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DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment
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 framewor...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519465/ https://www.ncbi.nlm.nih.gov/pubmed/34610009 http://dx.doi.org/10.1371/journal.pcbi.1009465 |
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author | Umarov, Ramzan Li, Yu Arner, Erik |
author_facet | Umarov, Ramzan Li, Yu Arner, Erik |
author_sort | Umarov, Ramzan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8519465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-85194652021-10-16 DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment Umarov, Ramzan Li, Yu Arner, Erik PLoS Comput Biol Research Article 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. Public Library of Science 2021-10-05 /pmc/articles/PMC8519465/ /pubmed/34610009 http://dx.doi.org/10.1371/journal.pcbi.1009465 Text en © 2021 Umarov et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Umarov, Ramzan Li, Yu Arner, Erik DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title | DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title_full | DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title_fullStr | DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title_full_unstemmed | DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title_short | DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
title_sort | deepcellstate: an autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8519465/ https://www.ncbi.nlm.nih.gov/pubmed/34610009 http://dx.doi.org/10.1371/journal.pcbi.1009465 |
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