R/gmap.ac.gpd.R
In QidiPeng/eQTLMAPT: eQTL Mediaiton Analysis with accelerated Permutation Testing approaches
Defines functions
gmap.ac.gpd
Documented in
gmap.ac.gpd
#'
#' @title Genomic Mediation analysis with Adaptive Petmutation scheme and
#' Adaptive Confunders and Generalized Pareto Distribution(GPD)
#'
#' @description The gmap.ac.gpd function performs genomic mediation analysis
#' with Adaptive Permutation scheme and Adaptive Confunders. It tests for
#' mediation effects for a set of user specified mediation trios(e.g., eQTL,
#' cis- and trans-genes) in the genome with the assumption of the presence of
#' cis-association. The gmap.ac.gpd function considers either a user provided
#' pool of potential confounding variables, real or constructed by other
#' methods, or all the PCs based on the feature data as the potential
#' confounder pool. When the empirical P-value is small enough, the GPD fit is
#' used to estimate a more accurate empirical P value.
#'
#' It returns the mediation p-values(nominal P-value, empirical P-values
#' obtained from ordinary calculations and empirical P-values estimated using
#' GPD fitting), the coefficient of linear models(e.g, t_stat, std.error,
#' beta, beta.total) and the proportions mediated(e.g., the percentage of
#' reduction in trans-effects after accounting for cis-mediation) based on the
#' mediation tests i) adjusting for known confounders only, and ii) adjusting
#' for known confounders and adaptively selected potential confounders for
#' each mediation trio.
#'
#' @details The function performs genomic mediation analysis with Adaptive
#' Permutation scheme and Adaptive Confunders. \code{Adaptive Permutation
#' scheme}{When using Fixed Permutation scheme, good estimation of
#' insignificant adjusted P-values can be achieved with few permutations while
#' many more are needed to estimate highly significant ones. Therefore, we
#' implemented an alternative permutation scheme that adapts the number of
#' permutations to the significance level of the variant–phenotype pairs}
#' \code{Adaptive Confunding adjustment} {One challenge in mediation test in
#' genomic studies is how to adjust unmeasured confounding variables for the
#' cis- and trans-genes (i.e., mediator-outcome) relationship.The current
#' function adaptively selects the variables to adjust for each mediation trio
#' given a large pool of constructed or real potential confounding variables.
#' The function allows the input of variables known to be potential cis- and
#' trans-genes (mediator-outcome) confounders in all mediation tests
#' (\code{known.conf}), and the input of the pool of candidate confounders
#' from which potential confounders for each mediation test will be adaptively
#' selected (\code{cov.pool}). When no pool is provided (\code{cov.pool =
#' NULL}), all the PCs based on feature profile (\code{fea.dat}) will be
#' constructed as the potential confounder pool.} \code{calculate Empirical
#' P-values using GPD fitting}{The use of a fixed number of permutations to
#' calculate empirical P-values has the disadvantage that the minimum
#' empirical P-value that can be calculated is 1/N. This makes a larger number
#' of permutations needed to calculate a smaller P-value. Therefore, we model
#' the tail of the permutation value as a Generalized Pareto
#' Distribution(GPD), enabling a smaller empirical P-value with fewer
#' permutation times.} \code{calculate Empirical P-values using GPD
#' fitting}{The use of a fixed number of permutations to calculate empirical
#' P-values has the disadvantage that the minimum empirical P-value that can
#' be calculated is 1/N. This makes a larger number of permutations needed to
#' calculate a smaller P-value. Therefore, we model the tail of the
#' permutation value as a Generalized Pareto Distribution(GPD), enabling a
#' smaller empirical P-value with fewer permutation times.}
#'
#' @param snp.dat The eQTL genotype matrix. Each row is an eQTL, each column is
#' a sample.
#' @param fea.dat A feature profile matrix. Each row is for one feature, each
#' column is a sample.
#' @param known.conf A known confounders matrix which is adjusted in all
#' mediation tests. Each row is a confounder, each column is a sample.
#' @param trios.idx A matrix of selected trios indexes (row numbers) for
#' mediation tests. Each row consists of the index (i.e., row number) of the
#' eQTL in \code{snp.dat}, the index of cis-gene feature in \code{fea.dat},
#' and the index of trans-gene feature in \code{fea.dat}. The dimension is the
#' number of trios by three.
#' @param cl Parallel backend if it is set up. It is used for parallel
#' computing. We set \code{cl}=NULL as default.
#' @param cov.pool The pool of candidate confounding variables from which
#' potential confounders are adaptively selected to adjust for each trio. Each
#' row is a covariate, each column is a sample. We set \code{cov.pool}=NULL as
#' default, which will calculate PCs of features as cov.pool.
#' @param pc.num If \code{cov.pool}=NULL, use the previous num PCs as
#' \code{cov.pool}.We set \code{pc.num}=30 as default. Please ensure the value
#' is less than the number of confusion variable number in the pool.
#' @param Minperm The minimum number of permutations. When the number of
#' permutation statistics better than the original statistic is greater than
#' \code{Minperm}, stop permutation and directly calculate the empirical P
#' value. If \code{Minperm}=0, only the nominal P-value is calculated. We set
#' \code{Minperm}=100 as default.
#' @param Maxperm Maximum number of permutation. We set \code{Maxperm}=10000 as
#' default.
#' @param gpd.perm Decide when to use GPD to fit estimation parameters. When the
#' proportion of permutation better than the original statistic is greater
#' than par, the GPD is fitted to estimate the empirical P-value. We set
#' \code{gpd.perm}=0.01 as default.
#' @param fdr The false discovery rate to select confounders. We set
#' \code{fdr}=0.05 as default.
#' @param fdr_filter The false discovery rate to filter common child and
#' intermediate variables. We set \code{fdr_filter}=0.1 as default.
#'
#' @return The algorithm will return a list of nperm, empirical.p,
#' empirical.p.gpd, nominal.p, beta, std.error, t_stat, beta.total,
#' beta.change. \item{nperm}{The actual number of permutations for testing
#' mediation.} \item{empirical.p}{The mediation empirical P-values with nperm
#' times permutation. A matrix with dimension of the number of trios.}
#' \item{empirical.p.gpd}{The mediation empirical P-values with nperm times
#' permutation using GPD fit. A matrix with dimension of the number of trios.}
#' \item{nominal.p}{The mediation nominal P-values. A matrix with dimension of
#' the number of trios.} \item{std.error}{The return std.error value of
#' feature1 for fit liner models. A matrix with dimension of the number of
#' trios.} \item{t_stat}{The return t_stat value of feature1 for fit liner
#' models. A matrix with dimension of the number of trios.} \item{beta}{The
#' return beta value of feature2 for fit liner models in the case of feature1.
#' A matrix with dimension of the number of trios.} \item{beta.total}{The
#' return beta value of feature2 for fit liner models without considering
#' feature1. A matrix with dimension of the number of trios.}
#' \item{beta.change}{The proportions mediated. A matrix with dimension of the
#' number of trios.} \item{pc.matrix}{PCs will be returned if the PCs based on
#' expression data are used as the pool of potential confounders. Each column
#' is a PC.} \item{sel.conf.ind}{An indicator matrix with dimension of the
#' number of trios by the number of covariates in \code{cov.pool} or
#' \code{pc.matrix}if the principal components (PCs) based on expression data
#' are used as the pool of potential confounders.}
#'
#' @references Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. (2016)
#' Fast and efficient QTL mapper for thousands of molecular phenotypes.
#' Bioinformatics. 2016;32:1479–1485. \doi{10.1093/bioinformatics/btv722}
#' @references Yang F, Wang J, Consortium G, Pierce BL, Chen LS. (2017)
#' Identifying cis-mediators for trans-eQTLs across many human tissues using
#' genomic mediation analysis. Genome Research. 2017;27:1859–1871.
#' \doi{10.1101/gr.216754.116}
#' @references Knijnenburg TA, Wessels LFA, Reinders MJT, Shmulevich I. (2009)
#' Fewer permutations, more accurate P-values. Bioinformatics.
#' 2009;25:i161–i168. \doi{10.1093/bioinformatics/btp211}
#'
#' @examples
#'
#' output <- gmap.ac.gpd(known.conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
#' trios.idx = dat$trios.idx[1:10,], Minperm = 100, Maxperm = 10000)
#'
#' \dontrun{
#' ## generate a cluster with 2 nodes for parallel computing
#' cl <- makeCluster(2)
#'
#' ## Use the specified candidate confusion variable pool
#' ## When the empirical P-value is less than 0.02, a more accurate
#' empirical P-value is estimated using the GPD fit.
#' output <- gmap.ac.gpd(known.conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
#' trios.idx = dat$trios.idx[1:10,], cl = cl, cov.pool = dat$cov.pool,
#' Minperm = 100, Maxperm = 10000, gpd.perm = 0.02)
#'
#' stopCluster(cl)
#' }
#'
#' @export
#' @importFrom parallel parLapply
#'
gmap.ac.gpd <- function(snp.dat, fea.dat, known.conf, trios.idx, cl = NULL, cov.pool = NULL, pc.num = 30,
Minperm = 100, Maxperm = 10000, gpd.perm = 0.01, fdr = 0.05, fdr_filter = 0.1){
confounders <- t(known.conf)
triomatrix <- array(NA, c(dim(fea.dat)[2], dim(trios.idx)[1], 3))
for (i in 1:dim(trios.idx)[1]) {
triomatrix[,i, ] <- cbind(round(snp.dat[trios.idx[i, 1], ], digits = 0),
fea.dat[trios.idx[i, 2], ], fea.dat[trios.idx[i, 3], ])
}
num_trio <- dim(triomatrix)[2]
# Adaptive Confunding adjustment
use.PC <- FALSE
if(is.null(cov.pool)){ # using PC
use.PC <- TRUE
res <- get.cov(cl, fea.dat = fea.dat, triomatrix = triomatrix, fdr = fdr, fdr_filter = fdr_filter)
pool_cov <- res$pool_cov
est_conf_pool_idx <- res$est_conf_pool_idx
all_pc <- res$pc.all
}else{ # using cov.pool
res <- get.cov(cl, cov.pool = cov.pool, triomatrix = triomatrix, fdr = fdr, fdr_filter = fdr_filter)
pool_cov <- res$pool_cov
est_conf_pool_idx <- res$est_conf_pool_idx
}
est_conf_pool_idx <- est_conf_pool_idx[ ,c(1:pc.num)]
if(!is.null(cl)){
known_output <- parLapply(cl, 1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm)
known_sel_pool_output <- parLapply(cl, 1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
pool_cov = pool_cov, est_conf_pool_idx = est_conf_pool_idx,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm, use.PC = use.PC)
}else{
known_output <- lapply(1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm)
known_sel_pool_output <- lapply(1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
pool_cov = pool_cov, est_conf_pool_idx = est_conf_pool_idx,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm, use.PC = use.PC)
}
nominal.p <- matrix(c(lapply(known_output, function(x) x$nominal.p),
lapply(known_sel_pool_output, function(x) x$nominal.p)), byrow = F, ncol = 2)
t_stat <- matrix(c(lapply(known_output, function(x) x$t_stat),
lapply(known_sel_pool_output, function(x) x$t_stat)), byrow = F, ncol = 2)
std.error <- matrix(c(lapply(known_output, function(x) x$std.error),
lapply(known_sel_pool_output, function(x) x$std.error)), byrow = F, ncol = 2)
beta <- matrix(c(lapply(known_output, function(x) x$beta),
lapply(known_sel_pool_output, function(x) x$beta)), byrow = F, ncol = 2)
beta.total <- matrix(c(lapply(known_output, function(x) x$beta.total),
lapply(known_sel_pool_output, function(x) x$beta.total)), byrow = F, ncol = 2)
beta.change <- matrix(c(lapply(known_output, function(x) x$beta.change),
lapply(known_sel_pool_output, function(x) x$beta.change)), byrow = F, ncol = 2)
empirical.p <- matrix(c(lapply(known_output, function(x) x$empirical.p),
lapply(known_sel_pool_output, function(x) x$empirical.p)), byrow = F, ncol = 2)
empirical.p.gpd <- matrix(c(lapply(known_output, function(x) x$empirical.p.gpd),
lapply(known_sel_pool_output, function(x) x$empirical.p.gpd)), byrow = F, ncol = 2)
nperm <- matrix(c(lapply(known_output, function(x) x$nperm),
lapply(known_sel_pool_output, function(x) x$nperm)), byrow = F, ncol = 2)
runtime <- matrix(c(lapply(known_output, function(x) x$runtime),
lapply(known_sel_pool_output, function(x) x$runtime)), byrow = F, ncol = 2)
if(use.PC){
output <- list(nperm = nperm, empirical.p = empirical.p, empirical.p.gpd = empirical.p.gpd, nominal.p = nominal.p,
std.error = std.error, t_stat = t_stat, beta = beta, beta.total = beta.total, beta.change = beta.change,
pc.matrix = all_pc, sel.conf.ind = est_conf_pool_idx, runtime = runtime)
}else{
output <- list(nperm = nperm, empirical.p = empirical.p, empirical.p.gpd = empirical.p.gpd, nominal.p = nominal.p,
std.error = std.error, t_stat = t_stat, beta = beta, beta.total = beta.total, beta.change = beta.change,
sel.conf.ind = est_conf_pool_idx, runtime = runtime)
}
return(output)
}
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Defines functions gmap.ac.gpd
Documented in gmap.ac.gpd
#'
#' @title Genomic Mediation analysis with Adaptive Petmutation scheme and
#' Adaptive Confunders and Generalized Pareto Distribution(GPD)
#'
#' @description The gmap.ac.gpd function performs genomic mediation analysis
#' with Adaptive Permutation scheme and Adaptive Confunders. It tests for
#' mediation effects for a set of user specified mediation trios(e.g., eQTL,
#' cis- and trans-genes) in the genome with the assumption of the presence of
#' cis-association. The gmap.ac.gpd function considers either a user provided
#' pool of potential confounding variables, real or constructed by other
#' methods, or all the PCs based on the feature data as the potential
#' confounder pool. When the empirical P-value is small enough, the GPD fit is
#' used to estimate a more accurate empirical P value.
#'
#' It returns the mediation p-values(nominal P-value, empirical P-values
#' obtained from ordinary calculations and empirical P-values estimated using
#' GPD fitting), the coefficient of linear models(e.g, t_stat, std.error,
#' beta, beta.total) and the proportions mediated(e.g., the percentage of
#' reduction in trans-effects after accounting for cis-mediation) based on the
#' mediation tests i) adjusting for known confounders only, and ii) adjusting
#' for known confounders and adaptively selected potential confounders for
#' each mediation trio.
#'
#' @details The function performs genomic mediation analysis with Adaptive
#' Permutation scheme and Adaptive Confunders. \code{Adaptive Permutation
#' scheme}{When using Fixed Permutation scheme, good estimation of
#' insignificant adjusted P-values can be achieved with few permutations while
#' many more are needed to estimate highly significant ones. Therefore, we
#' implemented an alternative permutation scheme that adapts the number of
#' permutations to the significance level of the variant–phenotype pairs}
#' \code{Adaptive Confunding adjustment} {One challenge in mediation test in
#' genomic studies is how to adjust unmeasured confounding variables for the
#' cis- and trans-genes (i.e., mediator-outcome) relationship.The current
#' function adaptively selects the variables to adjust for each mediation trio
#' given a large pool of constructed or real potential confounding variables.
#' The function allows the input of variables known to be potential cis- and
#' trans-genes (mediator-outcome) confounders in all mediation tests
#' (\code{known.conf}), and the input of the pool of candidate confounders
#' from which potential confounders for each mediation test will be adaptively
#' selected (\code{cov.pool}). When no pool is provided (\code{cov.pool =
#' NULL}), all the PCs based on feature profile (\code{fea.dat}) will be
#' constructed as the potential confounder pool.} \code{calculate Empirical
#' P-values using GPD fitting}{The use of a fixed number of permutations to
#' calculate empirical P-values has the disadvantage that the minimum
#' empirical P-value that can be calculated is 1/N. This makes a larger number
#' of permutations needed to calculate a smaller P-value. Therefore, we model
#' the tail of the permutation value as a Generalized Pareto
#' Distribution(GPD), enabling a smaller empirical P-value with fewer
#' permutation times.} \code{calculate Empirical P-values using GPD
#' fitting}{The use of a fixed number of permutations to calculate empirical
#' P-values has the disadvantage that the minimum empirical P-value that can
#' be calculated is 1/N. This makes a larger number of permutations needed to
#' calculate a smaller P-value. Therefore, we model the tail of the
#' permutation value as a Generalized Pareto Distribution(GPD), enabling a
#' smaller empirical P-value with fewer permutation times.}
#'
#' @param snp.dat The eQTL genotype matrix. Each row is an eQTL, each column is
#' a sample.
#' @param fea.dat A feature profile matrix. Each row is for one feature, each
#' column is a sample.
#' @param known.conf A known confounders matrix which is adjusted in all
#' mediation tests. Each row is a confounder, each column is a sample.
#' @param trios.idx A matrix of selected trios indexes (row numbers) for
#' mediation tests. Each row consists of the index (i.e., row number) of the
#' eQTL in \code{snp.dat}, the index of cis-gene feature in \code{fea.dat},
#' and the index of trans-gene feature in \code{fea.dat}. The dimension is the
#' number of trios by three.
#' @param cl Parallel backend if it is set up. It is used for parallel
#' computing. We set \code{cl}=NULL as default.
#' @param cov.pool The pool of candidate confounding variables from which
#' potential confounders are adaptively selected to adjust for each trio. Each
#' row is a covariate, each column is a sample. We set \code{cov.pool}=NULL as
#' default, which will calculate PCs of features as cov.pool.
#' @param pc.num If \code{cov.pool}=NULL, use the previous num PCs as
#' \code{cov.pool}.We set \code{pc.num}=30 as default. Please ensure the value
#' is less than the number of confusion variable number in the pool.
#' @param Minperm The minimum number of permutations. When the number of
#' permutation statistics better than the original statistic is greater than
#' \code{Minperm}, stop permutation and directly calculate the empirical P
#' value. If \code{Minperm}=0, only the nominal P-value is calculated. We set
#' \code{Minperm}=100 as default.
#' @param Maxperm Maximum number of permutation. We set \code{Maxperm}=10000 as
#' default.
#' @param gpd.perm Decide when to use GPD to fit estimation parameters. When the
#' proportion of permutation better than the original statistic is greater
#' than par, the GPD is fitted to estimate the empirical P-value. We set
#' \code{gpd.perm}=0.01 as default.
#' @param fdr The false discovery rate to select confounders. We set
#' \code{fdr}=0.05 as default.
#' @param fdr_filter The false discovery rate to filter common child and
#' intermediate variables. We set \code{fdr_filter}=0.1 as default.
#'
#' @return The algorithm will return a list of nperm, empirical.p,
#' empirical.p.gpd, nominal.p, beta, std.error, t_stat, beta.total,
#' beta.change. \item{nperm}{The actual number of permutations for testing
#' mediation.} \item{empirical.p}{The mediation empirical P-values with nperm
#' times permutation. A matrix with dimension of the number of trios.}
#' \item{empirical.p.gpd}{The mediation empirical P-values with nperm times
#' permutation using GPD fit. A matrix with dimension of the number of trios.}
#' \item{nominal.p}{The mediation nominal P-values. A matrix with dimension of
#' the number of trios.} \item{std.error}{The return std.error value of
#' feature1 for fit liner models. A matrix with dimension of the number of
#' trios.} \item{t_stat}{The return t_stat value of feature1 for fit liner
#' models. A matrix with dimension of the number of trios.} \item{beta}{The
#' return beta value of feature2 for fit liner models in the case of feature1.
#' A matrix with dimension of the number of trios.} \item{beta.total}{The
#' return beta value of feature2 for fit liner models without considering
#' feature1. A matrix with dimension of the number of trios.}
#' \item{beta.change}{The proportions mediated. A matrix with dimension of the
#' number of trios.} \item{pc.matrix}{PCs will be returned if the PCs based on
#' expression data are used as the pool of potential confounders. Each column
#' is a PC.} \item{sel.conf.ind}{An indicator matrix with dimension of the
#' number of trios by the number of covariates in \code{cov.pool} or
#' \code{pc.matrix}if the principal components (PCs) based on expression data
#' are used as the pool of potential confounders.}
#'
#' @references Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O. (2016)
#' Fast and efficient QTL mapper for thousands of molecular phenotypes.
#' Bioinformatics. 2016;32:1479–1485. \doi{10.1093/bioinformatics/btv722}
#' @references Yang F, Wang J, Consortium G, Pierce BL, Chen LS. (2017)
#' Identifying cis-mediators for trans-eQTLs across many human tissues using
#' genomic mediation analysis. Genome Research. 2017;27:1859–1871.
#' \doi{10.1101/gr.216754.116}
#' @references Knijnenburg TA, Wessels LFA, Reinders MJT, Shmulevich I. (2009)
#' Fewer permutations, more accurate P-values. Bioinformatics.
#' 2009;25:i161–i168. \doi{10.1093/bioinformatics/btp211}
#'
#' @examples
#'
#' output <- gmap.ac.gpd(known.conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
#' trios.idx = dat$trios.idx[1:10,], Minperm = 100, Maxperm = 10000)
#'
#' \dontrun{
#' ## generate a cluster with 2 nodes for parallel computing
#' cl <- makeCluster(2)
#'
#' ## Use the specified candidate confusion variable pool
#' ## When the empirical P-value is less than 0.02, a more accurate
#' empirical P-value is estimated using the GPD fit.
#' output <- gmap.ac.gpd(known.conf = dat$known.conf, fea.dat = dat$fea.dat, snp.dat = dat$snp.dat,
#' trios.idx = dat$trios.idx[1:10,], cl = cl, cov.pool = dat$cov.pool,
#' Minperm = 100, Maxperm = 10000, gpd.perm = 0.02)
#'
#' stopCluster(cl)
#' }
#'
#' @export
#' @importFrom parallel parLapply
#'
gmap.ac.gpd <- function(snp.dat, fea.dat, known.conf, trios.idx, cl = NULL, cov.pool = NULL, pc.num = 30,
Minperm = 100, Maxperm = 10000, gpd.perm = 0.01, fdr = 0.05, fdr_filter = 0.1){
confounders <- t(known.conf)
triomatrix <- array(NA, c(dim(fea.dat)[2], dim(trios.idx)[1], 3))
for (i in 1:dim(trios.idx)[1]) {
triomatrix[,i, ] <- cbind(round(snp.dat[trios.idx[i, 1], ], digits = 0),
fea.dat[trios.idx[i, 2], ], fea.dat[trios.idx[i, 3], ])
}
num_trio <- dim(triomatrix)[2]
# Adaptive Confunding adjustment
use.PC <- FALSE
if(is.null(cov.pool)){ # using PC
use.PC <- TRUE
res <- get.cov(cl, fea.dat = fea.dat, triomatrix = triomatrix, fdr = fdr, fdr_filter = fdr_filter)
pool_cov <- res$pool_cov
est_conf_pool_idx <- res$est_conf_pool_idx
all_pc <- res$pc.all
}else{ # using cov.pool
res <- get.cov(cl, cov.pool = cov.pool, triomatrix = triomatrix, fdr = fdr, fdr_filter = fdr_filter)
pool_cov <- res$pool_cov
est_conf_pool_idx <- res$est_conf_pool_idx
}
est_conf_pool_idx <- est_conf_pool_idx[ ,c(1:pc.num)]
if(!is.null(cl)){
known_output <- parLapply(cl, 1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm)
known_sel_pool_output <- parLapply(cl, 1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
pool_cov = pool_cov, est_conf_pool_idx = est_conf_pool_idx,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm, use.PC = use.PC)
}else{
known_output <- lapply(1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm)
known_sel_pool_output <- lapply(1:num_trio, getp.func, triomatrix = triomatrix, confounders = confounders,
pool_cov = pool_cov, est_conf_pool_idx = est_conf_pool_idx,
Minperm = Minperm, Maxperm = Maxperm, use.gpd=TRUE, gpd.perm = gpd.perm, use.PC = use.PC)
}
nominal.p <- matrix(c(lapply(known_output, function(x) x$nominal.p),
lapply(known_sel_pool_output, function(x) x$nominal.p)), byrow = F, ncol = 2)
t_stat <- matrix(c(lapply(known_output, function(x) x$t_stat),
lapply(known_sel_pool_output, function(x) x$t_stat)), byrow = F, ncol = 2)
std.error <- matrix(c(lapply(known_output, function(x) x$std.error),
lapply(known_sel_pool_output, function(x) x$std.error)), byrow = F, ncol = 2)
beta <- matrix(c(lapply(known_output, function(x) x$beta),
lapply(known_sel_pool_output, function(x) x$beta)), byrow = F, ncol = 2)
beta.total <- matrix(c(lapply(known_output, function(x) x$beta.total),
lapply(known_sel_pool_output, function(x) x$beta.total)), byrow = F, ncol = 2)
beta.change <- matrix(c(lapply(known_output, function(x) x$beta.change),
lapply(known_sel_pool_output, function(x) x$beta.change)), byrow = F, ncol = 2)
empirical.p <- matrix(c(lapply(known_output, function(x) x$empirical.p),
lapply(known_sel_pool_output, function(x) x$empirical.p)), byrow = F, ncol = 2)
empirical.p.gpd <- matrix(c(lapply(known_output, function(x) x$empirical.p.gpd),
lapply(known_sel_pool_output, function(x) x$empirical.p.gpd)), byrow = F, ncol = 2)
nperm <- matrix(c(lapply(known_output, function(x) x$nperm),
lapply(known_sel_pool_output, function(x) x$nperm)), byrow = F, ncol = 2)
runtime <- matrix(c(lapply(known_output, function(x) x$runtime),
lapply(known_sel_pool_output, function(x) x$runtime)), byrow = F, ncol = 2)
if(use.PC){
output <- list(nperm = nperm, empirical.p = empirical.p, empirical.p.gpd = empirical.p.gpd, nominal.p = nominal.p,
std.error = std.error, t_stat = t_stat, beta = beta, beta.total = beta.total, beta.change = beta.change,
pc.matrix = all_pc, sel.conf.ind = est_conf_pool_idx, runtime = runtime)
}else{
output <- list(nperm = nperm, empirical.p = empirical.p, empirical.p.gpd = empirical.p.gpd, nominal.p = nominal.p,
std.error = std.error, t_stat = t_stat, beta = beta, beta.total = beta.total, beta.change = beta.change,
sel.conf.ind = est_conf_pool_idx, runtime = runtime)
}
return(output)
}
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