@ARTICLE{Carvalho2007,
  author = {Benilton Carvalho and Henrik Bengtsson and Terence P Speed and Rafael
	A Irizarry},
  title = {{E}xploration, 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},
  keywords = {17189563},
  owner = {rscharpf},
  pii = {kxl042},
  pmid = {17189563},
  timestamp = {2007.07.07},
  url = {http://dx.doi.org/10.1093/biostatistics/kxl042},
}

@ARTICLE{Scharpf2007,
  author = {Robert B Scharpf and Jason C Ting and Jonathan Pevsner and Ingo Ruczinski},
  title = {{SNP}chip: {R} classes and methods for {SNP} array data.},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {627--628},
  number = {5},
  month = {Mar},
  abstract = {High-density single nucleotide polymorphism microarrays (SNP chips)
	provide information on a subject's genome, such as copy number and
	genotype (heterozygosity/homozygosity) at a SNP. While fluorescence
	in situ hybridization and karyotyping reveal many abnormalities,
	SNP chips provide a higher resolution map of the human genome that
	can be used to detect, e.g., aneuploidies, microdeletions, microduplications
	and loss of heterozygosity (LOH). As a variety of diseases are linked
	to such chromosomal abnormalities, SNP chips promise new insights
	for these diseases by aiding in the discovery of such regions, and
	may suggest targets for intervention. The R package SNPchip contains
	classes and methods useful for storing, visualizing and analyzing
	high density SNP data. Originally developed from the SNPscan web-tool,
	SNPchip utilizes S4 classes and extends other open source R tools
	available at Bioconductor. This has numerous advantages, including
	the ability to build statistical models for SNP-level data that operate
	on instances of the class, and to communicate with other R packages
	that add additional functionality. AVAILABILITY: The package is available
	from the Bioconductor web page at www.bioconductor.org. SUPPLEMENTARY
	INFORMATION: The supplementary material as described in this article
	(case studies, installation guidelines and R code) is available from
	http://biostat.jhsph.edu/~iruczins/publications/sm/},
  doi = {10.1093/bioinformatics/btl638},
  keywords = {Models, Oligonucleotide Array Sequence Analysis, Polymorphism, Single
	Nucleotide, Software, Statistical, 17204461},
  owner = {rscharpf},
  pii = {btl638},
  pmid = {17204461},
  timestamp = {2007.05.21},
  url = {http://dx.doi.org/10.1093/bioinformatics/btl638},
}

@TECHREPORT{Scharpf2007a,
  author = {Robert B Scharpf and Giovanni Parmigiani and Jonathan Pevsner and Ingo Ruczinski},
  title = {A hidden {M}arkov model for joint estimation of genotype and copy
	number in high-throughput {SNP} chips},
  institution = {Johns Hopkins University},
  number = {Working Paper 136},  
  month = {February},
  year = {2007},
}
