R/base_covGPA.R

In GPA: GPA (Genetic analysis incorporating Pleiotropy and Annotation)

.covGPA <- function( object, silent=FALSE, vDigitEst=1000, vDigitSE=1000 ) {
	# check correctness of arguments

	if ( silent != TRUE & silent != FALSE ) {
		stop( "Inappropriate value for 'silent' argument. It should be either TRUE or FALSE." )
	}

	if ( vDigitEst %% 10 != 0 | vDigitEst <= 0 ) {
		stop( "Inappropriate value for 'vDigitEst' argument. It should be multiples of 10, e.g., 10, 100, ..." )
	}

	if ( vDigitSE %% 10 != 0 | vDigitSE <= 0 ) {
		stop( "Inappropriate value for 'vDigitSE' argument. It should be multiples of 10, e.g., 10, 100, ..." )
	}

	# load fits

	emSetting <- get_setting(object)
	gwasPval <- get_gwasPval(object)

	# convert p-value & annotation vector to matrix, if needed, for consistency

	if ( is.vector(gwasPval) ) {
		gwasPval <- as.matrix(gwasPval)
	}
	if ( emSetting$useAnn ) {
		annMat <- get_annMat(object)
	} else {
		annMat <- NULL
	}

	# extract estimates

	empiricalNull <- emSetting$empiricalNull
	Z <- get_fit(object)$Z
	pis <- get_fit(object)$pis
	betaAlpha <- get_fit(object)$betaAlpha
	if ( empiricalNull ) {
		betaAlphaNull <- get_fit(object)$betaAlphaNull
	}

	nBin <- nrow(Z)
	nGWAS <- length(betaAlpha)

	if ( !is.null(annMat) ) {
		q1 <- get_fit(object)$q1
		nAnn <- ncol(annMat)
	}

	# extract information from estimates

	sumZ <- colSums(Z)

	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 ( !is.null(annMat) ) {
		nQ1 <- nAnn * nComp
	}

	# indexing

	locZero <- which( rowSums(binaryMat) == 0 )

	locNonzero <- seq_len(nPis)
	locNonzero <- locNonzero[ -locZero ]

	namePis <- names(pis)[ -locZero ]

	########################################################################
    #                                                                      #
    #                 empirical observed information                       #
    #                                                                      #
    ########################################################################

    if ( empiricalNull ) {

	    if ( !is.null(annMat) ) {
	    	smat <- matrix( NA, nBin, nPis - 1 + 2 * nAlpha + nQ1 )
		} else {
			smat <- matrix( NA, nBin, nPis - 1 + 2 * nAlpha )
		}

	} else {

	    if ( !is.null(annMat) ) {
	    	smat <- matrix( NA, nBin, nPis - 1 + nAlpha + nQ1 )
		} else {
			smat <- matrix( NA, nBin, nPis - 1 + nAlpha )
		}

	}

	# pi

	for ( i in seq_len(nPis-1) ) {
		smat[,i] <- Z[ , locNonzero[i] ] / pis[ locNonzero[i] ] -
			Z[ , locZero ] / pis[ locZero ]
	}

	# alpha

	for ( i in seq_len(nAlpha) ) {
		smat[ , nPis-1+i ] <- ( log(gwasPval[,i]) + 1 / betaAlpha[i] ) *
			rowSums( Z[ , binaryMat[,i] == 1, drop=FALSE ] )
	}

	if ( empiricalNull ) {

		# alpha0

		for ( i in seq_len(nAlpha) ) {
			smat[ , nPis-1+nAlpha+i ] <- ( log(gwasPval[,i]) + 1 / betaAlphaNull[i] ) *
				rowSums( Z[ , binaryMat[,i] == 0, drop=FALSE ] )
		}

		# q1

		if ( !is.null(annMat) ) {
			k <- 1

			for ( d in seq_len(nAnn) ) {
				for ( j in seq_len(nComp) ) {
					smat[ , nPis-1+2*nAlpha+k ] <-
						Z[,j] * ( annMat[,d] / q1[d] - ( 1 - annMat[,d] ) / ( 1 - q1[d] ) )

					k <- k + 1
				}
			}
		}

	} else {

		# q1

		if ( !is.null(annMat) ) {
			k <- 1

			for ( d in seq_len(nAnn) ) {
				for ( j in seq_len(nComp) ) {
					smat[ , nPis-1+nAlpha+k ] <-
						Z[,j] * ( annMat[,d] / q1[d] - ( 1 - annMat[,d] ) / ( 1 - q1[d] ) )

					k <- k + 1
				}
			}
		}

	}

	# empirical observed information

	infoEmp <- t(smat) %*% smat
	covEst <- solve(infoEmp)

	if ( empiricalNull ) {

		if ( !is.null(annMat) ) {
			rownames(infoEmp) <- colnames(infoEmp) <- rownames(covEst) <- colnames(covEst) <-
				c( paste("pi_",namePis,sep=""),
				paste("alpha_",seq_len(nAlpha),sep=""), paste("alpha0_",seq_len(nAlpha),sep=""),
				paste("q1_",rep(seq_len(nAnn),each=nComp),"_",rep(seq_len(nComp),nAnn),sep="") )
		} else {
			rownames(infoEmp) <- colnames(infoEmp) <- rownames(covEst) <- colnames(covEst) <-
				c( paste("pi_",namePis,sep=""),
				paste("alpha_",seq_len(nAlpha),sep=""), paste("alpha0_",seq_len(nAlpha),sep="") )
		}

	} else {

		if ( !is.null(annMat) ) {
			rownames(infoEmp) <- colnames(infoEmp) <- rownames(covEst) <- colnames(covEst) <-
				c( paste("pi_",namePis,sep=""), paste("alpha_",seq_len(nAlpha),sep=""),
				paste("q1_",rep(seq_len(nAnn),each=nComp),"_",rep(seq_len(nComp),nAnn),sep="") )
		} else {
			rownames(infoEmp) <- colnames(infoEmp) <- rownames(covEst) <- colnames(covEst) <-
				c( paste("pi_",namePis,sep=""), paste("alpha_",seq_len(nAlpha),sep="") )
		}

	}

	# 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 )

	# report

	if( !silent ) {
		locPi <- seq_len(nPis-1)
		locAlpha <- seq(from = nPis-1+1, to = nPis-1+nAlpha, by = 1)

		message( " " )
		message( "alpha: ", paste( round(betaAlpha*vDigitEst)/vDigitEst, collapse=" " ) )
		message( "     ( ", paste( round(sqrt(diag(covEst))[locAlpha]*vDigitSE)/vDigitSE,
															collapse=" " ), " )" )

		if ( empiricalNull ) {
			locAlpha0 <- seq(from = nPis-1+nAlpha+1, to = nPis-1+nAlpha+nAlpha, by = 1)
			message( "alpha0: ", paste( round(betaAlphaNull*vDigitEst)/vDigitEst,
																	collapse=" " ) )
			message( "     ( ", paste( round(sqrt(diag(covEst))[locAlpha0]*vDigitSE)/vDigitSE,
																collapse=" " ), " )" )
		}
		message( "GWAS combination: ", paste( combVec, collapse=" " ) )
		message( "pi: ", paste( round(pis*vDigitEst)/vDigitEst, collapse=" " ) )
		message( "  ( ", paste( round(piSE*vDigitSE)/vDigitSE, collapse=" " ), " )" )

		if ( !is.null(annMat) ) {
			# q

			message( "q: " )
			for ( d in seq_len(nAnn) ) {
				message( "Annotation #",d,":")
				if ( empiricalNull ) {
					locQ1 <- seq(from = nPis-1+2*nAlpha+nPis*(d-1)+1,
											 to = nPis-1+2*nAlpha+nPis*d, by = 1)
				} else {
					locQ1 <- seq(from = nPis-1+nAlpha+nPis*(d-1)+1,
											 to = nPis-1+nAlpha+nPis*d, by = 1)
				}

				message( "\t    ", paste( round(q1[d,]*vDigitEst)/vDigitEst, collapse=" " ) )
				message( "\t  ( ", paste( round(sqrt(diag(covEst))[locQ1]*vDigitSE)/
																	vDigitSE, collapse=" " ), " )" )
			}

			# ratio of q

			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 seq(from = 2, to = ncol(q1), by = 1) ) {
					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 )
				}
			}

			message( " " )
			message( "Ratio of q over baseline (",combVec[1],"):" )
			message( "GWAS combination: ", paste( combVec[-1], collapse=" " ) )
			for ( d in seq_len(nAnn) ) {
				message( "Annotation #",d,":")
				message( "\t    ", paste( round(q1ratio[d,]*vDigitEst)/vDigitEst,
																				collapse=" " ) )
				message( "\t  ( ", paste( round(q1ratioSE[d,]*vDigitSE)/vDigitSE,
																				collapse=" " ), " )" )
			}
		}
	}

	return( covEst )
}