f-scLVM: Scalable and versatile factor analysis for single-cell RNA-seq

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  Buettner, Florian ^a,f   Pratanwanich, Naruemon ^a   McCarthy, Davis J ^a,b   Marioni, John C ^a,c,d   Stegle, Oliver ^a,e  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b ST VINCENT'S INSTITUTE OF MEDICAL RESEARCH   (Australia)

^c CANCER RESEARCH UK CAMBRIDGE RESEARCH INSTITUTE   (United Kingdom)

^d WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^e EUROPEAN MOLECULAR BIOLOGY LABORATORY   (Germany)

^f INSTITUTE OF EPIDEMIOLOGY   (Germany)

Author keywords

Gene set annotations; Single cell RNA seq; Sparse factor analysis

Indexed keywords

DECOMPOSITION; FACTOR ANALYSIS; HUMAN; RNA SEQUENCE; ANIMAL; COMPUTER SIMULATION; DATA BASE; GENE EXPRESSION REGULATION; METABOLISM; MOUSE; MOUSE EMBRYONIC STEM CELL; NERVE CELL; REPRODUCIBILITY; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE; THEORETICAL MODEL;

ANIMALS; COMPUTER SIMULATION; DATABASES AS TOPIC; FACTOR ANALYSIS, STATISTICAL; GENE EXPRESSION REGULATION; MICE; MODELS, THEORETICAL; MOUSE EMBRYONIC STEM CELLS; NEURONS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 85033389355     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1334-8     Document Type: Article

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