The last year Rafa (@rafalab) and I have been hard at work on an R-package called quantro that can help you decide on how best to normalize your noisy high-throughput data such as DNA methylationRNASeq and ChIPSeq. One of the most successful and widely applied multi-sample normalization methods, quantile normalization, is a global normalization method and based on a set of assumptions that are not always appropriate depending on the type and source of variation. Until now, it has been left to the researcher to decide if these assumptions are appropriate.  quantro is a data-driven method to test for the assumptions of global normalization methods and helps researchers decide on “when to use quantile normalization?“.

I am happy to announce quantro was accepted as an R-package in the Bioconductor 3.0 release this fall and a pre-print of the manuscript has been posted on bioRxiv today!  

Here's a data-driven answer to one of the most FAQ I get: When should I quantile normalize? writen w @stephaniehicks:

— Rafael A Irizarry (@rafalab) December 5, 2014

 There is vignette is available to give an example of how the package works using the FlowSorted.DLPFC.450k data package in Bioconductor.

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