Scaden – deep learning–based cell composition analysis from tissue expression profiles

Researchers at the German Center for Neurodegenerative Diseases have developed 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. The researchers 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, they 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.

Overview of training data generation and cell type deconvolution with Scaden

(A) Artificial bulk samples are generated by subsampling random cells from an scRNA-seq dataset and merging their expression profiles. (B) Model training and parameter optimization on simulated tissue RNA-seq data by comparing cell fraction predictions to ground-truth cell composition. (C) Cell deconvolution of real tissue RNA-seq data using Scaden.

Availability – The source code for Scaden is available at

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. Science Advances 6(30) eaba2619. [article]

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