quantileAdjust {edgeR} | R Documentation |
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.
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)
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 |
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
Mark Robinson
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)