@article{Robinson:2010p249,
author = {Mark D Robinson and Davis J McCarthy and Gordon K Smyth}, 
journal = {Bioinformatics},
title = {edgeR: a Bioconductor package for differential expression analysis of digital gene expression data},
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).},
affiliation = {Cancer Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW 2010, Australia. mrobinson@wehi.edu.au},
number = {1},
pages = {139--40},
volume = {26},
year = {2010},
month = {Jan},
language = {eng},
keywords = {Signal Processing: Computer-Assisted, Algorithms, Programming Languages, Gene Expression Profiling, Software, Oligonucleotide Array Sequence Analysis}, 
date-added = {2010-04-15 14:54:42 +1000},
date-modified = {2010-04-16 17:12:51 +1000},
doi = {10.1093/bioinformatics/btp616},
pii = {btp616},
pmid = {19910308},
URL = {http://bioinformatics.oxfordjournals.org/cgi/content/full/26/1/139},
local-url = {file://localhost/Users/dmccarthy/Documents/Papers/2010/Robinson/Bioinformatics%202010%20Robinson.pdf},
uri = {papers://A9DE1D68-1058-44A3-8C56-88A0045B89C1/Paper/p249},
read = {Yes},
rating = {0}
}

@article{Anders:2010p792,
author = {Simon Anders and Wolfgang Huber}, 
journal = {Genome Biology},
title = {Differential expression analysis for sequence count data},
abstract = {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.},
affiliation = {European Molecular Biology Laboratory, Mayerhofstra{\ss}e 1, 69117 Heidelberg, Germany. sanders@fs.tum.de.},
number = {10},
pages = {R106},
volume = {11},
year = {2010},
month = {Oct},
language = {ENG},
date-added = {2011-02-01 12:48:55 +1100},
date-modified = {2011-02-01 13:09:25 +1100},
doi = {10.1186/gb-2010-11-10-r106},
pii = {gb-2010-11-10-r106},
pmid = {20979621},
local-url = {file://localhost/Users/dmccarthy/Documents/Papers/2010/Anders/Genome%20Biology%202010%20Anders.pdf},
uri = {papers://A9DE1D68-1058-44A3-8C56-88A0045B89C1/Paper/p792},
rating = {0}
}

@article{zhang2008model,
  title={Model-based analysis of ChIP-Seq (MACS)},
  author={Zhang, Y. and Liu, T. and Meyer, C.A. and Eeckhoute, J. and Johnson, D.S. and Bernstein, B.E. and Nussbaum, C. and Myers, R.M. and Brown, M. and Li, W. and others},
  journal={Genome Biol},
  volume={9},
  number={9},
  pages={R137},
  year={2008}
}

@article{ross2012differential,
  title={Differential oestrogen receptor binding is associated with clinical outcome in breast cancer},
  author={Ross-Innes, C.S. and Stark, R. and Teschendorff, A.E. and Holmes, K.A. and Ali, H.R. and Dunning, M.J. and Brown, G.D. and Gojis, O. and Ellis, I.O. and Green, A.R. and others},
  journal={Nature},
  volume={481},
  number={7381},
  pages={389--393},
  year={2012},
  publisher={Nature Publishing Group}
}


@article{li2011measuring,
  title={Measuring reproducibility of high-throughput experiments},
  author={Li, Q. and Brown, J.B. and Huang, H. and Bickel, P.},
  journal={Annals of Applied Statistics},
  year={2011}
}


