Journal Article

DZNE-2020-01279

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning–based cell composition analysis from tissue expression profiles

Menden, K. (First author)DZNE* ; Marouf, M. ; Oller, S. ; Dalmia, A.DZNE* ; Magruder, D. S. ; Kloiber, K. ; Heutink, P.DZNE* ; Bonn, S. (Last author)DZNE*

2020
Assoc. Washington, DC [u.a.]

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Please use a persistent id in citations: doi:10.1126/sciadv.aba2619

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.

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Contributing Institute(s):

1. Computational analysis of biological networks (AG Bonn 1)
2. Genome Biology of Neurodegenerative Diseases (AG Heutink)

Research Program(s):

1. 342 - Disease Mechanisms and Model Systems (POF3-342) (POF3-342)
2. 345 - Population Studies and Genetics (POF3-345) (POF3-345)

Appears in the scientific report 2020

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