GEO DataSets

(Submitter supplied) Recent advances in multiplexed single-cell transcriptomics experiments are facilitating 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. more...

Organism:

Homo sapiens

Type:

Expression profiling by high throughput sequencing

Platform:

GPL18573

96 Samples

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