betweensampleVariance: A generic function for computing the biological variance and...
In clippda: A package for the clinical proteomic profiling data analysis

Description Usage Arguments Value Author(s) Examples

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.

1	betweensampleVariance(Data, ...)

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

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.

Stephen Nyangoma

########################################
##### 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