articleMolecular Systems BiologyMay 8, 2023GOLD OA

Predicting cellular responses to complex perturbations in high‐throughput screens

MLMohammad LotfollahiAKAnna Klimovskaia SusmeljCDCarlo De DonnoLHLeon HetzelYJYuge Ji

Wellcome Sanger Institute · Center for Environmental Health · +9 more institutions

PubMed
Indexed incrossrefdoajpubmed

Abstract

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, 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 learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly…

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Keywords
  • Interpretability
  • In silico
  • Computer science
  • Computational biology
  • Modularity (biology)
  • Systems biology
  • Machine learning
  • Biology
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