qualityParameters {arrayMagic}R Documentation

Calculation of quality characteristics for DNA microarray hybridisations

Description

Several quality measures are calculated. The return value, i.e. a list of quality scores, should be used as input argument for the function qualityDiagnostics. For details on the quality measures read the value section.

Usage

qualityParameters(arrayDataObject, exprSetRGObject, spotIdentifier = "Name", slideNameColumn = "slideName", identifiersToBeSkipped=NA, resultFileName, verbose = TRUE)

Arguments

arrayDataObject object of type arrayData; required; default: missing
exprSetRGObject object of type exprSetRG; required; default: missing
spotIdentifier character string; required; specifies a column of getSpotAttr(arrayDataObject); the column is used to determine spot replicas; default: "Name"
slideNameColumn character string; required; specifies a column of getHybAttr(arrayDataObject); the column is used to extract the names of the hybridisations; if not found the hybridisations are consecutively numbered; default: "slideName"
identifiersToBeSkipped vector of character strings of spot identifiers to be excluded from calculations; required; default: NA
resultFileName character string; results are stored in a tab-deliminated file if supplied; default: missing
verbose logical; default TRUE

Details

For details on the quality measures read the value section.

Value

returns a list of results, i.e. a data.frame qualityParameters containing several scores for each hybridisation, as well as pairwise comparisons, i.e. a matrix slideDistance, a matrix slideDistanceLogRaw, a matrix slideDistanceGreen, a matrix slideDistanceGreenLogRaw, a matrix slideDistanceRed, a matrix slideDistanceRedLogRaw, and an integer replicateSpots, i.e. the number of detected spot replicas.
The matrix slideDistanceLogRaw contains a calculated distance (similarity) for each pair of slides$_{ij}$, i.e. the median absolute deviation (mad) taken over all spots of the log-ratio of the raw data; alike the matrix slideDistance the mad taken over all spots of the difference of the log-ratios (here: the difference of the normalised and transformed expression values of the two channels on the slide). Similarly the matrices slideDistanceGreen, slideDistanceGreenLogRaw, slideDistanceRed, and slideDistanceRedLogRaw contain calculated distances for each pair of slides$_{ij}$ based on the mad of the difference of the same channel (normalised or logged) taken over all spots.
A brief summary of all parameters given in the data.frame qualityParameters:
width a robust estimate of the noise, i.e. the median absolute deviation of the difference of the normalised channels taken over all spots, i.e. the "width" of the scatterplot
medianDistance a robust measure for the typical distance (similarity) of one slide with all other slides, i.e. the median of the "distances" between slides (c.f. slideDistance))
correlation(LogRaw) of the expression values between the two normalised (log raw) channels of the slide taken over all spots
meanSignalGreen the mean taken over all spots of the green raw data channel
meanSignalRed the mean taken over all spots of the red raw data channel
meanSignal mean taken over all spots of the raw data of both channels,
signalRangeGreen the range between the 10th and 95th percentile of the signal intensities given in the green raw data channel
signalRangeRed the range between the 10th and 95th percentile of the signal intensities given in the red raw data channel
backgroundRangeGreen the range between the 10th and 95th percentile of the background intensities given in the green raw data channel
backgroundRangeRed the range between the 10th and 95th percentile of the background intensities given in the red raw data channel
signalToBackgroundGreen the ratio of the median signal intensity and the median background intensity given in the green raw data channel
signalToBackgroundRed the ratio of the median signal intensity and the median background intensity given in the red raw data channel
spotReplicatesConcordanceGreen(LogRaw) the median of the standard deviations of all spot replicas for each unique identifier of the normalised (log raw) green channel is calculated; in case of duplicates, i.e. replicateSpots == 2, the Pearson and Spearman correlation is calculated instead
spotReplicatesConcordanceGreen(LogRaw) the median of the standard deviations of all spot replicas for each unique identifier of the normalised (log raw) green channel is calculated; in case of duplicates, i.e. replicateSpots == 2, the Pearson and Spearman correlation is calculated instead
greenvsAllGreen and redvsAllRed the correlation between each channel is measured against the averaged (median) channel over all hybridisations (like a virtual reference) separately for each channel

Author(s)

Andreas Buness <a.buness@dkfz.de>

See Also

qualityDiagnostics

Examples

      spotIdentifierVec <- c("A","A","Blank","B","B","Blank")
      hybNames <- "H1"
      R1 <- N1 <- c(1,1,9,2,2,10)
      R2 <- N2 <- c(2,2,7,4,4,8)
      rawDataIntensityValues <- array(0, dim=c(6,2,1))
      rawDataIntensityValues[,1,] <- R1
      rawDataIntensityValues[,2,] <- R2
      dimnames(rawDataIntensityValues) <- list(NULL, c("green","red"), NULL)
      spotAttr <- data.frame(Name=I(spotIdentifierVec))
      hybAttr <- data.frame(slideName=I(hybNames))
      arrayDataObject <- new("arrayData", intensities=rawDataIntensityValues, hybAttrList=list(red=hybAttr,green=hybAttr), spotAttr=spotAttr)
      indGreen <- 1
      indRed <- 2
      channels <- matrix( c(indGreen,indRed), nrow=length(indGreen), byrow=FALSE )
      colnames(channels) <- c("green","red")
      exprSetRGObject <- new("exprSetRG",       
      exprs <- matrix(c(R1,R2), nrow=6, byrow=FALSE), phenoData=        
          new("phenoData", pData=data.frame(matrix(0,nrow=2,ncol=1)),
              varLabels=list(rep("varLabel1",1))), channels=channels)           
      Re1 <- qualityParameters(arrayDataObject=arrayDataObject, exprSetRGObject=exprSetRGObject, identifiersToBeSkipped= "Blank")
      stopifnot(all.equal.numeric(as.numeric(Re1$qualityParameters["H1",c("correlation")]),c(1)))
      stopifnot(Re1$replicateSpots==2)

   

        

[Package arrayMagic version 1.10.0 Index]