You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
These embeddings are released for non-commercial academic research. Please tell us who you are and what you plan to use them for.
Log in or Sign Up to review the conditions and access this dataset content.
BioReasonCell — cell embeddings
Mean-pooled cell-state embeddings for the perturbation datasets used by
BioReasonCell. One vector per biological context, consumed by the
model as the <|CELL_START|> … <|CELL_END|> block.
mean_pool/
| folder | vectors | contexts |
|---|---|---|
mean_pool/genetic/ |
47 | CRISPRi / CRISPRa screens — Kaufman2025 (6 lines × 5 stimuli), Replogle (K562 gwps, RPE1), Norman, Papalexi, Marson (D1–D4), Xaira (HCT116, HEK293T), Xu2026, HepG2 / Jurkat / iPSC |
mean_pool/chemical/ |
47 | Tahoe drug screen — one per cell line |
Every file is a 1-D torch.float32 tensor of shape (2058,), the per-context mean of STATE
(st-se-replogle-full) cell embeddings over that context's control cells. 936 KB total.
These are cell-state vectors, not perturbation vectors: the same file is used for every perturbation measured in that context, and the perturbation itself reaches the model through the prompt text. The genetic and chemical sets are disjoint in filename, so the two folders can be merged into one directory if you prefer a single source.
Filenames are lookup keys — do not rename
A file's name (minus .pt) is exactly the key the loader resolves. Given a dataset row's
cell_file, the key is built by stemming twice:
from pathlib import Path
key = Path(Path(row["cell_file"]).stem).stem # collate.py stems an already-stemmed column
emb = torch.load(f"{cell_file_dir}/{key}.pt") # -> (2058,) float32
The double stem is deliberate and load-bearing. tahoe_Panc 03.27_merged.h5ad resolves to
tahoe_Panc 03.pt, because .27_merged is read as a suffix — so that file is correctly named and
renaming it to tahoe_Panc 03.27_merged.pt would break the lookup. Names also contain spaces
(tahoe_SW 1088_merged.pt, tahoe_AN3 CA_merged.pt); keep them.
Use
from huggingface_hub import snapshot_download
local = snapshot_download("wanglab/BioReasonCell-embeddings", repo_type="dataset")
# then point the training / eval config at the folder you want:
# cell_file_dir: {local}/mean_pool/chemical
The loader (bioreason_cell/dataset/cell_embedding_store.py) takes a directory of per-cell .pt
files and casts each to bfloat16, unsqueezing 1-D tensors to (1, 2058).
Coverage
Verified against the sampled batches under the double-stem transform: 47/47 keys present for
chemical (chemical_tahoe_v1.2_50k, chemical_tahoe_10k) and 47/47 for genetic
(genetic_v7.5_50k, genetic_v7.6_50k, genetic_v7.6_relabel, genetic_batch1_5k), with no
missing keys in either. Train and test splits draw from the same context pool, so one folder covers
both.
Related
wanglab/BioReasonCell-ReasoningData— perturbation rows, gene/pathway/cell annotationswanglab/BioReasonCell-ExperimentData— per-experiment train/validation/test splits
- Downloads last month
- 11