scGPT whole-human pretrained (Cui et al. 2024), augmented with a frozen ESM2-15B per-gene prior table from arcinstitute/SE-600M. The prior is projected through a learnable linear (5120 → 512) and added to the existing learnable gene-token embedding before the transformer body. Architecturally identical to scGPT otherwise: 12 transformer blocks, 8 heads, d_model=512, max_seq_len=1536.

Training

Source dataset: arcinstitute/State-Replogle-Filtered — CRISPRi essential-genome screens from Replogle et al. 2022 and Nadig et al. 2025. Training: 362,327 cells from K562 + Jurkat + HepG2 with 1,383 perturbations and 8,569 val pairs (held-out K562 perturbations). Evaluation: 109,207 RPE1 cells perturbed by the 1,047 genes overlapping the K562 training perturbation set, plus 10,691 real RPE1 controls.

Fine-tuned the scGPT whole-human pretrained checkpoint with the ESM2 gene prior injected at the gene embedding layer. Used --stop-metric pearson_delta (per-perturbation Pearson on Δ-expression) for early-stopping and best-checkpoint selection — this metric directly measures perturbation-effect prediction quality, whereas full-expression pearson is dominated by the unchanged-genes baseline.

Budget and stopping


Hardware	NVIDIA H100 PCIe (80 GB)
Train batch size	192
Eval batch size	192
Max epochs	30
Early-stop patience	10
Stop metric	`pearson_delta`
Epochs trained	19 (early-stopped)
Best epoch	9
Best val `pearson_delta`	0.1944
Training cells seen	3,420,399
Wall clock	254.6 min (~4.25 h)
AMP	fp16
Optimizer	Adam, lr=1e-4, StepLR γ=0.9

Test set metrics (cell-eval)

metric	mean	median	max
pearson_delta	0.5080	0.5057	0.6985
pr_auc	0.5304	0.5239	0.9234
roc_auc	0.3878	0.3868	0.4872
overlap_at_N	0.5533	0.5596	0.9260
de_sig_genes_recall	0.6401	0.6540	0.9552
de_direction_match	0.6174	0.6315	0.7602
discrimination_score_l1	0.5037	0.5014	1.0000
mae_delta	0.1650	0.1624	0.2221

These cell-eval full-profile numbers are not directly comparable to the scGPT or State paper headline numbers — those report different splits (within-cell-line on Norman, or 4-line leave-one-out via Replogle-Nadig). Companion runs in progress: (a) base scGPT without the ESM prior on the same K562/Jurkat/HepG2 → RPE1 split; (b) crosscoder-style model-diffing on the two finetuned models. The headline scientific question — does the ESM prior improve cross-cell-line transfer in scGPT — needs (a) before this artifact alone can answer it.

Known limitations

Single seed; no variance estimates.
Test set restricted to RPE1 perturbations that overlap K562 training perturbations (1,047 / 1,499 RPE1 perts); generalization to truly unseen perturbed genes is not assessed by this eval.
DE-aware metrics (pr_auc, roc_auc, de_sig_genes_recall, de_direction_match) are computed against K562-derived DE rankings; ideal for an RPE1 test set would be RPE1-derived DE rankings on the truth side.
Base scGPT (no ESM) companion run is in progress on separate hardware; the does-ESM-help claim is not yet established by this artifact alone.

Files

best_model.pt — fine-tuned weights (~210 MB)
args.json — scGPT architecture config (inherited from scGPT_human)
vocab.json — scGPT gene → token id mapping (inherited)
scgpt_esm_prior.safetensors — frozen ESM2-15B per-gene prior aligned to scGPT's 60,697-token vocab — required at load time, the model expects ESM-augmented embeddings
training_stats.json — epoch count, best metric, wall clock, wandb URL
predictions/scgpt_replogle_test.h5ad — .X = predicted expression, .layers['truth'] = ground truth; includes 10,691 real RPE1 controls; 119,898 cells × 6,546 genes
eval/agg_results.csv — cell-eval full-profile aggregated stats across 1,047 RPE1 test perturbations
eval/results.csv — cell-eval full-profile per-perturbation metrics

Usage

python

from pathlib import Path
import torch, json
from huggingface_hub import snapshot_download
from scgpt.model import TransformerGenerator
from scgpt.model.gene_priors import GenePriorEncoder
from scgpt.tokenizer.gene_tokenizer import GeneVocab

ckpt = Path(snapshot_download('matthewshu/scgpt-replogle-esm-ft'))
vocab = GeneVocab.from_file(str(ckpt / 'vocab.json'))
for tok in ('<pad>', '<cls>', '<eoc>'):
    if tok not in vocab: vocab.append_token(tok)
margs = json.load(open(ckpt / 'args.json'))
gene_prior = GenePriorEncoder.from_safetensors(
    ckpt / 'scgpt_esm_prior.safetensors', d_model=margs['embsize']
)
model = TransformerGenerator(
    ntoken=len(vocab), d_model=margs['embsize'], nhead=margs['nheads'],
    d_hid=margs['d_hid'], nlayers=margs['nlayers'], nlayers_cls=3,
    n_cls=1, vocab=vocab, dropout=margs.get('dropout', 0.0),
    pad_token=margs.get('pad_token', '<pad>'),
    pad_value=margs.get('pad_value', 0),
    pert_pad_id=margs.get('pert_pad_id', 2),
    use_fast_transformer=False, gene_prior=gene_prior,
)
state = torch.load(ckpt / 'best_model.pt', map_location='cpu')
model.load_state_dict(state)
model.eval()

Citation

If you use this model, please cite:

scGPT — Cui et al., Nature Methods (2024). https://www.nature.com/articles/s41592-024-02201-0
ESM2 — Lin et al., Science (2023). https://www.science.org/doi/10.1126/science.ade2574
Replogle CRISPRi — Replogle et al., Cell (2022). https://www.cell.com/cell/fulltext/S0092-8674(22)00472-X
Nadig CRISPRi — Nadig et al. (2025). https://doi.org/10.48550/arXiv.2502.18620
State / SE-600M — Adduri et al. (2025). https://www.biorxiv.org/content/10.1101/2025.06.26.661135v3