RPPanalyzer (version 1.3)

correctDilinterc: Dilution series intersect correction

Description

Consists of 3 functions: getIntercepts(), analyzeIntercepts() and getSignals(). The first one derives intercepts of dilution series in dependence of dilSeriesID (column in sampledescription.txt) and slide/pad/incubationRun/spottingRun number (colnames of arraydescription). A smoothing spline is used to extrapolate to 0. Nonparametric bootstrap is used to estimate uncertainty of the intercept estimate. The second function is used in the last one and does Analysis of Variances for nested models. The last one updates the original timeseries signal to (foreground expression - intercept).

Usage

correctDilinterc(dilseries, arraydesc, timeseries, exportNo) 
    getIntercepts(dilseries, arraydesc)
    analyzeIntercepts(intercepts, test="F", export)
    getSignals(timeseries, intercepts, arraydesc, exportNo)
	as.my(v)

Arguments

dilseries

foreground signal matrix as result of write.Data and import of resulting txt file, but just sample_type "control", i.e. dilution series

arraydesc

"arraydescription" matrix of the RPPA data set list

timeseries

foreground signal matrix as result of write.Data and import of resulting txt file, but just sample_type "measurement"

exportNo

integer of 1-4 which of the linear fits should be exported to the attribute of the result, variable for analyzeIntercepts(), 1: constant, 2: antibody, 3: antibody + slide (default) or antibody + slide + sample (dilSeriesID)

intercepts

output of getIntercepts(), data frame with columns for dilSeriesID and slide/pad/incubationRun/spottingRun number as well as antibody, estimated intercept and estimated error of intercept

test

test parameter for ANOVA (see documentation of anova), default is "F"

export

see exportNo

v

some variable

Value

• matrix with adapted signal intensities via subtraction of dilution intercept at concentration 0

Examples

library(RPPanalyzer)
# read data
dataDir <- system.file("extdata", package="RPPanalyzer")
setwd(dataDir)
rawdata <- read.Data(blocksperarray=12, spotter="aushon", printFlags=FALSE)
# write data
write.Data(rawdata,FileNameExtension="test_data")
# import raw data
fgRaw.tmp <- read.delim("test_dataexpression.txt",\cr
stringsAsFactors=FALSE, row.names=NULL, header=TRUE)
fgRaw <- read.delim("test_dataexpression.txt", skip=max(which(fgRaw.tmp[,1]==""))+1,\cr
stringsAsFactors=FALSE, row.names=NULL, header=TRUE)
# remove NAs
fgNAVec <- which(is.na(fgRaw[,"ID"]))
if(length(fgNAVec) > 0){
fgRaw <- fgRaw[-fgNAVec,]
}
colnames(fgRaw) <- sub("X","", gsub("\.","-", colnames(fgRaw)))
# correct data for BG noise
correctedData <- correctDilinterc(dilseries=fgRaw[which(fgRaw$sample_type=="control" &\cr
!is.na(fgRaw$dilSeriesID)),], arraydesc=rawdata$arraydescription,\cr
timeseries=fgRaw[which(fgRaw$sample_type=="measurement"),], exportNo=2)