TY - JOUR T1 - Deep-learning-based cell composition analysis from tissue expression profiles JF - bioRxiv DO - 10.1101/659227 SP - 659227 AU - Kevin Menden AU - Mohamed Marouf AU - Anupriya Dalmia AU - Peter Heutink AU - Stefan Bonn Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/07/16/659227.abstract 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-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, across tissues and species. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data. Due to this stability and flexibility, we surmise that deep learning-based cell deconvolution will become a mainstay across data types and algorithmic approaches. Scaden’s comprehensive software package is easy to use on novel as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.RNA-seqNext Generation RNA SequencingGEPgene expression profile matrixSVRSupport Vector RegressionDNNDeep Neural NetworkscRNA-seqsingle cell RNA-seqsimulated tissuetraining data generated by mixing proportions of scRNA-seq dataPBMCperipheral blood mononuclear cellsCCCconcordance correlation coefficientrPearson’s correlation coefficientCSCIBERSORTCSxCIBERSORTxCPMCell Population Mapping ER -