(R) A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). 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 expressions 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. 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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
- Yang, Jiaqi; Zhang, Wei; Wu, Baolin: A note on statistical method for genotype calling of high-throughput single-nucleotide polymorphism arrays (2013)
- Hansen, Kasper D.; Irizarry, Rafael A.; Wu, Zhijin: Removing technical variability in RNA-seq data using conditional quantile normalization (2012)
- Bengtsson, Henrik; Neuvial, Pierre; Speed, Terence P.: Tumorboost: normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays (2010) ioport
- Greenman, Chris D.; Bignell, Graham; Butler, Adam; Edkins, Sarah; Hinton, Jon: PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data (2010)
- Teo, Yik Y.: Exploratory data analysis in large-scale genetic studies (2010)
- Sampson, Joshua N.; Zhao, Hongyu: Genotyping and inflated type I error rate in genome-wide association case/control studies (2009) ioport
- Scharpf, Robert B.; Parmigiani, Giovanni; Pevsner, Jonathan; Ruczinski, Ingo: Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays (2008)
- Carvalho, Benilton; Bengtsson, Henrik; Speed, Terence P.; Irizarry, Rafael A.: Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data (2007)