Bulk RNA-seq

Note

More comprehensive examples are provided for these data types:

• scRNA-seq

• Flow cytometry

# !pip install 'lamindb[jupyter,bionty]'
!lamin init --storage test-bulkrna --modules bionty

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→ initialized lamindb: testuser1/test-bulkrna

import lamindb as ln
import bionty as bt
import pandas as pd
import anndata as ad
from pathlib import Path

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→ connected lamindb: testuser1/test-bulkrna

Ingest data

Access

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

(See Nextflow for running this with Nextflow.)

# pretend we're running a bulk RNA-seq pipeline
ln.track(
    transform=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.core.datasets.file_tsv_rnaseq_nfcore_salmon_merged_gene_counts(
    populate_registries=True
)
# move the count matrix into the output directory
path = path.rename(f"./test-bulkrna/output_dir/{path.name}")
# register the count matrix
ln.Artifact(path, description="Merged Bulk RNA counts").save()

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/tmp/ipykernel_3833/1244583888.py:3: FutureWarning: `name` will be removed soon, please pass 'nf-core RNA-seq' to `key` instead
  transform=ln.Transform(name="nf-core RNA-seq", reference="https://nf-co.re/rnaseq")

→ created Transform('AGTzE5YhMBnw0000'), started new Run('yoSk6VTH...') at 2025-07-21 11:25:11 UTC

• recommendation: to identify the script across renames, pass the uid: ln.track("AGTzE5YhMBnw")

/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/bionty/base/dev/_io.py:131: FutureWarning: Use synchronize_to instead of synchronize_to, synchronize_to will be removed in the future.
  remote_path.synchronize(localpath, error_no_origin=False, print_progress=True)

/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/bionty/base/dev/_io.py:131: FutureWarning: Use synchronize_to instead of synchronize_to, synchronize_to will be removed in the future.
  remote_path.synchronize(localpath, error_no_origin=False, print_progress=True)

/opt/hostedtoolcache/Python/3.12.11/x64/lib/python3.12/site-packages/bionty/base/dev/_io.py:131: FutureWarning: Use synchronize_to instead of synchronize_to, synchronize_to will be removed in the future.
  remote_path.synchronize(localpath, error_no_origin=False, print_progress=True)

Artifact(uid='OZlm5wVhZn8vW5pD0000', is_latest=True, key='output_dir/salmon.merged.gene_counts.tsv', description='Merged Bulk RNA counts', suffix='.tsv', size=3787, hash='xxw0k3au3KtxFcgtbEr4eQ', branch_id=1, space_id=1, storage_id=1, run_id=1, created_by_id=1, created_at=2025-07-21 11:25:13 UTC)

Transform

ln.track("s5V0dNMVwL9i0000")

Let’s query the artifact:

artifact = ln.Artifact.get(description="Merged Bulk RNA counts")
df = artifact.load()

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

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	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

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	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	97	98	99	100	101	102	103	104	105	106	107	108	109	110	111	112	113	114	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	YAL004W	YAL005C	YAL007C	YAL008W	YAL009W	YAL010C	YAL011W	YAL012W	YAL013W	YAL014C	YAL015C	YAL016C-A	YAL016C-B	YAL016W	YAL017W	YAL018C	YAL019W	YAL019W-A	YAL020C	YAL021C	YAL022C	YAL023C	YAL024C	YAL025C	YAL026C	YAL026C-A	YAL027W	YAL028W	YAL029C	YAL030W	YAL031C	YAL031W-A	YAL032C	YAL033W	YAL034C	YAL034C-B	YAL034W-A	YAL035W	YAL036C	YAL037C-A	YAL037C-B	YAL037W	YAL038W	YAL039C	YAL040C	YAL041W	YAL042C-A	YAL042W	YAL043C	YAL044C	YAL044W-A	YAL045C	YAL046C	YAL047C	YAL047W-A	YAL048C	YAL049C	YAL051W	YAL053W	YAL054C	YAL055W	YAL056C-A	YAL056W	YAL058W	YAL059C-A	YAL059W	YAL060W	YAL061W	YAL062W	YAL063C	YAL063C-A	YAL064C-A	YAL064W	YAL064W-B	YAL065C	YAL066W	YAL067C	YAL067W-A	YAL068C	YAL068W-A	YAL069W	YAR002C-A	YAR002W	YAR003W	YAR007C	YAR008W	YAR009C	YAR010C	YAR014C	YAR015W	YAR018C	YAR019C	YAR019W-A	YAR020C	YAR023C	YAR027W	YAR028W	YAR029W	YAR030C	YAR031W	YAR033W	YAR035C-A	YAR035W	YAR042W	YAR047C	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	YAL004W	SSA1	ERP2	FUN14	SPO7	MDM10	SWC3	CYS3	DEP1	SYN8	NTG1	YAL016C-A	YAL016C-B	TPD3	PSK1	LDS1	FUN30	YAL019W-A	ATS1	CCR4	FUN26	PMT2	LTE1	MAK16	DRS2	YAL026C-A	SAW1	FRT2	MYO4	SNC1	GIP4	YAL031W-A	PRP45	POP5	FUN19	YAL034C-B	MTW1	FUN12	RBG1	YAL037C-A	YAL037C-B	YAL037W	CDC19	CYC3	CLN3	CDC24	YAL042C-A	ERV46	PTA1	GCV3	YAL044W-A	YAL045C	AIM1	SPC72	YAL047W-A	GEM1	AIM2	OAF1	FLC2	ACS1	PEX22	YAL056C-A	GPB2	CNE1	YAL059C-A	ECM1	BDH1	BDH2	GDH3	FLO9	YAL063C-A	TDA8	YAL064W	YAL064W-B	YAL065C	YAL066W	SEO1	YAL067W-A	PAU8	YAL068W-A	YAL069W	ERP1	NUP60	SWD1	RFA1	SEN34	YAR009C	YAR010C	BUD14	ADE1	KIN3	CDC15	YAR019W-A	PAU7	YAR023C	UIP3	YAR028W	YAR029W	YAR030C	PRM9	MST28	YAR035C-A	YAT1	SWH1	YAR047C	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	1.0	6174.0	46.0	14.0	14.0	11.0	10.0	247.0	8.0	16.0	12.0	0.0	0.0	148.0	100.0	0.0	101.0	0.0	12.0	105.0	42.0	302.0	49.0	19.0	122.0	12.0	10.0	9.0	178.0	14.0	38.0	0.0	14.0	13.0	16.0	0.0	8.0	409.0	49.0	0.0	1.0	4.0	5710.0	9.0	34.0	61.0	0.0	141.0	63.0	33.0	5.0	0.0	2.0	20.0	0.0	18.0	26.0	38.0	116.0	3.0	6.0	0.0	61.0	23.0	0.0	17.0	49.0	30.0	5.0	4.643	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	2.0	1.0	2.0	64.0	57.0	27.0	56.0	8.0	16523.0	5741.0	55.0	53.0	20.0	21.0	0.0	0.0	1.0	25.0	18.0	1.0	2.0	17.0	1.0	0.0	2.0	104.0	0.0	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	345.089	6000.911	56.0	17.0	15.0	12.0	25.0	232.0	18.0	20.0	12.0	13.999	1.0	154.001	114.0	0.0	111.0	2.901	5.099	102.0	36.0	323.0	52.0	29.0	159.428	12.0	11.0	8.0	177.0	14.0	40.0	0.0	14.0	12.0	16.0	0.0	2.0	482.0	59.0	0.0	264.768	5.0	6162.232	23.0	44.0	65.0	12.533	133.467	67.0	58.0	0.0	3.0	3.0	23.0	1.0	20.0	37.0	60.0	129.0	13.0	2.0	1.0	49.0	27.0	0.0	23.0	53.0	19.0	27.0	23.28	1.0	0.0	0.0	0.0	2.0	0.0	3.0	0.0	5.0	0.0	1.0	78.0	60.0	17.0	67.0	14.0	17154.0	6178.0	61.0	63.0	14.0	35.0	0.0	0.0	1.0	34.0	13.0	2.0	0.0	15.0	17.0	0.0	5.0	105.0	0.0	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	1.0	13355.0	132.0	24.0	19.0	36.0	36.0	536.0	35.0	43.0	28.0	0.0	0.0	326.0	210.0	0.0	238.0	0.0	18.0	203.0	86.0	659.0	99.0	56.0	314.989	19.011	20.0	19.0	359.0	40.0	72.0	0.0	37.0	24.0	44.0	0.0	25.0	872.0	147.0	0.0	3.0	10.0	13457.0	39.0	92.0	140.0	0.0	291.0	135.0	123.0	14.0	1.0	7.0	40.0	0.0	46.0	65.0	119.0	262.0	7.0	5.0	0.0	133.0	49.0	0.0	42.0	114.0	82.0	37.0	13.228	1.0	0.0	2.0	1.0	0.0	0.0	4.0	0.0	15.0	1.0	1.0	156.0	121.0	35.0	126.0	18.0	33244.0	11826.0	168.0	151.0	28.0	72.0	0.0	0.0	5.0	74.0	56.0	4.0	2.0	42.0	20.0	0.0	4.0	198.0	2.0	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	0.0	8218.0	61.0	10.0	9.0	30.0	19.0	385.0	21.0	28.0	8.0	0.0	0.0	194.0	101.0	0.0	186.0	0.0	12.0	136.0	54.0	432.0	69.0	50.0	180.41	14.59	5.0	4.0	241.0	15.0	36.0	0.0	18.0	18.0	11.0	0.0	5.0	760.0	82.0	0.0	0.0	4.0	10313.0	12.0	50.0	89.0	0.0	168.0	77.0	47.0	7.0	0.0	8.0	19.0	0.0	26.0	34.0	82.0	133.0	5.0	1.0	0.0	72.0	39.0	0.0	33.0	43.0	9.0	13.0	4.535	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	3.0	0.0	0.0	85.0	61.0	22.0	95.0	4.0	36435.0	13470.0	68.0	91.0	25.0	26.0	0.0	0.0	2.0	26.0	16.0	0.0	1.0	12.0	5.0	0.0	12.0	127.0	1.0	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	1.0	4279.0	44.0	3.0	5.0	10.0	17.0	230.0	10.0	17.0	6.0	0.0	0.0	104.0	60.0	0.0	84.0	0.0	2.0	79.0	27.0	244.0	46.0	21.0	123.638	5.362	2.0	4.0	139.0	8.0	15.0	0.0	13.0	7.0	6.0	0.0	6.0	390.0	48.0	0.0	0.0	1.0	6339.0	5.0	36.0	50.0	0.0	102.0	44.0	25.0	3.0	0.0	6.0	14.0	0.0	10.0	12.0	39.0	68.0	1.0	2.0	0.0	38.0	27.0	0.0	20.0	29.0	5.0	4.0	1.109	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	64.0	40.0	14.0	45.0	5.0	17184.0	6132.0	50.0	39.0	14.0	18.0	0.0	0.0	0.0	19.0	11.0	0.0	0.0	9.0	4.0	0.0	2.0	75.0	0.0	6.891	0.0	1.0	0.0	0.0	0.0	11.0	7.0	1.0	0.0

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()

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	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 object:

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

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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()

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gene_id	Gfp_transgene_gene	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	YAL016C-A	YAL016C-B	YAL016W	YAL017W	YAL018C	YAL019W	YAL019W-A	YAL020C	YAL021C	YAL022C	YAL023C	YAL024C	YAL025C	YAL026C	YAL026C-A	YAL027W	YAL028W	YAL029C	YAL030W	YAL031C	YAL031W-A	YAL032C	YAL033W	YAL034C	YAL034C-B	YAL034W-A	YAL035W	YAL036C	YAL037C-A	YAL037C-B	YAL037W	YAL038W	YAL039C	YAL040C	YAL041W	YAL042C-A	YAL042W	YAL043C	YAL044C	YAL044W-A	YAL045C	YAL046C	YAL047C	YAL047W-A	YAL048C	YAL049C	YAL051W	YAL053W	YAL054C	YAL055W	YAL056C-A	YAL056W	YAL058W	YAL059C-A	YAL059W	YAL060W	YAL061W	YAL062W	YAL063C	YAL063C-A	YAL064C-A	YAL064W	YAL064W-B	YAL065C	YAL066W	YAL067C	YAL067W-A	YAL068C	YAL068W-A	YAL069W	YAR002C-A	YAR002W	YAR003W	YAR007C	YAR008W	YAR009C	YAR010C	YAR014C	YAR015W	YAR018C	YAR019C	YAR019W-A	YAR020C	YAR023C	YAR027W	YAR028W	YAR029W	YAR030C	YAR031W	YAR033W	YAR035C-A	YAR035W	YAR042W	YAR047C	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	1.000000	6174.000000	46.0	14.0	14.0	11.0	10.0	247.0	8.0	16.0	12.0	0.000	0.0	148.000000	100.0	0.0	101.0	0.000	12.000	105.0	42.0	302.0	49.0	19.0	122.000000	12.000	10.0	9.0	178.0	14.0	38.0	0.0	14.0	13.0	16.0	0.0	8.0	409.0	49.0	0.0	1.000000	4.0	5710.000000	9.0	34.0	61.0	0.000	141.000000	63.0	33.0	5.0	0.0	2.0	20.0	0.0	18.0	26.0	38.0	116.0	3.0	6.0	0.0	61.0	23.0	0.0	17.0	49.0	30.0	5.0	4.643000	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	2.0	1.0	2.0	64.0	57.0	27.0	56.0	8.0	16523.0	5741.0	55.0	53.0	20.0	21.0	0.0	0.0	1.0	25.0	18.0	1.0	2.0	17.0	1.0	0.0	2.0	104.0	0.0	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	345.088989	6000.911133	56.0	17.0	15.0	12.0	25.0	232.0	18.0	20.0	12.0	13.999	1.0	154.001007	114.0	0.0	111.0	2.901	5.099	102.0	36.0	323.0	52.0	29.0	159.427994	12.000	11.0	8.0	177.0	14.0	40.0	0.0	14.0	12.0	16.0	0.0	2.0	482.0	59.0	0.0	264.768005	5.0	6162.231934	23.0	44.0	65.0	12.533	133.466995	67.0	58.0	0.0	3.0	3.0	23.0	1.0	20.0	37.0	60.0	129.0	13.0	2.0	1.0	49.0	27.0	0.0	23.0	53.0	19.0	27.0	23.280001	1.0	0.0	0.0	0.0	2.0	0.0	3.0	0.0	5.0	0.0	1.0	78.0	60.0	17.0	67.0	14.0	17154.0	6178.0	61.0	63.0	14.0	35.0	0.0	0.0	1.0	34.0	13.0	2.0	0.0	15.0	17.0	0.0	5.0	105.0	0.0	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	1.000000	13355.000000	132.0	24.0	19.0	36.0	36.0	536.0	35.0	43.0	28.0	0.000	0.0	326.000000	210.0	0.0	238.0	0.000	18.000	203.0	86.0	659.0	99.0	56.0	314.989014	19.011	20.0	19.0	359.0	40.0	72.0	0.0	37.0	24.0	44.0	0.0	25.0	872.0	147.0	0.0	3.000000	10.0	13457.000000	39.0	92.0	140.0	0.000	291.000000	135.0	123.0	14.0	1.0	7.0	40.0	0.0	46.0	65.0	119.0	262.0	7.0	5.0	0.0	133.0	49.0	0.0	42.0	114.0	82.0	37.0	13.228000	1.0	0.0	2.0	1.0	0.0	0.0	4.0	0.0	15.0	1.0	1.0	156.0	121.0	35.0	126.0	18.0	33244.0	11826.0	168.0	151.0	28.0	72.0	0.0	0.0	5.0	74.0	56.0	4.0	2.0	42.0	20.0	0.0	4.0	198.0	2.0	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	0.000000	8218.000000	61.0	10.0	9.0	30.0	19.0	385.0	21.0	28.0	8.0	0.000	0.0	194.000000	101.0	0.0	186.0	0.000	12.000	136.0	54.0	432.0	69.0	50.0	180.410004	14.590	5.0	4.0	241.0	15.0	36.0	0.0	18.0	18.0	11.0	0.0	5.0	760.0	82.0	0.0	0.000000	4.0	10313.000000	12.0	50.0	89.0	0.000	168.000000	77.0	47.0	7.0	0.0	8.0	19.0	0.0	26.0	34.0	82.0	133.0	5.0	1.0	0.0	72.0	39.0	0.0	33.0	43.0	9.0	13.0	4.535000	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	3.0	0.0	0.0	85.0	61.0	22.0	95.0	4.0	36435.0	13470.0	68.0	91.0	25.0	26.0	0.0	0.0	2.0	26.0	16.0	0.0	1.0	12.0	5.0	0.0	12.0	127.0	1.0	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	1.000000	4279.000000	44.0	3.0	5.0	10.0	17.0	230.0	10.0	17.0	6.0	0.000	0.0	104.000000	60.0	0.0	84.0	0.000	2.000	79.0	27.0	244.0	46.0	21.0	123.638000	5.362	2.0	4.0	139.0	8.0	15.0	0.0	13.0	7.0	6.0	0.0	6.0	390.0	48.0	0.0	0.000000	1.0	6339.000000	5.0	36.0	50.0	0.000	102.000000	44.0	25.0	3.0	0.0	6.0	14.0	0.0	10.0	12.0	39.0	68.0	1.0	2.0	0.0	38.0	27.0	0.0	20.0	29.0	5.0	4.0	1.109000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	64.0	40.0	14.0	45.0	5.0	17184.0	6132.0	50.0	39.0	14.0	18.0	0.0	0.0	0.0	19.0	11.0	0.0	0.0	9.0	4.0	0.0	2.0	75.0	0.0	6.891	0.0	1.0	0.0	0.0	0.0	11.0	7.0	1.0	0.0

Curate

We define a simple Schema for Bulk RNA datasets that only expects genes with stable IDs to be stored in the dataset. Later, we can add additional metadata to the curated dataset such as the assay or the organism.

bulk_schema = ln.Schema(itype=bt.Gene.stable_id, otype="AnnData").save()

# set the organism to map to saccharomyces cerevisiae genes
bt.settings.organism = "saccharomyces cerevisiae"

curator = ln.curators.AnnDataCurator(adata, bulk_schema)
curator.validate()

Let’s create and save the artifact:

curated_af = curator.save_artifact(description="Curated bulk RNA counts")

Link additional metadata records:

efs = bt.ExperimentalFactor.lookup()
organism = bt.Organism.lookup()
features = ln.Feature.lookup()

curated_af.labels.add(efs.rna_seq, features.assay)
curated_af.labels.add(organism.saccharomyces_cerevisiae, features.organism)

curated_af.describe()

Show code cell output Hide code cell output

Artifact .h5ad · AnnData · dataset
├── General
│   ├── description: Curated bulk RNA counts
│   ├── uid: nUIzhbwMxkamDAS70000          hash: 6bieh8XjOCCz6bJToN4u1g
│   ├── size: 27.5 KB                      transform: bulkrna.ipynb
│   ├── space: all                         branch: all
│   ├── created_by: testuser1              created_at: 2025-07-21 11:25:14
│   ├── n_observations: 5
│   └── storage path: /home/runner/work/lamin-usecases/lamin-usecases/docs/test-bulkrna/nUIzhbwMxkamDAS70000
├── Linked features
│   └── assay                           cat[bionty.ExperimentalFactor]     RNA-Seq                                 
│       organism                        cat[bionty.Organism]               saccharomyces cerevisiae                
└── Labels
    └── .organisms                      bionty.Organism                    saccharomyces cerevisiae                
        .experimental_factors           bionty.ExperimentalFactor          RNA-Seq                                 

Query data

We have two files in the artifact registry:

ln.Artifact.df()

Show code cell output Hide code cell output

	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	nUIzhbwMxkamDAS70000	None	Curated bulk RNA counts	.h5ad	dataset	AnnData	28180	6bieh8XjOCCz6bJToN4u1g	None	5.0	md5	True	False	1	1	1.0	None	True	2	2025-07-21 11:25:14.393000+00:00	1	{'af': {'0': True}}	1
1	OZlm5wVhZn8vW5pD0000	output_dir/salmon.merged.gene_counts.tsv	Merged Bulk RNA counts	.tsv	None	None	3787	xxw0k3au3KtxFcgtbEr4eQ	None	NaN	md5	False	False	1	1	NaN	None	True	1	2025-07-21 11:25:13.104000+00:00	1	None	1

curated_af.view_lineage()

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