A Hierarchical, Data-Driven Approach to Modeling Single-Cell Populations Predicts Latent Causes of Cell-To-Cell Variability

Cell Systems

Volumn 6, Issue 5, 2018, Pages 593-603.e13

  Loos, Carolin ^a,b   Moeller, Katharina ^c   Fröhlich, Fabian ^a,b   Hucho, Tim ^c   Hasenauer, Jan ^a,b  

^a INSTITUTE OF EPIDEMIOLOGY   (Germany)

^b TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^c UNIVERSITY HOSPITAL OF COLOGNE   (Germany)

Author keywords

heterogeneity; mixture modeling; pain sensitization; single cell data; statistical inference; systems biology

Indexed keywords

MITOGEN ACTIVATED PROTEIN KINASE 1; MITOGEN ACTIVATED PROTEIN KINASE 3; NERVE GROWTH FACTOR; PROTEIN TYROSINE KINASE A;

ARTICLE; CELL CULTURE; CELL POPULATION; MICROSCOPY; PAIN; PRIORITY JOURNAL; SENSORY NERVE CELL; SIGNAL TRANSDUCTION; SINGLE CELL POPULATION; ANIMAL; BIOLOGICAL MODEL; BIOLOGICAL VARIATION; BIOLOGY; COMPUTER SIMULATION; MALE; PHYSIOLOGY; PROCEDURES; RAT; SINGLE CELL ANALYSIS; SPRAGUE DAWLEY RAT;

ANIMALS; BIOLOGICAL VARIATION, POPULATION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; MALE; MICROSCOPY; MODELS, BIOLOGICAL; RATS; RATS, SPRAGUE-DAWLEY; SENSORY RECEPTOR CELLS; SIGNAL TRANSDUCTION; SINGLE-CELL ANALYSIS;

EID: 85046341029     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2018.04.008     Document Type: Article

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