Deep generative modeling for single-cell transcriptomics

Nature Methods

Volumn 15, Issue 12, 2018, Pages 1053-1058

  Lopez, Romain ^a   Regier, Jeffrey ^a   Cole, Michael B ^b   Jordan, Michael I ^a,b   Yosef, Nir ^a,c,d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d CHAN ZUCKERBERG BIOHUB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; GENE EXPRESSION; STOCHASTIC MODEL; TRANSCRIPTOMICS; ALGORITHM; ANIMAL; BIOLOGICAL MODEL; BIOLOGY; BRAIN; CLUSTER ANALYSIS; CYTOLOGY; GENETIC VARIATION; HEMATOPOIETIC STEM CELL; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLISM; MONONUCLEAR CELL; MOUSE; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS;

TRANSCRIPTOME;

ALGORITHMS; ANIMALS; BRAIN; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; GENETIC VARIATION; HEMATOPOIETIC STEM CELLS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; LEUKOCYTES, MONONUCLEAR; MICE; MODELS, BIOLOGICAL; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85057586270     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/s41592-018-0229-2     Document Type: Article

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