DINOcell: Learning Generalizable Perturbation Effects through Self-Distillation
Li, J.; Tasdemir, N.; Kyriazopoulou Panagiotopoulou, S.; Xu, S.; Merrell, A.; Susanto, J.; Cass, A.; DeTomaso, D.; Steach, H.; Chow, J.; Wang, L.; Ravindra, N.; Kang, A.; Zheng, G.; Chua, M.; Knudsgaard, P.; He, E.; Ingrao, F.; Boroughs, A.; Copperman, B.; Charrington, N.; Abboud, K.; Berthoin, L.; Baca, D.; Leon, L.; Kotov, J.; Wozniak, G.; Cardozo, A.; Vucci, K.; Barner, A.; Yi, C.; Oh, C.; Simon, M.; Paliwal, S.; Drever, M.; Galvin, B.; Tan, M.; Guzman, G.; Loh, K.; Ong, A.; Sandoval, M.; Lim, M.; Ng, E.; Lincoln-Cabatu, B.; Wu, J.; Nguyen, A.; Patel, M.; Fua, A.; Wong, K.; Chen, J.; Yashin
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWPredicting cellular responses to therapeutics is a promising approach for novel target discovery. However, state-of-the-art computational models designed to predict perturbation effects struggle to generalize and outperform simple baselines. We present DINOcell, a weakly supervised framework that adapts self-distillation to single-cell transcriptomics for predicting perturbation effects. We demonstrate that DINOcell outperforms baselines in predicting the effects of single gene perturbations. Furthermore, DINOcell accurately predicts non-additive effects of combination perturbations, indicating its capacity to model complex genetic interactions. Finally, we show that DINOcell learns representations that capture biological signals and is a promising, generalizable approach for in silico perturbation modeling, providing a valuable tool for accelerating therapeutic target discovery.
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