1 |
Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 2002;18 Suppl 1:S96-S104.
DOI
|
2 |
Kadota K, Nishiyama T, Shimizu K. A normalization strategy for comparing tag count data. Algorithms Mol Biol 2012;7:5.
DOI
|
3 |
Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res 2011;21:2213-2223.
DOI
|
4 |
Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009;10:57-63.
DOI
|
5 |
Garber M, Grabherr MG, Guttman M, Trapnell C. Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 2011;8:469-477.
DOI
|
6 |
Auer PL, Srivastava S, Doerge RW. Differential expression: the next generation and beyond. Brief Funct Genomics 2012;11:57-62.
DOI
|
7 |
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004;5:R80.
DOI
|
8 |
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-140.
DOI
|
9 |
Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010;11:R106.
DOI
|
10 |
Wang L, Feng Z, Wang X, Wang X, Zhang X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 2010;26:136-138.
DOI
|
11 |
Hardcastle TJ, Kelly KA. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics 2010;11:422.
DOI
|
12 |
Calza S, Raffelsberger W, Ploner A, Sahel J, Leveillard T, Pawitan Y. Filtering genes to improve sensitivity in oligonucleotide microarray data analysis. Nucleic Acids Res 2007;35:e102.
DOI
|