@UNPUBLISHED{Scharpf2009,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{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}

 }