Nothing
# generic methods for "MosaicsHMM" class
setMethod(
f="show",
signature="MosaicsHMM",
definition=function( object ) {
# parameters for MOSAiCS-HMM
init <- object@init
initPiMat <- object@initPiMat
peakParam <- object@peakParam
analysisType <- peakParam@analysisType
signalModel <- peakParam@signalModel
FDR <- peakParam@FDR
maxgap <- peakParam@maxgap
minsize <- peakParam@minsize
thres <- peakParam@thres
bicMosaics <- object@bicMosaics
bicMosaicsHMM <- object@bicMosaicsHMM
# output
cat( "Summary: MOSAiCS-HMM model fitting (class: MosaicsHMM)\n" )
cat( "--------------------------------------------------\n" )
cat( "final model: " )
switch( analysisType,
"OS" = {
cat( "one-sample analysis " )
},
"TS" = {
cat( "two-sample analysis (with M & GC) " )
},
"IO" = {
cat( "two-sample analysis (input only) " )
}
)
switch( signalModel,
"1S" = {
cat( "with one signal component\n" )
},
"2S" = {
cat( "with two signal components\n" )
},
"2Sr" = {
cat( "with two robust signal components\n" )
}
)
cat( "setting for initialization:\n" )
if ( init == "mosaics" ) {
cat( "Initialized using MOSAiCS peak calling results.\n")
cat( "FDR = ", FDR, ", maxgap = ", maxgap,
", minsize = ", minsize, ", thres = ", thres, "\n", sep="" )
} else if ( init == "specify" ) {
cat( "initialized using the transition matrix user provided.\n")
cat( "Transition matrix:\n" )
cat( initPiMat[1,1], " ", initPiMat[1,2], "\n", sep="" )
cat( initPiMat[2,1], " ", initPiMat[2,2], "\n", sep="" )
}
cat( "BIC of MOSAiCS model = ", bicMosaics, "\n", sep="" )
cat( "BIC of MOSAiCS-HMM model = ", bicMosaicsHMM, "\n", sep="" )
cat( "--------------------------------------------------\n" )
}
)
setMethod(
f="print",
signature="MosaicsFit",
definition=function( x ) {
warning( "'print' method for 'MosaicsFit' class is not supported yet." )
}
)
setMethod(
f="plot",
signature=c("MosaicsHMM","missing"),
definition=function( x, y, seed=12345, parallel=FALSE, nCore=8, ... ) {
# check options: parallel computing (optional)
if ( parallel == TRUE ) {
message( "Info: Use 'parallel' package for parallel computing." )
if ( length(find.package('parallel',quiet=TRUE)) == 0 ) {
stop( "Please install 'parallel' package!" )
}
}
message( "Info: summarizing ChIP-seq data..." )
if ( x@mosaicsEst@analysisType=="OS" ) {
tagCount <- unlist(lapply( x@inputdata, function(x) x$Y ))
.plotGOFHMM(
mosaicsHMMEst=x@HMMfit, mosaicsEst=x@mosaicsEst,
tagCount=tagCount, input=0,
signalModel=x@peakParam@signalModel, k=3,
seed=seed, parallel=parallel, nCore=nCore )
} else {
tagCount <- unlist(lapply( x@inputdata, function(x) x$Y ))
input <- unlist(lapply( x@inputdata, function(x) x$X ))
.plotGOFHMM(
mosaicsHMMEst=x@HMMfit, mosaicsEst=x@mosaicsEst,
tagCount=tagCount, input=input,
signalModel=x@peakParam@signalModel, k=3,
seed=seed, parallel=parallel, nCore=nCore )
}
}
)
setMethod(
f="estimates",
signature="MosaicsHMM",
definition=function( object ) {
mosaicsEst <- object@mosaicsEst
pi0Vec <- t(sapply( object@HMMfit, function(x) x$pi0Vec ))
piMat <- t(sapply( object@HMMfit, function(x) x$piMat ))
piMat <- piMat[ , c(1,3,2,4) ]
colnames(pi0Vec) <- c( "pi0", "pi1" )
colnames(piMat) <- c( "pi00", "pi01", "pi10", "pi11" )
list(
pi0Vec=pi0Vec,
piMat=piMat,
a=mosaicsEst@a,
betaEst=mosaicsEst@betaEst,
b=mosaicsEst@b,
c=mosaicsEst@c,
p1=mosaicsEst@p1,
b1=mosaicsEst@b1,
c1=mosaicsEst@c1,
b2=mosaicsEst@b2,
c2=mosaicsEst@c2
)
}
)
setMethod(
f="seqDepth",
signature="MosaicsHMM",
definition=function( object ) {
return(object@seqDepth)
}
)
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