Empirical analysis of digital gene expression data in R
Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce counts, including ChIP-seq, SAGE and CAGE.
Yunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Xiaobei Zhou <xiaobei.zhou@uzh.ch>, Mark Robinson <mark.robinson@imls.uzh.ch>, Gordon Smyth <smyth@wehi.edu.au>
Yunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Mark Robinson <mark.robinson@imls.uzh.ch>, Davis McCarthy <dmccarthy@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>
MASS, statmod, splines, locfit, KernSmooth
GeneExpression, Transcription, AlternativeSplicing,
Coverage, DifferentialExpression, DifferentialSplicing,
GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression,
TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect,
MultipleComparison, Normalization, QualityControl