betr: Bayesian Estimation of Temporal Regulation (BETR): Calculate...

In betr: Identify differentially expressed genes in microarray time-course data

Description

Calculate the probability of differential expression of each feature in a microarray gene expression time-course data set.

Usage

betr(eset, cond=NULL, timepoint, replicate, twoColor = FALSE, twoCondition = NULL, alpha = 0.05, verbose=FALSE)

Arguments

eset	object of class matrix, ExpressionSet or exprSet containing log-ratios or log-values of expression for a series of microarrays
cond	character or factor vector giving the experimental group for each sample of eset. Not required for a single-condition time-course.
timepoint	numeric vector giving the time point for each sample of eset
replicate	character or factor vector giving the replicate ID of each sample of eset
twoColor	boolean indicating whether the data is from a two-color microarray platform
twoCondition	boolean indicating whether the data is from a two condition experiment (as opposed to a single condition experiment where the comparison is between baseline and subsequent time points)
alpha	the desired False Discovery Rate
verbose	whether to output more detailed information about the model fitting

Details

This function fits a model to estimate the probability of differential for each feature of time-course data set.

Value

a numeric vector of the probability of differential expression for each feature in the data set.

Author(s)

Martin Aryee

References

The algorithm is described in detail in: Aryee et al., An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation), BMC Bioinformatics. 2009 Dec 10;10:409.

Examples

library(Biobase)    
data(timeEset)
prob <- betr(timeEset, cond=pData(timeEset)$strain, 
    timepoint=pData(timeEset)$time, replicate=pData(timeEset)$rep, alpha=0.05)
head(prob)

betr documentation built on April 14, 2017, 5:16 a.m.