qsmooth  R Documentation 
This function applies a generalization of quantile normalization called smoothed quantile normalization. This function defines the qsmooth class and constructor.
qsmooth(object, group_factor, batch = NULL, norm_factors = NULL, window = 0.05)
object 
an object which is a 
group_factor 
a group level continuous or categorial
covariate associated with each sample or column in the

batch 
(Optional) batch covariate (multiple
batches are not allowed).
If batch covariate is provided, 
norm_factors 
optional normalization scaling factors. 
window 
window size for running median which is
a fraction of the number of rows in 
Quantile normalization is one of the most widely used normalization tools for data analysis in genomics. Although it was originally developed for gene expression microarrays it is now used across many different highthroughput applications including RNAseq and ChIPseq. The methodology relies on the assumption that observed changes in the empirical distribution of samples are due to unwanted variability. Because the data is transformed to remove these differences it has the potential to remove interesting biologically driven global variation. Therefore, applying quantile normalization, or other global normalization methods that rely on similar assumptions, may not be an appropriate depending on the type and source of variation.
This function computes a weight at every quantile that compares the variability between groups relative to within groups. In one extreme quantile normalization is applied and in the other extreme quantile normalization within each biological condition is applied. The weight shrinks the grouplevel quantile normalized data towards the overall reference quantiles if variability between groups is sufficiently smaller than the variability within groups. See the vignette for more details.
A object of the class qsmooth
that
contains a numeric vector of the qsmooth weights in
the qsmoothWeights
slot and a matrix of normalized
values after applying smoothed quantile normalization in
the qsmoothData
slot.
dat < cbind(matrix(rnorm(1000), nrow=100, ncol=10), matrix(rnorm(1000, .1, .7), nrow=100, ncol=10)) dat_qs < qsmooth(object = dat, group_factor = rep(c(0,1), each=10))
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