RPA: probe reliability and differential expression analysis

Description

RPA estimates probe-specific variances and differential gene expression using probe-level observations of differential gene expression.

Details

Package:	RPA
Type:	Package
Version:	1.1.2
Date:	2009-07-22
License:	GNU GPL 2 or any later version (at your option)
LazyLoad:	yes

RPA.pointestimate computes probe reliability and differential expression estimates: 'rpa.results <- RPA.pointestimate(affybatch)'. The other functions are provided for users who wish to investigate the details of the algorithm more closely.

Author(s)

Leo Lahti <leo.lahti@tkk.fi>

References

Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays. Lahti et al., TCBB/IEEE, to appear. See http://www.cis.hut.fi/projects/mi/software/RPA/

Examples

        ## Load example data set (Dilution affybatch).
        ## This is a toy example with a small example dataset
        ## for probe reliability analysis (4 arrays).
        ## For practical applications, a larger sample size is
        ## recommended.

        require(affy)
        require(affydata)
        data(Dilution)

        ## Run RPA analysis
        ## Compute RPA for the whole data set
        ## Slow, not executed here
        ##rpa.results <- RPA.pointestimate(Dilution)

        # Compute RPA for specific probesets only
        sets = geneNames(Dilution)[1:5]
        rpa.results <- RPA.pointestimate(Dilution,sets)

        ## Visualize the results for one of the probe sets
        plot(rpa.results[sets[[1]],])