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@Article{luo:etal:2013,
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  title = 	 {Affymetrix Microarray Suite User Guide},
author = {Affymetrix},
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  address =	 {Santa Clara, CA},
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@article{tripathi:etal:2013,
author = {Tripathi, Shailesh and Glazko, Galina V. and Emmert-Streib, Frank}, 
title = {Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential},
volume = {41}, 
number = {7}, 
pages = {e82}, 
year = {2013}, 
doi = {10.1093/nar/gkt054}, 
URL = {http://nar.oxfordjournals.org/content/41/7/e82.abstract}, 
eprint = {http://nar.oxfordjournals.org/content/41/7/e82.full.pdf+html}, 
journal = {Nucleic Acids Research} 
}

@article{smyth:2004,
author = {Smyth, Gordon K.}, 
title = {Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments},
volume = {3}, 
number = {1}, 
pages = {Article 3}, 
year = {2004}, 
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journal = {Statistical Applications in Genetics and Molecular Biology} 
}

@article{trapnell:etal:2012,
author = {Trapnell, Cole and Roberts, Adam and Goff, Loyal and Pertea, Geo and Kim, Daehwan and Kelley, David R. and Pimentel, Harold and Salzberg, Steven L. and Rinn, John L. and Pachter, Lior}, 
title = {{Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks}},
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journal = {Nature Protocols}
}

@Article{anders:etal:2010,
AUTHOR = {Anders, Simon and Huber, Wolfgang},
TITLE = {Differential expression analysis for sequence count data},
JOURNAL = {Genome Biology},
VOLUME = {11},
YEAR = {2010},
NUMBER = {10},
PAGES = {R106},
URL = {http://genomebiology.com/2010/11/10/R106},
DOI = {10.1186/gb-2010-11-10-r106},
PubMedID = {20979621},
ISSN = {1465-6906},
ABSTRACT = {High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.}
}

@article{robinson:etal:2010,
author = {Robinson, Mark D. and McCarthy, Davis J. and Smyth, Gordon K.}, 
title = {{edgeR}: a Bioconductor package for differential expression analysis of digital gene expression data},
volume = {26}, 
number = {1}, 
pages = {139-140}, 
year = {2010}, 
doi = {10.1093/bioinformatics/btp616}, 
abstract ={Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data.Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).Contact: mrobinson@wehi.edu.au}, 
URL = {http://bioinformatics.oxfordjournals.org/content/26/1/139.abstract}, 
eprint = {http://bioinformatics.oxfordjournals.org/content/26/1/139.full.pdf+html}, 
journal = {Bioinformatics} 
}
