> • parsing feature names of slot 'obs'
#> ✓    1 unique term (100.00%) is validated for name
#> →    returning existing schema with same hash: Schema(uid='DDujl63nz7wLhd948CgJ', name='my_obs_schema', n=1, itype='Feature', is_type=False, hash='KLviCQkYTAuSlRVPd3yngA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:21 UTC)
#> !    updated otype from None to DataFrame
#> ✓    linked: Schema(uid='DDujl63nz7wLhd948CgJ', name='my_obs_schema', n=1, itype='Feature', is_type=False, otype='DataFrame', hash='KLviCQkYTAuSlRVPd3yngA', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=2025-03-20 12:00:21 UTC)
#> ✓ saved 1 feature set for slot: 'var'

collection <- ln$Collection(
  list(artifact, artifact2),
  key = "my-RNA-seq-collection"
)$save()
collection$describe()
#> Collection 
#> └── General
#>     ├── .uid = '1Uj13Wi8sJPIgJRr0000'
#>     ├── .key = 'my-RNA-seq-collection'
#>     ├── .hash = 'DbgO9hDdS-KOydwDZDLp3g'
#>     ├── .created_by = testuser1 (Test User1)
#>     ├── .created_at = 2025-03-20 12:00:27
#>     └── .transform = 'introduction.Rmd'
collection$view_lineage()
#> ✖ `view_lineage()` is not yet implemented. Please view the lineage in the web interface.

collection$load()
#> AnnData object with n_obs × n_vars = 6 × 4
#>     obs: 'perturbation', 'artifact_uid'
collection$artifacts$all()
#> <QuerySet [Artifact(uid='RK57PCOxp5dRXuQn0000', is_latest=True, description='my RNA-seq', suffix='.h5ad', kind='dataset', otype='AnnData', size=19240, hash='gO44MDqttaaKNyBLVM-zzA', n_observations=3, space_id=1, storage_id=1, run_id=1, schema_id=4, created_by_id=1, created_at=2025-03-20 12:00:23 UTC), Artifact(uid='qte7EZLMuzOovNOb0000', is_latest=True, key='my_datasets/my_rnaseq2.h5ad', suffix='.h5ad', kind='dataset', otype='AnnData', size=19240, hash='Ti7bcnIOlk0fIt2TFW_VAw', n_observations=3, space_id=1, storage_id=1, run_id=1, schema_id=4, created_by_id=1, created_at=2025-03-20 12:00:26 UTC)]>
collection$artifacts$df()
#>                    uid                         key description suffix    kind
#> 3 RK57PCOxp5dRXuQn0000                        <NA>  my RNA-seq  .h5ad dataset
#> 4 qte7EZLMuzOovNOb0000 my_datasets/my_rnaseq2.h5ad        <NA>  .h5ad dataset
#>     otype  size                   hash n_files n_observations _hash_type
#> 3 AnnData 19240 gO44MDqttaaKNyBLVM-zzA    <NA>              3        md5
#> 4 AnnData 19240 Ti7bcnIOlk0fIt2TFW_VAw    <NA>              3        md5
#>   _key_is_virtual _overwrite_versions space_id storage_id schema_id version
#> 3            TRUE               FALSE        1          1         4    <NA>
#> 4            TRUE               FALSE        1          1         4    <NA>
#>   is_latest run_id          created_at created_by_id _aux _branch_code
#> 3      TRUE      1 2025-03-20 12:00:23             1 <NA>            1
#> 4      TRUE      1 2025-03-20 12:00:26             1 <NA>            1

Other examples

This section contains some additional examples of using {laminr}.

Download a dataset form another instance

This examples shows how to get an artifact from another instance (“laminlabs/cellxgene”).

artifact <- ln$Artifact$using("laminlabs/cellxgene")$get("7dVluLROpalzEh8mNyxk")
artifact
#> Artifact(uid='7dVluLROpalzEh8mNyxk', version='2023-12-15', is_latest=True, key='cell-census/2023-12-15/h5ads/02faf712-92d4-4589-bec7-13105059cf86.h5ad', description='Renal cell carcinoma, pre aPD1, kidney Puck_200727_12', suffix='.h5ad', otype='AnnData', size=13997860, hash='YNYuokfAoDFxdaRILjmU9w', n_observations=17612, space_id=1, storage_id=2, run_id=22, created_by_id=1, created_at=2024-01-11 09:13:23 UTC)

Tip

You can view detailed information about this dataset on LaminHub: https://lamin.ai/laminlabs/cellxgene/artifact/7dVluLROpalzEh8mNyxk.

You can search and query more CELLxGENE datasets here: https://lamin.ai/laminlabs/cellxgene/artifacts.

To download the dataset and load it into memory, run:

adata <- artifact$load()
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synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 97.5%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 98.0%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 98.5%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 98.9%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 99.4%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 99.9%... synchronizing 02faf712-92d4-4589-bec7-13105059cf86.h5ad: 100.0%→ completing transfer to track Artifact('7dVluLRO') as input
#> ✓ loaded 4 Gene records matching ensembl_gene_id: 'ENSG00000010610', 'ENSG00000153563', 'ENSG00000004468', 'ENSG00000170458'
#> ✓ created 23144 Gene records from Bionty matching ensembl_gene_id: 'ENSG00000000003', 'ENSG00000000005', 'ENSG00000000419', 'ENSG00000000457', 'ENSG00000000460', 'ENSG00000000938', 'ENSG00000000971', 'ENSG00000001036', 'ENSG00000001084', 'ENSG00000001167', 'ENSG00000001460', 'ENSG00000001461', 'ENSG00000001497', 'ENSG00000001561', 'ENSG00000001617', 'ENSG00000001626', 'ENSG00000001629', 'ENSG00000001630', 'ENSG00000001631', 'ENSG00000002016', ...
#> ! did not create Gene records for 106 non-validated ensembl_gene_ids: 'ENSG00000112096', 'ENSG00000182230', 'ENSG00000214783', 'ENSG00000215067', 'ENSG00000215271', 'ENSG00000221995', 'ENSG00000223797', 'ENSG00000224167', 'ENSG00000224739', 'ENSG00000225205', 'ENSG00000226380', 'ENSG00000226403', 'ENSG00000226747', 'ENSG00000226849', 'ENSG00000227220', 'ENSG00000227902', 'ENSG00000227925', 'ENSG00000228135', 'ENSG00000228139', 'ENSG00000228434', ...
#> • saving 106 new Gene records
#> ✓ 23254 unique terms (100.00%) are validated for ensembl_gene_id
#> • saving var schema: Schema(uid='iLR9Nz5YoNCvG2unsvmO', n=23254, dtype='num', itype='bionty.Gene', is_type=False, hash='EmfFYuBgOpnsSg5-Gw8mzg', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7f75b6b8bb90>)
#> • saving 11 new Feature records
#> ✓ 11 unique terms (100.00%) are validated for name
#> • saving obs schema: Schema(uid='WSGJL6nFKEAs3nd04Lde', n=11, itype='Feature', is_type=False, hash='FqF5x0w1rGBjZBGzh-QrHg', minimal_set=True, ordered_set=False, maximal_set=False, space_id=1, created_by_id=1, run_id=1, created_at=<django.db.models.expressions.DatabaseDefault object at 0x7f75f350f140>)
#> ✓ created 1 Tissue record from Bionty matching ontology_id: 'UBERON:0002113'
#> ✓ created 4 CellType records from Bionty matching ontology_id: 'CL:0000057', 'CL:0000763', 'CL:0001064', 'CL:0000066'
#> ✓ created 1 Disease record from Bionty matching ontology_id: 'MONDO:0005086'
#> ! did not create Phenotype record for 1 non-validated ontology_id: 'PATO:0000384'
#> ✓ created 1 ExperimentalFactor record from Bionty matching ontology_id: 'EFO:0030062'
#> ✓ created 1 DevelopmentalStage record from Bionty matching ontology_id: 'HsapDv:0000155'
#> ! did not create Ethnicity record for 1 non-validated ontology_id: 'unknown'
#> → mapped records: Organism(uid='1dpCL6Td'), Tissue(uid='2G2vSfyI'), CellType(uid='zQ4dyjEs'), CellType(uid='3VVi1D5u'), CellType(uid='bCXfFlJw'), CellType(uid='68LNvDH7'), Disease(uid='6XyVixMt'), ExperimentalFactor(uid='3y4ujHIQ'), DevelopmentalStage(uid='3nAjrqHq')
#> → transferred records: Artifact(uid='7dVluLROpalzEh8mNyxk'), Storage(uid='oIYGbD74'), Gene(uid='NbChbRuBbXnl'), Gene(uid='27tZaPokT674'), Gene(uid='4smSEXwJNay0'), Gene(uid='1aDv9cvtn3BZ'), Gene(uid='HZhdRBnJbeP6'), Gene(uid='73SZBKmzt7lg'), Gene(uid='67vS5sia9YAX'), Gene(uid='1wkMFOQjySDs'), Gene(uid='40MdF8JwA60p'), Gene(uid='598AjyY8Xd1u'), Gene(uid='2nh0YakbLxKd'), Gene(uid='7E8prJzZ9jMh'), Gene(uid='6P2BiY2s4ASZ'), Gene(uid='1f5Of53befjT'), Gene(uid='3EYo3R8AjbQg'), Gene(uid='QjnxkVHIr1Ij'), Gene(uid='3qZYRRuwl3YL'), Gene(uid='236m5u2AMkhY'), Gene(uid='4RczhUOCrnOD'), Gene(uid='3CnP9gFywXaE'), Gene(uid='30ur2TZPVGv7'), Gene(uid='3V1CnJ2Fseyc'), Gene(uid='vxbSKKBKrsBO'), Gene(uid='2jsU0E62tqwB'), Gene(uid='6ISLSbFGwyk6'), Gene(uid='1GkgCFYJJy9F'), Gene(uid='5R9FACMwDOuS'), Gene(uid='3qx6oJBJQcoT'), Gene(uid='1LFTwWWNyI2m'), Gene(uid='3t7rvzK4tKJM'), Gene(uid='5PnNY5Bb5bX2'), Gene(uid='1im6wZLBlUnf'), Gene(uid='6GF5Nw8yiGZ6'), Gene(uid='26x3VcTpb37b'), Gene(uid='6WPJfGaJ6I2K'), Gene(uid='54osXz3mbLfO'), Gene(uid='PieYmCuqL9b9'), Gene(uid='1puLmjfZj6jY'), Gene(uid='7BluZsjfwk0L'), Gene(uid='2H08Uhtcozo5'), Gene(uid='6xJtd5vC87wd'), Gene(uid='5aOxOXlTVvCO'), Gene(uid='2Atr2o6JqTHT'), Gene(uid='71XY3tPwlxpm'), Gene(uid='7HKWa8xrNwYQ'), Gene(uid='6ww18Aoa8NvY'), Gene(uid='6hqVSxvLfWCF'), Gene(uid='4FuXquue03Hn'), Gene(uid='2Pc750jQeBuK'), Gene(uid='Glj5taA0hGPx'), Gene(uid='6vc8rdUUMmld'), Gene(uid='72ltoOhwLawN'), Gene(uid='3Eua9MMuSc7b'), Gene(uid='49tFqEGzZxEA'), Gene(uid='5CXTEzmoiLcy'), Gene(uid='3HQmf8BDmyZz'), Gene(uid='1MqkPoqIZyja'), Gene(uid='7hHsqornqYqm'), Gene(uid='5HgW4NFjMdmP'), Gene(uid='4CNeHFIbFYJp'), Gene(uid='54uqltj9PgmN'), Gene(uid='25w9R5SKYOPa'), Gene(uid='vbsp1jbONvaj'), Gene(uid='4fy7ugwTYoK2'), Gene(uid='7JTFD3eAKNwJ'), Gene(uid='3G5b976chIMW'), Gene(uid='5ILZRV3F2hQ8'), Gene(uid='5IyCVjNwe3KV'), Gene(uid='krBYTJVPPY8i'), Gene(uid='5SZpPLVNTEDG'), Gene(uid='3Xqm80mYkh5Y'), Gene(uid='1YlQCYwfzatt'), Gene(uid='6coRfQYdTrD5'), Gene(uid='3voCQ3mO93Dz'), Gene(uid='1cd6Y5pWUbGa'), Gene(uid='7QlnxzcIyTsH'), Gene(uid='3OlyzhssKUAs'), Gene(uid='3eye2i3WNo2W'), Gene(uid='b1ySEMZRygiq'), Gene(uid='7XEXNRYrip1K'), Gene(uid='16cPRk98uWw7'), Gene(uid='1vUzQajeLVXl'), Gene(uid='1AeALrc2RYWe'), Gene(uid='3QtuLAJ4WKTm'), Gene(uid='5epqKbkXe2L4'), Gene(uid='62ALzDyEBXZ2'), Gene(uid='vH3HqvdAmYEH'), Gene(uid='3jeJWoShxFPU'), Gene(uid='75LIdg8d9hyZ'), Gene(uid='1MKaICkQ5s1E'), Gene(uid='13HachjLhtmx'), Gene(uid='7HbRENxcjVqJ'), Gene(uid='3idJBhAL00Gz'), Gene(uid='3JGvVj5QXMC3'), Gene(uid='mXaotlsHZVD0'), Gene(uid='BiUbHmbv2Uj5'), Gene(uid='1IUJQn5dMDcf'), Gene(uid='7QjSTvCCizol'), Gene(uid='7R4IinTXfYei'), Gene(uid='5okKUj47ZCje'), Gene(uid='5G38FFUbcL9r'), Gene(uid='4Ugk6Nyn58Px'), Gene(uid='38QEO33snTlq'), Gene(uid='3pqjpguuIOFN'), Gene(uid='1Au9DTbBtQl9'), Gene(uid='1Bk5vasv9RUY'), Feature(uid='MB1hKtGaQiqV'), Feature(uid='69RQ1pVPEo3o'), Feature(uid='tSNzi7e6BWnu'), Feature(uid='CBDJHomBrUIJ'), Feature(uid='Up1fqy1APH21'), Feature(uid='UnTz4XK8XGd0'), Feature(uid='xbMGSIMHV67J'), Feature(uid='DbVAV4BaQXAh'), Feature(uid='Xcti9JkM9uvG'), Feature(uid='9s20Imh64bYU'), Feature(uid='wCbvLeKe2rv8'), Reference(uid='XVqpeeo4rOGh'), Phenotype(uid='kniZxLLf'), Ethnicity(uid='5HWRj1OD'), ULabel(uid='dFJfE04f'), ULabel(uid='EhmpAjJO'), ULabel(uid='JJ3d8a2v'), ULabel(uid='hCKrqeRC')
#> • adding artifact ids [5] as inputs for run 1, adding parent transform 2
adata
#> AnnData object with n_obs × n_vars = 17612 × 23254
#>     obs: 'n_genes', 'n_UMIs', 'log10_n_UMIs', 'log10_n_genes', 'Cell_Type', 'cell_type_ontology_term_id', 'organism_ontology_term_id', 'tissue_ontology_term_id', 'assay_ontology_term_id', 'disease_ontology_term_id', 'self_reported_ethnicity_ontology_term_id', 'development_stage_ontology_term_id', 'sex_ontology_term_id', 'donor_id', 'is_primary_data', 'suspension_type', 'cell_type', 'assay', 'disease', 'organism', 'sex', 'tissue', 'self_reported_ethnicity', 'development_stage'
#>     var: 'gene', 'n_beads', 'n_UMIs', 'feature_is_filtered', 'feature_name', 'feature_reference', 'feature_biotype'
#>     uns: 'Cell_Type_colors', 'schema_version', 'title'
#>     obsm: 'X_spatial'

This artifact contains an AnnData object.

Tip

If you prefer a path to a local file or folder, call path <- artifact$cache().

Work with the dataset

Once you have loaded a dataset you can perform any analysis with it as you would normally. Here, marker genes are calculated for each of the provided cell type labels using {Seurat}.

library(Seurat)
#> Loading required package: SeuratObject
#> Loading required package: sp
#> 'SeuratObject' was built under R 4.4.0 but the current version is
#> 4.4.3; it is recomended that you reinstall 'SeuratObject' as the ABI
#> for R may have changed
#> 'SeuratObject' was built with package 'Matrix' 1.7.1 but the current
#> version is 1.7.2; it is recomended that you reinstall 'SeuratObject' as
#> the ABI for 'Matrix' may have changed
#> 
#> Attaching package: 'SeuratObject'
#> The following object is masked from 'package:anndata':
#> 
#>     Layers
#> The following objects are masked from 'package:base':
#> 
#>     intersect, t

# Create a Seurat object
seurat_obj <- CreateSeuratObject(
  counts = as(Matrix::t(adata$X), "CsparseMatrix"),
  meta.data = adata$obs
)
# Add gene metadata
seurat_obj[["RNA"]] <- AddMetaData(
  GetAssay(seurat_obj), adata$var
)
# Set cell identities to the provided cell type annotation
Idents(seurat_obj) <- "cell_type"
# Normalise the data
seurat_obj <- NormalizeData(seurat_obj)
#> Normalizing layer: counts
# Test for marker genes (the output is a data.frame)
markers <- FindAllMarkers(
  seurat_obj,
  features = Features(seurat_obj)[1:100] # Only test a few features for speed
)
#> Calculating cluster fibroblast
#> For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
#> (default method for FindMarkers) please install the presto package
#> --------------------------------------------
#> install.packages('devtools')
#> devtools::install_github('immunogenomics/presto')
#> --------------------------------------------
#> After installation of presto, Seurat will automatically use the more 
#> efficient implementation (no further action necessary).
#> This message will be shown once per session
#> Calculating cluster epithelial cell
#> Calculating cluster myeloid cell
#> Calculating cluster malignant cell
#> Warning: The following tests were not performed:
#> Warning: When testing epithelial cell versus all:
#>  Cell group 1 has fewer than 3 cells
# Display the marker genes
knitr::kable(markers)

	p_val	avg_log2FC	pct.1	pct.2	p_val_adj	cluster	gene
ENSG00000147113	0.0000001	1.8228103	0.019	0.005	0.0011654	fibroblast	ENSG00000147113
ENSG00000170004	0.0000002	2.5663044	0.021	0.006	0.0036485	fibroblast	ENSG00000170004
ENSG00000196139	0.0000003	-0.8318130	0.053	0.110	0.0058749	fibroblast	ENSG00000196139
ENSG00000132170	0.0006719	1.7151510	0.020	0.009	1.0000000	fibroblast	ENSG00000132170
ENSG00000205542	0.0007360	0.6442683	0.230	0.195	1.0000000	fibroblast	ENSG00000205542
ENSG00000163536	0.0025157	1.8914687	0.012	0.004	1.0000000	fibroblast	ENSG00000163536
ENSG00000067064	0.0063090	1.2509162	0.014	0.006	1.0000000	fibroblast	ENSG00000067064
ENSG00000105855	0.0068491	-0.5412708	0.022	0.041	1.0000000	fibroblast	ENSG00000105855
ENSG00000205542.1	0.0000002	1.3623005	0.310	0.195	0.0046479	myeloid cell	ENSG00000205542
ENSG00000196139.1	0.0015658	-0.5898982	0.040	0.108	1.0000000	myeloid cell	ENSG00000196139
ENSG00000196139.2	0.0000000	0.7939631	0.111	0.050	0.0000224	malignant cell	ENSG00000196139
ENSG00000205542.2	0.0000001	-0.8585382	0.193	0.247	0.0013456	malignant cell	ENSG00000205542
ENSG00000147113.1	0.0000018	-1.4976270	0.005	0.016	0.0415774	malignant cell	ENSG00000147113
ENSG00000170004.1	0.0000073	-2.2898276	0.006	0.018	0.1686987	malignant cell	ENSG00000170004
ENSG00000105855.1	0.0003828	0.7197716	0.041	0.019	1.0000000	malignant cell	ENSG00000105855
ENSG00000053371	0.0038080	0.8347505	0.029	0.014	1.0000000	malignant cell	ENSG00000053371
ENSG00000141385	0.0058269	1.0575502	0.019	0.007	1.0000000	malignant cell	ENSG00000141385
ENSG00000132170.1	0.0072852	-1.3878878	0.009	0.017	1.0000000	malignant cell	ENSG00000132170
ENSG00000163536.1	0.0076905	-1.8629158	0.004	0.010	1.0000000	malignant cell	ENSG00000163536

# Plot the marker genes
DotPlot(seurat_obj, features = unique(markers$gene)) +
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5))
#> Warning: Scaling data with a low number of groups may produce misleading
#> results

Slice a TileDB-SOMA array store

When artifacts contain TileDB-SOMA array stores they can be opened and sliced using the {tiledbsoma} package.

# Set some environment variables to avoid an issue with {tiledbsoma}
# https://github.com/chanzuckerberg/cellxgene-census/issues/1261
Sys.setenv(TILEDB_VFS_S3_REGION = "us-west-2")
Sys.setenv(AWS_DEFAULT_REGION = "us-west-2")
Sys.setenv(TILEDB_VFS_S3_NO_SIGN_REQUEST = "true")

# Define a filter to select specific cells
value_filter <- paste(
  "tissue == 'brain' &&",
  "cell_type %in% c('microglial cell', 'neuron') &&",
  "suspension_type == 'cell' &&",
  "assay == '10x 3\\' v3'"
)

# Get the artifact containing the CELLxGENE Census TileDB-SOMA store
census_artifact <- ln$Artifact$using("laminlabs/cellxgene")$get("FYMewVq5twKMDXVy0001")
# Open the SOMACollection
soma_collection <- census_artifact$open()
#> → completing transfer to track Artifact('FYMewVq5') as input
#> → mapped records: 
#> → transferred records: Artifact(uid='FYMewVq5twKMDXVy0001')
#> • adding artifact ids [6] as inputs for run 1, adding parent transform 2
# Slice the store to get a SOMADataFrame containing metadata for the cells of interest
cell_metadata <- soma_collection$get("census_data")$get("homo_sapiens")$obs$read(value_filter = value_filter)
# Concatenate the results to an arrow::Table
cell_metadata <- cell_metadata$concat()
# Convert to a data.frame
cell_metadata <- cell_metadata$to_data_frame()

cell_metadata
#> # A tibble: 66,418 × 28
#>    soma_joinid dataset_id                 assay assay_ontology_term_id cell_type
#>          <int> <fct>                      <fct> <fct>                  <fct>    
#>  1    48182177 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  2    48182178 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  3    48182185 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  4    48182187 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  5    48182188 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  6    48182189 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  7    48182190 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  8    48182191 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#>  9    48182192 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#> 10    48182194 c888b684-6c51-431f-972a-6… 10x … EFO:0009922            microgli…
#> # ℹ 66,408 more rows
#> # ℹ 23 more variables: cell_type_ontology_term_id <fct>,
#> #   development_stage <fct>, development_stage_ontology_term_id <fct>,
#> #   disease <fct>, disease_ontology_term_id <fct>, donor_id <fct>,
#> #   is_primary_data <lgl>, observation_joinid <chr>,
#> #   self_reported_ethnicity <fct>,
#> #   self_reported_ethnicity_ontology_term_id <fct>, sex <fct>, …

Save the results

Save results as new artifacts to the current LaminDB instance.

seurat_path <- tempfile(fileext = ".rds")
saveRDS(seurat_obj, seurat_path)

ln$Artifact$from_df(
  markers,
  key = "laminr-datasets/renal-cell-carcinoma-markers.parquet",
  description = "Marker genes for renal cell carcinoma dataset"
)$save()
#> • path content will be copied to default storage upon `save()` with key 'laminr-datasets/renal-cell-carcinoma-markers.parquet'
#> ✓ storing artifact '2Er38PcBpll58Bvc0000' at '/tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/2Er38PcBpll58Bvc0000.parquet'
#> Artifact(uid='2Er38PcBpll58Bvc0000', is_latest=True, key='laminr-datasets/renal-cell-carcinoma-markers.parquet', description='Marker genes for renal cell carcinoma dataset', suffix='.parquet', kind='dataset', otype='DataFrame', size=6456, hash='7evC4QdJjcvCSMKhAf5XMQ', n_observations=19, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-03-20 12:08:49 UTC)

ln$Artifact(
  seurat_path,
  key = "laminr-datasets/renal-cell-carcinoma.rds",
  description = "Seurat object for renal cell carcinoma dataset"
)$save()
#> • path content will be copied to default storage upon `save()` with key 'laminr-datasets/renal-cell-carcinoma.rds'
#> ✓ storing artifact 'sAVFIXGscjKU1Pod0000' at '/tmp/RtmppGs707/laminr-intro-20250320115956/.lamindb/sAVFIXGscjKU1Pod0000.rds'
#> Artifact(uid='sAVFIXGscjKU1Pod0000', is_latest=True, key='laminr-datasets/renal-cell-carcinoma.rds', description='Seurat object for renal cell carcinoma dataset', suffix='.rds', size=20908120, hash='foAxaMadrnwKmDQ8RifbKw', space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-03-20 12:08:50 UTC)

Finish tracking

Mark the analysis run as finished to create a time stamp and upload source code to the hub.

ln$finish()
#> ! no html report found; to attach one, create an .html export for your .Rmd file and then run: lamin save introduction.Rmd
#> → finished Run('q8rqesKD') after 8m at 2025-03-20 12:08:50 UTC

Save a notebook report (not needed for `.R` scripts)

Save a run report of your notebook (.Rmd or .qmd file) to your instance:

Render the notebook to HTML

In RStudio, click the “Knit” button

OR From the command line, run:

Rscript -e 'rmarkdown::render("introduction.Rmd")'

OR Use the rmarkdown package in R:
```
rmarkdown::render("introduction.Rmd")
```

Save it to your LaminDB instance:

Using the lamin_save() function in R:

lamin_save("introduction.Rmd")

OR Using the lamin CLI:

lamin save introduction.Rmd

Design

See https://docs.lamin.ai/introduction#design for more information on the design of LaminDB.