clippda (version 1.22.0)

betweensampleVariance: 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

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.

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')

[e-mail]=53

Data=OBJECT

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

rawData <- proteomicsExprsData(Data)

no.peaks <- [e-mail]

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