Single-cell RNA-seq denoising using a deep count autoencoder

Nature Communications

Volumn 10, Issue 1, 2019, Pages

  Eraslan, Gökcen ^a,b   Simon, Lukas M ^a   Mircea, Maria ^a   Mueller, Nikola S ^a   Theis, Fabian J ^a,b  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

SMALL CYTOPLASMIC RNA; RNA;

ANIMAL CELL; ARTICLE; DATA ANALYSIS; GENE EXPRESSION; NONHUMAN; NONLINEAR SYSTEM; RNA SEQUENCE; SIMULATION; SINGLE CELL ANALYSIS; ANIMAL; BIOLOGY; BLOOD CELL; CAENORHABDITIS ELEGANS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; MONONUCLEAR CELL; PHENOTYPE; PROCEDURES; SEQUENCE ANALYSIS; STATISTICAL MODEL;

ANIMALS; BLOOD CELLS; CAENORHABDITIS ELEGANS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; LEUKOCYTES, MONONUCLEAR; MODELS, STATISTICAL; PHENOTYPE; RNA; RNA, SMALL CYTOPLASMIC; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS;

EID: 85060401600     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-018-07931-2     Document Type: Article

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