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

1

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
########################################
##### 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

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.