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Deep unsupervised single-cell clustering (DUSC), is a hybrid approach that integrates feature-selection based on a deep learning architecture with a clustering algorithm, to find a compressed and informative representation of single-cell transcriptomic data.
DUSC is resilient to the inherent biological and technical noises in single-cell experiments and is used to generate cell clusters corresponding to cell types and sub-types.
We also introduce the denoising autoencoder with neuronal approximation (DAWN), which can efficiently estimate the number of latent features required for each dataset.