qtotalNormalized: Estimating size factors from the reads count table via...

In ABSSeq: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences

Description

Function of qtotal for esitmating size factors

Usage

qtotalNormalized(ma, qper = 0.95, qst = 0.1, qend = 0.95, qstep = 0.01,
  qbound = 0.05, mcut = 4, qcl = 1.5)

Arguments

ma	a count matrix
qper	quantile for assessing dispersion of data, default is 0.95, which serves to avoid outliers, should in (0,1]
qst	start of quantile for estimating cv ratio, should be in [0,1], default is 0.1
qend	end of quantile for estimating cv ratio, should be in [qbound,1-qbound], default is .95
qstep	step of quantile for estimating cv ratio (sliding window), should be in (0,1], default is 0.01
qbound	window size for estimating cv and shifted size factor, default is 0.05, a smaller window size is suitable if number of genes is large
mcut	cutoff of mean from sliding window to avoid abnormal cv, should >=0, default is 4
qcl	scale for outlier detection, should >=0, default is 1.5

Details

Given a matrix of count data, this function esitmates the size factors by qtotal method, which is based on assessing DE (CV) and ranking. The CV is estimated via sliding window.

Value

a vector with the estimates size factors, one element per column

Examples

data(simuN5)
counts <- simuN5$counts
qtotalNormalized(counts)

ABSSeq documentation built on Nov. 8, 2020, 5:07 p.m.