TY  - JOUR
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
TI  - Deep learning–based cell composition analysis from tissue expression profiles
JO  - Science advances
VL  - 6
IS  - 30
SN  - 2375-2548
CY  - Washington, DC [u.a.]
PB  - Assoc.
M1  - DZNE-2020-01279
SP  - eaba2619 -
PY  - 2020
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.
LB  - PUB:(DE-HGF)16
C6  - pmid:32832661
C2  - pmc:PMC7439569
DO  - DOI:10.1126/sciadv.aba2619
UR  - https://pub.dzne.de/record/153282
ER  -