Background: High density single nucleotide polymorphism microarrays (SNP chips) provide information on a subject's genome, such as the chromosomal copy numbers and the genotype (heterozygosity/homozygosity). In contrast to fluorescence in situ hybridization and karyotyping, SNP chips provide a high resolution map of the human genome that can be used to detect, for example, aneuploidies, microdeletions, microduplications, and loss of heterozygosity. 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 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, including the R packages Biobase and oligo. 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. Note: the build of callsConfidence is not in Bioconductor yet, please click here.

Collaborators: SNPchip, developed at the Pevsner Lab in the Kennedy Krieger Institute and the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, is a collaboration with Jon Pevsner, Rob Scharpf, and Jason Ting. Greatfully acknowledged is the input from Benilton Carvalho, Rafael Irizarry, and Giovanni Parmigiani.

Code: The R package is available from the Bioconductor webpage as Free Software in source code form under the terms of the GNU General Public License of the Free Software Foundation. Supplementary material such as a vignette and example code is available here.