Single-cell Assisted Deconvolutional Network

Scaden is a tool for bulk RNA-seq cell type deconvolutional that uses a deep neural network ensemble trained on artificial bulk data simulated with scRNA-seq datasets. This method was developed in the Genome Biology of Neurodegenerativ Diseases group at the DZNE Tübingen and the Medical Systems Biology group at the ZMNH. The main author is Kevin Menden.

A pre-print describing the method is available on Biorxiv: Deep-learning-based cell composition analysis from tissue expression profiles

Version 0.9.1

Changelog: + Added automatic removal of duplicate genes in Mixture file + Changed name of final prediction file + Added Scaden logo to main script

Version 0.9.0

This is the initial release version of Scaden. While this version contains full functionality for pre-processing, training and prediction, it does not contain thorough error messages, plotting functionality and a solid helper function for generation training data. These are all features planned for the release of v.1.0.0. The core functionality of Scaden is, however, implemented and fully operational. Please check the Usage section to learn how to use Scaden.