/
HIBAG.R
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HIBAG.R
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#######################################################################
#
# Package Name: HIBAG
# Description:
# HIBAG -- HLA Genotype Imputation with Attribute Bagging
#
# HIBAG R package, HLA Genotype Imputation with Attribute Bagging
# Copyright (C) 2011-2019 Xiuwen Zheng (zhengx@u.washington.edu)
# All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##########################################################################
#
# Attribute Bagging method -- HIBAG algorithm
#
.printMatching <- function(p)
{
w <- summary(p)
class(w) <- "table"
w <- c(w[1L],
"0.1% Qu." = quantile(p, 0.001, na.rm=TRUE, names=FALSE),
"1% Qu." = quantile(p, 0.01, na.rm=TRUE, names=FALSE),
w[2L], w[3L], w[5L], w[6L],
w[4L], "SD" = sd(p, na.rm=TRUE))
print(w)
}
##########################################################################
# To fit an attribute bagging model for predicting
#
hlaAttrBagging <- function(hla, snp, nclassifier=100L,
mtry=c("sqrt", "all", "one"), prune=TRUE, na.rm=TRUE, mono.rm=TRUE,
verbose=TRUE, verbose.detail=FALSE)
{
# check
stopifnot(inherits(hla, "hlaAlleleClass"))
stopifnot(inherits(snp, "hlaSNPGenoClass"))
stopifnot(is.numeric(nclassifier), length(nclassifier)==1L)
stopifnot(is.character(mtry) | is.numeric(mtry), length(mtry)>0L)
stopifnot(is.logical(prune), length(prune)==1L)
stopifnot(is.logical(na.rm), length(na.rm)==1L)
stopifnot(is.logical(mono.rm), length(mono.rm)==1L)
stopifnot(is.logical(verbose), length(verbose)==1L)
stopifnot(is.logical(verbose.detail), length(verbose.detail)==1L)
if (verbose.detail) verbose <- TRUE
with.matching <- (nclassifier > 0L)
if (!with.matching)
{
nclassifier <- -nclassifier
if (nclassifier == 0L) nclassifier <- 1L
}
# get the common samples
samp.id <- intersect(hla$value$sample.id, snp$sample.id)
# hla types
samp.flag <- match(samp.id, hla$value$sample.id)
hla.allele1 <- hla$value$allele1[samp.flag]
hla.allele2 <- hla$value$allele2[samp.flag]
if (na.rm)
{
if (any(is.na(c(hla.allele1, hla.allele2))))
{
warning("There are missing HLA alleles, ",
"and the corresponding samples have been removed.")
flag <- is.na(hla.allele1) | is.na(hla.allele2)
samp.id <- setdiff(samp.id, hla$value$sample.id[samp.flag[flag]])
samp.flag <- match(samp.id, hla$value$sample.id)
hla.allele1 <- hla$value$allele1[samp.flag]
hla.allele2 <- hla$value$allele2[samp.flag]
}
} else {
if (any(is.na(c(hla.allele1, hla.allele2))))
{
stop("There are missing HLA alleles!")
}
}
# SNP genotypes
samp.flag <- match(samp.id, snp$sample.id)
snp.geno <- snp$genotype[, samp.flag]
if (!is.integer(snp.geno))
storage.mode(snp.geno) <- "integer"
tmp.snp.id <- snp$snp.id
tmp.snp.position <- snp$snp.position
tmp.snp.allele <- snp$snp.allele
# remove mono-SNPs
if (mono.rm)
{
snpsel <- rowMeans(snp.geno, na.rm=TRUE)
snpsel[!is.finite(snpsel)] <- 0
snpsel <- (0 < snpsel) & (snpsel < 2)
if (sum(!snpsel) > 0L)
{
snp.geno <- snp.geno[snpsel, ]
if (verbose)
{
a <- sum(!snpsel)
if (a > 0L)
cat(sprintf("Exclude %d monomorphic SNP%s\n", a, .plural(a)))
}
tmp.snp.id <- tmp.snp.id[snpsel]
tmp.snp.position <- tmp.snp.position[snpsel]
tmp.snp.allele <- tmp.snp.allele[snpsel]
}
}
# check
if (length(samp.id) <= 0L)
stop("There is no common sample between 'hla' and 'snp'.")
if (length(dim(snp.geno)[1L]) <= 0L)
stop("There is no valid SNP markers.")
###################################################################
# initialize ...
n.snp <- dim(snp.geno)[1L] # Num. of SNPs
n.samp <- dim(snp.geno)[2L] # Num. of samples
HUA <- hlaUniqueAllele(c(hla.allele1, hla.allele2))
H <- factor(match(c(hla.allele1, hla.allele2), HUA))
levels(H) <- HUA
n.hla <- nlevels(H)
H1 <- as.integer(H[1L:n.samp]) - 1L
H2 <- as.integer(H[(n.samp+1L):(2L*n.samp)]) - 1L
# create an attribute bagging object (return an integer)
ABmodel <- .Call(HIBAG_Training, n.snp, n.samp, snp.geno, n.hla, H1, H2)
# number of variables randomly sampled as candidates at each split
mtry <- mtry[1L]
if (is.character(mtry))
{
if (mtry == "sqrt")
{
mtry <- ceiling(sqrt(n.snp))
} else if (mtry == "all")
{
mtry <- n.snp
} else if (mtry == "one")
{
mtry <- 1L
} else {
stop("Invalid mtry!")
}
} else if (is.numeric(mtry))
{
if (is.finite(mtry))
{
if ((0 < mtry) & (mtry < 1)) mtry <- n.snp*mtry
mtry <- ceiling(mtry)
if (mtry > n.snp) mtry <- n.snp
} else {
mtry <- ceiling(sqrt(n.snp))
}
} else {
stop("Invalid mtry value!")
}
if (mtry <= 0) mtry <- 1L
if (verbose)
{
cat(sprintf("Build a HIBAG model with %d individual classifier%s:\n",
nclassifier, .plural(nclassifier)))
cat("# of SNPs randomly sampled as candidates for each selection: ",
mtry, "\n", sep="")
cat("# of SNPs: ", n.snp, ", # of samples: ", n.samp, "\n", sep="")
s <- ifelse(!grepl("^KIR", hla$locus), "HLA", "KIR")
cat("# of unique ", s, " alleles: ", n.hla, "\n", sep="")
}
###################################################################
# training ...
# add new individual classifers
.Call(HIBAG_NewClassifiers, ABmodel, nclassifier, mtry, prune,
verbose, verbose.detail, NULL)
# output
mod <- list(n.samp = n.samp, n.snp = n.snp, sample.id = samp.id,
snp.id = tmp.snp.id, snp.position = tmp.snp.position,
snp.allele = tmp.snp.allele,
snp.allele.freq = 0.5*rowMeans(snp.geno, na.rm=TRUE),
hla.locus = hla$locus, hla.allele = levels(H),
hla.freq = prop.table(table(H)),
assembly = as.character(snp$assembly)[1L],
model = ABmodel,
appendix = list())
if (is.na(mod$assembly)) mod$assembly <- "unknown"
class(mod) <- "hlaAttrBagClass"
###################################################################
# calculate matching proportion
if (with.matching)
{
if (verbose)
cat("Calculating matching proportion:\n")
pd <- hlaPredict(mod, snp, verbose=FALSE)
mod$matching <- pd$value$matching
if (verbose)
{
.printMatching(mod$matching)
acc <- hlaCompareAllele(hla, pd, verbose=FALSE)$overall$acc.haplo
cat(sprintf("Accuracy with training data: %.1f%%\n", acc*100))
}
}
# out-of-bag accuracy
if (verbose)
{
mobj <- hlaModelToObj(mod)
acc <- sapply(mobj$classifiers, function(x) x$outofbag.acc)
cat(sprintf("Out-of-bag accuracy: %.1f%%\n", mean(acc)*100))
}
mod
}
##########################################################################
# To fit an attribute bagging model for predicting
#
hlaParallelAttrBagging <- function(cl, hla, snp, auto.save="",
nclassifier=100L, mtry=c("sqrt", "all", "one"), prune=TRUE, na.rm=TRUE,
mono.rm=TRUE, stop.cluster=FALSE, verbose=TRUE)
{
# check
stopifnot(is.null(cl) | is.numeric(cl) | inherits(cl, "cluster"))
stopifnot(inherits(hla, "hlaAlleleClass"))
stopifnot(inherits(snp, "hlaSNPGenoClass"))
stopifnot(is.character(auto.save), length(auto.save)==1L)
stopifnot(is.numeric(nclassifier), length(nclassifier)==1L)
stopifnot(is.character(mtry) | is.numeric(mtry), length(mtry)>0L)
stopifnot(is.logical(prune), length(prune)==1L)
stopifnot(is.logical(na.rm), length(na.rm)==1L)
stopifnot(is.logical(mono.rm), length(mono.rm)==1L)
stopifnot(is.logical(stop.cluster))
stopifnot(is.logical(verbose))
if (inherits(cl, "cluster"))
{
if (!requireNamespace("parallel", quietly=TRUE))
stop("The `parallel' package should be installed.")
} else if (is.numeric(cl))
{
if (cl > 1L)
{
if (!requireNamespace("parallel", quietly=TRUE))
stop("The `parallel' package should be installed.")
cl <- parallel::makeCluster(cl)
on.exit(parallel::stopCluster(cl))
stop.cluster <- FALSE
} else {
cl <- NULL
}
}
if (verbose)
{
if (!is.null(cl))
{
cat("Build a HIBAG model of", sprintf(
"%d individual classifier%s in parallel with %d compute node%s:\n",
nclassifier, if (nclassifier>1L) "s" else "",
length(cl), if (length(cl)>1L) "s" else ""
))
} else {
cat(sprintf(
"Build a HIBAG model of %d individual classifier%s:\n",
nclassifier, if (nclassifier>1L) "s" else ""
))
}
if (auto.save != "")
cat("The model is autosaved in '", auto.save, "'.\n", sep="")
}
# set random number
if (!is.null(cl))
{
RNGkind("L'Ecuyer-CMRG")
rand <- eval(parse(text=".Random.seed"))
parallel::clusterSetRNGStream(cl)
}
ans <- local({
total <- 0L
.DynamicClusterCall(cl,
fun = function(job, hla, snp, mtry, prune, na.rm, mono.rm)
{
eval(parse(text="library(HIBAG)"))
model <- hlaAttrBagging(hla=hla, snp=snp, nclassifier=0L,
mtry=mtry, prune=prune, na.rm=na.rm, mono.rm=mono.rm,
verbose=FALSE, verbose.detail=FALSE)
mobj <- hlaModelToObj(model)
hlaClose(model)
mobj
},
combine.fun = function(obj1, obj2)
{
if (is.null(obj1))
mobj <- obj2
else if (is.null(obj2))
mobj <- obj1
else
mobj <- hlaCombineModelObj(obj1, obj2)
if (auto.save != "")
save(mobj, file=auto.save)
if (verbose & !is.null(mobj))
{
z <- summary(mobj, show=FALSE)
cat(" -- avg out-of-bag acc:", sprintf(
"%0.2f%%, sd: %0.2f%%, min: %0.2f%%, max: %0.2f%%\n",
z$info["accuracy", "Mean"], z$info["accuracy", "SD"],
z$info["accuracy", "Min"], z$info["accuracy", "Max"]))
}
mobj
},
msg.fn = function(job, obj)
{
if (verbose)
{
z <- summary(obj, show=FALSE)
eval(parse(text="total <<- total + 1L"))
cat(sprintf(
"%s,%4d, job%3d, # of SNPs: %g, # of haplo: %g, acc: %0.1f%%\n",
format(Sys.time(), "%Y-%m-%d %H:%M:%S"),
total, as.integer(job), z$info["num.snp", "Mean"],
z$info["num.haplo", "Mean"],
z$info["accuracy", "Mean"]))
}
},
n = nclassifier, stop.cluster = stop.cluster,
hla=hla, snp=snp, mtry=mtry, prune=prune,
na.rm=na.rm, mono.rm=mono.rm
)
})
if (!is.null(cl) & !stop.cluster)
{
parallel::nextRNGStream(rand)
parallel::nextRNGSubStream(rand)
}
if (stop.cluster) cl <- NULL
if (auto.save != "")
ans <- get(load(auto.save))
mod <- hlaModelFromObj(ans)
if (verbose)
cat("Calculating matching proportion:\n")
pd <- hlaPredict(mod, snp, cl=cl, verbose=FALSE)
mod$matching <- pd$value$matching
if (verbose)
{
.printMatching(mod$matching)
acc <- hlaCompareAllele(hla, pd, verbose=FALSE)$overall$acc.haplo
cat(sprintf("Accuracy with training data: %.1f%%\n", acc*100))
# out-of-bag accuracy
mobj <- hlaModelToObj(mod)
acc <- sapply(mobj$classifiers, function(x) x$outofbag.acc)
cat(sprintf("Out-of-bag accuracy: %.1f%%\n", mean(acc)*100))
}
# output
if (auto.save != "")
{
mobj <- hlaModelToObj(mod)
save(mobj, file=auto.save)
invisible()
} else {
mod
}
}
##########################################################################
# To fit an attribute bagging model for predicting
#
hlaClose <- function(model)
{
stopifnot(inherits(model, "hlaAttrBagClass"))
.Call(HIBAG_Close, model$model)
invisible()
}
#######################################################################
# Predict HLA types from unphased SNP data
#
hlaPredict <- function(object, snp, cl=NULL,
type=c("response", "prob", "response+prob"), vote=c("prob", "majority"),
allele.check=TRUE, match.type=c("Position", "RefSNP+Position", "RefSNP"),
same.strand=FALSE, verbose=TRUE)
{
stopifnot(inherits(object, "hlaAttrBagClass"))
predict(object, snp, cl, type, vote, allele.check, match.type,
same.strand, verbose)
}
predict.hlaAttrBagClass <- function(object, snp, cl=NULL,
type=c("response", "prob", "response+prob"), vote=c("prob", "majority"),
allele.check=TRUE, match.type=c("Position", "RefSNP+Position", "RefSNP"),
same.strand=FALSE, verbose=TRUE, ...)
{
# check
stopifnot(inherits(object, "hlaAttrBagClass"))
stopifnot(is.null(cl) | is.numeric(cl) | inherits(cl, "cluster"))
stopifnot(is.logical(allele.check), length(allele.check)==1L)
stopifnot(is.logical(same.strand), length(same.strand)==1L)
stopifnot(is.logical(verbose), length(verbose)==1L)
type <- match.arg(type)
vote <- match.arg(vote)
match.type <- match.arg(match.type)
vote_method <- match(vote, c("prob", "majority"))
if (inherits(cl, "cluster"))
{
if (!requireNamespace("parallel", quietly=TRUE))
stop("The `parallel' package should be installed.")
if (length(cl) <= 1L) cl <- NULL
} else if (is.numeric(cl))
{
if (cl > 1L)
{
if (!requireNamespace("parallel", quietly=TRUE))
stop("The `parallel' package should be installed.")
cl <- parallel::makeCluster(cl)
on.exit(parallel::stopCluster(cl))
} else {
cl <- NULL
}
}
# if warning
if (!is.null(object$appendix$warning))
{
message(object$appendix$warning)
warning(object$appendix$warning)
}
if (verbose)
{
# get the number of classifiers
CNum <- .Call(HIBAG_GetNumClassifiers, object$model)
cat(sprintf(
"HIBAG model: %d individual classifier%s, %d SNPs, %d unique HLA alleles.\n",
CNum, .plural(CNum), length(object$snp.id),
length(object$hla.allele)))
if (vote_method == 1L)
{
cat("Predicting based on the averaged posterior probabilities",
"from all individual classifiers.\n")
} else
cat("Predicting by voting from all individual classifiers.\n")
if (!is.null(cl))
{
cat(sprintf(
"Run in parallel with %d compute node%s.\n",
length(cl), ifelse(length(cl)>1L, "s", "")
))
}
}
if (!inherits(snp, "hlaSNPGenoClass"))
{
# it should be a vector or a matrix
stopifnot(is.numeric(snp))
stopifnot(is.vector(snp) | is.matrix(snp))
if (is.vector(snp))
{
stopifnot(length(snp) == object$n.snp)
snp <- matrix(snp, ncol=1L)
} else {
stopifnot(nrow(snp) == object$n.snp)
}
geno.sampid <- 1L:ncol(snp)
assembly <- "auto-silent"
} else {
# a 'hlaSNPGenoClass' object
##################################################
# check assembly first
model.assembly <- as.character(object$assembly)[1L]
if (is.na(model.assembly))
model.assembly <- "unknown"
geno.assembly <- as.character(snp$assembly)[1L]
if (is.na(geno.assembly))
geno.assembly <- "unknown"
refstr <- sprintf("Model assembly: %s, SNP assembly: %s",
model.assembly, geno.assembly)
if (verbose)
cat(refstr, "\n", sep="")
if (model.assembly != geno.assembly)
{
if (any(c(model.assembly, geno.assembly) %in% "unknown"))
{
if (verbose)
message("The human genome references might not match!")
if (geno.assembly == "unknown")
assembly <- model.assembly
else
assembly <- geno.assembly
} else {
if (verbose)
message("The human genome references do not match!")
warning("The human genome references do not match! ",
refstr, ".")
assembly <- model.assembly
}
} else {
if (model.assembly != "unknown")
assembly <- model.assembly
else
assembly <- "auto"
}
##################################################
geno.sampid <- snp$sample.id
obj.id <- hlaSNPID(object, match.type)
geno.id <- hlaSNPID(snp, match.type)
# flag boolean
flag <- FALSE
if (length(obj.id) == length(geno.id))
{
if (all(obj.id == geno.id))
flag <- TRUE
}
# check and switch A/B alleles
if (flag)
{
if (allele.check)
{
snp <- hlaGenoSwitchStrand(snp, object,
match.type, same.strand, verbose)$genotype
} else {
snp <- snp$genotype
}
} else {
# snp selection
snp.sel <- match(obj.id, geno.id)
snp.allele <- snp$snp.allele
snp.allele[is.na(snp.allele)] <- ""
# tmp variable
tmp <- list(genotype = snp$genotype[snp.sel,],
sample.id = snp$sample.id,
snp.id = object$snp.id, snp.position = object$snp.position,
snp.allele = snp.allele[snp.sel],
assembly = snp$assembly)
flag <- is.na(tmp$snp.allele)
tmp$snp.allele[flag] <- object$snp.allele[
match(tmp$snp.id[flag], object$snp.id)]
if (is.vector(tmp$genotype))
tmp$genotype <- matrix(tmp$genotype, ncol=1L)
class(tmp) <- "hlaSNPGenoClass"
# the total number of missing snp
missing.cnt <- sum(flag)
# verbose
if (verbose)
{
if (missing.cnt > 0L)
{
cat(sprintf("There %s %d missing SNP%s (%0.1f%%).\n",
if (missing.cnt > 1L) "are" else "is",
missing.cnt,
if (missing.cnt > 1L) "s" else "",
100*missing.cnt/length(obj.id)))
}
}
# try alternative matching if possible
if (match.type == "RefSNP+Position")
{
# match by RefSNP IDs
s1 <- hlaSNPID(object, "RefSNP")
s2 <- hlaSNPID(snp, "RefSNP")
mcnt.id <- length(s1) - length(intersect(s1, s2))
# match by positions
s1 <- hlaSNPID(object, "Position")
s2 <- hlaSNPID(snp, "Position")
mcnt.pos <- length(s1) - length(intersect(s1, s2))
if (((mcnt.id < missing.cnt) |
(mcnt.pos < missing.cnt)) & verbose)
{
message(
"Hint:\n",
"The current SNP matching requires both RefSNP IDs ",
"and positions, and lower missing fraction(s) could ",
"be gained:\n",
sprintf("\t%0.1f%% by matching RefSNP IDs only, ",
100*mcnt.id/length(s1)),
"call 'predict(..., match.type=\"RefSNP\")'\n",
sprintf("\t%0.1f%% by matching positions only, ",
100*mcnt.pos/length(s1)),
"call 'predict(..., match.type=\"Position\")'\n",
"Any concern about SNP mismatching should be emailed ",
"to the genotyping platform provider.")
if (!is.null(object$appendix$platform))
{
message("The supported platform(s): ",
object$appendix$platform)
}
}
}
if (missing.cnt == length(obj.id))
{
stop("There is no overlapping of SNPs!")
} else if (missing.cnt > 0.5*length(obj.id))
{
warning("More than 50% of SNPs are missing!")
}
# switch
if (allele.check)
{
snp <- hlaGenoSwitchStrand(tmp, object, match.type,
same.strand, verbose)$genotype
} else {
snp <- tmp$genotype
}
}
}
# check
if (dim(snp)[1L] != object$n.snp)
{
stop("The number of SNPs is not valid, and ",
"it maybe due to duplicated 'snp.id' or ",
"incorrect dimension of genotypic matrix.")
}
# initialize ...
n.samp <- dim(snp)[2L]
n.hla <- length(object$hla.allele)
if (verbose)
cat(sprintf("Number of samples: %d\n", n.samp))
# parallel units
if (is.null(cl))
{
# pointer to functions for an extensible component
pm <- list(...)
pm <- pm$proc_ptr
# to predict HLA types
if (type %in% c("response", "response+prob"))
{
# the best-guess prediction
if (type == "response")
{
rv <- .Call(HIBAG_Predict_Resp, object$model, as.integer(snp),
n.samp, vote_method, verbose, pm)
names(rv) <- c("H1", "H2", "prob", "matching")
} else {
rv <- .Call(HIBAG_Predict_Resp_Prob, object$model,
as.integer(snp), n.samp, vote_method, verbose, pm)
names(rv) <- c("H1", "H2", "prob", "matching", "postprob")
}
res <- hlaAllele(geno.sampid,
H1 = object$hla.allele[rv$H1 + 1L],
H2 = object$hla.allele[rv$H2 + 1L],
locus = object$hla.locus, prob = rv$prob,
na.rm = FALSE, assembly = assembly)
res$value$matching <- rv$matching
if (!is.null(rv$postprob))
{
res$postprob <- rv$postprob
colnames(res$postprob) <- geno.sampid
m <- outer(object$hla.allele, object$hla.allele,
function(x, y) paste(x, y, sep="/"))
rownames(res$postprob) <- m[lower.tri(m, diag=TRUE)]
}
NA.cnt <- sum(is.na(res$value$allele1) | is.na(res$value$allele2))
} else {
# all probabilites
rv <- .Call(HIBAG_Predict_Resp_Prob, object$model,
as.integer(snp), n.samp, vote_method, verbose, pm)
names(rv) <- c("H1", "H2", "prob", "matching", "postprob")
res <- rv$postprob
colnames(res) <- geno.sampid
m <- outer(object$hla.allele, object$hla.allele,
function(x, y) paste(x, y, sep="/"))
rownames(res) <- m[lower.tri(m, diag=TRUE)]
NA.cnt <- sum(colSums(res) <= 0L)
}
} else {
# in parallel
rv <- parallel::clusterApply(cl=cl,
parallel::splitIndices(n.samp, length(cl)),
fun = function(idx, mobj, snp, type, vote)
{
if (length(idx) > 0L)
{
library(HIBAG)
m <- hlaModelFromObj(mobj)
on.exit(hlaClose(m))
pd <- hlaPredict(m, snp[,idx], type=type, vote=vote,
verbose=FALSE)
pd
} else
NULL
},
mobj=hlaModelToObj(object), snp=snp, type=type, vote=vote
)
if (type %in% c("response", "response+prob"))
{
res <- rv[[1L]]
for (i in 2L:length(rv))
{
if (!is.null(rv[[i]]))
res <- hlaCombineAllele(res, rv[[i]])
}
res$value$sample.id <- geno.sampid
if (!is.null(res$postprob))
colnames(res$postprob) <- geno.sampid
NA.cnt <- sum(is.na(res$value$allele1) | is.na(res$value$allele2))
} else {
res <- rv[[1L]]
for (i in 2L:length(rv))
{
if (!is.null(rv[[i]]))
res <- cbind(res, rv[[i]])
}
colnames(res) <- geno.sampid
NA.cnt <- sum(colSums(res) <= 0L)
}
}
if (NA.cnt > 0L)
{
if (NA.cnt > 1L) s <- "s" else s <- ""
warning(sprintf(
"No prediction output%s of %d individual%s ",
s, NA.cnt, s),
"(possibly due to missing SNPs.)")
}
# return
res
}
#######################################################################
# Merge predictions by voting
#
hlaPredMerge <- function(..., weight=NULL, equivalence=NULL)
{
# check "..."
pdlist <- list(...)
if (length(pdlist) <= 0L)
stop("No object is passed to 'hlaPredMerge'.")
for (i in 1:length(pdlist))
{
if (!inherits(pdlist[[i]], "hlaAlleleClass"))
{
stop("The object(s) passed to 'hlaPredMerge' ",
"should be 'hlaAlleleClass'.")
}
if (is.null(pdlist[[i]]$postprob))
{
stop("The object(s) passed to 'hlaPredMerge' should have ",
"a field of 'postprob' returned from ",
"'predict(..., type=\"response+prob\", vote=\"majority\")'.")
}
}
# check equivalence
stopifnot(is.null(equivalence) | is.data.frame(equivalence))
if (is.data.frame(equivalence))
{
if (ncol(equivalence) != 2L)
{
stop("'equivalence' should have two columns: ",
"the first for new equivalent alleles, and ",
"the second for the alleles possibly existed ",
"in the object(s) passed to 'hlaPredMerge'.")
}
}
# check locus and sample.id
samp.id <- pdlist[[1L]]$value$sample.id
locus <- pdlist[[1L]]$locus
for (i in 1L:length(pdlist))
{
if (!identical(samp.id, pdlist[[i]]$value$sample.id))
stop("The sample IDs should be the same.")
if (!identical(locus, pdlist[[i]]$locus))
stop("The locus should be the same.")
}
# check weight
if (!is.null(weight))
{
stopifnot(is.numeric(weight) & is.vector(weight))
if (length(pdlist) != length(weight))
stop("Invalid 'weight'.")
weight <- abs(weight)
weight <- weight / sum(weight)
} else {
weight <- rep(1/length(pdlist), length(pdlist))
}
#############################################################
# replace function
replace <- function(allele)
{
if (!is.null(equivalence))
{
i <- match(allele, equivalence[, 2L])
flag <- !is.na(i)
i <- i[flag]
allele[flag] <- equivalence[i, 1L]
}
allele
}
# all different alleles
hla.allele <- NULL
for (i in 1L:length(pdlist))
{
h <- unique(unlist(strsplit(rownames(pdlist[[i]]$postprob), "/")))
hla.allele <- unique(c(hla.allele, replace(h)))
}
hla.allele <- hlaUniqueAllele(hla.allele)
n.hla <- length(hla.allele)
n.samp <- length(samp.id)
prob <- matrix(0.0, nrow=n.hla*(n.hla+1L)/2, ncol=n.samp)
m <- outer(hla.allele, hla.allele, function(x, y) paste(x, y, sep="/"))
m <- m[lower.tri(m, diag=TRUE)]
# for-loop
for (i in 1L:length(pdlist))
{
p <- pdlist[[i]]$postprob
h <- replace(unlist(strsplit(rownames(p), "/")))
h1 <- h[seq(1L, length(h), 2L)]; h2 <- h[seq(2L, length(h), 2L)]
j1 <- match(paste(h1, h2, sep="/"), m)
j2 <- match(paste(h2, h1, sep="/"), m)
j1[is.na(j1)] <- j2[is.na(j1)]
# check
stopifnot(!any(is.na(j1)))
p <- p * weight[i]
for (j in seq_len(length(j1)))
prob[j1[j], ] <- prob[j1[j], ] + p[j, ]
}
colnames(prob) <- samp.id
rownames(prob) <- m
pb <- apply(prob, 2L, max)
pt <- unlist(strsplit(m[apply(prob, 2L, which.max)], "/"))
assembly <- pdlist[[1L]]$assembly
if (is.null(assembly)) assembly <- "auto"
rv <- hlaAllele(samp.id,
H1 = pt[seq(2L, length(pt), 2L)],
H2 = pt[seq(1L, length(pt), 2L)],
locus = locus,
locus.pos.start = pdlist[[1L]]$pos.start,
locus.pos.end = pdlist[[1L]]$pos.end,
prob = pb, na.rm = FALSE,
assembly = assembly)
rv$postprob <- prob
rv
}
#######################################################################
# summarize the "hlaAttrBagClass" object
#
summary.hlaAttrBagClass <- function(object, show=TRUE, ...)
{
obj <- hlaModelToObj(object)
summary(obj, show=show)
}
#######################################################################
# Save the parameters in a model of attribute bagging
#
hlaModelToObj <- function(model)
{
# check
stopifnot(inherits(model, "hlaAttrBagClass"))
# get the number of classifiers
CNum <- .Call(HIBAG_GetNumClassifiers, model$model)
# for each tree
res <- vector("list", CNum)
for (i in seq_len(CNum))
{
# get freq. and haplotypes, etc
v <- .Call(HIBAG_Classifier_GetHaplos, model$model, i)
names(v) <- c("freq", "hla", "haplo", "snpidx", "samp.num", "acc")
res[[i]] <- list(
samp.num = v$samp.num,
haplos = data.frame(freq = v$freq, hla = model$hla.allele[v$hla],
haplo = v$haplo, stringsAsFactors=FALSE),
snpidx = v$snpidx,
outofbag.acc = v$acc)
}
rv <- list(n.samp = model$n.samp, n.snp = model$n.snp,
sample.id = model$sample.id, snp.id = model$snp.id,
snp.position = model$snp.position, snp.allele = model$snp.allele,
snp.allele.freq = model$snp.allele.freq,
hla.locus = model$hla.locus,
hla.allele = model$hla.allele, hla.freq = model$hla.freq,
assembly = model$assembly,
classifiers = res,
matching = model$matching,
appendix = model$appendix)
class(rv) <- "hlaAttrBagObj"
rv
}
#######################################################################
# To combine two model objects of attribute bagging
#
hlaCombineModelObj <- function(obj1, obj2)
{
# check
stopifnot(inherits(obj1, "hlaAttrBagObj"))
stopifnot(inherits(obj2, "hlaAttrBagObj"))
stopifnot(identical(obj1$hla.locus, obj2$hla.locus))
stopifnot(identical(obj1$snp.id, obj2$snp.id))
stopifnot(identical(obj1$hla.allele, obj2$hla.allele))
stopifnot(identical(obj1$assembly, obj2$assembly))
samp.id <- unique(c(obj1$sample.id, obj2$sample.id))
if (!is.null(obj1$appendix) | !is.null(obj2$appendix))
{
appendix <- list(