betweensampleVariance {clippda} | R Documentation |
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.
betweensampleVariance(Data, ...)
Data |
An object of |
... |
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