Normalizing single-cell RNA sequencing data: Challenges and opportunities

Nature Methods

Volumn 14, Issue 6, 2017, Pages 565-571

  Vallejos, Catalina A ^a,b,c,d   Risso, Davide ^e,h   Scialdone, Antonio ^b   Dudoit, Sandrine ^e   Marioni, John C ^b,f,g  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^c ALAN TURING INSTITUTE   (United Kingdom)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

^e UNIVERSITY OF CALIFORNIA   (United States)

^f UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^g WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^h WEILL CORNELL MEDICAL COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANIMAL CELL; DATA ANALYSIS; MICROARRAY ANALYSIS; NONHUMAN; PRIORITY JOURNAL; REVIEW; RNA SEQUENCE; SINGLE CELL ANALYSIS; TECHNOLOGY; TRANSCRIPTOMICS; ALGORITHM; GENETICS; HIGH THROUGHPUT SEQUENCING; PROCEDURES; REFERENCE VALUE; SEQUENCE ANALYSIS; STANDARDS; STATISTICAL ANALYSIS;

RNA; TRANSCRIPTOME;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; REFERENCE VALUES; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85021816036     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.4292     Document Type: Review

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