MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Genome Biology

Volumn 16, Issue 1, 2015, Pages

  Finak, Greg ^a   McDavid, Andrew ^a   Yajima, Masanao ^a   Deng, Jingyuan ^a   Gersuk, Vivian ^b   Shalek, Alex K ^c,d,e   Slichter, Chloe K ^a   Miller, Hannah W ^a   McElrath, M Juliana ^a   Prlic, Martin ^a   Linsley, Peter S ^b   Gottardo, Raphael ^a  

^a Fred Hutchinson Cancer Research Center   (United States)

^b BENAROYA RESEARCH INSTITUTE   (United States)

^c Massachusetts Institute of Technology Cambridge   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

Bimodality; Cellular detection rate; Co expression; Empirical Bayes; Gene set enrichment analysis; Generalized linear model

Indexed keywords

DNA TRANSCRIPTION; EXPERIMENTAL THERAPY; GENE EXPRESSION; HUMAN; MODEL; RNA SEQUENCE; STATISTICAL MODEL; STOCHASTIC MODEL; TRANSCRIPTION REGULATION; TRANSCRIPTOMICS; ANIMAL; DENDRITIC CELL; GENE EXPRESSION PROFILING; GENETIC VARIATION; METABOLISM; MOUSE; PROCEDURES; SEQUENCE ANALYSIS; SINGLE CELL ANALYSIS; STATISTICAL ANALYSIS;

TRANSCRIPTOME;

ANIMALS; DATA INTERPRETATION, STATISTICAL; DENDRITIC CELLS; GENE EXPRESSION PROFILING; GENETIC VARIATION; HUMANS; LINEAR MODELS; MICE; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 84951574149     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-015-0844-5     Document Type: Article

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