RT Journal Article
SR Electronic
T1 Compositional perturbation autoencoder for single-cell response modeling
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.14.439903
DO 10.1101/2021.04.14.439903
A1 Lotfollahi, Mohammad
A1 Susmelj, Anna Klimovskaia
A1 De Donno, Carlo
A1 Ji, Yuge
A1 Ibarra, Ignacio L.
A1 Wolf, F. Alexander
A1 Yakubova, Nafissa
A1 Theis, Fabian J.
A1 Lopez-Paz, David
YR 2021
UL http://biorxiv.org/content/early/2021/04/15/2021.04.14.439903.abstract
AB Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug response across different cell types, doses, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions for unseen dosages and drug combinations. We show CPA accurately models single-cell perturbations across compounds, dosages, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying it captures features that distinguish different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design by enabling in-silico response prediction at the single-cell level.Competing Interest StatementF.J.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. and Dermagnostix. F.A.W. is a full-time employee of Cellarity Inc., and has ownership interest in Cellarity, Inc.