Jupyter Notebook

Validate & register bulk RNA-seq datasets#

Bulk RNA sequencing (RNA-seq) is a high-throughput technique that measures the gene expression levels of thousands of genes simultaneously, providing insights into overall cellular transcription patterns.

Here, we’ll demonstrate how to make a bulk RNA-seq count matrix data ware house ready and apply the TVR (transform, validate, register) workflow on it.

Setup#

!lamin init --storage test-bulkrna --schema bionty
Hide code cell output 
💡 creating schemas: core==0.47.5 bionty==0.30.4 
✅ saved: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:07:18)
✅ saved: Storage(id='mmsUAjmY', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna', type='local', updated_at=2023-09-06 17:07:18, created_by_id='DzTjkKse')

✅ loaded instance: testuser1/test-bulkrna
💡 did not register local instance on hub (if you want, call `lamin register`)


import lamindb as ln
from pathlib import Path
import lnschema_bionty as lb
import pandas as pd
import anndata as ad

✅ loaded instance: testuser1/test-bulkrna (lamindb 0.52.2)

Access #

We start by simulating a nf-core RNA-seq run which yields us a count matrix file.

(See Track Nextflow workflows for running this with Nextflow.)

# pretend we're running a bulk RNA-seq pipeline
ln.track(ln.Transform(name="nf-core RNA-seq", reference="https://nf-co.re/rnaseq"))
# create a directory for its output
Path("./test-bulkrna/output_dir").mkdir(exist_ok=True)
# get the count matrix
path = ln.dev.datasets.file_tsv_rnaseq_nfcore_salmon_merged_gene_counts(
    populate_registries=True
)
# move it into the output directory
path = path.rename(f"./test-bulkrna/output_dir/{path.name}")
# register it
ln.File(path, description="Merged Bulk RNA counts").save()
Hide code cell output 
✅ saved: Transform(id='Ug77SqHFdLKKcc', name='nf-core RNA-seq', type=notebook, reference='https://nf-co.re/rnaseq', updated_at=2023-09-06 17:07:20, created_by_id='DzTjkKse')

✅ saved: Run(id='vqS3oS1JHwjKPDqdQR4i', run_at=2023-09-06 17:07:20, transform_id='Ug77SqHFdLKKcc', created_by_id='DzTjkKse')

❗ file has more than one suffix (path.suffixes), using only last suffix: '.tsv'

💡 file in storage 'test-bulkrna' with key 'output_dir/salmon.merged.gene_counts.tsv'

Transform #

ln.track()

💡 notebook imports: anndata==0.9.2 lamindb==0.52.2 lnschema_bionty==0.30.4 pandas==1.5.3

❗ record with similar name exist! did you mean to load it?
id __ratio__
name
nf-core RNA-seq Ug77SqHFdLKKcc 85.5 
✅ saved: Transform(id='s5V0dNMVwL9iz8', name='Validate & register bulk RNA-seq datasets', short_name='bulkrna', version='0', type=notebook, updated_at=2023-09-06 17:07:26, created_by_id='DzTjkKse')

✅ saved: Run(id='b8QCUEVz2wApHJozC6Ip', run_at=2023-09-06 17:07:26, transform_id='s5V0dNMVwL9iz8', created_by_id='DzTjkKse')

Let’s query the file:

file = ln.File.filter(description="Merged Bulk RNA counts").one()

df = file.load()

💡 adding file gb9IBDEaYz037H5YVT4E as input for run b8QCUEVz2wApHJozC6Ip, adding parent transform Ug77SqHFdLKKcc

If we look at it, we realize it deviates far from the tidy data standard Wickham14, conventions of statistics & machine learning Hastie09, Murphy12 and the major Python & R data packages.

Variables are not in columns and observations are not in rows:

df
gene_id gene_name RAP1_IAA_30M_REP1 RAP1_UNINDUCED_REP1 RAP1_UNINDUCED_REP2 WT_REP1 WT_REP2
0 Gfp_transgene_gene Gfp_transgene_gene 0.0 0.000 0.0 0.0 0.0
1 HRA1 HRA1 0.0 8.572 0.0 0.0 0.0
2 snR18 snR18 3.0 8.000 4.0 8.0 8.0
3 tA(UGC)A TGA1 0.0 0.000 0.0 0.0 0.0
4 tL(CAA)A SUP56 0.0 0.000 0.0 0.0 0.0
... ... ... ... ... ... ... ...
120 YAR064W YAR064W 0.0 2.000 0.0 0.0 0.0
121 YAR066W YAR066W 3.0 13.000 8.0 5.0 11.0
122 YAR068W YAR068W 9.0 28.000 24.0 5.0 7.0
123 YAR069C YAR069C 0.0 0.000 0.0 0.0 1.0
124 YAR070C YAR070C 0.0 0.000 0.0 0.0 0.0

125 rows × 7 columns

Let’s change that and move observations into rows:

df = df.T

df
0 1 2 3 4 5 6 7 8 9 ... 115 116 117 118 119 120 121 122 123 124
gene_id Gfp_transgene_gene HRA1 snR18 tA(UGC)A tL(CAA)A tP(UGG)A tS(AGA)A YAL001C YAL002W YAL003W ... YAR050W YAR053W YAR060C YAR061W YAR062W YAR064W YAR066W YAR068W YAR069C YAR070C
gene_name Gfp_transgene_gene HRA1 snR18 TGA1 SUP56 TRN1 tS(AGA)A TFC3 VPS8 EFB1 ... FLO1 YAR053W YAR060C YAR061W YAR062W YAR064W YAR066W YAR068W YAR069C YAR070C
RAP1_IAA_30M_REP1 0.0 0.0 3.0 0.0 0.0 0.0 1.0 55.0 36.0 632.0 ... 4.357 0.0 1.0 0.0 1.0 0.0 3.0 9.0 0.0 0.0
RAP1_UNINDUCED_REP1 0.0 8.572 8.0 0.0 0.0 0.0 0.0 72.0 33.0 810.0 ... 15.72 0.0 0.0 0.0 3.0 2.0 13.0 28.0 0.0 0.0
RAP1_UNINDUCED_REP2 0.0 0.0 4.0 0.0 0.0 0.0 0.0 115.0 82.0 1693.0 ... 13.772 0.0 4.0 0.0 2.0 0.0 8.0 24.0 0.0 0.0
WT_REP1 0.0 0.0 8.0 0.0 0.0 1.0 0.0 60.0 63.0 1115.0 ... 13.465 0.0 0.0 0.0 1.0 0.0 5.0 5.0 0.0 0.0
WT_REP2 0.0 0.0 8.0 0.0 0.0 0.0 0.0 30.0 25.0 704.0 ... 6.891 0.0 1.0 0.0 0.0 0.0 11.0 7.0 1.0 0.0

7 rows × 125 columns

Now, it’s clear that the first two rows are in fact no observations, but descriptions of the variables (or features) themselves.

Let’s create an AnnData object to model this. First, create a dataframe for the variables:

var = pd.DataFrame({"gene_name": df.loc["gene_name"].values}, index=df.loc["gene_id"])

var.head()
gene_name
gene_id
Gfp_transgene_gene Gfp_transgene_gene
HRA1 HRA1
snR18 snR18
tA(UGC)A TGA1
tL(CAA)A SUP56

Now, let’s create an AnnData:

# we're also fixing the datatype here, which was string in the tsv
adata = ad.AnnData(df.iloc[2:].astype("float32"), var=var)

adata

AnnData object with n_obs × n_vars = 5 × 125
    var: 'gene_name'

The AnnData object is in tidy form and complies with conventions of statistics and machine learning:

adata.to_df()
gene_id Gfp_transgene_gene HRA1 snR18 tA(UGC)A tL(CAA)A tP(UGG)A tS(AGA)A YAL001C YAL002W YAL003W ... YAR050W YAR053W YAR060C YAR061W YAR062W YAR064W YAR066W YAR068W YAR069C YAR070C
RAP1_IAA_30M_REP1 0.0 0.000 3.0 0.0 0.0 0.0 1.0 55.0 36.0 632.0 ... 4.357 0.0 1.0 0.0 1.0 0.0 3.0 9.0 0.0 0.0
RAP1_UNINDUCED_REP1 0.0 8.572 8.0 0.0 0.0 0.0 0.0 72.0 33.0 810.0 ... 15.720 0.0 0.0 0.0 3.0 2.0 13.0 28.0 0.0 0.0
RAP1_UNINDUCED_REP2 0.0 0.000 4.0 0.0 0.0 0.0 0.0 115.0 82.0 1693.0 ... 13.772 0.0 4.0 0.0 2.0 0.0 8.0 24.0 0.0 0.0
WT_REP1 0.0 0.000 8.0 0.0 0.0 1.0 0.0 60.0 63.0 1115.0 ... 13.465 0.0 0.0 0.0 1.0 0.0 5.0 5.0 0.0 0.0
WT_REP2 0.0 0.000 8.0 0.0 0.0 0.0 0.0 30.0 25.0 704.0 ... 6.891 0.0 1.0 0.0 0.0 0.0 11.0 7.0 1.0 0.0

5 rows × 125 columns

Validate #

Let’s create a File object from this AnnData. Because this will validate the gene IDs and these are only defined given a species, we have to set a species context:

lb.settings.species = "saccharomyces cerevisiae"

Almost all gene IDs are validated:

genes = lb.Gene.from_values(adata.var.index, lb.Gene.stable_id)





✅ created 123 Gene records from Bionty matching stable_id: 'HRA1', 'snR18', 'tA(UGC)A', 'tL(CAA)A', 'tP(UGG)A', 'tS(AGA)A', 'YAL001C', 'YAL002W', 'YAL003W', 'YAL004W', 'YAL005C', 'YAL007C', 'YAL008W', 'YAL009W', 'YAL010C', 'YAL011W', 'YAL012W', 'YAL013W', 'YAL014C', 'YAL015C', ...





❗ did not create Gene records for 2 non-validated stable_ids: 'Gfp_transgene_gene', 'YAR062W'













# also register the 2 non-validated genes obtained from Bionty
ln.save(genes)













assays = lb.ExperimentalFactor.lookup()
assays.rna_seq









ExperimentalFactor(id='J0KT2nxy', name='RNA-Seq', ontology_id='EFO:0008896', description='Rna-Seq Is A Method That Involves Purifying Rna And Making Cdna, Followed By High-Throughput Sequencing.', molecule='RNA assay', instrument='assay by high throughput sequencer', updated_at=2023-09-06 17:07:21, bionty_source_id='9jlT', created_by_id='DzTjkKse')













Register #






curated_file = ln.File.from_anndata(
    adata, description="Curated bulk RNA counts", field=lb.Gene.stable_id
)









💡 file will be copied to default storage upon `save()` with key `None` ('.lamindb/EB8sohovIwZSS0NUknc9.h5ad')





💡 parsing feature names of X stored in slot 'var'





✅    123 terms (98.40%) are validated for stable_id





❗    2 terms (1.60%) are not validated for stable_id: Gfp_transgene_gene, YAR062W





✅    linked: FeatureSet(id='3A5lz691043XEAjlGD9p', n=123, type='number', registry='bionty.Gene', hash='8j8y_AHnWb5huZ2hXCDj', created_by_id='DzTjkKse')









Hence, let’s save this file:






curated_file.save()









✅ saved 1 feature set for slot: 'var'





✅ storing file 'EB8sohovIwZSS0NUknc9' at '.lamindb/EB8sohovIwZSS0NUknc9.h5ad'













features = ln.Feature.lookup()
curated_file.add_labels(assays.rna_seq, features.assay)
curated_file.add_labels(lb.settings.species, features.species)









✅ linked new feature 'assay' together with new feature set FeatureSet(id='6LV3sU3QelZDkn96zb6V', n=1, registry='core.Feature', hash='kP783F9lXSkcRi6OuPt2', updated_at=2023-09-06 17:07:28, modality_id='l3JmrZxw', created_by_id='DzTjkKse')





💡 no file links to it anymore, deleting feature set FeatureSet(id='6LV3sU3QelZDkn96zb6V', n=1, registry='core.Feature', hash='kP783F9lXSkcRi6OuPt2', updated_at=2023-09-06 17:07:28, modality_id='l3JmrZxw', created_by_id='DzTjkKse')





✅ linked new feature 'species' together with new feature set FeatureSet(id='mwSgS2LxAotCfepnDtay', n=2, registry='core.Feature', hash='qpGsDqo6CsCc8akr6kRl', updated_at=2023-09-06 17:07:28, modality_id='l3JmrZxw', created_by_id='DzTjkKse')













curated_file.describe()









💡 File(id='EB8sohovIwZSS0NUknc9', suffix='.h5ad', accessor='AnnData', description='Curated bulk RNA counts', size=28180, hash='6bieh8XjOCCz6bJToN4u1g', hash_type='md5', updated_at=2023-09-06 17:07:28)

Provenance:
  🗃️ storage: Storage(id='mmsUAjmY', root='/home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna', type='local', updated_at=2023-09-06 17:07:18, created_by_id='DzTjkKse')
  💫 transform: Transform(id='s5V0dNMVwL9iz8', name='Validate & register bulk RNA-seq datasets', short_name='bulkrna', version='0', type=notebook, updated_at=2023-09-06 17:07:28, created_by_id='DzTjkKse')
  👣 run: Run(id='b8QCUEVz2wApHJozC6Ip', run_at=2023-09-06 17:07:26, transform_id='s5V0dNMVwL9iz8', created_by_id='DzTjkKse')
  👤 created_by: User(id='DzTjkKse', handle='testuser1', email='testuser1@lamin.ai', name='Test User1', updated_at=2023-09-06 17:07:18)
Features:
  var: FeatureSet(id='3A5lz691043XEAjlGD9p', n=123, type='number', registry='bionty.Gene', hash='8j8y_AHnWb5huZ2hXCDj', updated_at=2023-09-06 17:07:28, created_by_id='DzTjkKse')
    'OAF1', 'TRN1', 'None', 'None', 'CYS3', 'ACS1', 'RFA1', 'None', 'None', 'None', ...
  external: FeatureSet(id='mwSgS2LxAotCfepnDtay', n=2, registry='core.Feature', hash='qpGsDqo6CsCc8akr6kRl', updated_at=2023-09-06 17:07:28, modality_id='l3JmrZxw', created_by_id='DzTjkKse')
    🔗 species (1, bionty.Species): 'saccharomyces cerevisiae'
    🔗 assay (1, bionty.ExperimentalFactor): 'RNA-Seq'
Labels:
  🏷️ species (1, bionty.Species): 'saccharomyces cerevisiae'
  🏷️ experimental_factors (1, bionty.ExperimentalFactor): 'RNA-Seq'















Example queries#


We have two files in the file registry:






ln.File.filter().df()













  
    

      
      storage_id
      key
      suffix
      accessor
      description
      version
      size
      hash
      hash_type
      transform_id
      run_id
      initial_version_id
      updated_at
      created_by_id
    

    

      id
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      gb9IBDEaYz037H5YVT4E
      mmsUAjmY
      output_dir/salmon.merged.gene_counts.tsv
      .tsv
      None
      Merged Bulk RNA counts
      None
      3787
      xxw0k3au3KtxFcgtbEr4eQ
      md5
      Ug77SqHFdLKKcc
      vqS3oS1JHwjKPDqdQR4i
      None
      2023-09-06 17:07:25
      DzTjkKse
    

    

      EB8sohovIwZSS0NUknc9
      mmsUAjmY
      None
      .h5ad
      AnnData
      Curated bulk RNA counts
      None
      28180
      6bieh8XjOCCz6bJToN4u1g
      md5
      s5V0dNMVwL9iz8
      b8QCUEVz2wApHJozC6Ip
      None
      2023-09-06 17:07:28
      DzTjkKse
    

  











curated_file.view_flow()








https://d33wubrfki0l68.cloudfront.net/7e1558b3b9d9196ec4b8308a093129480f6c4b02/ffa33/_images/060838102583556be3cba0f350a7e5f7777fbfa74c2b8f2c02a40c76bf206049.svg




Let’s by query by gene:






genes = lb.Gene.lookup()













genes.spo7









Gene(id='tpzwPcGZFK1y', symbol='SPO7', stable_id='YAL009W', ncbi_gene_ids='851224', biotype='protein_coding', description='Putative regulatory subunit of Nem1p-Spo7p phosphatase holoenzyme; regulates nuclear growth by controlling phospholipid biosynthesis, required for normal nuclear envelope morphology, premeiotic replication, and sporulation [Source:SGD;Acc:S000000007]', synonyms='', updated_at=2023-09-06 17:07:28, species_id='nn8c', bionty_source_id='1LRa', created_by_id='DzTjkKse')













# a feature set containing RNA measurements and SPO7 gene expression
feature_set = ln.FeatureSet.filter(genes=genes.spo7, modality__name="rna").first()













# files that link to this feature set
ln.File.filter(feature_sets=feature_set).df()













  
    

      
      storage_id
      key
      suffix
      accessor
      description
      version
      size
      hash
      hash_type
      transform_id
      run_id
      initial_version_id
      updated_at
      created_by_id
    

    

      id
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      gb9IBDEaYz037H5YVT4E
      mmsUAjmY
      output_dir/salmon.merged.gene_counts.tsv
      .tsv
      None
      Merged Bulk RNA counts
      None
      3787
      xxw0k3au3KtxFcgtbEr4eQ
      md5
      Ug77SqHFdLKKcc
      vqS3oS1JHwjKPDqdQR4i
      None
      2023-09-06 17:07:25
      DzTjkKse
    

  











# clean up test instance
!lamin delete --force test-bulkrna
!rm -r test-bulkrna










Hide code cell output




💡 deleting instance testuser1/test-bulkrna
✅     deleted instance settings file: /home/runner/.lamin/instance--testuser1--test-bulkrna.env





✅     instance cache deleted
✅     deleted '.lndb' sqlite file
❗     consider manually deleting your stored data: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna