betweensampleVariance {clippda} | R Documentation

## A generic function for computing the biological variance and mean differences between cases and controls

### Description

This generic function fits a regression model to the averaged replicate data. The outputs are the between sample variance, and the differences in mean expression between cases and controls, adjusted for confounders.

### Usage

    betweensampleVariance(Data, ...)

### Arguments

`Data` | An object of `aclinicalProteomicsData` class.
`...` | Some methods for this generic function may take additional, optional arguments. At present none do.

### Value

It returns a list with the following components:

`betweensamplevariance` | A vector of the between-sample variance for each peak.
`differences` | A vector of the differences in mean expression values between the cases and controls, adjusted for confounders for each peak.
`significance` | A dataframe, or a vector of the differential-expression p-values for each peak.

### Author(s)

Stephen Nyangoma

### Examples

    ########################################
    ##### methods for the generic function
    ########################################

    showMethods("betweensampleVariance")

    ###################################################
    # Creating data of a aclinicalProteomicsData class
    ###################################################

    data(liverdata)

    data(liver_pheno)

    OBJECT=new("aclinicalProteomicsData")

    OBJECT@rawSELDIdata=as.matrix(liverdata)

    OBJECT@covariates=c("tumor" , "sex")

    OBJECT@phenotypicData=as.matrix(liver_pheno)

    OBJECT@variableClass=c('numeric','factor','factor')

    OBJECT@no.peaks=53

    Data=OBJECT

    #################################################################################
    # Data manipulation carried out internally by the betweensampleVariance function
    #################################################################################

    rawData <- proteomicsExprsData(Data)

    no.peaks <- Data@no.peaks

    JUNK_DATA <- sampleClusteredData(rawData,no.peaks)

    JUNK_DATA=negativeIntensitiesCorrection(JUNK_DATA)

    # we use the log-basetwo2 expression values

    LOG_DATA <- log2(JUNK_DATA)

    #######################################################################################
    # compute biological variation, difference to be estimated, and the p-values
    #######################################################################################

    BiovarDiffSig <- betweensampleVariance(OBJECT)

    BiovarDiffSig

[Package clippda version 1.40.0 Index]