@article{Bolstad2003,
author = {Bolstad, B.M. and Irizarry, R.A and Astrand, M. and Speed, T.P.},
title = {{A comparison of normalization methods for high density oligonucleotide array data based on 
variance and bias}},
journal = {Bioinformatics},
volume = {19},
number = {2},
pages = {185-193},
year = {2003},
abstract = {Motivation: When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations.  Results: We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably.  Availability: Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org.  Contact: bolstad@stat.berkeley.edu.  Supplementary information: Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
},
URL = {http://bioinformatics.oupjournals.org/cgi/content/abstract/19/2/185},
eprint = {http://bioinformatics.oupjournals.org/cgi/reprint/19/2/185.pdf}
}



@Misc{Fox2002,
  author = 	 {John Fox},
  year={2002},
  title = 	 {{Robust regression: an appendix to an R and S-PLUS companion to applied regression}}
}


@Book{Huber1981,
  author = 	 {Huber, P. J.},
  title = 	 {{Robust Statistics}},
  publisher = 	 {Wiley series in probability and mathematical statistics},
  year = 	 {2004},
  address = 	 {New York}
}

@article{Irizarry2003,
author = {Irizarry, Rafael A. and Bolstad, Benjamin M. and Collin, Francois and Cope, Leslie M. and Hobbs, Bridget and Speed, Terence P.},
title = {{Summaries of Affymetrix GeneChip probe level data}},
journal = {Nucl. Acids Res.},
volume = {31},
number = {4},
pages = {e15},
year = {2003},
abstract = {High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11-20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.
},
URL = {http://nar.oupjournals.org/cgi/content/abstract/31/4/e15},
eprint = {http://nar.oupjournals.org/cgi/reprint/31/4/e15.pdf}
}

@Article{Smyth2004,
  author =       {Smyth, Gordon K.},
  title =        {{Linear models and empirical Bayes methods for assessing differential expression in            
                 microarray experiments}},
  journal =      {Statistical Applications in Genetics and Molecular Biology},
  year =         {2004},
  volume =       {3},
  number =       {1},
  pages =        {article 3}
}

@incollection{Smyth2005,
author = {Smyth, Gordon K.},
title = "Limma: linear models for microarray data.",
year = {2005},
editor = {R. Gentleman and V. Carey and S. Dudoit and R. Irizarry and W. Huber},
booktitle = {Bioinformatics and Computational Biology Solutions using R and Bioconductor},
publisher = "Springer",
address = "New York"
}


@Article{TaiSpeed2006,
  author =       {Tai, Yu Chuan and Speed, Terence P.},
  title =        {{A multivariate empirical Bayes statistic for replicated microarray time course data}},
  journal =      {{Annals of Statistics}},
  year =         {2006},
  volume =        {34},
  number = {5},
  pages ={2387-2412}
}

@incollection{Tai2005,
author = {Tai, Yu Chuan and Speed, Terence P.},
title = "Statistical Analysis of Microarray Time Course Data",
year = {2005},
editor = {U. Nuber},
booktitle = {DNA Microarrays},
publisher = "BIOS Scientific Publishers Limited, Taylor \& Francis",
address ={4 Park Square, Milton Park, Abingdon OX14 4RN},
note={Chapter 20}
}


@PhdThesis{Taithesis2005,
  author = 	 {Tai, Yu Chuan},
  title = 	 {{Multivariate empirical Bayes models for replicated microarray time course data}},
  school = 	 {Division of Biostatistics, University of California, Berkeley},
  year = 	 {2005},
  month ={May}
}

@Article{Tomancak2002,
AUTHOR = {Tomancak, Pavel and Beaton, Amy and Weiszmann, Richard and Kwan, Elaine and Shu, ShengQiang and Lewis, Suzanna and Richards, Stephen and Ashburner, Michael and Hartenstein, Volker and Celniker, Susan and Rubin, Gerald},
TITLE = {Systematic determination of patterns of gene expression during Drosophila embryogenesis},
JOURNAL = {Genome Biology},
VOLUME = {3},
YEAR = {2002},
NUMBER = {12},
PAGES = {research0088.1-0088.14},
URL = {http://genomebiology.com/2002/3/12/research/0088},
NOTE = {The microarray time course data can be downloaded from \url{www.fruitfly.org/cgi-bin/ex/insitu.pl}},
ISSN = {1465-6906},
ABSTRACT = {BACKGROUND:Cell-fate specification and tissue differentiation during development are largely achieved by the regulation of gene transcription.RESULTS:As a first step to creating a comprehensive atlas of gene-expression patterns during Drosophila embryogenesis, we examined 2,179 genes by in situ hybridization to fixed Drosophila embryos. Of the genes assayed, 63.7% displayed dynamic expression patterns that were documented with 25,690 digital photomicrographs of individual embryos. The photomicrographs were annotated using controlled vocabularies for anatomical structures that are organized into a developmental hierarchy. We also generated a detailed time course of gene expression during embryogenesis using microarrays to provide an independent corroboration of the in situ hybridization results. All image, annotation and microarray data are stored in publicly available database. We found that the RNA transcripts of about 1% of genes show clear subcellular localization. Nearly all the annotated expression patterns are distinct. We present an approach for organizing the data by hierarchical clustering of annotation terms that allows us to group tissues that express similar sets of genes as well as genes displaying similar expression patterns.CONCLUSIONS:Analyzing gene-expression patterns by in situ hybridization to whole-mount embryos provides an extremely rich dataset that can be used to identify genes involved in developmental processes that have been missed by traditional genetic analysis. Systematic analysis of rigorously annotated patterns of gene expression will complement and extend the types of analyses carried out using expression microarrays.},
}
