ABSDataSet: ABSDataSet object and constructors

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

Description

ABSDataSet object and constructors

Usage

ABSDataSet(counts, groups, normMethod = c("user", "qtotal", "total", "quartile",
  "geometric", "TMM"), sizeFactor = 0, paired = FALSE, minDispersion = NULL, minRates = 0.1,
  maxRates = 0.3, LevelstoNormFC = 100)

Arguments

counts	a matrix or table with at least two columns and one row,
groups	a factor with two groups, whose length should be equal with sample size
normMethod	method for estimating the size factors, should be one of 'user', 'qtotal', 'total', 'quartile', 'geometric' and 'TMM'. See normalFactors for description.
sizeFactor	size factors for 'user' method, self-defined size factors by user.
paired	switch for differential expression detection in paired samples.
minDispersion	a positive double for user-defined penalty of dispersion estimation
minRates	low bounder rate of baseline estimation for counts difference, default is 0.1
maxRates	up bounder rate of baseline estimation for counts difference, default is 0.3. Setting minRates equal with maxRates will result in a testing on user-define rate,
LevelstoNormFC	maximal level of average standard deviation in fold-change normalization according to expression level, default is 100.

Details

The function contructs an ABSDataSet object with counts table and groups. It also checks the structure of counts and groups.The ABSDataSet is a class, used to store the input values, intermediate calculations and results of an analysis of differential expression. It also contains information for the running time of an analysis.

Value

An ABSDataSet object.

Examples

counts <- matrix(1:4,ncol=2)
groups <- factor(c("a","b"))
obj <- ABSDataSet(counts, groups)
obj <- ABSDataSet(counts, groups, paired=TRUE)

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