Microarray technology is a powerful tool for measuring genome-wide expression levels. These arrays have become a standard tool in medical science and basic biology research. In these technologies, a number of critical steps are required to convert the raw data, referred to as probe-level data, into the expression-level measures relied upon by biologists and clinicians. These data manipulations, referred to as pre-processing, have enormous influence on the quality of the ultimate measurements and on the studies that rely upon them. Affymetrix GeneChip expression array technology is the most widely used commercial platform. Our group has previously demonstrated that the use of the alternative pre-processing methodology can substantially improve accuracy and precision of gene expression measurements, relative to the ad-hoc procedures introduced by the manufacturers of this technology. Although a large number of tools exist for the analysis of expression measurements, software for the analysis of probe-level data is quite limited. The further improvement of pre-processing procedures is an important evolving research field and requires the availability of appropriate software. Through our Bioconductor affy R package we provide a flexible environment that is the premier open source tool for the analysis of Affymetrix probe-level data. The software is freely available to all and has become widely used by the research community. In fact, our thousands of users include various members of the research and development team at Affymetrix. Since its first release in May 2002, we have added various extensions, stand-alone software that implements the most used algorithms, and a web-tool for assessment of competing pre-processing algorithms. Furthermore, various commercial products have ported some of our tools making them available to an even larger base of users. Our proposed goal is to continue the support of our software and further develop our tools to increase their usefulness to the research community.
| Kumar, M Senthil; Slud, Eric V; Okrah, Kwame et al. (2018) Analysis and correction of compositional bias in sparse sequencing count data. BMC Genomics 19:799 |
| Hicks, Stephanie C; Irizarry, Rafael A (2015) quantro: a data-driven approach to guide the choice of an appropriate normalization method. Genome Biol 16:117 |
| Timp, Winston; Bravo, Hector Corrada; McDonald, Oliver G et al. (2014) Large hypomethylated blocks as a universal defining epigenetic alteration in human solid tumors. Genome Med 6:61 |
| Parker, Hilary S; Leek, Jeffrey T (2012) The practical effect of batch on genomic prediction. Stat Appl Genet Mol Biol 11:Article 10 |
| McCall, Matthew N; Jaffee, Harris A; Irizarry, Rafael A (2012) fRMA ST: frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays. Bioinformatics 28:3153-4 |
| Jaffe, Andrew E; Murakami, Peter; Lee, Hwajin et al. (2012) Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies. Int J Epidemiol 41:200-9 |
| Jaffe, Andrew E; Feinberg, Andrew P; Irizarry, Rafael A et al. (2012) Significance analysis and statistical dissection of variably methylated regions. Biostatistics 13:166-78 |
| Leek, Jeffrey T; Johnson, W Evan; Parker, Hilary S et al. (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882-3 |
| Scharpf, Robert B; Irizarry, Rafael A; Ritchie, Matthew E et al. (2011) Using the R Package crlmm for Genotyping and Copy Number Estimation. J Stat Softw 40:1-32 |
| McCall, Matthew N; Murakami, Peter N; Lukk, Margus et al. (2011) Assessing affymetrix GeneChip microarray quality. BMC Bioinformatics 12:137 |
Showing the most recent 10 out of 31 publications
