@ARTICLE{Scharpf2007a,
  author = {Robert B Scharpf and Giovanni Parmigiani and Jonathan Pevsner and
	Ingo Ruczinski},
  title = {Hidden {M}arkov models for the assessment of chromosomal alterations
	using high-throughput {SNP} arrays},
  journal = {Annals of Applied Statistics},
  year = {2008},
}

@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, Statistical; Oligonucleotide Array Sequence Analysis; Polymorphism,
	Single Nucleotide; Software},
  owner = {rscharpf},
  pii = {638},
  pmid = {17204461},
  timestamp = {2007.11.26},
  url = {http://dx.doi.org/10.1093/bioinformatics/btl638}
}
