Duplicate from rednaSander/omicsfm-data

Co-authored-by: Sander Heyndrickx <rednaSander@users.noreply.huggingface.co>

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README.md

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+---
+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.

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