Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
| Package: | ruv |
| Type: | Package |
| Version: | 0.9.7.1 |
| Date: | 2019-08-30 |
| License: | GPL |
| LazyLoad: | yes |
| URL: | http://www-personal.umich.edu/~johanngb/ruv/ |
Gagnon-Bartsch, J.A. and T.P. Speed (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics. <doi:10.1093/biostatistics/kxr034>
Gagnon-Bartsch, J.A., L. Jacob, and T.P. Speed (2013). Removing Unwanted Variation from High Dimensional Data with Negative Controls. Technical report. Available at: http://statistics.berkeley.edu/tech-reports/820
Molania, R., J. A. Gagnon-Bartsch, A. Dobrovic, and T. P. Speed (2019). A new normalization for the Nanostring nCounter gene expression assay. Nucleic Acids Research. <doi:10.1093/nar/gkz433>