scTREAT: Latent Diffusion for Single-Cell Drug-Response Prediction
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The Flow has not summarised this story yet — read it at arXiv Machine Learning.
PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.
scDEFT is a deep learning framework that treats a drug as a conditioning operator on single‑cell representations, enabling prediction of drug‑induced state changes and responder status. The model learns drug‑conditioned cell latents via feature‑wise linear modulation, aggregates them over transcriptional neighborhoods, and ranks latent dimensions to identify genes distinguishing responders from non‑responders. Applied to a harmonized inflammatory bowel disease atlas of 1.16 million cells, scDEFT achieves 45% of the baseline‑to‑reproducibility ceiling in state‑change prediction and stratifies responders before treatment with an AUROC of 0.70, outperforming standard predictors.
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