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Single cell RNA-seq denoising using a deep count autoencoder

View ORCID ProfileGökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, Fabian J. Theis
doi: https://doi.org/10.1101/300681
Gökcen Eraslan
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
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  • ORCID record for Gökcen Eraslan
Lukas M. Simon
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
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Maria Mircea
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
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Nikola S. Mueller
TUM School of Life Sciences Weihenstephan, Technische Universität München, Freising, Germany
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Fabian J. Theis
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, GermanyDepartment of Mathematics, Technische Universität München, Garching, Germany
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Abstract

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.

  • List of abbreviations

    scRNA-seq
    single-cell RNA sequencing
    tSNE
    t-distributed stochastic neighbor embedding
    DCA
    deep count autoencoder
    AE
    autoencoder
    PCA
    principal component analysis
    H1
    human embryonic stem cells
    DEC
    definitive endoderm cells
    MEP
    megakaryocyte-erythroid progenitors
    GMP
    granulocyte-macrophage progenitors
    MSE
    mean squared error
    ZINB
    zero-inflated negative binomial
    CITE-seq
    Cellular Indexing of Transcriptome and Epitopes by sequencing
    NK
    natural killer cells
    DPT
    diffusion pseudotime
    ReLU
    rectified linear unit.
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    Posted April 13, 2018.
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    Single cell RNA-seq denoising using a deep count autoencoder
    Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, Fabian J. Theis
    bioRxiv 300681; doi: https://doi.org/10.1101/300681
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    Single cell RNA-seq denoising using a deep count autoencoder
    Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller, Fabian J. Theis
    bioRxiv 300681; doi: https://doi.org/10.1101/300681

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