RT Journal Article
SR Electronic
T1 Single cell RNA-seq denoising using a deep count autoencoder
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 300681
DO 10.1101/300681
A1 Gökcen Eraslan
A1 Lukas M. Simon
A1 Maria Mircea
A1 Nikola S. Mueller
A1 Fabian J. Theis
YR 2018
UL http://biorxiv.org/content/early/2018/04/13/300681.abstract
AB Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNAseq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a zero-inflated negative binomial noise model, and nonlinear gene-gene or gene-dispersion interactions are captured. Our method scales linearly with the number of cells and can therefore be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.scRNA-seqsingle-cell RNA sequencingtSNEt-distributed stochastic neighbor embeddingDCAdeep count autoencoderAEautoencoderPCAprincipal component analysisH1human embryonic stem cellsDECdefinitive endoderm cellsMEPmegakaryocyte-erythroid progenitorsGMPgranulocyte-macrophage progenitorsMSEmean squared errorZINBzero-inflated negative binomialCITE-seqCellular Indexing of Transcriptome and Epitopes by sequencingNKnatural killer cellsDPTdiffusion pseudotimeReLUrectified linear unit.