Phân tích RNA-seq dễ như 1-2-3 với limma, Glimma và edgeR
Tóm tắt
Từ khóa
Tài liệu tham khảo
M Robinson, 2010, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data., Bioinformatics., 26, 139-140, 10.1093/bioinformatics/btp616
M Ritchie, 2015, limma powers differential expression analyses for RNA-sequencing and microarray studies., Nucleic Acids Res., 43, e47, 10.1093/nar/gkv007
W Huber, 2015, Orchestrating high-throughput genomic analysis with Bioconductor., Nat Methods., 12, 115-121, 10.1038/nmeth.3252
S Su, 2016, Glimma: Interactive HTML graphics for RNA-seq data
J Sheridan, 2015, A pooled shRNA screen for regulators of primary mammary stem and progenitor cells identifies roles for Asap1 and Prox1., BMC Cancer., 15, 221, 10.1186/s12885-015-1187-z
Y Liao, 2013, The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote., Nucleic Acids Res., 41, e108, 10.1093/nar/gkt214
Y Liao, 2014, featureCounts: an efficient general purpose program for assigning sequence reads to genomic features., Bioinformatics., 30, 923-30, 10.1093/bioinformatics/btt656
2016, Mus.musculus: Annotation package for the Mus.musculus object
2016, Homo.sapiens: Annotation package for the Homo.sapiens object
S Durinck, 2005, BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis., Bioinformatics., 21, 3439-40, 10.1093/bioinformatics/bti525
S Durinck, 2009, Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt., Nat Protoc., 4, 1184-91, 10.1038/nprot.2009.97
M Robinson, 2010, A scaling normalization method for differential expression analysis of RNA-seq data., Genome Biol., 11, R25, 10.1186/gb-2010-11-3-r25
C Law, 2014, voom: Precision weights unlock linear model analysis tools for RNA-seq read counts., Genome Biol., 15, R29, 10.1186/gb-2014-15-2-r29
R Liu, 2015, Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses., Nucleic Acids Res., 43, e97, 10.1093/nar/gkv412
R Liu, 2016, Transcriptional profiling of the epigenetic regulator Smchd1., Genom Data., 7, 144-7, 10.1016/j.gdata.2015.12.027
G Smyth, 2004, Linear models and empirical bayes methods for assessing differential expression in microarray experiments., Stat Appl Genet Mol Biol., 3, 10.2202/1544-6115.1027
D McCarthy, 2009, Testing significance relative to a fold-change threshold is a TREAT., Bioinformatics., 25, 765-71, 10.1093/bioinformatics/btp053
D Wu, 2012, Camera: a competitive gene set test accounting for inter-gene correlation., Nucleic Acids Res., 40, e133, 10.1093/nar/gks461
A Subramanian, 2005, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles., Proc Natl Acad Sci U S A., 102, 15545-50, 10.1073/pnas.0506580102
E Lim, 2010, Transcriptome analyses of mouse and human mammary cell subpopulations reveal multiple conserved genes and pathways., Breast Cancer Res., 12, R21, 10.1186/bcr2560
D Wu, 2010, ROAST: rotation gene set tests for complex microarray experiments., Bioinformatics., 26, 2176-82, 10.1093/bioinformatics/btq401
2016, R: A language and environment for statistical computing.
G Warnes, 2016, gplots: Various R Programming Tools for Plotting Data
Y Xie, 2014, knitr: A comprehensive tool for reproducible research in R, Implementing Reproducible Computational Research.
Y Xie, 2015, Dynamic Documents with R and knitr.
Y Xie, 2016, knitr: A General-Purpose Package for Dynamic Report Generation in R