theislab/Embpy_Data

Embpy Static Embeddings

This dataset contains static gene and protein embeddings packaged with embpy. Embedding values are stored in Zarr arrays, while row identifiers, provenance, species metadata, and AnnData-like .uns metadata are stored in sidecar metadata files.

Summary

• Repository: theislab/Embpy_Data
• Schema version: embpy.static_embedding_package.v1
• Generated at: 2026-06-01T15:07:46+00:00
• Number of embeddings: 11
• Total indexed entities: 201,717
• Species keys: human_9606
• NCBI taxonomy IDs: 9606
• Default gene identifier policy: ensembl_id
• Unresolved gene identifier policy: drop

Available Embeddings

key	entity	species	id type	rows	dims	description
crispr_gene_effect	gene	human_9606	ensembl_id	17,087	1,178	DepMap CRISPR gene effect matrix, genes as rows after transposition.
crispr_gene_effect_1178	gene	human_9606	ensembl_id	17,087	1,178	DepMap CRISPR gene effect embedding, scaled, 1178d.
crispr_gene_effect_205	gene	human_9606	ensembl_id	17,916	205	DepMap CRISPR gene effect embedding, scaled, 205d.
gene2vec	gene	human_9606	ensembl_id	18,795	200	Gene2Vec co-expression embedding, 200d.
genept	gene	human_9606	ensembl_id	18,807	3,072	GenePT GPT-3.5 text embedding, 3072d, Ensembl-keyed.
genept_scaled	gene	human_9606	ensembl_id	17,728	3,072	GenePT GPT-3.5 text embedding, z-scored, 3072d.
omics	gene	human_9606	ensembl_id	19,385	256	Omics 256d static gene embedding, Ensembl-keyed.
pops	gene	human_9606	ensembl_id	18,383	256	PoPS 256d gene features, Ensembl-keyed.
string_functional_9606	protein	human_9606	string_protein_id	19,699	512	STRING/SPACE functional PPI embedding for human proteins, species 9606, 512d.
string_node2vec_9606	protein	human_9606	string_protein_id	19,622	128	STRING node2vec PPI embedding for human proteins, species 9606, 128d.
wikicrow	gene	human_9606	ensembl_id	17,208	4,096	WikiCrow text embedding, scaled, 4096d.

File Layout

manifest.json
embeddings/<model_key>/
  values.zarr/
  metadata/
    index.parquet
    index.csv
    metadata.json
    uns.json

The dense matrix is stored under values.zarr. The metadata/index.parquet file maps row positions to entity_id values and any preserved aliases such as source IDs or gene symbols.

Loading With embpy

from embpy.pp import HFHandler

hf = HFHandler("theislab/Embpy_Data")
embedding = hf.download_embedding("crispr_gene_effect")
matrix = embedding["embeddings"]
ids = embedding["ids"]

For a local checkout or downloaded snapshot:

from embpy import load_static_embedding_package

store = load_static_embedding_package("/path/to/package", key="crispr_gene_effect")
tp53 = store.get("ENSG00000141510")
tp53_by_symbol = store.get("TP53", id_type="symbol")

Missing identifiers raise by default. Use missing="drop" or missing="nan" when a partial result is acceptable.

Metadata And Species

This card reports species using package-level defaults. Regenerate the package with a current embpy build to also write per-embedding species, taxonomy_id, and species_key fields into metadata/metadata.json.

Validation

The package was designed to be validated locally before upload:

python -m embpy.scripts.package_static_embeddings validate --package /path/to/package

This package intentionally skips 2 source collection(s) containing 2642 file(s). Those collections usually contain per-species artifacts and should be packaged only when a species/taxonomy ID is selected explicitly.

License And Attribution

This repository aggregates embeddings derived from multiple upstream resources. Please check the per-embedding metadata and upstream sources for the applicable licenses and citation terms.