ref.bib

@article{Ritchie2015,
abstract = {limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.},
author = {Ritchie, M. E. and Phipson, B. and Wu, D. and Hu, Y. and Law, C. W. and Shi, W. and Smyth, G. K.},
doi = {10.1093/nar/gkv007},
issn = {0305-1048},
journal = {Nucleic Acids Research},
month = jan,
pages = {gkv007--},
title = {{limma powers differential expression analyses for RNA-sequencing and microarray studies}},
url = {http://nar.oxfordjournals.org/content/early/2015/01/20/nar.gkv007.full},
year = {2015}
}

@article{Law2014,
abstract = {Normal linear modeling methods are developed for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation, and then enters these into a limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.},
author = {Law, Charity W and Chen, Yunshun and Shi, Wei and Smyth, Gordon K},
doi = {10.1186/gb-2014-15-2-r29},
file = {:C$\backslash$:/Users/belinda.phipson/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Law et al. - 2014 - Voom precision weights unlock linear model analysis tools for RNA-seq read counts.pdf:pdf},
issn = {1465-6906},
journal = {Genome Biology},
number = {2},
pages = {R29},
title = {{Voom: precision weights unlock linear model analysis tools for RNA-seq read counts}},
url = {http://genomebiology.com/2014/15/2/R29},
volume = {15},
year = {2014}
}

@article{robinson2010tmm,
author = {Robinson, Mark D and Oshlack, Alicia},
doi = {10.1186/gb-2010-11-3-r25},
file = {:D$\backslash$:/NHMRC/papers/tmm.pdf:pdf},
journal = {Genome Biology},
number = {3},
pages = {R25},
pmid = {20196867},
title = {{A scaling normalization method for differential expression analysis of RNA-seq data}},
url = {http://genomebiology.com/2010/11/3/R25},
volume = {11},
year = {2010}
}

@article{McCarthy2009,
author = {McCarthy, Davis J and Smyth, Gordon K},
doi = {10.1093/bioinformatics/btp053},
file = {:C$\backslash$:/Users/belinda.phipson/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/McCarthy, Smyth - 2009 - Testing significance relative to a fold-change threshold is a TREAT.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Gene Expression Profiling,Gene Expression Profiling: methods,Internet,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Software},
month = mar,
number = {6},
pages = {765--71},
pmid = {19176553},
title = {{Testing significance relative to a fold-change threshold is a TREAT.}},
url = {http://bioinformatics.oxfordjournals.org/content/25/6/765.short},
volume = {25},
year = {2009}
}

@article{wu2010roast,
author = {Wu, D and Lim, E and Vaillant, F and Asselin-Labat, M L and Visvader, J E and Smyth, G K},
journal = {Bioinformatics},
number = {17},
pages = {2176--2182},
publisher = {Oxford Univ Press},
title = {{ROAST: rotation gene set tests for complex microarray experiments}},
volume = {26},
year = {2010}
}

@article{wu2012camera,
author = {Wu, D and Smyth, G K},
journal = {Nucleic Acids Research},
number = {17},
pages = {e133----e133},
publisher = {Oxford University Press},
title = {{Camera: a competitive gene set test accounting for inter-gene correlation}},
volume = {40},
year = {2012}
}

@article{Liao2014,
author = {Liao, Yang and Smyth, Gordon K and Shi, Wei},
doi = {10.1093/bioinformatics/btt656},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Genome,Genomics,Genomics: methods,High-Throughput Nucleotide Sequencing,Histones,Histones: chemistry,Histones: genetics,Sequence Analysis, RNA,Software},
month = apr,
number = {7},
pages = {923--30},
pmid = {24227677},
title = {{featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.}},
url = {http://bioinformatics.oxfordjournals.org/content/30/7/923.short},
volume = {30},
year = {2014}
}

@article{liao2013subread,
author = {Liao, Yang and Smyth, Gordon K and Shi, Wei},
journal = {Nucleic Acids Research},
pages = {16 pages},
title = {{The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote}},
volume = {41},
year = {2013}
}

@article{Dobin2013,
author = {Dobin, Alexander and Davis, Carrie A and Schlesinger, Felix and Drenkow, Jorg and Zaleski, Chris and Jha, Sonali and Batut, Philippe and Chaisson, Mark and Gingeras, Thomas R},
doi = {10.1093/bioinformatics/bts635},
file = {:C$\backslash$:/Users/belinda.phipson/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Dobin et al. - 2013 - STAR ultrafast universal RNA-seq aligner.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
keywords = {Algorithms,Cluster Analysis,Gene Expression Profiling,Genome, Human,Humans,RNA Splicing,Sequence Alignment,Sequence Alignment: methods,Sequence Analysis, RNA,Sequence Analysis, RNA: methods,Software},
month = jan,
number = {1},
pages = {15--21},
pmid = {23104886},
title = {{STAR: ultrafast universal RNA-seq aligner.}},
url = {http://bioinformatics.oxfordjournals.org/content/early/2012/10/25/bioinformatics.bts635},
volume = {29},
year = {2013}
}

@article{trapnell2009tophat,
author = {Trapnell, Cole and Pachter, Lior and Salzberg, Steven L},
doi = {doi:10.1093/bioinformatics/btp120},
journal = {Bioinformatics},
number = {9},
pages = {1105--1111},
title = {{TopHat: discovering splice junctions with RNA-seq}},
volume = {25},
year = {2009}
}

@article{Langmead2012,
author = {Langmead, Ben and Salzberg, Steven L},
doi = {10.1038/nmeth.1923},
file = {:C$\backslash$:/Users/belinda.phipson/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Langmead, Salzberg - 2012 - Fast gapped-read alignment with Bowtie 2.pdf:pdf},
issn = {1548-7105},
journal = {Nature methods},
keywords = {Algorithms,Computational Biology,Computational Biology: methods,Databases, Genetic,Genome, Human,Genome, Human: genetics,Humans,Sequence Alignment,Sequence Alignment: methods,Sequence Analysis, DNA,Sequence Analysis, DNA: methods},
month = apr,
number = {4},
pages = {357--9},
pmid = {22388286},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
shorttitle = {Nat Meth},
title = {{Fast gapped-read alignment with Bowtie 2.}},
url = {http://dx.doi.org/10.1038/nmeth.1923},
volume = {9},
year = {2012}
}

@article{Lun2016,
author = {Lun, Aaron T L and Chen, Yunshun and Smyth, Gordon K},
doi = {10.1007/978-1-4939-3578-9\_19},
issn = {1940-6029},
journal = {Methods in molecular biology (Clifton, N.J.)},
month = jan,
pages = {391--416},
pmid = {27008025},
title = {{It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/27008025},
volume = {1418},
year = {2016}
}

@article{Fu2015,
abstract = {Expansion and remodelling of the mammary epithelium requires a tight balance between cellular proliferation, differentiation and death. To explore cell survival versus cell death decisions in this organ, we deleted the pro-survival gene Mcl-1 in the mammary epithelium. Mcl-1 was found to be essential at multiple developmental stages including morphogenesis in puberty and alveologenesis in pregnancy. Moreover, Mcl-1-deficient basal cells were virtually devoid of repopulating activity, suggesting that this gene is required for stem cell function. Profound upregulation of the Mcl-1 protein was evident in alveolar cells at the switch to lactation, and Mcl-1 deficiency impaired lactation. Interestingly, EGF was identified as one of the most highly upregulated genes on lactogenesis and inhibition of EGF or mTOR signalling markedly impaired lactation, with concomitant decreases in Mcl-1 and phosphorylated ribosomal protein S6. These data demonstrate that Mcl-1 is essential for mammopoiesis and identify EGF as a critical trigger of Mcl-1 translation to ensure survival of milk-producing alveolar cells.},
author = {Fu, Nai Yang and Rios, Anne C and Pal, Bhupinder and Soetanto, Rina and Lun, Aaron T L and Liu, Kevin and Beck, Tamara and Best, Sarah A and Vaillant, Fran\c{c}ois and Bouillet, Philippe and Strasser, Andreas and Preiss, Thomas and Smyth, Gordon K and Lindeman, Geoffrey J and Visvader, Jane E},
doi = {10.1038/ncb3117},
issn = {1476-4679},
journal = {Nature cell biology},
keywords = {Animals,Apoptosis,Apoptosis: genetics,Base Sequence,Cell Differentiation,Cell Differentiation: genetics,Cell Line,Cell Proliferation,Cell Proliferation: genetics,Cell Survival,Epidermal Growth Factor,Epidermal Growth Factor: antagonists \& inhibitors,Epidermal Growth Factor: biosynthesis,Epidermal Growth Factor: metabolism,Female,Gene Knockout Techniques,Lactation,Lactation: genetics,Lactation: metabolism,Mammary Glands, Animal,Mammary Glands, Animal: metabolism,Mice,Mice, Inbred C57BL,Myeloid Cell Leukemia Sequence 1 Protein,Myeloid Cell Leukemia Sequence 1 Protein: biosynth,Myeloid Cell Leukemia Sequence 1 Protein: genetics,Phosphorylation,Pregnancy,Ribosomal Protein S6,Ribosomal Protein S6: metabolism,Sequence Analysis, RNA,Stem Cells,Stem Cells: cytology,TOR Serine-Threonine Kinases,TOR Serine-Threonine Kinases: antagonists \& inhibi,Up-Regulation},
month = apr,
number = {4},
pages = {365--75},
pmid = {25730472},
title = {{EGF-mediated induction of Mcl-1 at the switch to lactation is essential for alveolar cell survival.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/25730472},
volume = {17},
year = {2015}
}

@article{brooks2011conservation,
author = {Brooks, Angela N and Yang, Li and Duff, Michael O and Hansen, Kasper D and Park, Jung W and Dudoit, Sandrine and Brenner, Steven E and Graveley, Brenton R},
journal = {Genome Research},
number = {2},
pages = {193--202},
publisher = {Cold Spring Harbor Lab},
title = {{Conservation of an RNA regulatory map between Drosophila and mammals}},
volume = {21},
year = {2011}
}

@article{robinson2010edgeR,
author = {Robinson, M D and McCarthy, D J and Smyth, G K},
journal = {Bioinformatics},
number = {1},
pages = {139--140},
publisher = {Oxford Univ Press},
title = {{edgeR: a Bioconductor package for differential expression analysis of digital gene expression data}},
volume = {26},
year = {2010}
}

@article{Risso2011,
author = {Risso, Davide and Schwartz, Katja and Sherlock, Gavin and Dudoit, Sandrine},
doi = {10.1186/1471-2105-12-480},
file = {:C$\backslash$:/Users/belinda.phipson/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Risso et al. - 2011 - GC-content normalization for RNA-Seq data.pdf:pdf},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Base Composition,Gene Expression Profiling,Saccharomyces cerevisiae,Saccharomyces cerevisiae: genetics,Sequence Analysis, RNA,Sequence Analysis, RNA: methods,Transcriptome},
month = jan,
number = {1},
pages = {480},
pmid = {22177264},
title = {{GC-content normalization for RNA-Seq data.}},
url = {http://www.biomedcentral.com/1471-2105/12/480},
volume = {12},
year = {2011}
}

@article{Su2017,
  doi = {10.1093/bioinformatics/btx094},
  url = {https://doi.org/10.1093/bioinformatics/btx094},
  year = {2017},
  month = feb,
  publisher = {Oxford University Press ({OUP})},
  volume = {33},
  number = {13},
  pages = {2050--2052},
  author = {Shian Su and Charity W Law and Casey Ah-Cann and Marie-Liesse Asselin-Labat and Marnie E Blewitt and Matthew E Ritchie},
  editor = {Bonnie Berger},
  title = {Glimma: interactive graphics for gene expression analysis},
  journal = {Bioinformatics}
}

@article{Law2018,
  doi = {10.12688/f1000research.9005.3},
  url = {https://doi.org/10.12688/f1000research.9005.3},
  year = {2018},
  month = dec,
  publisher = {F1000 Research Ltd},
  volume = {5},
  pages = {1408},
  author = {Charity W. Law and Monther Alhamdoosh and Shian Su and Xueyi Dong and Luyi Tian and Gordon K. Smyth and Matthew E. Ritchie},
  title = {{RNA}-seq analysis is easy as 1-2-3 with limma,  Glimma and {edgeR}},
  journal = {F1000Research}
}