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 read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE.
Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Yunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>, Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>, Mark Robinson <mark.robinson@imls.uzh.ch>
methods, graphics, stats, utils, locfit, Rcpp
jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi,
SummarizedExperiment, org.Hs.eg.db
GeneExpression, Transcription, AlternativeSplicing,
Coverage, DifferentialExpression, DifferentialSplicing,
DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics,
DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression,
TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE,
Normalization, QualityControl, MultipleComparison,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Epigenetics, Genetics, ImmunoOncology, SystemsBiology,
Transcriptomics