Literature DB >> 17204461

SNPchip: R classes and methods for SNP array data.

Robert B Scharpf1, Jason C Ting, Jonathan Pevsner, Ingo Ruczinski.   

Abstract

UNLABELLED: 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/

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Year:  2007        PMID: 17204461     DOI: 10.1093/bioinformatics/btl638

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

Review 1.  The properties of high-dimensional data spaces: implications for exploring gene and protein expression data.

Authors:  Robert Clarke; Habtom W Ressom; Antai Wang; Jianhua Xuan; Minetta C Liu; Edmund A Gehan; Yue Wang
Journal:  Nat Rev Cancer       Date:  2008-01       Impact factor: 60.716

2.  Hidden Markov models for the assessment of chromosomal alterations using high-throughput SNP arrays.

Authors:  Robert B Scharpf; Giovanni Parmigiani; Jonathan Pevsner; Ingo Ruczinski
Journal:  Ann Appl Stat       Date:  2008-06-01       Impact factor: 2.083

3.  R classes and methods for SNP array data.

Authors:  Robert B Scharpf; Ingo Ruczinski
Journal:  Methods Mol Biol       Date:  2010

4.  CNAReporter: a GenePattern pipeline for the generation of clinical reports of genomic alterations.

Authors:  Yuri Kotliarov; Serdar Bozdag; Hangjiong Cheng; Stefan Wuchty; Jean-Claude Zenklusen; Howard A Fine
Journal:  BMC Med Genomics       Date:  2010-04-09       Impact factor: 3.063

  4 in total

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