genAFold: Calculate parameters for differential expression test base on...

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

Description

Calculate aFold for each gene and general sd

Usage

genAFold(nncounts, cond, preval = 0.05, qforkappa = 0, pair = FALSE,
  priorgenesd)

Arguments

nncounts	matrix for read count.
cond	factor for conditions. If provide only one condition, fold-change estimation will be suppressed.
preval	pre-defined scale control for variance normalization, default is 0.05, a large value generally increases the fold-changes (decreases penalty of variances) under low expression.
qforkappa	quantile for estimating kappa(>=qforkappa), default is 0 (without trimming of data). Please set up a value in [0,1) if you want to trim the low expressed data.
pair	switch for paired samples, default is false
priorgenesd	prior value for general SD of fold change, if provided, the estimation of general SD will be replaced by this value.

Details

shifted and calculate a set of parameters from normalized counts table before callDEs

Value

A list with log2 foldchange, general SD for calculating pvalue, variance stabilized counts and expression level adjusted counts (used for PCA analysis)

Note

This function should run after normalFactors.

Examples

data(simuN5)
obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups))
mtx <- counts(obj,TRUE)
aFold <- genAFold(mtx,factor(simuN5$groups))
hist(aFold[[1]])

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