#' The rqt class constructor
#'
#' This function generates rqt class objects
#' @param phenotype Phenotype (a vector of length
#' \code{N}, where \code{N} - number of individuals).
#' @param genotype Genotype - an object of class
#' \code{SummarizedExperiment}. Should contain one assay
#' (matrix, \code{N}
#' by \code{M} where \code{N} - number of individuals, \code{M}
#' - number of genetic variants).
#' @param covariates Covariates, a data frame \code{N}
#' by \code{K} where \code{N} - number of individuals, \code{K}
#' - number of covariates
#' @param results A list of two: test statistics:
#' (\code{Q1}, \code{Q2}, \code{Q3}),
#' p-values: (\code{p1.Q1}, \code{p2.Q2}, \code{p3.Q3})
#' @return Object of class \code{rqt}
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' print(obj)
#' @rdname rqt-methods
#' @export
rqt <- function(phenotype=NULL, genotype=NULL, covariates=NULL,
results=NULL) {
if(is.null(phenotype)) {
phenotype <- c()
}
if(is.null(genotype)) {
genotype <- matrix()
colnames(genotype) <- "geno"
genotype <- SummarizedExperiment(genotype)
}
if(is.null(covariates)) {
covariates <- data.frame()
}
if(is.null(results)) {
results <- list()
}
new("rqt", phenotype=phenotype,
genotype=genotype,
covariates=covariates,
results=results)
}
#' geneTest
#' This function performs a gene-level test based on combined effect sizes.
#'
#' @param perm Integer indicating the number of permutations
#' to compute p-values. Default: 0.
#' @param STT Numeric indicating soft truncation threshold (STT)
#' to convert to gamma parameter (must be <= 0.4).
#' Needed for an optimal parameter a in Gamma-distribution. Default: 0.2.
#' See, for example, Fridley, et al 2013: "Soft truncation thresholding
#' for gene set analysis of RNA-seq data: Application to a vaccine study".
#' @param weight Logical value. Indicates using weights (see Lee et al 2016).
#' Default: FALSE.
#' @param cumvar.threshold Numeric value indicating
#' the explained variance threshold for PCA-like methods. Default: 75
#' @param method Method used to reduce multicollinerity and account for LD.
#' Default: \code{pca}.
#' Another methods available: \code{lasso}, \code{ridge}, \code{pls}.
#' @param out.type Character, indicating a type of phenotype.
#' Possible values: \code{D} (dichotomous or binary),
#' \code{C} (continous or quantitative).
#' @param scaleData A logic parameter (TRUE/FALSE) indicating scaling of
#' the genotype dataset.
#' @param asym.pval Indicates Monte Carlo approximation for p-values. Default: FALSE.
#' @param penalty A value of \code{penalty} parameter for LASSO/ridge regression.
#' Default: 0.001
#' @param verbose Indicates verbosing output. Default: FALSE.
#' @return Object of class \code{rqt}
#' @rdname rqt-geneTest
#' @export
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' res <- geneTest(obj, method="pca", out.type = "D")
#' print(res)
setMethod("geneTest", signature = "rqt",
function(obj, perm=0, STT=0.2, weight=FALSE,
cumvar.threshold=75, out.type="D",
method="pca", scaleData=FALSE, asym.pval=FALSE,
penalty=0.001,
verbose=FALSE) {
# Prepare test: load distribution table and prepare #
# some other information #
avail.methods <- c("pca", "lasso", "ridge", "pls", "none")
if(!(method %in% avail.methods)) {
stop(paste("Unknown method:", method))
}
if(!is.null(cumvar.threshold)) {
if(cumvar.threshold > 100) {
warning("Warning: cumvar.threshold > 100 and will be set to 100.")
cumvar.threshold <- 100
}
if(cumvar.threshold <= 0) {
warning("Warning: cumvar.threshold <= 0 and will be set to 1.")
cumvar.threshold <- 1
}
}
# Load data #
phenotype <- phenotype(obj)
genotype <- assays(genotype(obj))[[1]]
covariates <- covariates(obj)
# Dimensions
phenoSize <- length(phenotype)
genoSize <- dim(genotype)
if(weight & scaleData) {
if(verbose) {
print("Warining! You can not use scaling in presence of weights!")
print("Parameter weight will be set to FALSE.")
}
weight <- FALSE
}
# Start the tests #
if(perm==0){
rslt0 <- geneTestOne(phenotype=phenotype,
genotype=genotype,
covariates=covariates,
STT=STT,
weight=weight,
cumvar.threshold=cumvar.threshold,
out.type=out.type, method=method,
scaleData = scaleData, penalty=penalty, verbose=verbose)
if(asym.pval) {
rsltMC <- do.call(rbind,
lapply(1:dim(genotype)[1],
function(k){
yP <- phenotype[sample(1:phenoSize, phenoSize,
replace=FALSE)]
t.res <- geneTestOne(phenotype=yP,genotype=genotype,
covariates=covariates,STT=STT,
weight=weight,
cumvar.threshold=cumvar.threshold,
out.type=out.type, method=method,
scaleData = scaleData,
penalty=penalty)
if(is.na(t.res)) {
t.res <- list( data.frame(Q1=NA, Q2=NA, Q3=NA),
data.frame(pVal1=1,pVal2=1,pVal3=1) )
names(t.res) <- c("Qstatistic", "pValue")
}
tt.res <- t.res$Qstatistic
}))
if(!is.na(rslt0)) {
nn <- genoSize[1]+1
rslt <- list("Qstatistic"= data.frame(
Q1=rslt0$Qstatistic$Q1,
Q2=rslt0$Qstatistic$Q2,
Q3=rslt0$Qstatistic$Q3),
"pValue" = data.frame(
pVal1 = (length(rsltMC[,1][rsltMC[,1] >=
rslt0$Qstatistic$Q1])+1)/nn,
pVal2 = (length(rsltMC[,2][rsltMC[,2] >=
rslt0$Qstatistic$Q2])+1)/nn,
pVal3 = (length(rsltMC[,3][rsltMC[,3] >=
rslt0$Qstatistic$Q3])+1)/nn),
beta = rslt0$beta,
model = rslt0$model)
} else {
rslt <- list( Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=1,pVal2=1,pVal3=1),
beta = NA,
model= rslt0$model )
}
} else {
rslt <- rslt0
}
} else {
rsltPP <- do.call(rbind, lapply(1:perm, function(k){
yP <- phenotype[sample(1:phenoSize, phenoSize,replace=FALSE)]
t.res <- geneTestOne(phenotype=yP,genotype=genotype,
covariates=covariates,STT=STT,
weight=weight,
cumvar.threshold=cumvar.threshold,
out.type=out.type, method=method,
scaleData = scaleData, penalty=penalty, verbose=verbose)
if(is.na(t.res)) {
t.res <- list( data.frame(Q1=NA, Q2=NA, Q3=NA),
data.frame(pVal1=1,pVal2=1,pVal3=1) )
names(t.res) <- c("Qstatistic", "pValue")
}
pv <- t.res$pValue
}))
rslt0 <- as.numeric(geneTestOne(phenotype=phenotype,
genotype=genotype,
covariates=covariates,
STT=STT, weight=weight,
cumvar.threshold=cumvar.threshold,
out.type=out.type, method=method,
scaleData = scaleData, penalty=penalty))
if(!is.na(rslt0)) {
rslt <- list(Qstatistic= data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue = data.frame(
pVal1 = (length(rsltPP[,1][rsltPP[,1] <
rslt0$pValue[1]])+1)/(perm+1),
pVal2 = (length(rsltPP[,2][rsltPP[,2] <
rslt0$pValue[2]])+1)/(perm+1),
pVal3 = (length(rsltPP[,3][rsltPP[,3] <
rslt0$pValue[3]])+1)/(perm+1)),
beta = rslt0$beta,
model= rslt0$model)
} else {
rslt <- list( Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=1,pVal2=1,pVal3=1),
beta = NA, model = NA)
}
}
results(obj) <- rslt
return(obj)
})
#' This function performs a gene-level meta-analysis based on
#' combined effect sizes.
#'
#' @param perm Integer indicating the number of permutations
#' to compute p-values. Default: 0.
#' @param STT Numeric indicating soft truncation threshold (STT)
#' to convert to gamma parameter (must be <= 0.4).
#' Needed for an optimal parameter a in Gamma-distribution. Default: 0.2.
#' See, for example, Fridley, et al 2013: "Soft truncation thresholding
#' for gene set analysis of RNA-seq data: Application to a vaccine study".
#' @param weight Logical value. Indicates using weights (see Lee et al 2016).
#' Default: FALSE.
#' @param cumvar.threshold Numeric value indicating
#' the explained variance threshold for PCA-like methods. Default: 75
#' @param method Method used to reduce multicollinerity and account for LD.
#' Default: \code{pca}.
#' Another methods available: \code{lasso}, \code{ridge}, \code{pls}.
#' @param out.type Character, indicating a type of phenotype.
#' Possible values: \code{D} (dichotomous or binary),
#' \code{C} (continous or quantitative).
#' @param scaleData A logic parameter (TRUE/FALSE) indicating scaling of
#' the genotype dataset.
#' @param asym.pval Indicates Monte Carlo approximation for p-values. Default: FALSE.
#' @param comb.test Statistical test for combining p-values.
#' @param penalty Value of \code{penalty} parameter for LASSO/ridge regression.
#' Default: 0.001
#' @param verbose Indicates verbosing output. Default: FALSE.
#' @return A list of two: (i) final.pvalue -
#' a final p-value across all studies;
#' (ii) pvalueList - p-values for each study;
#' @export
#' @docType methods
#' @rdname rqt-geneTestMeta
#' @examples
#'data1 <- data.matrix(read.table(system.file("extdata/phengen2.dat",
#' package="rqt"), skip=1))
#'pheno <- data1[,1]
#'geno <- data1[, 2:dim(data1)[2]]
#'colnames(geno) <- paste(seq(1, dim(geno)[2]))
#'geno.obj <- SummarizedExperiment(geno)
#'obj1 <- rqt(phenotype=pheno, genotype=geno.obj)
#'
#'data2 <- data.matrix(read.table(system.file("extdata/phengen3.dat",
#' package="rqt"), skip=1))
#'pheno <- data2[,1]
#'geno <- data2[, 2:dim(data2)[2]]
#'colnames(geno) <- paste(seq(1, dim(geno)[2]))
#'geno.obj <- SummarizedExperiment(geno)
#'obj2 <- rqt(phenotype=pheno, genotype=geno.obj)
#'
#'data3 <- data.matrix(read.table(system.file("extdata/phengen.dat",
#' package="rqt"), skip=1))
#'pheno <- data3[,1]
#'geno <- data3[, 2:dim(data3)[2]]
#'colnames(geno) <- paste(seq(1, dim(geno)[2]))
#'geno.obj <- SummarizedExperiment(geno)
#'obj3 <- rqt(phenotype=pheno, genotype=geno.obj)
#'
#'res.meta <- geneTestMeta(list(obj1, obj2, obj3))
#'print(res.meta)
setMethod("geneTestMeta", signature="list",
function(objects, perm=0, STT=0.2, weight=FALSE,
cumvar.threshold=75, out.type="D",
method="pca", scaleData=FALSE, asym.pval=FALSE,
comb.test="wilkinson", penalty=0.001,
verbose=FALSE) {
if(cumvar.threshold > 100) {
warning("Warning: cumvar.threshold > 100
and will be set to 100.")
cumvar.threshold <- 100
}
if(inherits(objects, "list") == FALSE) {
stop("objects must be a list of rqt class objects!")
}
### Meta-analysis ###
numStudies <- length(objects)
pv <- rep(NA_real_, numStudies)
for(i in 1:numStudies) {
res <- geneTest(objects[[i]],
STT=STT,
weight=weight,
cumvar.threshold=cumvar.threshold,
out.type=out.type,
method=method,
perm = perm,
scaleData=scaleData,
asym.pval=asym.pval,
penalty=penalty,
verbose=verbose)
if(length(results(res)) != 0) {
pv[i] <- results(res)$pValue$pVal3
}
}
### Combining p-values via some comb.test ###
comb.res <- list()
switch(comb.test,
wilkinson={
# Wilkinson
comb.res <- wilkinsonp(pv)
},
fisher={
# Fisher
chi.comb <- sum(-2*log(pv[!is.na(pv)]))
df <- 2*length(pv)
comb.res[["p"]] <- 1-pchisq(q=chi.comb, df=df)
},
minimump={
# minimump
comb.res <- minimump(pv)
},
sump={
# sump
comb.res <- sump(pv)
},
sumlog={
# sumlog
comb.res <- sumlog(pv)
},
meanp={
comb.res <- meanp(pv)
},
logitp={
comb.res <- logitp(pv)
},
votep={
comb.res <- votep(pv)
},
{
# Wilkinson
comb.res <- wilkinsonp(pv)
}
)
#### End of combining p-values ####
### End of meta-analysis ###
return(list(final.pvalue=comb.res[["p"]],
pvalueList=pv))
})
#' Get a given STT
#' @param L TODO
#' @param STT Numeric indicating soft truncation threshold (STT) to convert
#' to gamma parameter (must be <= 0.4).
#' @return a number from gamma distribution
get.a <- function(L,STT=0.2) {
aa <- diff <- seq(0,1,length=200)
asize <- length(aa)
diff <- unlist(lapply(1:asize,
function(i) {
abs(get.stt(L,aa[i], STT) - STT)
} ))
return(aa[which.min(diff)])
}
get.stt <- function(L,a,STT=0.2){
ans <- 1-pgamma(L*qgamma(1-STT,a,1),L*a,1)
return(ans)
}
#' get.reg.family
#' @param out.type out.type
#' @return reg.family
get.reg.family <- function(out.type="D") {
if(out.type == "D") {
reg.family <- "binomial"
} else if(out.type == "C") {
reg.family <- "gaussian"
} else {
stop(paste("Unknown out.type:", out.type))
}
return(reg.family)
}
# geneTestOne: the basic function for
# gene-level association test
# Some parts of this code were adopted from the
# supplementary code provided by Lee et al 2016
# "Gene-set association tests for next-generation sequencing data"
#
#' get.reg.family
#' @param phenotype phenotype
#' @param genotype genotype
#' @param covariates covariates
#' @param STT STT
#' @param weight weight
#' @param cumvar.threshold cumvar.threshold
#' @param method method
#' @param out.type out.type
#' @param scaleData scaleData
#' @param penalty penalty
#' @param verbose verbose
#' @return rslt
geneTestOne <- function(phenotype, genotype, covariates, STT=0.2, weight=FALSE,
cumvar.threshold=75, method="pca", out.type="D",
scaleData=FALSE, penalty=0.001, verbose=FALSE) {
reg.family <- get.reg.family(out.type)
rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=NA, pVal2=NA, pVal3=NA),
beta=NA)
res.preproc <- list()
tryCatch({
if(dim(genotype)[2] > 1) {
# Removing constant columns #
preddata <- data.frame(genotype[,apply(genotype, 2,
var, na.rm=TRUE) != 0])
indexes <- NA
### Dimensionality reduction and account for LD ###
if(method != "none") {
tmp <- cor(preddata)
tmp[upper.tri(tmp)] <- 0
diag(tmp) <- 0
preddata <- preddata[,!apply(tmp,2,function(x) any(x > 0.99))]
res.preproc <- preprocess(data=preddata,
pheno=phenotype, method=method,
reg.family=reg.family,
scaleData=scaleData,
cumvar.threshold=cumvar.threshold,
out.type=out.type,
penalty=penalty,
verbose=verbose)
if(method == "pls") {
phenotype <- res.preproc[["Y"]]
reg.family <- get.reg.family("C")
}
} else {
res.preproc[["S"]] <- preddata
res.preproc[["model"]] <- "none"
}
#### Regression after data preprocessing ####
if(method %in% c("pca", "pls", "none")) {
S <- res.preproc[["S"]]
if(method == "pca") {
indexes <- res.preproc[["indexes"]]
}
# Build null model #
null.model <- build.null.model(y=phenotype, x=covariates,
reg.family=reg.family)
res <- simple.multvar.reg(null.model=null.model, Z=S)
S <- res[["S"]]
fit <- res[["fit"]]
if(dim(coef(summary(fit)))[2] >= 2) {
coef.multivar <- coef(summary(fit))[-1,1:2]
}
if(length(coef.multivar) != 2) {
beta.multivar <- coef.multivar[,1]
se.multivar <- coef.multivar[,2]
}
if(length(coef.multivar) == 2) {
beta.multivar <- coef.multivar[1]
se.multivar <- coef.multivar[2]
}
vMat <- vcov(fit)[-1,-1]
alpha <- 1/(se.multivar^2)
alpha <- alpha/sum(1/(se.multivar^2))
if(length(coef(fit)) > 2) {
mean.vif <- mean(vif(fit), na.rm=TRUE)
} else {
mean.vif <- summary(fit)$coefficients[2,2]
}
} else if(method %in% c("lasso", "ridge")) {
S <- res.preproc[["S"]]
# Build null model #
null.model <- build.null.model(y=phenotype, x=covariates,
reg.family=reg.family)
res <- simple.multvar.reg(null.model=null.model, Z=S)
S <- res[["S"]]
fit <- res[["fit"]]
if(dim(coef(summary(fit)))[2] >= 2) {
coef.multivar <- coef(summary(fit))[-1,1:2]
}
if(length(coef.multivar) != 2) {
beta.multivar <- coef.multivar[,1]
se.multivar <- coef.multivar[,2]
}
if(length(coef.multivar) == 2) {
beta.multivar <- coef.multivar[1]
se.multivar <- coef.multivar[2]
}
vMat <- vcov(fit)[-1,-1]
alpha <- 1/(se.multivar^2)
alpha <- alpha/sum(1/(se.multivar^2))
if(length(coef(fit)) > 2) {
mean.vif <- mean(vif(fit), na.rm=TRUE)
} else {
mean.vif <- summary(fit)$coefficients[2,2]
}
#
# if(sum(coef.multivar) == 0) {
# print("All coefficients in LASSO/ridge regression
# are equal to 0. No relationships between predictors
# and outcome were discovered.
# P-values will be set to 1.")
#
# rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
# pValue= data.frame(pVal1=1,pVal2=1,pVal3=1),
# beta=NA, var.pooled=NA, mean.vif=NA,
# model=res.preproc[["model"]])
#
# return(rslt)
#
# } else {
# beta.multivar <- coef.multivar[coef.multivar != 0L]
#
# vMat <- vcov_ridge(x=as.matrix(preddata),
# y=phenotype,
# rmod=fit)$vcov[coef.multivar != 0L, coef.multivar != 0L]
#
# nl <- length(which(coef.multivar != 0L))
# vMat <- as.matrix(vMat, nrow=nl, ncol=nl, byrow=TRUE)
# se.multivar <- sqrt(diag(vMat))
# }
#
# alpha <- as.matrix((1/(se.multivar^2)), ncol=1)
# alpha <- alpha/sum(1/(se.multivar^2))
#
# # Temporary disabled:
# #mean.vif <- mean(vif(fit), na.rm=TRUE)
# #print(mean.vif)
# mean.vif <- NA
}
beta.pool.base <- 0
beta.pool <- 0
var.pool <- NA
var.pool.base <- NA
if(length(coef.multivar) != 0) {
#=================== QTest1 ===================#
if(weight == FALSE) {
var.pool.base <- t(alpha) %*% vMat %*% alpha
beta.pool.base <- t(alpha) %*% beta.multivar
zScore.base <- beta.pool.base/sqrt(var.pool.base)
QStat1 <- zScore.base^2
pVal1 <- pchisq(QStat1, df=1, lower.tail=FALSE)
} else {
if(!(method %in% c("pca", "pls"))) {
maf.S <- apply(S,2, function(v)mean(na.omit(v))/2)
} else {
maf.S <- apply(S,2,
function(v) mean(na.omit(abs(v)))/2 )
}
w.S0 <- qbeta(maf.S,1,25,lower.tail=FALSE)
WS <- diag(w.S0)
if(length(beta.multivar)==1){
WS <- w.S0
}
var.pool <- t(alpha) %*% WS %*% vMat %*% WS %*% alpha
beta.pool <- t(alpha) %*% WS %*% beta.multivar
z.score <- beta.pool/sqrt(var.pool)
QStat1 <- z.score^2
pVal1 <- pchisq(QStat1, df=1, lower.tail=FALSE)
}
#=================== QTest-2 ===================#
QStat2.eigen <- eigen(vMat)
U2 <- QStat2.eigen$vectors
l2 <- QStat2.eigen$values
na.l2 <- which(l2/mean(l2) < 0.01)
if(length(na.l2) > 0){
l2 <- l2[-na.l2]
U2 <- U2[,-na.l2]
}
p2 <- pchisq((t(U2) %*% beta.multivar)^2/l2, df=1, lower.tail=FALSE)
a <- get.a(length(p2), STT)
q2 <- 2*(qgamma(p2, a, 1, lower.tail=FALSE))
QStat2 <- sum(q2)
pVal2 <- pchisq(QStat2, df=2*a*length(q2), lower.tail=FALSE)
#=================== QTest-3 ===================#
if(weight==FALSE){
cov.beta <- c(t(alpha) %*% vMat)
b.star <- beta.multivar - as.vector(beta.pool.base)*cov.beta/var.pool.base[1]
vMat.star <- vMat - (cov.beta%*%t(cov.beta))/var.pool.base[1]
} else {
w.vMat<-WS %*% vMat %*% WS
cov.beta <- c(t(alpha)%*%w.vMat)
b.star <- WS %*% beta.multivar - beta.pool*cov.beta/var.pool[1]
vMat.star <- w.vMat - (cov.beta%*%t(cov.beta))/var.pool[1]
}
if(length(beta.multivar) !=1 ) {
QStat3.eigen <- eigen(vMat.star)
U3 <- QStat3.eigen$vectors
l3 <- QStat3.eigen$values
na.l3 <- which(l3/mean(l3) < 0.001)
if(length(na.l3) > 0) {
l3<-l3[-na.l3]
U3<-U3[,-na.l3]
}
q2.proj <- (t(U3) %*% b.star)^2/l3
p2.1 <- pchisq(q2.proj, df=1, lower.tail=FALSE)
a <- get.a(length(p2.1),STT)
QStat2.proj <- sum(2*qgamma(p2.1,a,1,lower.tail=FALSE))
pVal2.proj <- pchisq(QStat2.proj,df=2*a*length(l3),
lower.tail=FALSE)
if(pVal2.proj == 0) {
pVal2.proj <- 1e-8
}
QStat2.1 <- qchisq(pVal2.proj,df=1,lower.tail=FALSE)
pi0 <- seq(0,1,by=0.1)
pVal3.can <- QStat3 <- rep(1,11)
pVal3.can[1] <- pVal2.proj
QStat3[1] <- QStat2.1
pVal3.can[11] <- pVal1
QStat3[11] <- QStat1
for(h in 2:10){
QStat3[h] <- pi0[h]*QStat1 + (1-pi0[h])*QStat2.1
pVal3.can[h] <- davies(QStat3[h],
c(pi0[h],(1-pi0[h])),
c(1,1))$Qq
if(pVal3.can[h] <= 0 | pVal3.can[h] > 1) {
pVal3.can[h] <- imhof(QStat3[h],
c(pi0[h],(1-pi0[h])),
c(1,1))$Qq
}
if(pVal3.can[h]<=0|pVal3.can[h]>1){
pVal3.can[h]<-liu(QStat3[h],c(pi0[h],
(1-pi0[h])),c(1,1))[1]
}
}
QStat3final <- QStat3[which.min(pVal3.can)]
pVal3 <- (sum(null.dist.Q3[null.dist.Q3[,1] >
-log10(min(pVal3.can)),2])+1) /
(sum(null.dist.Q3[,2])+1)
}
if(length(beta.multivar)==1){
pVal3 <- pVal1
QStat3final <- QStat1
}
rslt <- list(Qstatistic=data.frame(Q1=QStat1, Q2=QStat2, Q3=QStat3final),
pValue=data.frame(pVal1,pVal2,pVal3),
beta=ifelse(weight, beta.pool, beta.pool.base),
var.pooled=ifelse(weight, var.pool, var.pool.base),
mean.vif=mean.vif,
model=res.preproc[["model"]])
}
if(length(coef.multivar)==0) {
rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=1,pVal2=1,pVal3=1),
beta=NA, var.pooled=NA, mean.vif=NA,
model=res.preproc[["model"]])
}
} else {
# Simple regression:
# Build null model #
null.model <- build.null.model(y=phenotype, x=covariates,
reg.family=reg.family)
res <- simple.multvar.reg(null.model=null.model, Z=genotype)
reg.coef <- coef(summary(res[["fit"]]))
if(length(coef(res[["fit"]])) > 2) {
mean.vif <- mean(vif(res[["fit"]]), na.rm=TRUE)
} else {
mean.vif <- summary(res[["fit"]])$coefficients[2,2]
}
if(dim(reg.coef)[1] == 2) {
rslt <- list(Qstatistic=data.frame(Q1=reg.coef[2,3],
Q2=reg.coef[2,3], Q3=reg.coef[2,3]),
pValue=data.frame(pVal1=reg.coef[2,4],
pVal2=reg.coef[2,4],pVal3=reg.coef[2,4]),
beta=reg.coef[2,1],
var.pooled=reg.coef[2,2],
mean.vif=mean.vif, model=res.preproc[["model"]])
} else {
rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=NA,pVal2=NA,pVal3=NA),
beta=NA, var.pooled=NA, mean.vif=NA,
model=res.preproc[["model"]])
#rslt <- NA
}
}
},error=function(e) {
print(e)
print(alpha)
print(vMat)
rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue=data.frame(pVal1=NA,pVal2=NA,pVal3=NA),
beta=NA, var.pooled=NA, mean.vif=NA,
model=res.preproc[["model"]])
#rslt <- NA
}, finally=rslt)
if(is.na(rslt$pValue$pVal3)) {
rslt <- list(Qstatistic=data.frame(Q1=NA, Q2=NA, Q3=NA),
pValue= data.frame(pVal1=NA,pVal2=NA,pVal3=NA),
beta=NA, var.pooled=NA, mean.vif=NA,
model=res.preproc[["model"]])
#rslt <- NA
}
return(rslt)
}
#' Common methods for class rqt
#'
#' @name rqt-general
#' @rdname rqt-general
#'
#' @aliases show.rqt
#' @aliases summary.rqt
#' @aliases print.rqt
#'
#' @title General functions of \code{rqt}
#' such as accessors and printing.
#'
#' @description Common methods for class rqt.
#' This document lists a series of basic methods for the class rqt
#' @return None
NULL
#' This function performs an access to phenotype
#'
#' @description A phenotype accessor
#' @param obj An object of \code{rqt} class.
#' @return phenotype returns the phenotype
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' phenotype(obj)
#' @rdname rqt-phenotype
#' @docType methods
#' @export
setMethod("phenotype", signature(obj = "rqt"), function(obj) {
return(slot(obj, "phenotype"))
})
#' This function performs an access to genotype.
#'
#' @description A genotype accessor
#' @param obj An object of \code{rqt} class.
#' @return genotype returns the genotype
#' @rdname rqt-genotype
#' @docType methods
#' @export
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' genotype(obj)
setMethod("genotype", signature(obj = "rqt"), function(obj) {
return(slot(obj, "genotype"))
})
#' This function performs an access to covariates
#'
#' @description An accessor to covariates
#' @param obj An object of \code{rqt} class.
#' @return covariates returns the covariates
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' covariates(obj)
#' @rdname rqt-covariates
#' @docType methods
#' @export
setMethod("covariates", signature(obj = "rqt"), function(obj) {
return(slot(obj, "covariates"))
})
#' This function performs an access to covariates
#'
#' @description An accessor to results
#' @param obj An object of \code{rqt} class.
#' @return results returns the results
#' @examples
#' data <- data.matrix(read.table(system.file("extdata/test.bin1.dat",
#' package="rqt"), header=TRUE))
#' pheno <- data[,1]
#' geno <- data[, 2:dim(data)[2]]
#' colnames(geno) <- paste(seq(1, dim(geno)[2]))
#' geno.obj <- SummarizedExperiment(geno)
#' obj <- rqt(phenotype=pheno, genotype=geno.obj)
#' res <- geneTest(obj, method="pca", out.type = "D")
#' results(res)
#' @rdname rqt-results
#' @docType methods
#' @export
setMethod("results", signature(obj = "rqt"), function(obj) {
return(slot(obj, "results"))
})
setReplaceMethod("results", signature(obj = "rqt"),
function(obj, value) {
slot(obj, "results") <- value
obj
}
)
setMethod("show", signature(object = "rqt"), function(object) {
cat("Phenotype:\n")
print(head(phenotype(object)))
cat("...\n\n")
cat("Genotype:\n")
print(head(assays(genotype(object))[[1]]))
cat("...\n\n")
cat("Covariates:\n")
print(head(covariates(object)))
cat("\n\n")
cat("Results:\n\n")
print(results(object))
})
setMethod("summary", signature="rqt", function(object) {
cat("Phenotype:\n")
print(summary(phenotype(object)))
cat("...\n\n")
cat("Genotype:\n")
print(summary(assays(genotype(object))[[1]]))
cat("...\n\n")
cat("Covariates:\n")
cat(summary(covariates(object)))
cat("\n\n")
cat("Results:\n\n")
print(summary(results(object)))
})
