BALLI (version 0.2.0)

tecVarEstim: Technical Variance Estimation

Description

Estimate technical variance by using voom-trend. The code is derived from voom function in limma package

Usage

tecVarEstim(counts, design = NULL, lib.size = NULL, span = 0.5, ...)

Arguments

counts

a DGEList object

design

design matrix with samples in row and coefficient(s) to be estimated in column

lib.size

numeric vector containing total library sizes for each sample

span

width of the lowess smoothing window as a proportion

...

other arguments are passed to lmFit.

Value

an TecVarList object with the following components:

targets

matrix containing covariables, library sizes and normalization foctors of each sample

design

design matrix with samples in row and covariable(s) to be estimated in column

logcpm

logcpm values of each gene and each sample

tecVar

estimated techical variance of each gene and each sample

Examples

# NOT RUN {
expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50))))
group <- c(rep("A",10),rep("B",10))
design <- model.matrix(~group, data = expr)
dge <- DGEList(counts=expr, group=group)
dge <- calcNormFactors(dge)
tecVarEstim(dge,design)
# }