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# Several binarization methods for time series. <measurements> is a list of matrices with the
# genes in the rows, each specifying one time series.
# If <method> is "kmeans", k-means binarization is used.
# <nstart> and <iter.max> are the corresponding parameters for k-means. This clustering
# is employed for binarization.
# If <method> is "edgeDetector", an edge detector is used for binarization.
# If <method> is "edgeDetector" and <edge> is "firstEdge",
# the first "significant" edge in the sorted data is the threshold for the binarization.
# If <edge> is "maxEdge", the algorithm searches for the edge with the highest gradient.
# With the <scaling> factor, the size of the first edge can be adapted.
# If <method> is "scanStatistic", a scan statistic is used to binarize the data
# If <dropInsignificant> is true, insignificant genes (that are not recommended by the statistic) are removed.
# Returns a list containing the binarized matrix list and (for k-means) a vector
# of thresholds and (for scan statistic) a vector specifying genes to remove.
binarizeTimeSeries <- function(measurements, method=c("kmeans","edgeDetector","scanStatistic"), nstart=100, iter.max=1000, edge=c("firstEdge","maxEdge"), scaling=1, windowSize=0.25, sign.level=0.1, dropInsignificant=FALSE)
{
if (!is.null(dim(measurements)))
fullData <- measurements
else
# in case of list, paste all matrices before clustering
{
fullData <- measurements[[1]]
for (m in measurements[-1])
{
fullData <- cbind(fullData,m)
}
}
#switch between the different methods
switch(match.arg(method),
kmeans={
cluster <- apply(fullData,1,function(gene)
# cluster data using k-means
{
cl_res <- kmeans(gene, 2, nstart=nstart,iter.max=iter.max)
if (cl_res$centers[1] > cl_res$centers[2])
# exchange clusters if necessary, so that smaller numbers
# are binarized to 0, and larger numbers are binarized to 1
group <- abs(cl_res$cluster-2)
else
group <- cl_res$cluster-1
# calculate the binarization threshold
threshold <- min(cl_res$centers) + dist(cl_res$centers)[1]/2
list(bin=group, threshold=threshold)
})
if (is.null(dim(measurements)))
# split up the collated binarized measurements into a list of matrices of the original size
{
startIndex <- 0
binarizedTimeSeries <- lapply(measurements,function(m)
{
currentSplit <- (startIndex+1):(startIndex+ncol(m))
startIndex <<- startIndex + ncol(m)
t(sapply(cluster,function(cl)
cl$bin[currentSplit]))
})
}
else
{
binarizedTimeSeries <- t(sapply(cluster,function(cl)
cl$bin))
}
#colnames(binarizedTimeSeries)<-colnames(fullData)
return(list(binarizedMeasurements=binarizedTimeSeries,
thresholds=sapply(cluster,function(cl)cl$threshold)))
},
edgeDetector={
#switch between the different edgedetectors
switch(match.arg(edge),
firstEdge={
cluster <- apply(fullData,1,function(gene)
# cluster data using edgedetector
{
cl_res <- edgeDetector(gene,scaling,edge="firstEdge")
list(bin=cl_res$bindata,thresholds=cl_res$thresholds)
})
if (is.null(dim(measurements)))
# split up the collated binarized measurements into a list of matrices of the original size
{
startIndex <- 0
binarizedTimeSeries <- lapply(measurements,function(m)
{
currentSplit <- (startIndex+1):(startIndex+ncol(m))
startIndex <<- startIndex + ncol(m)
t(sapply(cluster,function(cl)
cl$bin[currentSplit]))
})
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
else
{
binarizedTimeSeries <- t(sapply(cluster,function(cl)
cl$bin))
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
return(list(binarizedMeasurements=binarizedTimeSeries,thresholds= threshlist))
},
maxEdge={
cluster <- apply(fullData,1,function(gene)
# cluster data using edgedetector
{
cl_res <- edgeDetector(gene,edge="maxEdge")
list(bin=cl_res$bindata,thresholds=cl_res$thresholds)
})
if (is.null(dim(measurements)))
# split up the collated binarized measurements into a list of matrices of the original size
{
startIndex <- 0
binarizedTimeSeries <- lapply(measurements,function(m)
{
currentSplit <- (startIndex+1):(startIndex+ncol(m))
startIndex <<- startIndex + ncol(m)
t(sapply(cluster,function(cl)
cl$bin[currentSplit]))
})
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
else
{
binarizedTimeSeries <- t(sapply(cluster,function(cl)
cl$bin))
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
return(list(binarizedMeasurements=binarizedTimeSeries,thresholds= threshlist))
},
stop("'method' must be one of \"firstEdge\",\"maxEdge\"")
)
},
scanStatistic={
cluster <- apply(fullData,1,function(gene)
# cluster data using scanStatistic
{
cl_res <- scanStatistic(gene,windowSize,sign.level)
list(bin= cl_res$bindata,thresholds=cl_res$thresholds,reject=cl_res$reject)
})
significant <- sapply(cluster,function(cl)(cl$reject==FALSE))
# remove not recommended genes
if (dropInsignificant)
{
significant <- sapply(cluster,function(cl)(cl$reject==FALSE))
cluster <- cluster[significant]
}
if (is.null(dim(measurements)))
# split up the collated binarized measurements into a list of matrices of the original size
{
startIndex <- 0
binarizedTimeSeries <- lapply(measurements,function(m)
{
currentSplit <- (startIndex+1):(startIndex+ncol(m))
startIndex <<- startIndex + ncol(m)
t(sapply(cluster,function(cl)
cl$bin[currentSplit]))
})
rejectlist<-sapply(cluster,function(cl) cl$reject)
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
else
{
binarizedTimeSeries <- t(sapply(cluster,function(cl)
cl$bin))
rejectlist<-sapply(cluster,function(cl) cl$reject)
threshlist<-sapply(cluster,function(cl) cl$thresholds)
}
if(any(rejectlist))
{
warning("The following genes show a uniform behaviour and should possibly be excluded from binarization:", paste(rownames(fullData)[!significant],collapse=" "))
}
return(list(binarizedMeasurements=binarizedTimeSeries,thresholds=threshlist,reject=rejectlist))
},
stop("'method' must be one of \"kmeans\",\"edgeDetector\",\"scanStatistic\"")
)
}
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