DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.

Autor: Ramzan Umarov, Yu Li, Erik Arner
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: PLoS Computational Biology, Vol 17, Iss 10, p e1009465 (2021)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1009465
Popis: Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.
Databáze: Directory of Open Access Journals
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