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Deep learning-based cell composition analysis from tissue expression profiles

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Deep learning-based cell composition analysis from tissue expression profiles. / Menden, Kevin; Marouf, Mohamed; Oller, Sergio; Dalmia, Anupriya; Magruder, Daniel Sumner; Kloiber, Karin; Heutink, Peter; Bonn, Stefan.

In: SCI ADV, Vol. 6, No. 30, 22.07.2020, p. eaba2619.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Menden, K, Marouf, M, Oller, S, Dalmia, A, Magruder, DS, Kloiber, K, Heutink, P & Bonn, S 2020, 'Deep learning-based cell composition analysis from tissue expression profiles', SCI ADV, vol. 6, no. 30, pp. eaba2619. https://doi.org/10.1126/sciadv.aba2619

APA

Menden, K., Marouf, M., Oller, S., Dalmia, A., Magruder, D. S., Kloiber, K., Heutink, P., & Bonn, S. (2020). Deep learning-based cell composition analysis from tissue expression profiles. SCI ADV, 6(30), eaba2619. https://doi.org/10.1126/sciadv.aba2619

Vancouver

Menden K, Marouf M, Oller S, Dalmia A, Magruder DS, Kloiber K et al. Deep learning-based cell composition analysis from tissue expression profiles. SCI ADV. 2020 Jul 22;6(30):eaba2619. https://doi.org/10.1126/sciadv.aba2619

Bibtex

@article{18ee660ee49d4815ae19ca8bcbd7fa4a,
title = "Deep learning-based cell composition analysis from tissue expression profiles",
abstract = "We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.",
author = "Kevin Menden and Mohamed Marouf and Sergio Oller and Anupriya Dalmia and Magruder, {Daniel Sumner} and Karin Kloiber and Peter Heutink and Stefan Bonn",
note = "Copyright {\textcopyright} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).",
year = "2020",
month = jul,
day = "22",
doi = "10.1126/sciadv.aba2619",
language = "English",
volume = "6",
pages = "eaba2619",
journal = "SCI ADV",
issn = "2375-2548",
publisher = "American Association for the Advancement of Science",
number = "30",

}

RIS

TY - JOUR

T1 - Deep learning-based cell composition analysis from tissue expression profiles

AU - Menden, Kevin

AU - Marouf, Mohamed

AU - Oller, Sergio

AU - Dalmia, Anupriya

AU - Magruder, Daniel Sumner

AU - Kloiber, Karin

AU - Heutink, Peter

AU - Bonn, Stefan

N1 - Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

PY - 2020/7/22

Y1 - 2020/7/22

N2 - We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

AB - We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden's software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

U2 - 10.1126/sciadv.aba2619

DO - 10.1126/sciadv.aba2619

M3 - SCORING: Journal article

C2 - 32832661

VL - 6

SP - eaba2619

JO - SCI ADV

JF - SCI ADV

SN - 2375-2548

IS - 30

ER -