quantileAdjust {edgeR}R Documentation

Normalizes a dataset by using a quantile adjustment

Description

The function adjusts (you might say normalizes) a dataset, creating pseudodata that represents quantile-adjusted data as if all samples had the same library size, while estimating the dispersion parameter.

Usage

 
quantileAdjust(object, N = prod(object$lib.size)^(1/ncol(object$data)), alpha = 0, null.hypothesis = FALSE, n.iter = 5, r.init = NULL, tol = 0.001, verbose=TRUE) 

Arguments

object list containing the raw data with elements data (table of counts), group (vector indicating group) and lib.size (vector of library sizes)
N library size to normalize to; default is the geometric mean of the original library sizes
alpha weight to put on the individual tag's likelihood
null.hypothesis logical, whether to calculate the means and percentile under the null hypothesis; default is TRUE
n.iter number of iterations in estimating the size parameter
r.init initialized value of the size parameter; if NULL, then the common value on unadjusted data is used
tol tolerance in estimating the size parameter
verbose whether to write comments, default true

Value

list containing several elements used in downstream function calls. r is the dispersion estimate, pseudo is the quantile-adjusted pseudodata, ps is a list containing the abundance estimates, N is the common library size and p and mu are the percentiles and means, respectively that the quantile is based on

Author(s)

Mark Robinson

Examples

set.seed(0)
y<-matrix(rnbinom(40,size=1,mu=10),ncol=4)
d<-list(data=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
qA<-quantileAdjust(d,alpha=100)

[Package edgeR version 1.0.4 Index]