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000153282 1001_ $$0P:(DE-2719)2812499$$aMenden, Kevin$$b0$$eFirst author$$udzne
000153282 245__ $$aDeep learning–based cell composition analysis from tissue expression profiles
000153282 260__ $$aWashington, DC [u.a.]$$bAssoc.$$c2020
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000153282 520__ $$aWe 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.
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000153282 7001_ $$00000-0002-3877-7793$$aMarouf, Mohamed$$b1
000153282 7001_ $$00000-0002-8994-1549$$aOller, Sergio$$b2
000153282 7001_ $$0P:(DE-2719)2812478$$aDalmia, Anupriya$$b3$$udzne
000153282 7001_ $$0P:(DE-HGF)0$$aMagruder, Daniel Sumner$$b4
000153282 7001_ $$aKloiber, Karin$$b5
000153282 7001_ $$0P:(DE-2719)2810728$$aHeutink, Peter$$b6$$udzne
000153282 7001_ $$0P:(DE-2719)2810547$$aBonn, Stefan$$b7$$eLast author$$udzne
000153282 773__ $$0PERI:(DE-600)2810933-8$$a10.1126/sciadv.aba2619$$gVol. 6, no. 30, p. eaba2619 -$$n30$$peaba2619 -$$tScience advances$$v6$$x2375-2548$$y2020
000153282 8564_ $$uhttps://advances.sciencemag.org/content/6/30/eaba2619
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