A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

Genome Biology

Volumn 17, Issue 1, 2016, Pages

  Korthauer, Keegan D ^a,b   Chu, Li Fang ^c   Newton, Michael A ^c,d   Li, Yuan ^d   Thomson, James ^c,e,f   Stewart, Ron ^c   Kendziorski, Christina ^c,d  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

^d University of Wisconsin Madison   (United States)

^e UNIVERSITY OF WISCONSIN SCHOOL OF MEDICINE AND PUBLIC HEALTH   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Cellular heterogeneity; Differential expression; Mixture modeling; Single cell RNA seq

Indexed keywords

GENE EXPRESSION; STATISTICAL MODEL; ALGORITHM; BIOLOGY; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; SOFTWARE; STATISTICS AND NUMERICAL DATA;

RNA;

ALGORITHMS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; RNA; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; SOFTWARE;

EID: 84992327075     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-016-1077-y     Document Type: Article

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