R/methods_GPA.R
In carter-allen/GPA: GPA (Genetic analysis incorporating Pleiotropy and Annotation)
# generic methods for "GPA" class
setMethod(
f = "get_fit",
signature = "GPA",
definition = function(x) x@fit
)
setMethod(
f = "get_setting",
signature = "GPA",
definition = function(x) x@setting
)
setMethod(
f = "get_gwasPval",
signature = "GPA",
definition = function(x) x@gwasPval
)
setMethod(
f = "get_annMat",
signature = "GPA",
definition = function(x) x@annMat
)
setMethod(
f="show",
signature="GPA",
definition=function( object ) {
# summary of GPA fit
vDigit <- 1000
# constants
nBin <- nrow(get_gwasPval(object))
nGWAS <- ncol(get_gwasPval(object))
nAnn <- ncol(get_annMat(object))
binaryList <- vector( "list", nGWAS )
for ( k in seq_len(nGWAS) ) {
binaryList[[k]] <- c( 0, 1 )
}
binaryMat <- expand.grid( binaryList )
nComp <- nrow(binaryMat)
combVec <- apply( binaryMat, 1, function(bm) paste( bm, collapse="" ) )
# parameters for GPA
emSetting <- get_setting(object)
pis <- get_fit(object)$pis
betaAlpha <- get_fit(object)$betaAlpha
if ( emSetting$empiricalNull ) {
betaAlphaNull <- get_fit(object)$betaAlphaNull
}
q1 <- get_fit(object)$q1
nPis <- length(pis)
nAlpha <- length(betaAlpha)
# estimate covariance matrix
covMat <- cov( object, silent=TRUE )
seVec <- sqrt(diag(covMat))
# SE for pi00, using Delta method
piSE <- seVec[ seq_len(nPis-1) ]
gderiv <- as.matrix( c( rep( -1, (nPis-1) ),
rep( 0, nrow(covMat) - (nPis-1) ) ) )
pi00SE <- sqrt( t(gderiv) %*% covMat %*% gderiv )
piSE <- c( pi00SE, piSE )
betaAlphaSE <- seVec[ seq(from = (nPis-1+1), to = (nPis-1+nAlpha), by = 1) ]
if ( emSetting$empiricalNull ) {
betaAlphaNullSE <- seVec[ seq(from = (nPis-1+nAlpha+1),
to = (nPis-1+nAlpha+nAlpha), by = 1) ]
}
# output
cat( "Summary: GPA model fitting results (class: GPA)\n" )
cat( "--------------------------------------------------\n" )
cat( "Data summary:\n" )
cat( "\tNumber of GWAS data: ", nGWAS , "\n", sep="" )
cat( "\tNumber of SNPs: ", nBin , "\n", sep="" )
if ( emSetting$useAnn ) {
cat( "\tNumber of annotation data: ", nAnn, "\n", sep="" )
} else {
cat( "\tNumber of annotation data: (not provided)\n", sep="" )
}
cat( "Model setting:\n" )
if ( emSetting$empiricalNull ) {
cat( "\tNull distribution is empirically estimated.\n" )
} else {
cat( "\tTheoretical null distribution is assumed.\n" )
}
if ( emSetting$pleiotropyH0 ) {
cat( "\tGPA is fitted under H0 of pleiotropy LRT.\n" )
}
cat( "Parameter estimates (standard errors):\n" )
cat( "\talpha: ", paste( round(betaAlpha*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(betaAlphaSE*vDigit)/vDigit, collapse=" " ), " )",
"\n", sep="" )
if ( emSetting$empiricalNull ) {
cat( "\talphaNull: ", paste( round(betaAlphaNull*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(betaAlphaNullSE*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
cat( "\tGWAS combination: ", paste( combVec, collapse=" " ), "\n", sep="" )
cat( "\tpi: ", paste( round(pis*vDigit)/vDigit, collapse=" " ), "\n", sep="" )
cat( "\t ( ", paste( round(piSE*vDigit)/vDigit, collapse=" " ), " )", "\n",
sep="" )
if ( emSetting$useAnn ) {
cat( "\tq:\n" )
for ( d in seq_len(nAnn) ) {
cat( "\tAnnotation #",d,":\n", sep="" )
cat( "\t ", paste( round(q1[d,]*vDigit)/vDigit, collapse=" " ), "\n",
sep="" )
if ( emSetting$empiricalNull ) {
locQ1 <- (nPis-1+2*nAlpha+nPis*(d-1)+1):(nPis-1+2*nAlpha+nPis*d)
} else {
locQ1 <- (nPis-1+nAlpha+nPis*(d-1)+1):(nPis-1+nAlpha+nPis*d)
}
cat( "\t ( ", paste( round(seVec[locQ1]*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
}
if ( emSetting$useAnn ) {
q1ratio <- q1ratioSE <- matrix( NA, nrow(q1), (ncol(q1)-1) )
for ( d in seq_len(nAnn) ) {
# estimates
q1ratio[d,] <- q1[d,-1] / q1[d,1]
# SE
for ( j in 2:ncol(q1) ) {
qderiv <- rep( 0, nrow(q1)*ncol(q1) )
qderiv[ ncol(q1) * (d-1) + 1 ] <- - q1[d,j] / q1[d,1]^2
qderiv[ ncol(q1) * (d-1) + j ] <- 1 / q1[d,1]
gderiv <- as.matrix( c( rep( 0, nrow(covMat) - nrow(q1)*ncol(q1) ),
qderiv ) )
q1ratioSE[d,(j-1)] <- sqrt( t(gderiv) %*% covMat %*% gderiv )
}
}
cat( "\n" )
cat( "\tRatio of q over baseline (",combVec[1],"):\n", sep="" )
cat( "\tGWAS combination: ", paste( combVec[-1], collapse=" " ),"\n", sep="" )
for ( d in seq_len(nAnn) ) {
cat( "\tAnnotation #",d,":\n", sep="" )
cat( "\t ", paste( round(q1ratio[d,]*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(q1ratioSE[d,]*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
}
cat( "--------------------------------------------------\n" )
}
)
setMethod(
f="print",
signature="GPA",
definition=function( x ) {
# return posterior probability matrix
return(get_fit(x)$Z)
}
)
setMethod(
f="fdr",
signature="GPA",
definition=function( object, pattern=NULL ) {
if ( is.null(pattern) ) {
# if pattern is not specified, return marginal local FDR
margfdr <- 1 - get_fit(object)$Zmarg
colnames(margfdr) <- colnames(get_gwasPval(object))
return(margfdr)
} else {
# if pattern is provided, return FDR matching the pattern
ppmat <- print(object)
# parse pattern & convert it to regular expression
ptvec <- strsplit( pattern, "" )[[1]]
if ( length(grep( "(1|\\*)", ptvec )) < length(ptvec) ) {
stop( "Invalid 'pattern' argument! Please use only '1' or '*' for the pattern." )
}
ptreg <- paste( gsub( "\\*", "(0|1)", ptvec ), collapse="" )
locp <- grep( ptreg, colnames(ppmat) )
# return related FDR
if ( length(locp) > 0 ) {
psel <- 1 - rowSums(ppmat[ , locp, drop=FALSE ])
return(psel)
} else {
stop( "Invalid 'pattern' argument! Please provide correct pattern." )
}
}
}
)
setMethod(
f="cov",
signature="GPA",
definition=function( object, silent=FALSE, vDigitEst=1000, vDigitSE=1000, ... ) {
# calculate covariance matrix using empirical information matrix
.covGPA( object, silent=silent, vDigitEst=vDigitEst, vDigitSE=vDigitSE )
}
)
setMethod(
f="se",
signature="GPA",
definition=function( object, ... ) {
# return estimates & standard error
# covariance matrix
covEst <- .covGPA( object, silent=TRUE, vDigitEst=1000, vDigitSE=1000 )
seVec <- list()
# extract estimates
empiricalNull <- get_setting(object)$empiricalNull
useAnn <- get_setting(object)$useAnn
pis <- get_fit(object)$pis
betaAlpha <- get_fit(object)$betaAlpha
if ( empiricalNull ) {
betaAlphaNull <- get_fit(object)$betaAlphaNull
}
nGWAS <- length(betaAlpha)
if ( useAnn ) {
q1 <- get_fit(object)$q1
nAnn <- nrow(q1)
}
# extract information from estimates
binaryList <- vector( "list", nGWAS )
for ( k in seq_len(nGWAS) ) {
binaryList[[k]] <- c( 0, 1 )
}
binaryMat <- expand.grid( binaryList )
nComp <- nrow(binaryMat)
combVec <- apply( binaryMat, 1, function(bm) paste( bm, collapse="" ) )
nPis <- length(pis)
nAlpha <- length(betaAlpha)
if ( useAnn ) {
nQ1 <- nAnn * nComp
}
# SE for pi, using Delta method
piSE <- sqrt(diag(covEst))[ seq_len(nPis-1) ]
gderiv <- as.matrix( c( rep( -1, (nPis-1) ),
rep( 0, nrow(covEst) - (nPis-1) ) ) )
pi00SE <- sqrt( t(gderiv) %*% covEst %*% gderiv )
piSE <- c( pi00SE, piSE )
names(piSE)[1] <- paste("pi_",names(pis)[1],sep="")
# return estimates and SE
locPi <- seq_len(nPis-1)
locAlpha <- seq(from = (nPis-1+1),to = (nPis-1+nAlpha),by = 1)
seVec$betaAlpha <- sqrt(diag(covEst))[locAlpha]
if ( empiricalNull ) {
locAlpha0 <- seq(from = (nPis-1+nAlpha+1),to = (nPis-1+nAlpha+nAlpha), by = 1)
seVec$betaAlphaNull <- sqrt(diag(covEst))[locAlpha0]
}
seVec$pis <- piSE
if ( useAnn ) {
# q
seVec$q1 <- c()
for ( d in seq_len(nAnn) ) {
if ( empiricalNull ) {
locQ1 <- seq((nPis-1+2*nAlpha+nPis*(d-1)+1),(nPis-1+2*nAlpha+nPis*d))
} else {
locQ1 <- seq((nPis-1+nAlpha+nPis*(d-1)+1),(nPis-1+nAlpha+nPis*d))
}
seVec$q1 <- rbind( seVec$q1, sqrt(diag(covEst))[locQ1] )
}
# ratio of q
q1ratioSE <- matrix( NA, nrow(q1), (ncol(q1)-1) )
for ( d in seq_len(nAnn) ) {
for ( j in seq(1,ncol(q1)) ) {
qderiv <- rep( 0, nrow(q1)*ncol(q1) )
qderiv[ ncol(q1) * (d-1) + 1 ] <- - q1[d,j] / q1[d,1]^2
qderiv[ ncol(q1) * (d-1) + j ] <- 1 / q1[d,1]
gderiv <- as.matrix( c( rep( 0, nrow(covEst) - nrow(q1)*ncol(q1) ), qderiv ) )
q1ratioSE[d,(j-1)] <- sqrt( t(gderiv) %*% covEst %*% gderiv )
}
}
seVec$q1ratio <- q1ratioSE
}
return( seVec )
}
)
setMethod(
f="estimates",
signature="GPA",
definition=function( object, ... ) {
# return parameter estimates
paramEst <- list()
paramEst$pis <- get_fit(object)$pis
paramEst$betaAlpha <- get_fit(object)$betaAlpha
if ( get_setting(object)$empiricalNull ) {
paramEst$betaAlphaNull <- get_fit(object)$betaAlphaNull
}
if ( get_setting(object)$useAnn ) {
paramEst$q1 <- get_fit(object)$q1
# ratio of q
paramEst$q1ratio <- matrix( NA, nrow(paramEst$q1), (ncol(paramEst$q1)-1) )
for ( d in seq_len(nrow(paramEst$q1)) ) {
paramEst$q1ratio[d,] <- paramEst$q1[d,-1] / paramEst$q1[d,1]
}
}
return(paramEst)
}
)
carter-allen/GPA documentation built on April 21, 2020, 11:18 a.m.
R Package Documentation
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# generic methods for "GPA" class
setMethod(
f = "get_fit",
signature = "GPA",
definition = function(x) x@fit
)
setMethod(
f = "get_setting",
signature = "GPA",
definition = function(x) x@setting
)
setMethod(
f = "get_gwasPval",
signature = "GPA",
definition = function(x) x@gwasPval
)
setMethod(
f = "get_annMat",
signature = "GPA",
definition = function(x) x@annMat
)
setMethod(
f="show",
signature="GPA",
definition=function( object ) {
# summary of GPA fit
vDigit <- 1000
# constants
nBin <- nrow(get_gwasPval(object))
nGWAS <- ncol(get_gwasPval(object))
nAnn <- ncol(get_annMat(object))
binaryList <- vector( "list", nGWAS )
for ( k in seq_len(nGWAS) ) {
binaryList[[k]] <- c( 0, 1 )
}
binaryMat <- expand.grid( binaryList )
nComp <- nrow(binaryMat)
combVec <- apply( binaryMat, 1, function(bm) paste( bm, collapse="" ) )
# parameters for GPA
emSetting <- get_setting(object)
pis <- get_fit(object)$pis
betaAlpha <- get_fit(object)$betaAlpha
if ( emSetting$empiricalNull ) {
betaAlphaNull <- get_fit(object)$betaAlphaNull
}
q1 <- get_fit(object)$q1
nPis <- length(pis)
nAlpha <- length(betaAlpha)
# estimate covariance matrix
covMat <- cov( object, silent=TRUE )
seVec <- sqrt(diag(covMat))
# SE for pi00, using Delta method
piSE <- seVec[ seq_len(nPis-1) ]
gderiv <- as.matrix( c( rep( -1, (nPis-1) ),
rep( 0, nrow(covMat) - (nPis-1) ) ) )
pi00SE <- sqrt( t(gderiv) %*% covMat %*% gderiv )
piSE <- c( pi00SE, piSE )
betaAlphaSE <- seVec[ seq(from = (nPis-1+1), to = (nPis-1+nAlpha), by = 1) ]
if ( emSetting$empiricalNull ) {
betaAlphaNullSE <- seVec[ seq(from = (nPis-1+nAlpha+1),
to = (nPis-1+nAlpha+nAlpha), by = 1) ]
}
# output
cat( "Summary: GPA model fitting results (class: GPA)\n" )
cat( "--------------------------------------------------\n" )
cat( "Data summary:\n" )
cat( "\tNumber of GWAS data: ", nGWAS , "\n", sep="" )
cat( "\tNumber of SNPs: ", nBin , "\n", sep="" )
if ( emSetting$useAnn ) {
cat( "\tNumber of annotation data: ", nAnn, "\n", sep="" )
} else {
cat( "\tNumber of annotation data: (not provided)\n", sep="" )
}
cat( "Model setting:\n" )
if ( emSetting$empiricalNull ) {
cat( "\tNull distribution is empirically estimated.\n" )
} else {
cat( "\tTheoretical null distribution is assumed.\n" )
}
if ( emSetting$pleiotropyH0 ) {
cat( "\tGPA is fitted under H0 of pleiotropy LRT.\n" )
}
cat( "Parameter estimates (standard errors):\n" )
cat( "\talpha: ", paste( round(betaAlpha*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(betaAlphaSE*vDigit)/vDigit, collapse=" " ), " )",
"\n", sep="" )
if ( emSetting$empiricalNull ) {
cat( "\talphaNull: ", paste( round(betaAlphaNull*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(betaAlphaNullSE*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
cat( "\tGWAS combination: ", paste( combVec, collapse=" " ), "\n", sep="" )
cat( "\tpi: ", paste( round(pis*vDigit)/vDigit, collapse=" " ), "\n", sep="" )
cat( "\t ( ", paste( round(piSE*vDigit)/vDigit, collapse=" " ), " )", "\n",
sep="" )
if ( emSetting$useAnn ) {
cat( "\tq:\n" )
for ( d in seq_len(nAnn) ) {
cat( "\tAnnotation #",d,":\n", sep="" )
cat( "\t ", paste( round(q1[d,]*vDigit)/vDigit, collapse=" " ), "\n",
sep="" )
if ( emSetting$empiricalNull ) {
locQ1 <- (nPis-1+2*nAlpha+nPis*(d-1)+1):(nPis-1+2*nAlpha+nPis*d)
} else {
locQ1 <- (nPis-1+nAlpha+nPis*(d-1)+1):(nPis-1+nAlpha+nPis*d)
}
cat( "\t ( ", paste( round(seVec[locQ1]*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
}
if ( emSetting$useAnn ) {
q1ratio <- q1ratioSE <- matrix( NA, nrow(q1), (ncol(q1)-1) )
for ( d in seq_len(nAnn) ) {
# estimates
q1ratio[d,] <- q1[d,-1] / q1[d,1]
# SE
for ( j in 2:ncol(q1) ) {
qderiv <- rep( 0, nrow(q1)*ncol(q1) )
qderiv[ ncol(q1) * (d-1) + 1 ] <- - q1[d,j] / q1[d,1]^2
qderiv[ ncol(q1) * (d-1) + j ] <- 1 / q1[d,1]
gderiv <- as.matrix( c( rep( 0, nrow(covMat) - nrow(q1)*ncol(q1) ),
qderiv ) )
q1ratioSE[d,(j-1)] <- sqrt( t(gderiv) %*% covMat %*% gderiv )
}
}
cat( "\n" )
cat( "\tRatio of q over baseline (",combVec[1],"):\n", sep="" )
cat( "\tGWAS combination: ", paste( combVec[-1], collapse=" " ),"\n", sep="" )
for ( d in seq_len(nAnn) ) {
cat( "\tAnnotation #",d,":\n", sep="" )
cat( "\t ", paste( round(q1ratio[d,]*vDigit)/vDigit, collapse=" " ),
"\n", sep="" )
cat( "\t ( ", paste( round(q1ratioSE[d,]*vDigit)/vDigit, collapse=" " ),
" )", "\n", sep="" )
}
}
cat( "--------------------------------------------------\n" )
}
)
setMethod(
f="print",
signature="GPA",
definition=function( x ) {
# return posterior probability matrix
return(get_fit(x)$Z)
}
)
setMethod(
f="fdr",
signature="GPA",
definition=function( object, pattern=NULL ) {
if ( is.null(pattern) ) {
# if pattern is not specified, return marginal local FDR
margfdr <- 1 - get_fit(object)$Zmarg
colnames(margfdr) <- colnames(get_gwasPval(object))
return(margfdr)
} else {
# if pattern is provided, return FDR matching the pattern
ppmat <- print(object)
# parse pattern & convert it to regular expression
ptvec <- strsplit( pattern, "" )[[1]]
if ( length(grep( "(1|\\*)", ptvec )) < length(ptvec) ) {
stop( "Invalid 'pattern' argument! Please use only '1' or '*' for the pattern." )
}
ptreg <- paste( gsub( "\\*", "(0|1)", ptvec ), collapse="" )
locp <- grep( ptreg, colnames(ppmat) )
# return related FDR
if ( length(locp) > 0 ) {
psel <- 1 - rowSums(ppmat[ , locp, drop=FALSE ])
return(psel)
} else {
stop( "Invalid 'pattern' argument! Please provide correct pattern." )
}
}
}
)
setMethod(
f="cov",
signature="GPA",
definition=function( object, silent=FALSE, vDigitEst=1000, vDigitSE=1000, ... ) {
# calculate covariance matrix using empirical information matrix
.covGPA( object, silent=silent, vDigitEst=vDigitEst, vDigitSE=vDigitSE )
}
)
setMethod(
f="se",
signature="GPA",
definition=function( object, ... ) {
# return estimates & standard error
# covariance matrix
covEst <- .covGPA( object, silent=TRUE, vDigitEst=1000, vDigitSE=1000 )
seVec <- list()
# extract estimates
empiricalNull <- get_setting(object)$empiricalNull
useAnn <- get_setting(object)$useAnn
pis <- get_fit(object)$pis
betaAlpha <- get_fit(object)$betaAlpha
if ( empiricalNull ) {
betaAlphaNull <- get_fit(object)$betaAlphaNull
}
nGWAS <- length(betaAlpha)
if ( useAnn ) {
q1 <- get_fit(object)$q1
nAnn <- nrow(q1)
}
# extract information from estimates
binaryList <- vector( "list", nGWAS )
for ( k in seq_len(nGWAS) ) {
binaryList[[k]] <- c( 0, 1 )
}
binaryMat <- expand.grid( binaryList )
nComp <- nrow(binaryMat)
combVec <- apply( binaryMat, 1, function(bm) paste( bm, collapse="" ) )
nPis <- length(pis)
nAlpha <- length(betaAlpha)
if ( useAnn ) {
nQ1 <- nAnn * nComp
}
# SE for pi, using Delta method
piSE <- sqrt(diag(covEst))[ seq_len(nPis-1) ]
gderiv <- as.matrix( c( rep( -1, (nPis-1) ),
rep( 0, nrow(covEst) - (nPis-1) ) ) )
pi00SE <- sqrt( t(gderiv) %*% covEst %*% gderiv )
piSE <- c( pi00SE, piSE )
names(piSE)[1] <- paste("pi_",names(pis)[1],sep="")
# return estimates and SE
locPi <- seq_len(nPis-1)
locAlpha <- seq(from = (nPis-1+1),to = (nPis-1+nAlpha),by = 1)
seVec$betaAlpha <- sqrt(diag(covEst))[locAlpha]
if ( empiricalNull ) {
locAlpha0 <- seq(from = (nPis-1+nAlpha+1),to = (nPis-1+nAlpha+nAlpha), by = 1)
seVec$betaAlphaNull <- sqrt(diag(covEst))[locAlpha0]
}
seVec$pis <- piSE
if ( useAnn ) {
# q
seVec$q1 <- c()
for ( d in seq_len(nAnn) ) {
if ( empiricalNull ) {
locQ1 <- seq((nPis-1+2*nAlpha+nPis*(d-1)+1),(nPis-1+2*nAlpha+nPis*d))
} else {
locQ1 <- seq((nPis-1+nAlpha+nPis*(d-1)+1),(nPis-1+nAlpha+nPis*d))
}
seVec$q1 <- rbind( seVec$q1, sqrt(diag(covEst))[locQ1] )
}
# ratio of q
q1ratioSE <- matrix( NA, nrow(q1), (ncol(q1)-1) )
for ( d in seq_len(nAnn) ) {
for ( j in seq(1,ncol(q1)) ) {
qderiv <- rep( 0, nrow(q1)*ncol(q1) )
qderiv[ ncol(q1) * (d-1) + 1 ] <- - q1[d,j] / q1[d,1]^2
qderiv[ ncol(q1) * (d-1) + j ] <- 1 / q1[d,1]
gderiv <- as.matrix( c( rep( 0, nrow(covEst) - nrow(q1)*ncol(q1) ), qderiv ) )
q1ratioSE[d,(j-1)] <- sqrt( t(gderiv) %*% covEst %*% gderiv )
}
}
seVec$q1ratio <- q1ratioSE
}
return( seVec )
}
)
setMethod(
f="estimates",
signature="GPA",
definition=function( object, ... ) {
# return parameter estimates
paramEst <- list()
paramEst$pis <- get_fit(object)$pis
paramEst$betaAlpha <- get_fit(object)$betaAlpha
if ( get_setting(object)$empiricalNull ) {
paramEst$betaAlphaNull <- get_fit(object)$betaAlphaNull
}
if ( get_setting(object)$useAnn ) {
paramEst$q1 <- get_fit(object)$q1
# ratio of q
paramEst$q1ratio <- matrix( NA, nrow(paramEst$q1), (ncol(paramEst$q1)-1) )
for ( d in seq_len(nrow(paramEst$q1)) ) {
paramEst$q1ratio[d,] <- paramEst$q1[d,-1] / paramEst$q1[d,1]
}
}
return(paramEst)
}
)
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