@UNPUBLISHED{Scharpf2009,
  author = {Robert B Scharpf and Ingo Ruczinski and Benilton Carvalho and Betty
	Doan and Aravinda Chakravarti and Rafael Irizarry},
  title = {A multilevel model to address batch effects in copy number estimation
	using SNP arrays},
  month = {May},
  year = {2009},
  url={http://www.bepress.com/cgi/viewcontent.cgi?article=1193&context=jhubiostat}
}

@ARTICLE{Korn2008,
  author = {Joshua M Korn and Finny G Kuruvilla and Steven A McCarroll and Alec
	Wysoker and James Nemesh and Simon Cawley and Earl Hubbell and Jim
	Veitch and Patrick J Collins and Katayoon Darvishi and Charles Lee
	and Marcia M Nizzari and Stacey B Gabriel and Shaun Purcell and Mark
	J Daly and David Altshuler},
  title = {Integrated genotype calling and association analysis of SNPs, common
	copy number polymorphisms and rare CNVs.},
  journal = {Nat Genet},
  year = {2008},
  volume = {40},
  pages = {1253--1260},
  number = {10},
  month = {Oct},
  abstract = {Accurate and complete measurement of single nucleotide (SNP) and copy
	number (CNV) variants, both common and rare, will be required to
	understand the role of genetic variation in disease. We present Birdsuite,
	a four-stage analytical framework instantiated in software for deriving
	integrated and mutually consistent copy number and SNP genotypes.
	The method sequentially assigns copy number across regions of common
	copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies
	rare CNVs via a hidden Markov model (HMM), and generates an integrated
	sequence and copy number genotype at every locus (for example, including
	genotypes such as A-null, AAB and BBB in addition to AA, AB and BB
	calls). Such genotypes more accurately depict the underlying sequence
	of each individual, reducing the rate of apparent mendelian inconsistencies.
	The Birdsuite software is applied here to data from the Affymetrix
	SNP 6.0 array. Additionally, we describe a method, implemented in
	PLINK, to utilize these combined SNP and CNV genotypes for association
	testing with a phenotype.},
  doi = {10.1038/ng.237},
  institution = {Broad Institute of Harvard and Massachusetts Institute of Technology,
	Cambridge, Massachusetts 02142, USA. jkorn@broad.mit.edu},
  owner = {rscharpf},
  pii = {ng.237},
  pmid = {18776909},
  timestamp = {2008.10.07},
  url = {http://dx.doi.org/10.1038/ng.237}
}

@ARTICLE{Carvalho2007a,
  author = {Benilton Carvalho and Henrik Bengtsson and Terence P Speed and Rafael
	A Irizarry},
  title = {Exploration, normalization, and genotype calls of high-density oligonucleotide
	SNP array data.},
  journal = {Biostatistics},
  year = {2007},
  volume = {8},
  pages = {485--499},
  number = {2},
  month = {Apr},
  abstract = {In most microarray technologies, a number of critical steps are required
	to convert raw intensity measurements into the data relied upon by
	data analysts, biologists, and clinicians. These data manipulations,
	referred to as preprocessing, can influence the quality of the ultimate
	measurements. In the last few years, the high-throughput measurement
	of gene expression is the most popular application of microarray
	technology. For this application, various groups have demonstrated
	that the use of modern statistical methodology can substantially
	improve accuracy and precision of the gene expression measurements,
	relative to ad hoc procedures introduced by designers and manufacturers
	of the technology. Currently, other applications of microarrays are
	becoming more and more popular. In this paper, we describe a preprocessing
	methodology for a technology designed for the identification of DNA
	sequence variants in specific genes or regions of the human genome
	that are associated with phenotypes of interest such as disease.
	In particular, we describe a methodology useful for preprocessing
	Affymetrix single-nucleotide polymorphism chips and obtaining genotype
	calls with the preprocessed data. We demonstrate how our procedure
	improves existing approaches using data from 3 relatively large studies
	including the one in which large numbers of independent calls are
	available. The proposed methods are implemented in the package oligo
	available from Bioconductor.},
  doi = {10.1093/biostatistics/kxl042},
  institution = {Department of Biostatistics, Johns Hopkins University, Baltimore,
	MD 21205, USA.},
  keywords = {Algorithms; Alleles; Data Interpretation, Statistical; Genotype; Humans;
	Oligonucleotide Array Sequence Analysis; Oligonucleotides; Polymorphism,
	Single Nucleotide},
  owner = {rscharpf},
  pii = {kxl042},
  pmid = {17189563},
  timestamp = {2008.08.07},
  url = {http://dx.doi.org/10.1093/biostatistics/kxl042}
}