author = {Robert B Scharpf and Ingo Ruczinski and Benilton Carvalho and Betty
	Doan and Aravinda Chakravarti and Rafael A Irizarry},
  title = {A multilevel model to address batch effects in copy number estimation
	using SNP arrays.},
  journal = {Biostatistics},
  year = {2011},
  volume = {12},
  pages = {33--50},
  number = {1},
  month = {Jan},
  abstract = {Submicroscopic changes in chromosomal DNA copy number dosage are common
	and have been implicated in many heritable diseases and cancers.
	Recent high-throughput technologies have a resolution that permits
	the detection of segmental changes in DNA copy number that span thousands
	of base pairs in the genome. Genomewide association studies (GWAS)
	may simultaneously screen for copy number phenotype and single nucleotide
	polymorphism (SNP) phenotype associations as part of the analytic
	strategy. However, genomewide array analyses are particularly susceptible
	to batch effects as the logistics of preparing DNA and processing
	thousands of arrays often involves multiple laboratories and technicians,
	or changes over calendar time to the reagents and laboratory equipment.
	Failure to adjust for batch effects can lead to incorrect inference
	and requires inefficient post hoc quality control procedures to exclude
	regions that are associated with batch. Our work extends previous
	model-based approaches for copy number estimation by explicitly modeling
	batch and using shrinkage to improve locus-specific estimates of
	copy number uncertainty. Key features of this approach include the
	use of biallelic genotype calls from experimental data to estimate
	batch-specific and locus-specific parameters of background and signal
	without the requirement of training data. We illustrate these ideas
	using a study of bipolar disease and a study of chromosome 21 trisomy.
	The former has batch effects that dominate much of the observed variation
	in the quantile-normalized intensities, while the latter illustrates
	the robustness of our approach to a data set in which approximately
	27\% of the samples have altered copy number. Locus-specific estimates
	of copy number can be plotted on the copy number scale to investigate
	mosaicism and guide the choice of appropriate downstream approaches
	for smoothing the copy number as a function of physical position.
	The software is open source and implemented in the R package crlmm
	at Bioconductor (http:www.bioconductor.org).},
  doi = {10.1093/biostatistics/kxq043},
  institution = {Department of Oncology, Johns Hopkins University School of Medicine,
	Baltimore, MD 21205, USA. rscharpf@jhsph.edu},
  language = {eng},
  medline-pst = {ppublish},
  owner = {rscharpf},
  pii = {kxq043},
  pmcid = {PMC3006124},
  pmid = {20625178},
  timestamp = {2011.02.26},
  url = {http://dx.doi.org/10.1093/biostatistics/kxq043}

  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}