qsmooth: qsmooth

In qsmooth: Smooth quantile normalization

Description

This function applies a generalization of quantile normalization called smoothed quantile normalization. This function defines the qsmooth class and constructor.

Usage

qsmooth(object, group_factor, batch = NULL, norm_factors = NULL, window = 0.05)

Arguments

object	an object which is a matrix or data.frame with observations (e.g. probes or genes) on the rows and samples as the columns. Alternatively, a user can provide a SummarizedExperiment object and the assay(object, "counts") will be used as input for the qsmooth normalization.
group_factor	a group level continuous or categorial covariate associated with each sample or column in the object. The order of the group_factor must match the order of the columns in object.
batch	(Optional) batch covariate (multiple batches are not allowed). If batch covariate is provided, Combat() from sva is used prior to qsmooth normalization to remove batch effects. See Combat() for more details.
norm_factors	optional normalization scaling factors.
window	window size for running median which is a fraction of the number of rows in object. Default is 0.05.

Details

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 high-throughput 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 group-level 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.

Value

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.

Examples

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))

qsmooth documentation built on Nov. 8, 2020, 5:26 p.m.