rednaSander/omicsfm-data

OmicsFM transcriptomics datasets

Bulk and single-cell expression matrices used to train and evaluate OmicsFM, prepared as model-ready AnnData from public databases.

The proteomics modality lives separately in omicsfm-data-proteomics.

Naming

Every dataset is <modality>_<identifier authority>, so the name states both what was measured and which identifiers index the features:

suffix	authority	example
_uniprot	UniProt accessions	A0A024RBG1
_hgnc	HGNC gene symbols	TSPAN6
_ensembl	Ensembl gene IDs	ENSG00000237491

The _uniprot datasets share one 20,272-accession vocabulary with the proteomics data, so a feature index means the same protein across every modality. That shared space is what the models consume.

The gene-space datasets are the native measurement spaces, kept for comparison against methods that operate on genes. Bulk and single-cell use different gene identifiers, so those two are not comparable feature by feature; only the _uniprot versions are.

Contents

dataset	split	samples	features
bulk_transcriptomics_uniprot	train	614,169	20,272
bulk_transcriptomics_uniprot	valid	34,945	20,272
bulk_transcriptomics_uniprot	test	31,102	20,272
bulk_transcriptomics_hgnc	train	614,169	67,186
bulk_transcriptomics_hgnc	valid	34,945	67,186
bulk_transcriptomics_hgnc	test	31,102	67,186
sc_transcriptomics_uniprot	train	3,546,382	20,272
sc_transcriptomics_uniprot	valid	361,037	20,272
sc_transcriptomics_uniprot	test	642,687	20,272
sc_transcriptomics_ensembl	train	3,546,382	61,497
sc_transcriptomics_ensembl	valid	361,037	61,497
sc_transcriptomics_ensembl	test	642,687	61,497

Splits are grouped so that no study spans a boundary: bulk by ARCHS4 series, single-cell by Census dataset. Assignment is deterministic given seed 42.

Sources

modality	source
bulk	ARCHS4 human gene-level counts (kallisto, raw estimated counts)
single-cell	CELLxGENE Census

These are model-ready derivatives, not the primary data: quality filtered, harmonised to a controlled vocabulary, and projected onto the shared UniProt space. The construction pipeline is in the OmicsFM repository under transcriptomics/.

Use

from huggingface_hub import hf_hub_download
import anndata as ad

path = hf_hub_download("rednaSander/omicsfm-data",
                       "sc_transcriptomics_uniprot/test.h5ad",
                       repo_type="dataset")
adata = ad.read_h5ad(path)

Tokenisation caches are not included: omicsfm.data.ExpressionDataset rebuilds them on first use.

Citation

Publication in preparation. Please also cite ARCHS4 and CELLxGENE Census.