An interpretable framework for clustering single-cell RNA-Seq datasets

BMC Bioinformatics

Volumn 19, Issue 1, 2018, Pages

  Zhang, Jesse M ^a   Fan, Jue ^b   Fan, H Christina ^b   Rosenfeld, David ^b   Tse, David N ^a  

^a Stanford University   (United States)

^b BD Genomics   (United States)

Author keywords

Clustering; Feature selection; Interpretability; Single cell RNA seq

Indexed keywords

COMPUTATIONAL EFFICIENCY; CYTOLOGY; FEATURE EXTRACTION; POPULATION STATISTICS; RNA;

CELL TYPES; CLUSTERING; CLUSTERING PROBLEMS; HYPERPARAMETERS; INTERPRETABILITY; MULTIPLE LEVELS; SINGLE CELLS; UNSUPERVISED ANALYSIS;

CELLS;

ARTICLE; HUMAN; SOFTWARE; VELOCITY; ALGORITHM; CLUSTER ANALYSIS; GENETIC DATABASE; METABOLISM; MONONUCLEAR CELL; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; STANDARD;

ALGORITHMS; CLUSTER ANALYSIS; DATABASES, GENETIC; HUMANS; LEUKOCYTES, MONONUCLEAR; REFERENCE STANDARDS; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85043484245     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-018-2092-7     Document Type: Article

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