OPS-Eval: Leakage-Resistant Evaluation for Optical Pooled Screens

Benchmark artifacts for evaluating representation learning on pooled CRISPR microscopy data. This dataset accompanies a submission to the NeurIPS 2026 Evaluations and Datasets Track.

Contents

Directory/File	Description	Size
montages/	Per-gene montage images (4 channels x 2 phases, ~10 PNGs per gene)	~66 GB
cell_embeddings/	Pre-extracted 512-dim cell embeddings per sgRNA (.npz)	~15 GB
splits_v1.json	Gene-disjoint train/val/test split (3,628 / 694 / 1,000 genes)	1.4 MB
splits_v1_random.json	Random image-level split (comparison, demonstrates leakage)	1.4 MB
splits_v1_sgrna_disjoint.json	Guide-disjoint split (comparison)	1.3 MB
gene_metadata.parquet	Per-gene metadata: cluster labels, mitotic index, guide count, 98-dim features	1.9 MB
sgrna_metadata.parquet	Per-sgRNA metadata	48 KB
leakage_audit_v1.json	Formal verification of zero gene overlap across splits	99 KB
manifest_v1.json	SHA-256 checksums for all montage images	4 KB
external_gene_pairs_v1.json	STRING + CORUM gene relationships for co-functional retrieval	14 MB
results/	All experiment results (baseline ladder, ablations, split sensitivity)	2.4 MB
idr0071/	External validation on independent idr0071 dataset (A549 cells)	7.4 MB
cellpaint_posh/	Cell Painting POSH replication: features (.pq) and sgRNA library (.csv)	1.5 GB

Montage Image Structure

Each gene directory contains ~10 PNG files following the naming convention:

{GENE}.{phase}-montage-{channel}.png

• Phases: interphase, mitotic
• Channels: 0=DNA/DAPI, 1=Tubulin, 2=gH2AX, 3=Actin, 4=Label (segmentation mask)
• Dimensions: ~2700x2000 px per montage
• Band structure: 5 horizontal bands per montage (sgRNA 1-4 + non-targeting control), each containing ~100 tiled single-cell crops at ~100x100 px

Total: 5,322 genes, ~53,000 montage images, ~2.1M single cells.

Cell Embeddings

Pre-extracted 512-dimensional cell-level embeddings (float16) from a frozen ResNet-18 encoder. One .npz file per gene, with keys corresponding to individual sgRNAs.

Quick Start

from huggingface_hub import hf_hub_download, snapshot_download
import json

# Download just the splits
path = hf_hub_download("cspeters119/ops-eval", "splits_v1.json", repo_type="dataset")
splits = json.load(open(path))
print(f"Loaded splits for {len(splits)} genes")

# Download a single gene's montages
snapshot_download("cspeters119/ops-eval", repo_type="dataset",
                  allow_patterns="montages/AAAS/*")

# Download everything (warning: ~85 GB)
snapshot_download("cspeters119/ops-eval", repo_type="dataset")

Code

See the supplementary material for evaluation code, baseline implementations, and one-command reproduction harness (bash scripts/run_all.sh --tiny).

License

MIT