Single cell RNA-seq denoising using a deep count autoencoder

Count based loss function is necessary to identify celltypes in simulated data with high
levels of dropout noise.
(c) Helmholtz Zentrum München

Scientists from the Helmholtz Zentrum München developed a deep count autoencoder (DCA) to denoise single cell RNA-seq datasets.

The deep count autoencoder network (DCA) denoises scRNA-seq data and removes the dropout effect by taking the count structure, overdispersed nature and sparsity of the data into account using a deep autoencoder with zero-inflated negative binomial (ZINB) loss function. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery. The software can be downloaded from:

For more information, see the preprint: Eraslan G. et al. Single cell RNA-seq denoising using a deep count autoencoder. bioRxiv, 2018. DOI: 10.1101/300681


FASTGenomics: Platform for single-cell RNA sequencing data

(c) Comma Soft AG 2018,

FASTGenomics is a platform for single cell transcriptomics to analyze single cell expression data. Researchers from biology, medicine, and computational biology can use FASTGenomics to use the latest algorithms together with big data storage solutions and carry comfortably out their own studies. The platform is intuitive to use, meets high data protection standards, and promotes exchange and re-use in the scientific community.

For more information, see the preprint: Scholz C.J. et al. FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data. bioRxiv, 2018. DOI: 10.1101/272476

Sparse2big kick-off meeting

The kick-off meeting for our new sparse2big consortium took place at the Helmholtz Center Munich. Sparse2big focuses on method development to deal with sparse data, in particular in single cell genomics data. Eight Helmholtz Centers work together: Joachim Schultze, Uwe Ohler, ... and collaborate with industry: Comma Soft AG, IBM, ... .

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