dohClusterCustommedSegments {speaq}

Use CluPA for alignment with additional information

Description

This function integrates some additional information from user such as references for each specific segment, segment ignorance, maximum step size.. to align spectra using CluPA.

Usage

dohClusterCustommedSegments(
  X,
  peakList,
  refInd,
  segmentInfoMat,
  minSegSize = 128,
  maxShift = 100,
  acceptLostPeak = TRUE,
  verbose = TRUE
)

Arguments

X

The spectral dataset in the matrix format in which each row contains a single sample

peakList

The peak lists of the spectra

refInd

The index of the reference spectrum.

segmentInfoMat

The matrix containing the additional information for segments from the users. This parameter must be a matrix.

minSegSize

The minimum size of the segments which could be considered for alignment.

maxShift

The maximum number of the points for a shift step.

acceptLostPeak

This is an option for users, TRUE is the default value. If the users believe that all the peaks in the peak list are true positive, change it to FALSE.

verbose

A boolean value to allow print out process information.

Details

Each row of the segmentInfoMat matrix includes 5 values. For example, it could be imported from a CSV file consisting of following content: # begin,end,forAlign,ref,maxShift 100,200,0,0,0 450,680,1,0,50 # Each column could be explained as the following: * begin: the starting point of the segment. * end: the end point of the segment. * forAlign: the segment is aligned (1) or not (0). * ref: the index of the reference spectrum. If 0, the algorithm will select the reference found by the reference finding step. * maxShift: the maximum number of points of a shift to left/right. It is worth to note that only segments with forAlign=1 (column 3) will be taken into account for spectral alignment.

Value

The aligned spectral segments.

Author(s)

Trung Nghia Vu

See Also

dohCluster

Examples

cat("\n Please see more examples in the vignettes file.")
res=makeSimulatedData();
X=res$data;
groupLabel=res$label;
peakList <- detectSpecPeaks(X,
                            nDivRange = c(128),
                            scales = seq(1, 16, 2),
                            baselineThresh = 50000,
                            SNR.Th = -1,
                            verbose=FALSE
);
resFindRef<- findRef(peakList);
refInd <- resFindRef$refInd;
segmentInfoMat=matrix(data=c(100,200,0,0,0,
                             50,680,1,0,50),nrow=2,ncol=5,byrow=TRUE
)
colnames(segmentInfoMat)=c("begin","end","forAlign","ref","maxShift")
segmentInfoMat
maxShift = 50;
Yc <- dohClusterCustommedSegments(X,
                                  peakList = peakList,
                                  refInd = refInd,
                                  maxShift  = maxShift,
                                  acceptLostPeak = TRUE,
                                  segmentInfoMat = segmentInfoMat,
                                  minSegSize = 128,
                                  verbose=FALSE)