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...

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Autores principales:	Ramzan Umarov, Yu Li, Erik Arner
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/1e922526e25248f6b5086385720b05c1
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spelling	oai:doaj.org-article:1e922526e25248f6b5086385720b05c12021-11-25T05:40:33ZDeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.1553-734X1553-735810.1371/journal.pcbi.1009465https://doaj.org/article/1e922526e25248f6b5086385720b05c12021-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009465https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Drug 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.Ramzan UmarovYu LiErik ArnerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 10, p e1009465 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ramzan Umarov Yu Li Erik Arner DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.
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	article
author	Ramzan Umarov Yu Li Erik Arner
author_facet	Ramzan Umarov Yu Li Erik Arner
author_sort	Ramzan Umarov
title	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_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_sort	deepcellstate: an autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1e922526e25248f6b5086385720b05c1
work_keys_str_mv	AT ramzanumarov deepcellstateanautoencoderbasedframeworkforpredictingcelltypespecifictranscriptionalstatesinducedbydrugtreatment AT yuli deepcellstateanautoencoderbasedframeworkforpredictingcelltypespecifictranscriptionalstatesinducedbydrugtreatment AT erikarner deepcellstateanautoencoderbasedframeworkforpredictingcelltypespecifictranscriptionalstatesinducedbydrugtreatment
_version_	1718414503626932224

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

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