SC3: Consensus clustering of single-cell RNA-seq data

Nature Methods

Volumn 14, Issue 5, 2017, Pages 483-486

  Kiselev, Vladimir Yu ^a   Kirschner, Kristina ^b   Schaub, Michael T ^c,d   Andrews, Tallulah ^a   Yiu, Andrew ^a   Chandra, Tamir ^a,e   Natarajan, Kedar N ^a,f   Reik, Wolf ^a,e,g   Barahona, Mauricio ^h   Green, Anthony R ^b   Hemberg, Martin ^a  

^a WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c University of Namur   (Belgium)

^d UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

^e BABRAHAM INSTITUTE   (United Kingdom)

^f EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^g UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^h IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTOME;

ARTICLE; CARCINOGENESIS; CELLS BY BODY ANATOMY; CONSENSUS; DEVELOPMENTAL STAGE; GOLD STANDARD; HUMAN; MARKER GENE; MYELOPROLIFERATIVE NEOPLASM; PRIORITY JOURNAL; RNA SEQUENCE; SANGER SEQUENCING; SINGLE CELL ANALYSIS; SUPPORT VECTOR MACHINE; WORKFLOW; CLUSTER ANALYSIS; CYTOLOGY; GENE EXPRESSION PROFILING; HEMATOPOIETIC STEM CELL; HIGH THROUGHPUT SEQUENCING; INFORMATION PROCESSING; PROCEDURES; SEQUENCE ANALYSIS;

CLUSTER ANALYSIS; DATASETS AS TOPIC; GENE EXPRESSION PROFILING; HEMATOPOIETIC STEM CELLS; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 85016121177     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.4236     Document Type: Article

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