| --- |
| license: cc-by-4.0 |
| tags: |
| - biology |
| - transcriptomics |
| - single-cell |
| - bulk-rna-seq |
| --- |
| |
| # OmicsFM transcriptomics datasets |
|
|
| Bulk and single-cell expression matrices used to train and evaluate |
| [OmicsFM](https://huggingface.co/rednaSander/omicsfm), prepared as model-ready |
| AnnData from public databases. |
|
|
| The proteomics modality lives separately in |
| [omicsfm-data-proteomics](https://huggingface.co/datasets/rednaSander/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](https://archs4.org) human gene-level counts (kallisto, raw estimated counts) | |
| | single-cell | [CELLxGENE Census](https://chanzuckerberg.github.io/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 |
|
|
| ```python |
| 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. |
|
|
