title: quantro: a data-driven approach to guide the choice of an appropriate normalization method.
abstract: Normalization is an essential step in the analysis of high-throughput data. Multi-sample global normalization methods, such as quantile normalization, have been successfully used to remove technical variation. However, these methods rely on the assumption that observed global changes across samples are due to unwanted technical variability. Applying global normalization methods has the potential to remove biologically driven variation. Currently, it is up to the subject matter experts to determine if the stated assumptions are appropriate. Here, we propose a data-driven alternative. We demonstrate the utility of our method (quantro) through examples and simulations. A software implementation is available from http://www.bioconductor.org/packages/release/bioc/html/quantro.html .
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115-5450, USA. shicks@jimmy.harvard.edu.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. shicks@jimmy.harvard.edu.
authors.forename: Stephanie C
authors.initials: SC
authors.lastname: Hicks
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02115-5450, USA. rafa@jimmy.harvard.edu.|Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. rafa@jimmy.harvard.edu.
authors.forename: Rafael A
authors.initials: RA
authors.lastname: Irizarry
mesh_terms: D018899:CpG Islands; D019175:DNA Methylation; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008297:Male; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software