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Compositional perturbation autoencoder for single-cell response modeling

23 Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high24 throughput study of drug and genetic perturbations. However, an exhaustive exploration of the com25 binatorial perturbation space is experimentally unfeasible, so computational methods are needed to 26 predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation 27 Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of 28 deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional 29 drug response across different cell types, doses, and drug combinations. The model produces easy30 to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions 31 for unseen dosages and drug combinations. We show CPA accurately models single-cell perturba32 tions across compounds, dosages, species, and time. We further demonstrate that CPA predicts 33 combinatorial genetic interactions of several types, implying it captures features that distinguish 34 different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 35 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our 36 model will facilitate efficient experimental design by enabling in-silico response prediction at the 37 single-cell level. 38