RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

F1000Research

Volumn 5, Issue , 2016, Pages

  Smyth, Gordon K ^a,d   Law, Charity W ^a,b   Alhamdoosh, Monther ^c   Su, Shian ^a   Ritchie, Matthew E ^a,b,d  

^a WALTER AND ELIZA HALL INSTITUTE OF MEDICAL RESEARCH   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c CSL LIMITED   (Australia)

^d UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Data analysis; Gene expression; RNA sequencing

Indexed keywords

ANIMAL TISSUE; ARTICLE; BAYES THEOREM; GENE EXPRESSION; GENE MAPPING; GENETIC ALGORITHM; MOUSE; NONHUMAN; RNA SEQUENCE; SEQUENCE ANALYSIS; SOFTWARE; STATISTICAL MODEL;

EID: 85010866381     PISSN: 20461402     EISSN: 1759796X     Source Type: Journal    
DOI: 10.12688/f1000research.9005.2     Document Type: Article

References (26)

1. Robinson MD McCarthy DJ Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140. 19910308 10.1093/bioinformatics/btp616 2796818
2. Ritchie ME Phipson B Wu D: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. 25605792 10.1093/nar/gkv007 4402510
3. Huber W Carey VJ Gentleman R: Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015;12(2):115-121. 25633503 10.1038/nmeth.3252 4509590
4. Su S Ritchie ME: Glimma: Interactive HTML graphics for RNA-seq data.2016; R package version 1.1.1.
5. Sheridan JM Ritchie ME Best SA: A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1. BMC Cancer. 2015;15(1):221. 25879659 10.1186/s12885-015-1187-z 4399223
6. Liao Y Smyth GK Shi W: The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41(10):e108. 23558742 10.1093/nar/gkt214 3664803
7. Liao Y Smyth GK Shi W: featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923-30. 24227677 10.1093/bioinformatics/btt656
8. Bioconductor Core Team: Mus.musculus: Annotation package for the Mus.musculus object.2016; R package version 1.3.1. Reference Source
9. Bioconductor Core Team: Homo.sapiens: Annotation package for the Homo.sapiens object.2016; R package version 1.3.1. Reference Source
10. Durinck S Moreau Y Kasprzyk A: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;21(16):3439-40. 16082012 10.1093/bioinformatics/bti525
11. Durinck S Spellman PT Birney E: Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4(8):1184-91. 19617889 10.1038/nprot.2009.97 3159387
12. Robinson MD Oshlack A: A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. 20196867 10.1186/gb-2010-11-3-r25 2864565
13. Law CW Chen Y Shi W: voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. 24485249 10.1186/gb-2014-15-2-r29 4053721
14. Liu R Holik AZ Su S: Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 2015;43(15):e97. 25925576 10.1093/nar/gkv412 4551905
15. Liu R Chen K Jansz N: Transcriptional profiling of the epigenetic regulator Smchd1. Genom Data. 2016;7:144-7. 26981392 10.1016/j.gdata.2015.12.027 4778621
16. Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3(1): Article3. 16646809 10.2202/1544-6115.1027
17. McCarthy DJ Smyth GK: Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics. 2009;25(6):765-71. 19176553 10.1093/bioinformatics/btp053 2654802
18. Wu D Smyth GK: Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;40(17):e133. 22638577 10.1093/nar/gks461 3458527
19. Subramanian A Tamayo P Mootha VK: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. 16199517 10.1073/pnas.0506580102 1239896
20. Lim E Wu D Pal B: Transcriptome analyses of mouse and human mammary cell subpopulations reveal multiple conserved genes and pathways. Breast Cancer Res. 2010;12(2):R21. 20346151 10.1186/bcr2560 2879567
21. Wu D Lim F Vaillant F: ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics. 2010;26(17):2176-82. 20610611 10.1093/bioinformatics/btq401 2922896
22. R Development Core Team: R: A language and environment for statistical computing.R Foundation for Statistical Computing, Vienna,2016. Reference Source
23. Warnes GR Bolker B Bonebakker L: gplots: Various R Programming Tools for Plotting Data.2016; R package version 3.0.1. Reference Source
24. Xie Y: knitr: A comprehensive tool for reproducible research in R. In: V. Stodden, F. Leisch, and R. D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC,2014; ISBN 978-1466561595. Reference Source
25. Xie Y: Dynamic Documents with R and knitr. Chapman and Hall/CRC, Boca Raton, Florida, 2nd edition,2015; ISBN 978-1498716963. Reference Source
26. Xie Y: knitr: A General-Purpose Package for Dynamic Report Generation in R.2016; R package version 1.12.3. Reference Source