Datasets:

wozhen
/

ambimod

license: mit
task_categories:
  - tabular-regression
tags:
  - perturbation-prediction
  - Perturb-seq
  - benchmark
  - zero-shot
  - protein-embeddings
size_categories:
  - 1M<n<10M
configs:
  - config_name: cohort
    data_files:
      - split: train
        path: viewer/cohort/*.parquet
  - config_name: annotations
    data_files:
      - split: train
        path: viewer/annotations/*.parquet
  - config_name: responses
    data_files:
      - split: train
        path: viewer/responses/*.parquet
  - config_name: embeddings_esm2_650m
    data_files:
      - split: train
        path: viewer/embeddings/embedding_esm2_650m.parquet
  - config_name: embeddings_esm2_3b
    data_files:
      - split: train
        path: viewer/embeddings/embedding_esm2_3b.parquet
  - config_name: embeddings_protrek
    data_files:
      - split: train
        path: viewer/embeddings/embedding_protrek.parquet
  - config_name: splits
    data_files:
      - split: train
        path: viewer/splits/splits.parquet
  - config_name: rpe1_splits
    data_files:
      - split: train
        path: viewer/splits/rpe1_splits.parquet

AMBIMOD benchmark data (K562)

Derived evaluation data for the AMBIMOD zero-shot perturbation-prediction benchmark. The data is organized into Dataset-Viewer configs (browse each tab above) plus a ready-to-use archive for the benchmark code.

Dataset Access

Two equivalent access paths:

A. Load individual tables with datasets (what the Dataset Viewer shows):

from datasets import load_dataset

cohort = load_dataset("wozhen/ambimod", "cohort", split="train")
splits = load_dataset("wozhen/ambimod", "splits", split="train")
responses = load_dataset("wozhen/ambimod", "responses", split="train")

B. Download the benchmark archive (exact layout the code expects — cache/, data/splits/, checksums):

huggingface-cli download wozhen/ambimod --repo-type dataset --local-dir .
tar xzf ambimod-benchmark-data.tar.gz

Configs (Dataset Viewer tabs)

Config	Rows	Columns	Content
`cohort`	490	tf, n_cells, sequence_length, sequence, uniprot_header	quality-controlled K562 TF cohort (UniProt-reviewed human sequences)
`annotations`	490+490	tf ↔ DBD family (InterPro); tf ↔ paralog list	leakage-control annotations
`responses`	2,450,000	tf, replicate (1–5), gene_idx, gene_id, log1p_delta	held-out evaluation responses: log1p-delta per TF × replicate × 1,000 target genes
`embeddings_esm2_650m`	627,200	tf, dim, value	ESM2-650M embeddings (490 × 1280, L2-normalized)
`embeddings_esm2_3b`	1,254,400	tf, dim, value	ESM2-3B embeddings (490 × 2560)
`embeddings_protrek`	501,760	tf, dim, value	ProTrek-650M embeddings (490 × 1024)
`splits`	7,350	design, seed, role, tf	the 15 frozen K562 splits (random / dbd_family / embed_ball × 5 seeds), train/test membership
`rpe1_splits`	6,534	seed, role, perturbation	frozen RPE1 split manifests (reference for the raw-data pipeline)

Embeddings are stored in long format (tf, dim, value) for Viewer compatibility; the benchmark archive (ambimod-benchmark-data.tar.gz) contains the same values as dense (490, d) float32 arrays.

Raw Data Sources

K562 GWPS + RPE1 essentialome (Perturb-seq): https://gwps.wi.mit.edu/
K562/RPE1 figshare mirror (RPE1 h5ad, direct download): https://figshare.com/ndownloader/files/42362275
Norman 2019 CRISPRa (GEARS-official file, Harvard Dataverse): https://dataverse.harvard.edu/api/access/datafile/6154020

Embedding provenance

All embedding caches were computed by us from public model weights applied to the cohort's UniProt-reviewed sequences — no third-party embedding files are redistributed. Checkpoints used (all public):

Embedding	Checkpoint	Pooling
`esm2_650m`	`facebook/esm2_t33_650M_UR50D`	residue mean (1022-aa length-weighted chunks), L2-normalized
`esm2_3b`	`facebook/esm2_t36_3B_UR50D`	same
`protrek`	`westlake-repl/ProTrek_650M`	official `get_protein_repr`

scripts/recompute_embeddings.py in the code repository rebuilds any cache from these checkpoints and fails on checksum mismatch. The RPE1 patched ProTrek cache and its gated sanity receipt are documented in EMBEDDINGS.md in the code repository.

Usage with the benchmark code

pip install -e git+https://github.com/ambimod/ambimod#egg=ambimod
python -c "from mechid.benchmark import load_benchmark; s = load_benchmark(); print(len(s))"
# -> 15

License

MIT (derived arrays and manifests). Embeddings were computed from public protein sequences with public models (ESM2, ProTrek). The raw datasets remain under their original licenses and are not redistributed.