Nothing
R/other_useful_functions.R
In RAINBOWR: Genome-Wide Association Study with SNP-Set Methods
Defines functions
plotHaploNetwork
plotPhyloTree
estNetwork
estPhylo
convertBlockList
MAF.cut
See
Documented in
convertBlockList
estNetwork
estPhylo
MAF.cut
plotHaploNetwork
plotPhyloTree
See
#' Function to view the first part of data (like head(), tail())
#'
#' @param data Your data. 'vector', 'matrix', 'array' (whose dimensions <= 4), 'data.frame' are supported format.
#' If other formatted data is assigned, str(data) will be returned.
#' @param fh From head. If this argument is TRUE, first part (row) of data will be shown (like head() function).
#' If FALSE, last part (row) of your data will be shown (like tail() function).
#' @param fl From left. If this argument is TRUE, first part (column) of data will be shown (like head() function).
#' If FALSE, last part (column) of your data will be shown (like tail() function).
#' @param rown The number of rows shown in console.
#' @param coln The number of columns shown in console.
#' @param rowst The start point for the direction of row.
#' @param colst The start point for the direction of column.
#' @param narray The number of dimensions other than row and column shown in console.
#' This argument is effective only your data is array (whose dimensions >= 3).
#' @param drop When rown = 1 or coln = 1, the dimension will be reduced if this argument is TRUE.
#' @param save.variable If you want to assign the result to a variable, please set this agument TRUE.
#' @param verbose If TRUE, print the first part of data.
#'
#' @return If save.variable is FALSE, NULL. If TRUE, the first part of your data will be returned.
#'
#'
See <- function(data, fh = TRUE, fl = TRUE, rown = 6, coln = 6,
rowst = 1, colst = 1, narray = 2, drop = FALSE,
save.variable = FALSE, verbose = TRUE) {
islist <- is.list(data)
isvec <- is.vector(data)
isfac <- is.factor(data)
if ((isvec | isfac) & (!islist)) {
n.data <- length(data)
if (fh) {
start <- min(rowst, n.data)
end <- min(rowst + rown - 1, n.data)
} else {
start <- max(n.data - rowst - rown + 2, 1)
end <- max(n.data - rowst + 1, 1)
}
data.show <- data[start:end]
dim.show <- length(data)
} else {
ismat <- is.matrix(data)
isdf <- is.data.frame(data)
if (ismat | isdf) {
n.data.row <- nrow(data)
if (fh) {
start.row <- min(rowst, n.data.row)
end.row <- min(rowst + rown - 1, n.data.row)
} else {
start.row <- max(n.data.row - rowst - rown + 2, 1)
end.row <- max(n.data.row - rowst + 1, 1)
}
n.data.col <- ncol(data)
if (fl) {
start.col <- min(colst, n.data.col)
end.col <- min(colst + coln - 1, n.data.col)
} else {
start.col <- max(n.data.col - colst - coln + 2, 1)
end.col <- max(n.data.col - colst + 1, 1)
}
class.each <- rep(NA, end.col - start.col + 1)
for (i in 1:(end.col - start.col + 1)) {
class.each[i] <- class(data[, i])
}
class.show <- paste0("<", class.each, ">")
data.show <-
data[start.row:end.row, start.col:end.col, drop = drop]
data.show <-
as.data.frame(rbind(class.show, apply(data.show, 2, as.character)))
data.rowname <- rownames(data)
if (!is.null(data.rowname)) {
rownames(data.show) <- c("class", rownames(data)[start.row:end.row])
} else {
rownames(data.show) <-
c("class", paste0("NULL_", start.row:end.row))
}
dim.show <- dim(data)
} else {
isarray <- is.array(data)
if (isarray) {
n.array <- length(dim(data))
n.data.row <- nrow(data)
if (fh) {
start.row <- min(rowst, n.data.row)
end.row <- min(rowst + rown - 1, n.data.row)
} else {
start.row <- max(n.data.row - rowst - rown + 2, 1)
end.row <- max(n.data.row - rowst + 1, 1)
}
n.data.col <- ncol(data)
if (fl) {
start.col <- min(colst, n.data.col)
end.col <- min(colst + coln - 1, n.data.col)
} else {
start.col <- max(n.data.col - colst - coln + 2, 1)
end.col <- max(n.data.col - colst + 1, 1)
}
if (n.array == 1) {
data.show <- data[start.row:end.row, drop = drop]
}
if (n.array == 2) {
data.show <- data[start.row:end.row, start.col:end.col, drop = drop]
}
if (n.array >= 3) {
start.other <- 1
end.other <- pmin(rep(narray, n.array - 2), dim(data)[-c(1:2)])
indices <- c(list(start.row:end.row, start.col:end.col),
lapply(X = end.other, FUN = function(end.other.now) {
start.other:end.other.now
}))
data.show <- R.utils::extract(x = data,
indices = indices,
dims = 1:n.array,
drop = drop)
}
dim.show <- dim(data)
} else {
warning("We cannot offer the simple view of your data. Instead we will offer the structure of your data.")
data.show <- str(data)
dim.show <- NULL
}
}
}
if (verbose) {
if (!is.null(data.show)) {
print(data.show)
}
print(paste0("class: ", paste(class(data), collapse = " & ")))
print(paste0("dimension: ", paste(dim.show, collapse = " x ")))
}
if (save.variable) {
return(data.show)
}
}
#' Function to remove the minor alleles
#'
#' @param x.0 A \eqn{n \times m} original marker genotype matrix.
#' @param map.0 Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' @param min.MAF Specifies the minimum minor allele frequency (MAF).
#' If a marker has a MAF less than min.MAF, it is removed from the original marker genotype data.
#' @param max.HE Specifies the maximum heterozygous rate (HE).
#' If a marker has a HE more than max.HE, it is removed from the original marker genotype data.
#' @param max.MS Specifies the maximum missing rate (MS).
#' If a marker has a MS more than max.MS, it is removed from the original marker genotype data.
#' @param return.MAF If TRUE, MAF will be returned.
#'
#' @return
#' \describe{
#' \item{$x}{The modified marker genotype data whose SNPs with MAF <= min.MAF were removed.}
#' \item{$map}{The modified map information whose SNPs with MAF <= min.MAF were removed.}
#' \item{$before}{Minor allele frequencies of the original marker genotype.}
#' \item{$after}{Minor allele frequencies of the modified marker genotype.}
#'}
#'
MAF.cut <- function(x.0, map.0 = NULL, min.MAF = 0.05, max.HE = 0.999,
max.MS = 0.05, return.MAF = FALSE) {
x.unique <- sort(unique(c(x.0)), decreasing = FALSE)
len.x.unique <- length(x.unique)
if (len.x.unique == 2) {
is.scoring1 <- all(x.unique == c(-1, 1))
is.scoring2 <- all(x.unique == c(0, 2))
} else {
if (len.x.unique == 3) {
is.scoring1 <- all(x.unique == c(-1, 0, 1))
is.scoring2 <- all(x.unique == c(0, 1, 2))
} else {
stop("Something wrong with your genotype data!!")
}
}
if (is.scoring1) {
freq <- apply(X = x.0 + 1, MARGIN = 2,
FUN = function(x) {
return(mean(x, na.rm = TRUE) / 2)
})
freq.hetero <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x == 0, na.rm = TRUE))
})
} else {
if (is.scoring2) {
freq <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x, na.rm = TRUE) / 2)
})
freq.hetero <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x == 1, na.rm = TRUE))
})
} else {
stop("Genotype data should be scored with (-1, 0, 1) or (0, 1, 2)!!")
}
}
MAF.before <- pmin(freq, 1 - freq)
mark.remain.MAF <- MAF.before >= min.MAF
mark.remain.HE <- freq.hetero <= max.HE
MS.rate <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(is.na(x)))
})
mark.remain.MS <- MS.rate <= max.MS
mark.remain <- mark.remain.MAF & mark.remain.HE & mark.remain.MS
x <- x.0[, mark.remain]
if (!is.null(map.0)) {
map <- map.0[mark.remain,]
} else {
map <- NULL
}
if (return.MAF) {
MAF.after <- MAF.before[mark.remain]
freq.hetero.after <- freq.hetero[mark.remain]
return(list(
data = list(x = x, map = map),
MAF = list(before = MAF.before, after = MAF.after),
HE = list(before = freq.hetero, after = freq.hetero.after)
))
} else {
return(list(x = x, map = map))
}
}
#' Function to convert haplotype block list from PLINK to RAINBOWR format
#'
#' @param fileNameBlocksDetPlink File name of the haplotype block list generated by PLINK (See reference).
#' The file names must contain ".blocks.det" in the tail.
#' @param map Data frame with the marker names in the first column.
#' The second and third columns contain the chromosome and map position.
#' @param blockNamesHead You can specify the header of block names for the returned data.frame.
#' @param imputeOneSNP As default, blocks including only one SNP will be discarded from the returned data.
#' If you want to include them when creating haplotype-block list for RAINBOWR,
#' please set `imputeOneSNP = TRUE`.
#' @param insertZeros When naming blocks, whether or not inserting zeros to the name of blocks.
#' For example, if there are 1,000 blocks in total, the function will name the block 1 as
#' "block_1" when `insertZeros = FALSE` and "block_0001" when `insertZeros = TRUE`.
#' @param n.core Setting n.core > 1 will enable parallel execution on a machine with multiple cores.
#' This argument is not valid when `parallel.method = "furrr"`.
#' @param parallel.method Method for parallel computation. We offer three methods, "mclapply", "furrr", and "foreach".
#'
#' When `parallel.method = "mclapply"`, we utilize \code{\link[pbmcapply]{pbmclapply}} function in the `pbmcapply` package
#' with `count = TRUE` and \code{\link[parallel]{mclapply}} function in the `parallel` package with `count = FALSE`.
#'
#' When `parallel.method = "furrr"`, we utilize \code{\link[furrr]{future_map}} function in the `furrr` package.
#' With `count = TRUE`, we also utilize \code{\link[progressr]{progressor}} function in the `progressr` package to show the progress bar,
#' so please install the `progressr` package from github (\url{https://github.com/HenrikBengtsson/progressr}).
#' For `parallel.method = "furrr"`, you can perform multi-thread parallelization by
#' sharing memories, which results in saving your memory, but quite slower compared to `parallel.method = "mclapply"`.
#'
#' When `parallel.method = "foreach"`, we utilize \code{\link[foreach]{foreach}} function in the `foreach` package
#' with the utilization of \code{\link[parallel]{makeCluster}} function in `parallel` package,
#' and \code{\link[doParallel]{registerDoParallel}} function in `doParallel` package.
#' With `count = TRUE`, we also utilize \code{\link[utils]{setTxtProgressBar}} and
#' \code{\link[utils]{txtProgressBar}} functions in the `utils` package to show the progress bar.
#'
#' We recommend that you use the option `parallel.method = "mclapply"`, but for Windows users,
#' this parallelization method is not supported. So, if you are Windows user,
#' we recommend that you use the option `parallel.method = "foreach"`.
#' @param count When count is TRUE, you can know how far RGWAS has ended with percent display.
#'
#' @return
#' \describe{
#' A data.frame object of
#' \item{$block}{Block names for SNP-set methods in RAINBOWR}
#' \item{$marker}{Marker names in each block for SNP-set methods in RAINBOWR}
#'}
#'
#' Purcell, S. and Chang, C. (2018). PLINK 1.9, www.cog-genomics.org/plink/1.9/.
#' Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4.
#' Gaunt T, RodrÃguez S, Day I (2007) Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX'. BMC Bioinformatics, 8.
#' Taliun D, Gamper J, Pattaro C (2014) Efficient haplotype block recognition of very long and dense genetic sequences. BMC Bioinformatics, 15.
#'
convertBlockList <- function(fileNameBlocksDetPlink,
map,
blockNamesHead = "haploblock_",
imputeOneSNP = FALSE,
insertZeros = FALSE,
n.core = 1,
parallel.method = "mclapply",
count = FALSE) {
if (requireNamespace("data.table", quietly = TRUE)) {
blockDataRaw <- data.frame(data.table::fread(input = fileNameBlocksDetPlink))
} else {
blockDataRaw <- read.csv(fileNameBlocksDetPlink, check.names = FALSE, header = TRUE, sep = "")
}
nBlocks <- nrow(blockDataRaw)
blockNos <- rep(1:nBlocks, blockDataRaw[, 5])
if (insertZeros) {
blockNames <- sprintf(fmt = paste0(blockNamesHead,
"%0", floor(log10(max(blockNos))) + 1, "i"),
blockNos)
} else {
blockNames <- paste0(blockNamesHead, blockNos)
}
marker <- map[, 1]
blockSNPsList <- RAINBOWR::parallel.compute(vec = blockDataRaw[, 6],
func = function(x) {
stringr::str_split(string = x,
pattern = "\\|")[[1]]
},
n.core = n.core,
parallel.method = parallel.method,
count = count)
blockSNPs <- unlist(blockSNPsList)
if (!all(blockSNPs %in% marker)) {
chr <- map[, 2]
pos <- map[, 3]
chrPos <- paste0(chr, "_", pos)
chrPosBlockSt <- apply(X = blockDataRaw[, 1:2],
MARGIN = 1,
FUN = paste, collapse = "_")
chrPosBlockEd <- apply(X = blockDataRaw[, c(1, 3)],
MARGIN = 1,
FUN = paste, collapse = "_")
blockSNPsList <- RAINBOWR::parallel.compute(vec = 1:nBlocks,
func = function(x) {
markerSt <- which(chrPos == chrPosBlockSt[x])
markerEd <- which(chrPos == chrPosBlockEd[x])
return(markerSt:markerEd)
},
n.core = n.core,
parallel.method = parallel.method,
count = count)
blockSNPs <- marker[unlist(blockSNPsList)]
}
if (imputeOneSNP) {
mrkHaploBlockIds <- rep(NA, length(marker))
names(mrkHaploBlockIds) <- marker
mrkHaploBlockIds[blockSNPs] <- blockNos
mrkHaploBlockIds[is.na(mrkHaploBlockIds)] <- (max(mrkHaploBlockIds, na.rm = TRUE) + 1):
(max(mrkHaploBlockIds, na.rm = TRUE) + sum(is.na(mrkHaploBlockIds)))
mrkHaploBlockIdsFac <- factor(mrkHaploBlockIds, levels = unique(mrkHaploBlockIds))
mrkHaploBlockIds <- as.numeric(mrkHaploBlockIdsFac)
if (insertZeros) {
blockNames <- sprintf(fmt = paste0(blockNamesHead,
"%0", floor(log10(max(mrkHaploBlockIds))) + 1, "i"),
mrkHaploBlockIds)
} else {
blockNames <- paste0(blockNamesHead, mrkHaploBlockIds)
}
blockListDf <- data.frame(block = blockNames,
marker = marker)
} else {
blockListDf <- data.frame(block = blockNames,
marker = blockSNPs)
}
return(blockListDf)
}
#' Function to estimate & plot phylogenetic tree
#'
#' @param blockInterest A \eqn{n \times M} matrix representing the marker genotype that belongs to the haplotype block of interest.
#' If this argument is NULL, this argument will automatically be determined by `geno`,
#' @param gwasRes You can use the results (data.frame) of haplotype-based (SNP-set) GWAS by `RGWAS.multisnp` function.
#' @param nTopRes Haplotype blocks (or gene sets, SNP-sets) with top `nTopRes` p-values by `gwasRes` will be used.
#' @param gene.set If you have information of gene (or haplotype block), you can use it to perform kernel-based GWAS.
#' You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2).
#' In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
#' which correspond to the marker names of "geno" argument.
#' @param indexRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker index in `geno`.
#' @param chrInterest You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the chromosome number to this argument.
#' @param posRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the position in the chromosome to this argument.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' @param pheno Data frame where the first column is the line name (gid).
#' The remaining columns should be a phenotype to test.
#' @param geno Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' Columns 4 and higher contain the marker scores for each line, coded as {-1, 0, 1} = {aa, Aa, AA}.
#' @param ZETA A list of covariance (relationship) matrix (K: \eqn{m \times m}) and its design matrix (Z: \eqn{n \times m}) of random effects.
#' Please set names of list "Z" and "K"! You can use more than one kernel matrix.
#' For example,
#'
#' ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D))
#' \describe{
#' \item{Z.A, Z.D}{Design matrix (\eqn{n \times m}) for the random effects. So, in many cases, you can use the identity matrix.}
#' \item{K.A, K.D}{Different kernels which express some relationships between lines.}
#' }
#' For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
#' @param chi2Test If TRUE, chi-square test for the relationship between haplotypes & subpopulations will be performed.
#' @param thresChi2Test The threshold for the chi-square test.
#' @param plotTree If TRUE, the function will return the plot of phylogenetic tree.
#' @param distMat You can assign the distance matrix of the block of interest.
#' If NULL, the distance matrix will be computed in this function.
#' @param distMethod You can choose the method to calculate distance between accessions.
#' This argument corresponds to the `method` argument in the `dist` function.
#' @param evolutionDist If TRUE, the evolution distance will be used instead of the pure distance.
#' The `distMat` will be converted to the distance matrix by the evolution distance.
#' @param subpopInfo The information of subpopulations. This argument should be a vector of factor.
#' @param groupingMethod If `subpopInfo` argument is NULL, this function estimates subpopulation information from marker genotype.
#' You can choose the grouping method from `kmeans`, `kmedoids`, and `hclust`.
#' @param nGrp The number of groups (or subpopulations) grouped by `groupingMethod`.
#' If this argument is 0, the subpopulation information will not be estimated.
#' @param nIterClustering If `groupingMethod` = `kmeans`, the clustering will be performed multiple times.
#' This argument specifies the number of classification performed by the function.
#' @param kernelTypes In the function, similarlity matrix between accessions will be computed from marker genotype to estimate genotypic values.
#' This argument specifies the method to compute similarity matrix:
#' If this argument is `addNOIA` (or one of other options in `methodGRM` in `calcGRM`),
#' then the `addNOIA` (or corresponding) option in the `calcGRM` function will be used,
#' and if this argument is `phylo`, the gaussian kernel based on phylogenetic distance will be computed from phylogenetic tree.
#' You can assign more than one kernelTypes for this argument; for example, kernelTypes = c("addNOIA", "phylo").
#' @param nCores The number of cores used for optimization.
#' @param hOpt Optimized hyper parameter for constructing kernel when estimating haplotype effects.
#' If hOpt = "optimized", hyper parameter will be optimized in the function.
#' If hOpt = "tuned", hyper parameter will be replaced by the median of off-diagonal of distance matrix.
#' If hOpt is numeric, that value will be directly used in the function.
#' @param hOpt2 Optimized hyper parameter for constructing kernel when estimating haplotype effects of nodes.
#' If hOpt2 = "optimized", hyper parameter will be optimized in the function.
#' If hOpt2 = "tuned", hyper parameter will be replaced by the median of off-diagonal of distance matrix.
#' If hOpt2 is numeric, that value will be directly used in the function.
#' @param maxIter Max number of iterations for optimization algorithm.
#' @param rangeHStart The median of off-diagonal of distance matrix multiplied by rangeHStart will be used
#' as the initial values for optimization of hyper parameters.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colNodeBase A vector of two integers or chracters specifying color of nodes for the positive and negative genotypic values respectively.
#' @param colTipBase A vector of integers or chracters specifying color of tips for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param edgeColoring If TRUE, the edge branch of phylogenetic tree wiil be colored.
#' @param tipLabel If TRUE, lavels for tips will be shown.
#' @param ggPlotTree If TRUE, the function will return the ggplot version of phylogenetic tree.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for tip with positive / negative effects.
#' alpha for node will be same as the alpha for tip with negative effects.
#' @param verbose If this argument is TRUE, messages for the current step_s will be shown.
#'
#' @return
#' \describe{A list / lists of
#' \item{$haplotypeInfo}{\describe{A list of haplotype information with
#' \item{$haploCluster}{A vector indicating each individual belongs to which haplotypes.}
#' \item{$haploBlock}{Marker genotype of haplotype block of interest for the representing haplotypes.}
#' }
#' }
#' \item{$subpopInfo}{The information of subpopulations.}
#' \item{$distMats}{\describe{A list of distance matrix:
#' \item{$distMat}{Distance matrix between haplotypes.}
#' \item{$distMatEvol}{Evolutionary distance matrix between haplotypes.}
#' \item{$distMatNJ}{Phylogenetic distance matrix between haplotypes including nodes.}
#' }
#' }
#' \item{$pValChi2Test}{A p-value of the chi-square test for the dependency between haplotypes & subpopulations.
#' If `chi2Test = FALSE`, `NA` will be returned.}
#' \item{$njRes}{The result of phylogenetic tree by neighborhood-joining method}
#' \item{$gvTotal}{Estimated genotypic values by kernel regression for each haplotype.}
#' \item{$gvTotalForLine}{Estimated genotypic values by kernel regression for each individual.}
#' \item{$minuslog10p}{\eqn{-log_{10}(p)} for haplotype block of interest.
#' p is the p-value for the siginifacance of the haplotype block effect.}
#' \item{$hOpts}{Optimized hyper parameters, hOpt1 & hOpt2.}
#' \item{$EMMResults}{\describe{A list of estimated results of kernel regression:
#' \item{$EM3Res}{Estimated results of kernel regression for the estimation of haplotype effects. (1st step)}
#' \item{$EMMRes}{Estimated results of kernel regression for the estimation of haplotype effects of nodes. (2nd step)}
#' \item{$EMM0Res}{Estimated results of kernel regression for the null model.}
#' }
#' }
#' \item{$clusterNosForHaplotype}{A list of cluster Nos of individuals that belong to each haplotype.}
#'}
#'
estPhylo <- function(blockInterest = NULL, gwasRes = NULL, nTopRes = 1, gene.set = NULL,
indexRegion = 1:10, chrInterest = NULL, posRegion = NULL, blockName = NULL,
pheno = NULL, geno = NULL, ZETA = NULL,
chi2Test = TRUE, thresChi2Test = 5e-2, plotTree = TRUE,
distMat = NULL, distMethod = "manhattan", evolutionDist = FALSE,
subpopInfo = NULL, groupingMethod = "kmedoids",
nGrp = 3, nIterClustering = 100, kernelTypes = "addNOIA",
nCores = parallel::detectCores() - 1, hOpt = "optimized",
hOpt2 = "optimized", maxIter = 20, rangeHStart = 10 ^ c(-1:1),
saveName = NULL, saveStyle = "png",
pchBase = c(1, 16), colNodeBase = c(2, 4), colTipBase = c(3, 5, 6),
cexMax = 2, cexMin = 0.7, edgeColoring = TRUE, tipLabel = TRUE,
ggPlotTree = FALSE, cexMaxForGG = 0.12, cexMinForGG = 0.06,
alphaBase = c(0.9, 0.3), verbose = TRUE) {
if (requireNamespace("ggtree", quietly = TRUE) &
requireNamespace("phylobase", quietly = TRUE)) {
ggPlotTree <- ggPlotTree
} else {
warning(paste0("If you want to plot phylogenetic trees, please install `ggtree` and `phylobase` packages from Bioconductor! \n",
"We switched `ggPlotTree = FALSE`."))
ggPlotTree <- FALSE
}
if (!is.null(geno)) {
M <- t(geno[, -c(1:3)])
map <- geno[, 1:3]
nLine <- nrow(M)
lineNames <- rownames(M)
if (is.null(ZETA)) {
K <- calcGRM(M)
Z <- diag(nLine)
rownames(Z) <- colnames(Z) <- lineNames
ZETA <- list(A = list(Z = Z, K = K))
}
if (is.null(subpopInfo)) {
if (verbose) {
print("Now clustering...")
}
if (nGrp > 0) {
if (groupingMethod == "kmeans") {
bwSSRatios <- rep(NA, nIterClustering)
kmResList <- NULL
for (iterNo in 1:nIterClustering) {
kmResNow <- kmeans(x = M, centers = nGrp)
bwSSRatio <- kmResNow$betweenss / (kmResNow$betweenss + kmResNow$tot.withinss)
bwSSRatios[iterNo] <- bwSSRatio
kmResList <- c(kmResList, list(kmResNow))
}
maxNo <- which(bwSSRatios == max(bwSSRatios))
maxNoNow <- sample(maxNo, 1)
clusterNos <- match(kmResList[[maxNoNow]]$cluster, unique(kmResList[[maxNoNow]]$cluster))
} else if (groupingMethod == "kmedoids") {
kmResNow <- cluster::pam(x = M, k = nGrp, pamonce = 5)
clusterNos <- match(kmResNow$clustering, unique(kmResNow$clustering))
} else if (groupingMethod == "hclust") {
distMat <- Rfast::Dist(x = M, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(M)
tre <- hclust(as.dist(m = distMat))
cutreeRes <- cutree(tree = tre, k = nGrp)
clusterNos <- match(cutreeRes, unique(cutreeRes))
} else {
stop("We only offer 'kmeans', 'kmedoids', and 'hclust' methods for grouping methods.")
}
clusterRes <- factor(paste0("cluster_", clusterNos))
names(clusterRes) <- lineNames
subpopInfo <- clusterRes
} else {
subpopInfo <- NULL
}
} else {
if (!is.factor(subpopInfo)) {
subpopInfo <- as.factor(subpopInfo)
}
}
nGrp <- length(levels(subpopInfo))
if (is.null(blockInterest)) {
if (!is.null(gwasRes)) {
gwasResOrd <- gwasRes[order(gwasRes[, 4], decreasing = TRUE), ]
if (nTopRes == 1) {
blockName <- as.character(gwasResOrd[1, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterest <- M[, geno[, 1] %in% mrkInBlock]
} else {
blockInterest <- NULL
blockNames <- rep(NA, nTopRes)
for (topNo in 1:nTopRes) {
blockName <- as.character(gwasResOrd[topNo, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterestNow <- M[, geno[, 1] %in% mrkInBlock]
blockNames[topNo] <- blockName
blockInterest <- c(blockInterest, list(blockInterestNow))
}
names(blockInterest) <- blockNames
}
} else if (!is.null(posRegion)) {
if (is.null(chrInterest)) {
stop("Please input the chromosome number of interest!")
} else {
chrCondition <- map[, 2] %in% chrInterest
posCondition <- (posRegion[1] <= map[, 3]) & (posRegion[2] >= map[, 3])
indexRegion <- which(chrCondition & posCondition)
}
}
blockInterest <- M[, indexRegion]
}
} else {
blockInterestCheck <- !is.null(blockInterest)
ZETACheck <- !is.null(ZETA)
if (blockInterestCheck & ZETACheck) {
lineNames <- rownames(blockInterest)
nLine <- nrow(blockInterest)
} else {
stop("Please input 'geno', or both 'blockInterest' and 'ZETA'.")
}
}
estPhyloEachBlock <- function(blockInterest,
blockName = NULL) {
stringBlock <- apply(blockInterest, 1, function(x) paste0(x, collapse = ""))
blockInterestUnique <- blockInterest[!duplicated(stringBlock), ]
nHaplo <- length(unique(stringBlock))
haploClusterNow <- as.numeric(factor(stringBlock))
names(haploClusterNow) <- lineNames
blockInterestUniqueSorted <- blockInterestUnique[order(unique(stringBlock)), ]
haploNames <- paste0("haplo_", 1:nHaplo)
rownames(blockInterestUniqueSorted) <- haploNames
haploCluster <- haploNames[haploClusterNow]
names(haploCluster) <- lineNames
if (!is.null(subpopInfo)) {
tableRes <- table(haploCluster = haploCluster,
subpop = subpopInfo)[haploNames, ]
if (verbose) {
cat("Tabular for haplotype x subpopulation: \n")
print(head(tableRes))
cat("\nThe number of haplotypes: ", nHaplo, "\n")
}
if (chi2Test) {
chi2TestRes <- chisq.test(tableRes)
pValChi2Test <- chi2TestRes$p.value
if (verbose) {
cat("\n")
print(chi2TestRes)
cat("Haplotypes & Subpopulations are: \n")
if (pValChi2Test <= thresChi2Test) {
cat("\tSiginificantly dependent\n")
} else {
cat("\tindependent\n")
}
}
} else {
pValChi2Test <- NA
}
}
haplotypeInfo <- list(haploCluster = haploCluster,
haploBlock = blockInterestUniqueSorted)
nMrkInBlock <- ncol(blockInterest)
if (is.null(distMat)) {
distMat <- Rfast::Dist(x = blockInterestUniqueSorted, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(blockInterestUniqueSorted)
}
if (evolutionDist) {
inLog <- 1 - 4 * (distMat / (ncol(blockInterest) * 2)) / 3
inLog[inLog <= 0] <- min(inLog[inLog > 0]) / 10
dist4Nj <- - 3 * log(inLog) / 4
} else {
dist4Nj <- distMat
}
njRes <- ape::nj(X = as.dist(dist4Nj))
distNodes <- ape::dist.nodes(njRes) / sqrt(nMrkInBlock)
minuslog10ps <- c()
gvEstTotals <- gvEstTotalForLines <-
hOptsList <- EMMResultsList <- list()
hOptBase <- hOpt
hOptBase2 <- hOpt2
for (kernelType in kernelTypes) {
if (verbose) {
print(paste0("Now optimizing for kernelType: ", kernelType))
}
if (!is.null(pheno)) {
rownames(distNodes)[1:nHaplo] <- colnames(distNodes)[1:nHaplo] <- haploNames
nTotal <- nrow(distNodes)
nNode <- nTotal - nHaplo
ZgKernelPart <- as.matrix(Matrix::sparseMatrix(i = 1:nLine,
j = haploClusterNow,
x = rep(1, nLine),
dims = c(nLine, nHaplo),
dimnames = list(lineNames, haploNames)))
hInv <- median((distNodes ^ 2)[upper.tri(distNodes ^ 2)])
h <- 1 / hInv
hStarts <- h * rangeHStart
hStarts <- split(hStarts, factor(1:length(rangeHStart)))
if (kernelType %in% c("phylo", "gaussian", "exponential")) {
if (hOptBase == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects...")
}
maximizeFunc <- function(h) {
if (kernelType == "phylo") {
gKernel <- exp(- h * distNodes ^ 2)
gKernelPart <- gKernel[1:nHaplo, 1:nHaplo]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = h)
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- try(EM3.cpp(y = pheno[, 2], ZETA = ZETANow), silent = TRUE)
if (!("try-error" %in% class(EM3Res))) {
LL <- EM3Res$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo <- which.min(unlist(lapply(solnList, function(x) x$objective)))
soln <- solnList[[solnNo]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln <- nlminb(start = hStarts[[1]], objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt <- soln$par
} else if (hOptBase == "tuned") {
hOpt <- h
} else if (!is.numeric(hOptBase)) {
stop("`hOpt` should be either one of 'optimized', 'tuned', or numeric!!")
}
if (kernelType == "phylo") {
gKernel <- exp(- hOpt * distNodes)
gKernelPart <- gKernel[1:nHaplo, 1:nHaplo]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = hOpt)
}
} else {
hOpt <- NA
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType)
}
if (verbose) {
print("Now estimating genotypic values...")
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- EM3.cpp(y = pheno[, 2], ZETA = ZETANow)
LL <- EM3Res$LL
gvEst <- EM3Res$u.each[(nLine + 1):(nLine + nHaplo), ]
EMMRes0 <- EMM.cpp(y = pheno[, 2], ZETA = ZETA)
LL0 <- EMMRes0$LL
pVal <- pchisq(2 * (LL - LL0), df = 1, lower.tail = FALSE)
minuslog10p <- - log10(pVal)
if (EM3Res$weights[2] <= 1e-06) {
warning("This block seems to have no effect on phenotype...")
plotNode <- FALSE
gvEstTotal <- c(gvEst, rep(NA, nNode))
names(gvEstTotal) <- rownames(distNodes)
cexTip <- cexMax / 2
pchTip <- pchBase[2]
EMMRes <- NA
hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
} else {
plotNode <- TRUE
ZgKernel <- diag(nTotal)
rownames(ZgKernel) <- colnames(ZgKernel) <- rownames(distNodes)
gvEst2 <- matrix(c(gvEst, rep(NA, nNode)))
rownames(gvEst2) <- rownames(distNodes)
if (hOptBase2 == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects of nodes...")
}
maximizeFunc2 <- function(h) {
gKernel <- exp(- h * distNodes ^ 2)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel))
EMMRes <- try(EMM.cpp(y = gvEst2, ZETA = ZETA2), silent = TRUE)
if (!("try-error" %in% class(EMMRes))) {
LL <- EMMRes$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList2 <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList2 <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo2 <- which.min(unlist(lapply(solnList2, function(x) x$objective)))
soln2 <- solnList2[[solnNo2]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln2 <- nlminb(start = hStarts[[1]], objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt2 <- soln2$par
} else if (hOptBase2 == "tuned") {
hOpt2 <- h
} else if (!is.numeric(hOptBase2)) {
stop("`hOpt2` should be either one of 'optimized', 'tuned', or numeric!!")
}
gKernel2 <- exp(- hOpt2 * distNodes ^ 2)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel2))
EMMRes <- EMM.cpp(y = gvEst2, ZETA = ZETA2)
gvNode <- EMMRes$u[(nHaplo + 1):nTotal] + EMMRes$beta
gvEstTotal <- c(gvEst, gvNode)
names(gvEstTotal) <- rownames(distNodes)
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexNode <- (abs(gvScaled4Cex) + cexMin)[(nHaplo + 1):nTotal]
pchNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, pchBase[1], pchBase[2])
colNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, colNodeBase[1], colNodeBase[2])
cexTip <- (abs(gvScaled4Cex) + cexMin)[1:nHaplo]
pchTip <- ifelse(gvScaled[1:nHaplo] > 0, pchBase[1], pchBase[2])
colorForNodeFac <- factor(gvScaled[(nHaplo + 1):nTotal] > 0,
levels = c("TRUE", "FALSE"))
colorForNodeDF <- data.frame(model.matrix(~ colorForNodeFac - 1))
rownames(colorForNodeDF) <- names(colorForNodeFac)
colnames(colorForNodeDF) <- c("gvNode +", "gvNode -")
colorForNodeDF$node <- rownames(colorForNodeDF)
gvScaled4CexForGG <- sign(gvScaled) * sqrt(abs(gvScaled)) *
(cexMaxForGG - cexMinForGG) / max(sqrt(abs(gvScaled)))
cexNodeForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[(nHaplo + 1):nTotal]
cexTipForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[1:nHaplo]
alphaTip <- ifelse(gvScaled[1:nHaplo] > 0, alphaBase[1], alphaBase[2])
alphaNode <- alphaBase[2]
}
} else {
plotNode <- FALSE
nNode <- njRes$Nnode
nTotal <- nNode + nHaplo
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- rownames(distNodes)
minuslog10p <- NA
cexTip <- cexMax / 2
pchTip <- pchBase[2]
EM3Res <- EMMRes <- EMMRes0 <- NA
hOpt <- hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
}
gvEstTotalForLine <- (gvEstTotal[haploNames])[haploClusterNow]
names(gvEstTotalForLine) <- lineNames
hOpts <- c(hOpt = hOpt,
hOpt2 = hOpt2)
EMMResults <- list(EM3Res = EM3Res,
EMMRes = EMMRes,
EMMRes0 = EMMRes0)
gvEstTotals[[kernelType]] <- gvEstTotal
gvEstTotalForLines[[kernelType]] <- gvEstTotalForLine
minuslog10ps[kernelType] <- minuslog10p
hOptsList[[kernelType]] <- hOpts
EMMResultsList[[kernelType]] <- EMMResults
if (!is.null(subpopInfo)) {
if (length(colTipBase) != nGrp) {
stop("The length of 'colTipBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colTipNo <- as.numeric(subpopInfo)
colTip <- colTipBase[colTipNo]
names(colTipNo) <- names(colTip) <- lineNames
colTip <- tapply(colTip, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
edgeCol <- as.numeric(colTip[njRes$edge[, 2]])
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
clusterNosRatioDF$node <- 1:nHaplo
} else {
colTip <- rep("gray", nHaplo)
names(colTip) <- haploNames
colTipBase <- "gray"
edgeCol <- colTip[njRes$edge[, 2]]
clusterNosForHaplotype <- NA
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Tip"
clusterNosRatioDF$node <- 1:nHaplo
}
edgeCol[is.na(edgeCol)] <- "gray"
if (plotTree) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_tree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
if (edgeColoring) {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = edgeCol)
} else {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = "gray30")
}
if (plotNode) {
ape::nodelabels(pch = pchNode, cex = cexNode, col = colNode)
}
if (tipLabel) {
ape::tiplabels(pch = pchTip, cex = cexTip, col = colTip)
}
title(main = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (plotNode) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Tip (gv:+)", "Tip (gv:-)",
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, 2))
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colTipBase,
pch = pchTip)
} else {
legend("topleft", legend = "Tip",
col = colTipBase,
pch = pchTip)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotTree) {
trPhylo41 <- as(njRes, 'phylo4')
if (edgeColoring) {
dataForTipCol <- data.frame(color = colTip)
} else {
dataForTipCol <- data.frame(color = rep("gray30", nHaplo))
}
rownames(dataForTipCol) <- njRes$tip.label
trPhylo42 <- phylobase::phylo4d(trPhylo41, dataForTipCol)
nodeData <- data.frame(randomTrait = gvEstTotal[(nHaplo + 1):nTotal],
color = rep("gray", phylobase::nNodes(trPhylo41)),
row.names = phylobase::nodeId(trPhylo41, "internal"))
phylobase::nodeData(trPhylo42) <- nodeData
plt <- ggtree::ggtree(tr = trPhylo42,
ggtree::aes(col = I(ggplot2::.data$color)),
layout = "equal_angle")
if (plotNode) {
piesNode <- ggtree::nodepie(colorForNodeDF, cols = 1:2,
color = colNodeBase,
alpha = alphaNode)
plt <- plt + ggtree::geom_inset(insets = piesNode,
width = cexNodeForGG,
height = cexNodeForGG)
}
if (tipLabel) {
if (!is.null(subpopInfo)) {
colTipBaseForPie <- colTipBase
} else {
colTipBaseForPie <- rep("gray", nGrp)
}
if (!all(is.na(EMMRes))) {
piesPlus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[1], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[1])
piesMinus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[2], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[2])
plt <- plt + ggtree::geom_inset(insets = piesPlus,
width = cexTipForGG[alphaTip == alphaBase[1]],
height = cexTipForGG[alphaTip == alphaBase[1]])
plt <- plt + ggtree::geom_inset(insets = piesMinus,
width = cexTipForGG[alphaTip == alphaBase[2]],
height = cexTipForGG[alphaTip == alphaBase[2]])
} else {
piesTip <- ggtree::nodepie(clusterNosRatioDF,
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = mean(alphaBase))
plt <- plt + ggtree::geom_inset(insets = piesTip,
width = cexTipForGG,
height = cexTipForGG)
}
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_ggtree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
return(list(haplotypeInfo = haplotypeInfo,
subpopInfo = subpopInfo,
pValChi2Test = pValChi2Test,
distMats = list(distMat = distMat,
distMatEvol = dist4Nj,
distMatNJ = distNodes),
njRes = njRes,
gvTotal = gvEstTotals,
gvTotalForLine = gvEstTotalForLines,
minuslog10p = minuslog10ps,
hOpts = hOptsList,
EMMResults = EMMResultsList,
clusterNosForHaplotype = clusterNosForHaplotype))
}
if (is.matrix(blockInterest)) {
return(estPhyloEachBlock(blockInterest = blockInterest,
blockName = blockName))
} else if (is.list(blockInterest)) {
nTopRes <- length(blockInterest)
allResultsList <- rep(list(NULL), nTopRes)
blockNames <- names(blockInterest)
if (is.null(blockNames)) {
blockNames <- paste0("block_", 1:nTopRes)
}
names(allResultsList) <- blockNames
for (topNo in 1:nTopRes) {
allResultsList[[topNo]] <- estPhyloEachBlock(blockInterest = blockInterest[[topNo]],
blockName = blockNames[topNo])
}
return(allResultsList)
}
}
#' Function to estimate & plot haplotype network
#'
#' @param blockInterest A \eqn{n \times M} matrix representing the marker genotype that belongs to the haplotype block of interest.
#' If this argument is NULL, this argument will automatically be determined by `geno`,
#' @param gwasRes You can use the results (data.frame) of haplotype-based (SNP-set) GWAS by `RGWAS.multisnp` function.
#' @param nTopRes Haplotype blocks (or gene sets, SNP-sets) with top `nTopRes` p-values by `gwasRes` will be used.
#' @param gene.set If you have information of gene (or haplotype block), you can use it to perform kernel-based GWAS.
#' You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2).
#' In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
#' which correspond to the marker names of "geno" argument.
#' @param indexRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker index in `geno`.
#' @param chrInterest You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the chromosome number to this argument.
#' @param posRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the position in the chromosome to this argument.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' @param pheno Data frame where the first column is the line name (gid).
#' The remaining columns should be a phenotype to test.
#' @param geno Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' Columns 4 and higher contain the marker scores for each line, coded as {-1, 0, 1} = {aa, Aa, AA}.
#' @param ZETA A list of covariance (relationship) matrix (K: \eqn{m \times m}) and its design matrix (Z: \eqn{n \times m}) of random effects.
#' Please set names of list "Z" and "K"! You can use more than one kernel matrix.
#' For example,
#'
#' ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D))
#' \describe{
#' \item{Z.A, Z.D}{Design matrix (\eqn{n \times m}) for the random effects. So, in many cases, you can use the identity matrix.}
#' \item{K.A, K.D}{Different kernels which express some relationships between lines.}
#' }
#' For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
#' @param chi2Test If TRUE, chi-square test for the relationship between haplotypes & subpopulations will be performed.
#' @param thresChi2Test The threshold for the chi-square test.
#' @param plotNetwork If TRUE, the function will return the plot of haplotype network.
#' @param distMat You can assign the distance matrix of the block of interest.
#' If NULL, the distance matrix will be computed in this function.
#' @param evolutionDist If TRUE, the evolution distance will be used instead of the pure distance.
#' The `distMat` will be converted to the distance matrix by the evolution distance when you use `complementHaplo = "phylo"`.
#' @param distMethod You can choose the method to calculate distance between accessions.
#' This argument corresponds to the `method` argument in the `dist` function.
#' @param complementHaplo how to complement unobserved haplotypes.
#' When `complementHaplo = "all"`, all possible haplotypes will be complemented from the observed haplotypes.
#' When `complementHaplo = "never"`, unobserved haplotypes will not be complemented.
#' When `complementHaplo = "phylo"`, unobserved haplotypes will be complemented as nodes of phylogenetic tree.
#' When `complementHaplo = "TCS"`, unobserved haplotypes will be complemented by TCS methods (Clement et al., 2002).
#' @param subpopInfo The information of subpopulations. This argument should be a vector of factor.
#' @param groupingMethod If `subpopInfo` argument is NULL, this function estimates subpopulation information from marker genotype.
#' You can choose the grouping method from `kmeans`, `kmedoids`, and `hclust`.
#' @param nGrp The number of groups (or subpopulations) grouped by `groupingMethod`.
#' If this argument is 0, the subpopulation information will not be estimated.
#' @param nIterClustering If `groupingMethod` = `kmeans`, the clustering will be performed multiple times.
#' This argument specifies the number of classification performed by the function.
#' @param iterRmst The number of iterations for RMST (randomized minimum spanning tree).
#' @param networkMethod Either one of 'mst' (minimum spanning tree),
#' 'msn' (minimum spanning network), and 'rmst' (randomized minimum spanning tree).
#' 'rmst' is recommended.
#' @param autogamous This argument will be valid only when you use `complementHaplo = "all"` or `complementHaplo = "TCS"`.
#' This argument specifies whether the plant is autogamous or not. If autogamous = TRUE,
#' complemented haplotype will consist of only homozygous sites ({-1, 1}).
#' If FALSE, complemented haplotype will consist of both homozygous & heterozygous sites ({-1, 0, 1}).
#' @param probParsimony Equal to the argument `prob` in `haplotypes::parsimnet` function:
#'
#' A numeric vector of length one in the range [0.01, 0.99] giving the probability of parsimony as defined in Templeton et al. (1992).
#' In order to set maximum connection steps to Inf (to connect all the haplotypes in a single network), set the probability to NULL.
#' @param nMaxHaplo The maximum number of haplotypes. If the number of total (complemented + original) haplotypes are larger than `nMaxHaplo`,
#' we will only show the results only for the original haplotypes to reduce the computational time.
#' @param kernelTypes In the function, similarlity matrix between accessions will be computed from marker genotype to estimate genotypic values.
#' This argument specifies the method to compute similarity matrix:
#' If this argument is `addNOIA` (or one of other options in `methodGRM` in `calcGRM`),
#' then the `addNOIA` (or corresponding) option in the `calcGRM` function will be used,
#' and if this argument is `diffusion`, the diffusion kernel based on Laplacian matrix will be computed from network.
#' You can assign more than one kernelTypes for this argument; for example, kernelTypes = c("addNOIA", "diffusion").
#' @param nCores The number of cores used for optimization.
#' @param hOpt Optimized hyper parameter for constructing kernel when estimating haplotype effects.
#' If hOpt = "optimized", hyper parameter will be optimized in the function.
#' If hOpt is numeric, that value will be directly used in the function.
#' @param hOpt2 Optimized hyper parameter for constructing kernel when estimating complemented haplotype effects.
#' If hOpt2 = "optimized", hyper parameter will be optimized in the function.
#' If hOpt2 is numeric, that value will be directly used in the function.
#' @param maxIter Max number of iterations for optimization algorithm.
#' @param rangeHStart The median of off-diagonal of distance matrix multiplied by rangeHStart will be used
#' as the initial values for optimization of hyper parameters.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param plotWhichMDS We will show the MDS (multi-dimensional scaling) plot,
#' and this argument is a vector of two integers specifying that will define which MDS dimension will be plotted.
#' The first and second integers correspond to the horizontal and vertical axes, respectively.
#' @param colConnection A vector of two integers or characters specifying the colors of connection between nodes for the original and complemented haplotypes, respectively.
#' @param ltyConnection A vector of two characters specifying the line types of connection between nodes for the original and complemented haplotypes, respectively.
#' @param lwdConnection A vector of two integers specifying the line widths of connection between nodes for the original and complemented haplotypes, respectively.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colCompBase A vector of two integers or characters specifying color of complemented haplotypes for the positive and negative genotypic values respectively.
#' @param colHaploBase A vector of integers or characters specifying color of original haplotypes for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param ggPlotNetwork If TRUE, the function will return the ggplot version of haplotype network.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for original haplotype with positive / negative effects.
#' alpha for complemented haplotype will be same as the alpha for original haplotype with negative effects.
#' @param verbose If this argument is TRUE, messages for the current steps will be shown.
#'
#' @return
#' \describe{A list / lists of
#' \item{$haplotypeInfo}{\describe{A list of haplotype information with
#' \item{$haploCluster}{A vector indicating each individual belongs to which haplotypes.}
#' \item{$haploBlock}{Marker genotype of haplotype block of interest for the representing haplotypes.}
#' }
#' }
#' \item{$subpopInfo}{The information of subpopulations.}
#' \item{$pValChi2Test}{A p-value of the chi-square test for the dependency between haplotypes & subpopulations.
#' If `chi2Test = FALSE`, `NA` will be returned.}
#' \item{$mstResults}{\describe{A list of estimated results of MST / MSN / RMST:
#' \item{$mstRes}{Estimated results of MST / MSN / RMST for the data including original haplotypes.}
#' \item{$mstResComp}{Estimated results of MST / MSN / RMST for the data including both original and complemented haplotype.}
#' }
#' }
#' \item{$distMats}{\describe{A list of distance matrix:
#' \item{$distMat}{Distance matrix between haplotypes.}
#' \item{$distMatComp}{Distance matrix between haplotypes (including unobserved ones).}
#' \item{$laplacianMat}{Laplacian matrix between haplotypes (including unobserved ones).}
#' }
#' }
#' \item{$gvTotal}{Estimated genotypic values by kernel regression for each haplotype.}
#' \item{$gvTotalForLine}{Estimated genotypic values by kernel regression for each individual.}
#' \item{$minuslog10p}{\eqn{-log_{10}(p)} for haplotype block of interest.
#' p is the p-value for the siginifacance of the haplotype block effect.}
#' \item{$hOpts}{Optimized hyper parameters, hOpt1 & hOpt2.}
#' \item{$EMMResults}{\describe{A list of estimated results of kernel regression:
#' \item{$EM3Res}{Estimated results of kernel regression for the estimation of haplotype effects. (1st step)}
#' \item{$EMMRes}{Estimated results of kernel regression for the estimation of haplotype effects of nodes. (2nd step)}
#' \item{$EMM0Res}{Estimated results of kernel regression for the null model.}
#' }
#' }
#' \item{$clusterNosForHaplotype}{A list of cluster Nos of individuals that belong to each haplotype.}
#'}
#'
estNetwork <- function(blockInterest = NULL, gwasRes = NULL, nTopRes = 1, gene.set = NULL,
indexRegion = 1:10, chrInterest = NULL, posRegion = NULL, blockName = NULL,
pheno = NULL, geno = NULL, ZETA = NULL, chi2Test = TRUE,
thresChi2Test = 5e-2, plotNetwork = TRUE, distMat = NULL,
distMethod = "manhattan", evolutionDist = FALSE, complementHaplo = "phylo",
subpopInfo = NULL, groupingMethod = "kmedoids", nGrp = 3,
nIterClustering = 100, iterRmst = 100, networkMethod = "rmst",
autogamous = FALSE, probParsimony = 0.95, nMaxHaplo = 1000,
kernelTypes = "addNOIA", nCores = parallel::detectCores() - 1,
hOpt = "optimized", hOpt2 = "optimized", maxIter = 20,
rangeHStart = 10 ^ c(-1:1), saveName = NULL, saveStyle = "png",
plotWhichMDS = 1:2, colConnection = c("grey40", "grey60"),
ltyConnection = c("solid", "dashed"), lwdConnection = c(1.5, 0.8),
pchBase = c(1, 16), colCompBase = c(2, 4),
colHaploBase = c(3, 5, 6), cexMax = 2, cexMin = 0.7,
ggPlotNetwork = FALSE, cexMaxForGG = 0.025, cexMinForGG = 0.008,
alphaBase = c(0.9, 0.3), verbose = TRUE) {
if (requireNamespace("ggplot2", quietly = TRUE) &
requireNamespace("scatterpie", quietly = TRUE)) {
ggPlotNetwork <- ggPlotNetwork
} else {
warning(paste0("If you want to plot haplotype network, please install `ggplot2` and `scatterpie` packages! \n",
"We switched `ggPlotNetwork = FALSE`."))
ggPlotNetwork <- FALSE
}
if (!is.null(geno)) {
M <- t(geno[, -c(1:3)])
map <- geno[, 1:3]
nLine <- nrow(M)
lineNames <- rownames(M)
if (is.null(ZETA)) {
K <- calcGRM(M)
Z <- diag(nLine)
rownames(Z) <- colnames(Z) <- lineNames
ZETA <- list(A = list(Z = Z, K = K))
}
if (is.null(subpopInfo)) {
if (verbose) {
print("Now clustering...")
}
if (nGrp > 0) {
if (groupingMethod == "kmeans") {
bwSSRatios <- rep(NA, nIterClustering)
kmResList <- NULL
for (iterNo in 1:nIterClustering) {
kmResNow <- kmeans(x = M, centers = nGrp)
bwSSRatio <- kmResNow$betweenss / (kmResNow$betweenss + kmResNow$tot.withinss)
bwSSRatios[iterNo] <- bwSSRatio
kmResList <- c(kmResList, list(kmResNow))
}
maxNo <- which(bwSSRatios == max(bwSSRatios))
maxNoNow <- sample(maxNo, 1)
clusterNos <- match(kmResList[[maxNoNow]]$cluster, unique(kmResList[[maxNoNow]]$cluster))
} else if (groupingMethod == "kmedoids") {
kmResNow <- cluster::pam(x = M, k = nGrp, pamonce = 5)
clusterNos <- match(kmResNow$clustering, unique(kmResNow$clustering))
} else if (groupingMethod == "hclust") {
distMat <- Rfast::Dist(x = M, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(M)
tre <- hclust(as.dist(m = distMat))
cutreeRes <- cutree(tree = tre, k = nGrp)
clusterNos <- match(cutreeRes, unique(cutreeRes))
} else {
stop("We only offer 'kmeans', 'kmedoids', and 'hclust' methods for grouping methods.")
}
clusterRes <- factor(paste0("cluster_", clusterNos))
names(clusterRes) <- lineNames
subpopInfo <- clusterRes
} else {
subpopInfo <- NULL
}
} else {
if (!is.factor(subpopInfo)) {
subpopInfo <- as.factor(subpopInfo)
}
}
nGrp <- length(levels(subpopInfo))
if (is.null(blockInterest)) {
if (!is.null(gwasRes)) {
gwasResOrd <- gwasRes[order(gwasRes[, 4], decreasing = TRUE), ]
if (nTopRes == 1) {
blockName <- as.character(gwasResOrd[1, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterest <- M[, geno[, 1] %in% mrkInBlock]
} else {
blockInterest <- NULL
blockNames <- rep(NA, nTopRes)
for (topNo in 1:nTopRes) {
blockName <- as.character(gwasResOrd[topNo, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterestNow <- M[, geno[, 1] %in% mrkInBlock]
blockNames[topNo] <- blockName
blockInterest <- c(blockInterest, list(blockInterestNow))
}
names(blockInterest) <- blockNames
}
} else if (!is.null(posRegion)) {
if (is.null(chrInterest)) {
stop("Please input the chromosome number of interest!")
} else {
chrCondition <- map[, 2] %in% chrInterest
posCondition <- (posRegion[1] <= map[, 3]) & (posRegion[2] >= map[, 3])
indexRegion <- which(chrCondition & posCondition)
}
}
blockInterest <- M[, indexRegion]
}
} else {
blockInterestCheck <- !is.null(blockInterest)
ZETACheck <- !is.null(ZETA)
if (blockInterestCheck & ZETACheck) {
lineNames <- rownames(blockInterest)
nLine <- nrow(blockInterest)
} else {
stop("Please input 'geno', or both 'blockInterest' and 'ZETA'.")
}
}
estNetworkEachBlock <- function(blockInterest,
blockName = NULL) {
stringBlock <- apply(blockInterest, 1, function(x) paste0(x, collapse = ""))
blockInterestUnique <- blockInterest[!duplicated(stringBlock), ]
nHaplo <- length(unique(stringBlock))
haploClusterNow <- as.numeric(factor(stringBlock))
lineNames <- rownames(blockInterest)
names(haploClusterNow) <- lineNames
blockInterestUniqueSorted <- blockInterestUnique[order(unique(stringBlock)), ]
haploNames <- paste0("h", 1:nHaplo)
rownames(blockInterestUniqueSorted) <- haploNames
stringBlockUniqueSorted <- apply(blockInterestUniqueSorted, 1,
function(x) paste0(x, collapse = ""))
haploCluster <- haploNames[haploClusterNow]
names(haploCluster) <- lineNames
if (!is.null(subpopInfo)) {
tableRes <- table(haploCluster = haploCluster,
subpop = subpopInfo)[haploNames, ]
if (verbose) {
cat("Tabular for haplotype x subpopulation: \n")
print(head(tableRes))
cat("\nThe number of haplotypes: ", nHaplo, "\n")
}
if (chi2Test) {
chi2TestRes <- chisq.test(tableRes)
pValChi2Test <- chi2TestRes$p.value
if (verbose) {
cat("\n")
print(chi2TestRes)
cat("Haplotypes & Subpopulations are: \n")
if (pValChi2Test <= thresChi2Test) {
cat("\tSiginificantly dependent\n")
} else {
cat("\tindependent\n")
}
}
} else {
pValChi2Test <- NA
}
}
haplotypeInfo <- list(haploCluster = haploCluster,
haploBlock = blockInterestUniqueSorted)
nMrkInBlock <- ncol(blockInterest)
if (is.null(distMat)) {
distMat <- Rfast::Dist(x = blockInterestUniqueSorted, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(blockInterestUniqueSorted)
}
if (networkMethod == "rmst") {
mstRes <- pegas::rmst(d = distMat, B = iterRmst)
mstResPlus <- attr(mstRes, "alter.links")
mstResAll <- rbind(mstRes,
mstResPlus)
} else if (networkMethod == "mst") {
mstRes <- pegas::mst(d = distMat)
mstResPlus <- NULL
mstResAll <- mstRes
} else if (networkMethod == "msn") {
mstRes <- pegas::msn(d = distMat)
mstResPlus <- attr(mstRes, "alter.links")
mstResAll <- rbind(mstRes,
mstResPlus)
} else {
stop("We only offer 'rmst', 'mst', and 'msn' for `networkMethod`!!")
}
if (complementHaplo %in% c("all", "never")) {
if (complementHaplo == "all") {
blockInterestComp <- do.call(
what = rbind,
args = sapply(1:nrow(mstResAll), function(eachComb) {
blocksNow <- blockInterestUniqueSorted[mstResAll[eachComb, 1:2], ]
diffBlocks <- diff(blocksNow)
whichDiff <- which(diffBlocks != 0)
nDiff <- length(whichDiff)
blocksPart <- blocksNow[, whichDiff, drop = FALSE]
gridList <- lapply(apply(blocksPart, 2, function(eachMrk) {
gridCand <- min(eachMrk):max(eachMrk)
if (autogamous) {
gridCand <- gridCand[gridCand != 0]
}
return(list(gridCand))
}), function(x) x[[1]])
newBlocksPart <- as.matrix(expand.grid(gridList))
nNewBlocks <- nrow(newBlocksPart)
newBlocks <- matrix(data = rep(blocksNow[1, ], nNewBlocks),
nrow = nNewBlocks,
ncol = ncol(blocksNow),
byrow = TRUE)
colnames(newBlocks) <- colnames(blocksNow)
newBlocks[, whichDiff] <- newBlocksPart
return(newBlocks)
}, simplify = FALSE)
)
if (nrow(blockInterestComp) > nMaxHaplo) {
warning("There are too many complemented haplotypes... We will show the results only for the original haplotypes.")
blockInterestComp <- blockInterestUniqueSorted
}
} else if (complementHaplo == "never") {
blockInterestComp <- blockInterestUniqueSorted
}
blockInterestComp <- blockInterestComp[!duplicated(blockInterestComp), ]
stringBlockComp <- apply(blockInterestComp, 1,
function(x) paste0(x, collapse = ""))
nHaploComp <- nrow(blockInterestComp)
blockInterestCompSorted <- blockInterestComp[order(stringBlockComp), ]
stringBlockCompSorted <- apply(blockInterestCompSorted, 1,
function(x) paste0(x, collapse = ""))
matchString <- match(stringBlockCompSorted, stringBlockUniqueSorted)
namesBlockInterestComp <- rownames(blockInterestUniqueSorted)[matchString]
nComp <- sum(is.na(namesBlockInterestComp))
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp[is.na(namesBlockInterestComp)] <- compNames
rownames(blockInterestCompSorted) <- namesBlockInterestComp
haplotypeInfo$haploBlockCompSorted <- blockInterestCompSorted
distMatComp <- Rfast::Dist(x = blockInterestCompSorted, method = distMethod)
rownames(distMatComp) <- colnames(distMatComp) <- rownames(blockInterestCompSorted)
} else if (complementHaplo == "phylo") {
haplotypeInfo$haploBlockCompSorted <- NULL
if (evolutionDist) {
inLog <- 1 - 4 * (distMat / (ncol(blockInterest) * 2)) / 3
inLog[inLog <= 0] <- min(inLog[inLog > 0]) / 10
dist4Nj <- - 3 * log(inLog) / 4
} else {
dist4Nj <- distMat
}
njRes <- ape::nj(X = as.dist(dist4Nj))
distMatComp <- ape::dist.nodes(njRes)
nHaploComp <- nrow(distMatComp)
nComp <- nHaploComp - nHaplo
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp <- c(haploNames, compNames)
rownames(distMatComp) <- colnames(distMatComp) <- namesBlockInterestComp
} else if (complementHaplo == "TCS") {
extractParsimnetRes <- function (parsimnetRes) {
nHapEachNet <- parsimnetRes@nhap
distForEachNet <- parsimnetRes@d
nNet <- length(distForEachNet)
nTotalEachNet <- unlist(lapply(distForEachNet, nrow))
nCompEachNet <- nTotalEachNet - nHapEachNet
haploNamesByTCS <- unlist(sapply(1:nNet, function(netNo) {
distNow <- distForEachNet[[netNo]]
haploNamesNow <- rownames(distNow)
nHapNow <- nHapEachNet[netNo]
nTotalNow <- nTotalEachNet[netNo]
if (nCompEachNet[netNo] >= 1) {
for (compNo in (nHapNow + 1):nTotalNow) {
compNameNow <- haploNamesNow[compNo]
x <- stringr::str_split(string = compNameNow, pattern = "_")[[1]]
haploNos <- as.numeric(x[1:2])
if (is.na(x[3])) {
compNameNew <- paste(haploNamesNow[haploNos],
collapse = "_")
} else {
compNameNew <- paste(c(haploNamesNow[haploNos],
x[3]), collapse = "_")
}
haploNamesNow[compNo] <- compNameNew
}
}
return(haploNamesNow)
}, simplify = FALSE))
return(list(nCompEachNet = nCompEachNet,
distForEachNet = distForEachNet,
haploNamesByTCS = haploNamesByTCS))
}
haplotypeInfo$haploBlockCompSorted <- NULL
if (requireNamespace("haplotypes", quietly = TRUE)) {
if (autogamous) {
parsimnetRes <- haplotypes::parsimnet(x = distMat / 2,
seqlength = nMrkInBlock,
prob = probParsimony)
parsimnetResAll <- haplotypes::parsimnet(x = distMat / 2,
seqlength = nMrkInBlock,
prob = NULL)
} else {
parsimnetRes <- haplotypes::parsimnet(x = distMat,
seqlength = nMrkInBlock)
parsimnetResAll <- haplotypes::parsimnet(x = distMat,
seqlength = nMrkInBlock,
prob = NULL)
}
} else {
stop("R package `haplotypes` should be correctly installed when you use the option `complementHaplo = 'TCS'` !")
}
parsimnetResults <- list(parsimnetRes = parsimnetRes,
parsimnetResForOneNet = parsimnetResAll)
haplotypeInfo$parsimnetResults <- parsimnetResults
extractInfoByTCS <- extractParsimnetRes(parsimnetRes = parsimnetRes)
extractInfoByTCSAll <- extractParsimnetRes(parsimnetRes = parsimnetResAll)
haploNamesByTCS <- extractInfoByTCS$haploNamesByTCS
distMatByTCSAll <- extractInfoByTCSAll$distForEachNet$net1
distMatComp <- distMatByTCSAll[haploNamesByTCS, haploNamesByTCS]
if (autogamous) {
distMatComp <- distMatComp * 2
}
nComp <- sum(extractInfoByTCS$nCompEachNet)
nHaploComp <- nHaplo + nComp
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp <- haploNamesByTCS
namesBlockInterestComp[!(namesBlockInterestComp %in% haploNames)] <- compNames
rownames(distMatComp) <- colnames(distMatComp) <- namesBlockInterestComp
}
if (networkMethod == "rmst") {
mstResComp <- pegas::rmst(d = distMatComp, B = iterRmst)
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
} else if (networkMethod == "mst") {
mstResComp <- pegas::mst(d = distMatComp)
mstResCompPlus <- NULL
mstResCompAll <- mstResComp
} else if (networkMethod == "msn") {
mstResComp <- pegas::msn(d = distMatComp)
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
} else {
stop("We only offer 'rmst', 'mst', and 'msn' for `networkMethod`!!")
}
L <- as.matrix(Matrix::sparseMatrix(i = mstResCompAll[, 1],
j = mstResCompAll[, 2],
x = rep(-1, nrow(mstResCompAll)),
dims = c(nHaploComp, nHaploComp),
dimnames = list(namesBlockInterestComp,
namesBlockInterestComp)))
L <- L + t(L)
diag(L) <- - apply(L, 2, sum)
nTotal <- nrow(L)
plotPlus <- !is.null(mstResCompPlus)
minuslog10ps <- c()
gvEstTotals <- gvEstTotalForLines <-
hOptsList <- EMMResultsList <- list()
hOptBase <- hOpt
hOptBase2 <- hOpt2
for (kernelType in kernelTypes) {
if (verbose) {
print(paste0("Now optimizing for kernelType: ", kernelType))
}
if (!is.null(pheno)) {
ZgKernelPart <- as.matrix(Matrix::sparseMatrix(i = 1:nLine,
j = haploClusterNow,
x = rep(1, nLine),
dims = c(nLine, nHaplo),
dimnames = list(lineNames, haploNames)))
h <- 1
hStarts <- h * rangeHStart
hStarts <- split(hStarts, factor(1:length(rangeHStart)))
if (kernelType %in% c("diffusion", "gaussian", "exponential")) {
if (hOptBase == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects...")
}
maximizeFunc <- function(h) {
if (kernelType == "diffusion") {
gKernel <- expm::expm(- h * L)
gKernelPart <- gKernel[haploNames, haploNames]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = h)
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- try(EM3.cpp(y = pheno[, 2], ZETA = ZETANow), silent = TRUE)
if (!("try-error" %in% class(EM3Res))) {
LL <- EM3Res$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo <- which.min(unlist(lapply(solnList, function(x) x$objective)))
soln <- solnList[[solnNo]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln <- nlminb(start = hStarts[[1]], objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt <- soln$par
} else if (!is.numeric(hOptBase)) {
stop("`hOpt` should be either one of 'optimized' or numeric!!")
}
if (kernelType == "diffusion") {
gKernel <- expm::expm(- hOpt * L)
gKernelPart <- gKernel[haploNames, haploNames]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = hOpt)
}
} else {
hOpt <- NA
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType)
}
if (verbose) {
print("Now estimating genotypic values...")
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- EM3.cpp(y = pheno[, 2], ZETA = ZETANow)
gvEst <- EM3Res$u.each[(nLine + 1):(nLine + nHaplo), ]
LL <- EM3Res$LL
EMMRes0 <- EMM.cpp(y = pheno[, 2], ZETA = ZETA)
LL0 <- EMMRes0$LL
pVal <- pchisq(2 * (LL - LL0), df = 1, lower.tail = FALSE)
minuslog10p <- - log10(pVal)
if ((EM3Res$weights[2] <= 1e-06)) {
warning("This block seems to have no effect on phenotype...")
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- namesBlockInterestComp
gvEstTotal[haploNames] <- gvEst
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
EMMRes <- NA
hOpt2 <- NA
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
} else {
if (existComp) {
ZgKernel <- diag(nTotal)
rownames(ZgKernel) <- colnames(ZgKernel) <- namesBlockInterestComp
gvEst2 <- matrix(rep(NA, nTotal))
rownames(gvEst2) <- namesBlockInterestComp
gvEst2[haploNames, ] <- gvEst
if (hOptBase2 == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating complemented haplotype effects...")
}
maximizeFunc2 <- function(h) {
gKernel <- expm::expm(- h * L)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel))
EMMRes <- try(EMM.cpp(y = gvEst2, ZETA = ZETA2), silent = TRUE)
if (!("try-error" %in% class(EMMRes))) {
LL <- EMMRes$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList2 <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList2 <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo2 <- which.min(unlist(lapply(solnList2, function(x) x$objective)))
soln2 <- solnList2[[solnNo2]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln2 <- nlminb(start = hStarts[[1]], objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt2 <- soln2$par
} else if (!is.numeric(hOptBase2)) {
stop("`hOpt2` should be either one of 'optimized' or numeric!!")
}
gKernel2 <- expm::expm(- hOpt2 * L)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel2))
EMMRes <- EMM.cpp(y = gvEst2, ZETA = ZETA2)
u <- EMMRes$u
names(u) <- namesBlockInterestComp
gvPlus <- u[compNames] + EMMRes$beta
gvEstTotal <- gvEst2[, 1]
gvEstTotal[compNames] <- gvPlus
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexComp <- (abs(gvScaled4Cex) + cexMin)[compNames]
pchComp <- ifelse(gvScaled[compNames] > 0, pchBase[1], pchBase[2])
colComp <- ifelse(gvScaled[compNames] > 0, colCompBase[1], colCompBase[2])
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexAll[compNames]
} else {
gvEstTotal <- gvEst
names(gvEstTotal) <- haploNames
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
cexComp <- cexMax / 4
colComp <- colCompBase[2]
pchComp <- pchBase[2]
EMMRes <- NA
hOpt2 <- NA
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexMaxForGG / 2
}
}
} else {
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- namesBlockInterestComp
minuslog10p <- NA
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
EM3Res <- EMMRes <- EMMRes0 <- NA
hOpt <- hOpt2 <- NA
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
}
gvEstTotalForLine <- (gvEstTotal[haploNames])[haploClusterNow]
names(gvEstTotalForLine) <- lineNames
hOpts <- c(hOpt = hOpt,
hOpt2 = hOpt2)
EMMResults <- list(EM3Res = EM3Res,
EMMRes = EMMRes,
EMMRes0 = EMMRes0)
gvEstTotals[[kernelType]] <- gvEstTotal
gvEstTotalForLines[[kernelType]] <- gvEstTotalForLine
minuslog10ps[kernelType] <- minuslog10p
hOptsList[[kernelType]] <- hOpts
EMMResultsList[[kernelType]] <- EMMResults
if (!is.null(subpopInfo)) {
if (length(colHaploBase) != nGrp) {
stop("The length of 'colHaploBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colHaploNo <- as.numeric(subpopInfo)
colHaplo <- colHaploBase[colHaploNo]
names(colHaploNo) <- names(colHaplo) <- lineNames
colHaplo <- tapply(colHaplo, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
} else {
colHaplo <- rep("gray", nHaplo)
names(colHaplo) <- haploNames
clusterNosForHaplotype <- NA
}
nDimMDS <- max(plotWhichMDS)
mdsResComp <- cmdscale(d = distMatComp,
k = nDimMDS,
eig = TRUE)
rangeX <- range(mdsResComp$points[, plotWhichMDS[1]])
rangeY <- range(mdsResComp$points[, plotWhichMDS[2]])
if (plotNetwork) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
rangeX[1] <- rangeX[1] - abs(diff(rangeX)) / 4
rangeY[2] <- rangeY[2] + abs(diff(rangeY)) / 3
plot(x = mdsResComp$points[haploNames, plotWhichMDS[1]],
y = mdsResComp$points[haploNames, plotWhichMDS[2]],
col = colHaplo,
pch = pchHaplo, cex = cexHaplo,
xlab = paste0("MDS", plotWhichMDS[1]),
ylab = paste0("MDS", plotWhichMDS[2]),
xlim = rangeX,
ylim = rangeY)
if (existComp) {
points(x = mdsResComp$points[compNames, plotWhichMDS[1]],
y = mdsResComp$points[compNames, plotWhichMDS[2]],
col = colComp,
pch = pchComp, cex = cexComp)
}
segments(x0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[2]],
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
segments(x0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[2]],
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
title(main = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (existComp) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)",
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, 2))
}
} else if (!is.null(pheno)) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
col = rep(colHaploBase, each = 2),
pch = rep(pchBase, nGrp))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)"),
col = rep(colHaploBase, each = 2),
pch = pchBase)
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colHaploBase,
pch = pchHaplo)
} else {
legend("topleft", legend = "Haplotype",
col = colHaploBase,
pch = pchHaplo)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotNetwork) {
mdsPoints <- data.frame(mdsResComp$points[, plotWhichMDS[1:2]])
colnames(mdsPoints) <- paste0("MDS", plotWhichMDS[1:2])
mdsPoints$name <- rownames(mdsPoints)
if (!is.null(subpopInfo)) {
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
colHaploBaseForPie <- colHaploBase
} else {
nGrp <- 1
colHaploBaseForPie <- "gray"
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Haplotype"
}
if (existComp) {
if (!all(is.na(EMMRes))) {
clusterNosRatioDF$`gvComp +` <- 0
clusterNosRatioDF$`gvComp -` <- 0
colorForCompFac <- factor(gvScaled[compNames] > 0,
levels = c("TRUE", "FALSE"))
colorForCompDF <- data.frame(model.matrix(~ colorForCompFac - 1))
rownames(colorForCompDF) <- names(colorForCompFac)
colnames(colorForCompDF) <- c("gvComp +", "gvComp -")
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
clusterNosRatioDF$`Complement` <- 0
colorForCompDF <- data.frame(matrix(data = NA,
nrow = nComp, ncol = 1))
rownames(colorForCompDF) <- compNames
colnames(colorForCompDF) <- "Complement"
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
colCompBase <- "gray60"
}
colorForCompDFComp <- data.frame(matrix(data = 0,
nrow = nComp,
ncol = nGrp,
dimnames = list(rownames(colorForCompDF),
colnames(clusterNosRatioDF)[1:nGrp])))
colorForCompDF <- cbind(colorForCompDFComp,
colorForCompDF)
colorForAllCompDF <- data.frame(matrix(data = 0,
nrow = nTotal,
ncol = ncol(colorForCompDF)))
rownames(colorForAllCompDF) <- rownames(mdsPoints)
colnames(colorForAllCompDF) <- colnames(colorForCompDF)
colorForAllCompDF[haploNames, ] <- clusterNosRatioDF
colorForAllCompDF[compNames, ] <- colorForCompDF
alphaAll <- rep(NA, nTotal)
names(alphaAll) <- rownames(mdsPoints)
alphaAll[haploNames] <- alphaHaplo
alphaComp <- rep(alphaBase[2], nComp)
alphaAll[compNames] <- alphaComp
} else {
colorForAllCompDF <- clusterNosRatioDF
if (EM3Res$weights[2] > 1e-06) {
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
}
alphaAll <- alphaHaplo
colCompBase <- NULL
}
alpha2 <- alphaBase[2]
cexAll <- max(diff(rangeX), diff(rangeY)) * cexAll
mdsPointsForPlotDF <- cbind(mdsPoints,
colorForAllCompDF)
colnames(mdsPointsForPlotDF)[1:2] <- paste0("MDS", 1:2)
mdsPointsForPlotDF$cex <- cexAll
plt <- ggplot2::ggplot(data = mdsPointsForPlotDF,
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2))
if (any(alphaAll == alpha2)) {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha2, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha2) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
} else {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
}
mdsSegmentsDF <- data.frame(x1 = mdsPoints[mstResComp[, 1], 1],
y1 = mdsPoints[mstResComp[, 1], 2],
x2 = mdsPoints[mstResComp[, 2], 1],
y2 = mdsPoints[mstResComp[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsDF,
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
mdsSegmentsPlusDF <- data.frame(x1 = mdsPoints[mstResCompPlus[, 1], 1],
y1 = mdsPoints[mstResCompPlus[, 1], 2],
x2 = mdsPoints[mstResCompPlus[, 2], 1],
y2 = mdsPoints[mstResCompPlus[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsPlusDF,
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network_ggplot")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 15.5, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 1300, height = 700)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 1300, height = 700)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 1300, height = 700)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
return(list(haplotypeInfo = haplotypeInfo,
subpopInfo = subpopInfo,
mstResults = list(mstRes = mstRes,
mstResComp = mstResComp),
distMats = list(distMat = distMat,
distMatComp = distMatComp,
laplacianMat = L),
pValChi2Test = pValChi2Test,
gvTotal = gvEstTotals,
gvTotalForLine = gvEstTotalForLines,
minuslog10p = minuslog10ps,
hOpts = hOptsList,
EMMResults = EMMResultsList,
clusterNosForHaplotype = clusterNosForHaplotype))
}
if (is.matrix(blockInterest)) {
return(estNetworkEachBlock(blockInterest = blockInterest,
blockName = blockName))
} else if (is.list(blockInterest)) {
nTopRes <- length(blockInterest)
allResultsList <- rep(list(NULL), nTopRes)
blockNames <- names(blockInterest)
if (is.null(blockNames)) {
blockNames <- paste0("block_", 1:nTopRes)
}
names(allResultsList) <- blockNames
for (topNo in 1:nTopRes) {
allResultsList[[topNo]] <- estNetworkEachBlock(blockInterest = blockInterest[[topNo]],
blockName = blockNames[topNo])
}
return(allResultsList)
}
}
#' Function to plot phylogenetic tree from the estimated results
#'
#' @param estPhyloRes The estimated results of phylogenetic analysis by `estPhylo` function for one
#' @param traitName Name of trait of interest. This will be used in the title of the plots.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' This will be used in the title of the plots.
#' @param plotTree If TRUE, the function will return the plot of phylogenetic tree.
#' @param subpopInfo The information of subpopulations.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colNodeBase A vector of two integers or chracters specifying color of nodes for the positive and negative genotypic values respectively.
#' @param colTipBase A vector of integers or chracters specifying color of tips for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param edgeColoring If TRUE, the edge branch of phylogenetic tree wiil be colored.
#' @param tipLabel If TRUE, lavels for tips will be shown.
#' @param ggPlotTree If TRUE, the function will return the ggplot version of phylogenetic tree.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for tip with positive / negative effects.
#' alpha for node will be same as the alpha for tip with negative effects.
#'
#' @return Draw plots of phylogenetic tree.
#'
plotPhyloTree <- function(estPhyloRes, traitName = NULL, blockName = NULL, plotTree = TRUE,
subpopInfo = estPhyloRes$subpopInfo, saveName = NULL, saveStyle = "png",
pchBase = c(1, 16), colNodeBase = c(2, 4), colTipBase = c(3, 5, 6),
cexMax = 2, cexMin = 0.7, edgeColoring = TRUE, tipLabel = TRUE,
ggPlotTree = FALSE, cexMaxForGG = 0.12, cexMinForGG = 0.06, alphaBase = c(0.9, 0.3)) {
if (requireNamespace("ggtree", quietly = TRUE) &
requireNamespace("phylobase", quietly = TRUE)) {
ggPlotTree <- ggPlotTree
} else {
warning(paste0("If you want to plot phylogenetic trees, please install `ggtree` and `phylobase` packages from Bioconductor! \n",
"We switched `ggPlotTree = FALSE`."))
ggPlotTree <- FALSE
}
haplotypeInfo <- estPhyloRes$haplotypeInfo
haploCluster <- haplotypeInfo$haploCluster
blockInterestUniqueSorted <- haplotypeInfo$haploBlock
nHaplo <- nrow(blockInterestUniqueSorted)
haploNames <- rownames(blockInterestUniqueSorted)
haploClusterNow <- match(haploCluster, haploNames)
lineNames <- names(haploCluster)
names(haploClusterNow) <- lineNames
nGrp <- length(levels(subpopInfo))
nMrkInBlock <- ncol(blockInterestUniqueSorted)
njRes <- estPhyloRes$njRes
distNodes <- estPhyloRes$distMats$distMatNJ
nTotal <- nrow(distNodes)
nNode <- nTotal - nHaplo
kernelTypes <- names(estPhyloRes$gvTotal)
for (kernelType in kernelTypes) {
EMMResults <- estPhyloRes$EMMResults[[kernelType]]
EM3Res <- EMMResults$EM3Res
EMMRes <- EMMResults$EMMRes
gvEstTotal <- estPhyloRes$gvTotal[[kernelType]]
minuslog10p <- estPhyloRes$minuslog10p[kernelType]
nullPheno <- all(is.na(gvEstTotal))
if (!nullPheno) {
if (EM3Res$weights[2] <= 1e-06) {
plotNode <- FALSE
cexTip <- cexMax / 2
pchTip <- pchBase[2]
hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
} else {
plotNode <- TRUE
gvNode <- gvEstTotal[(nHaplo + 1):nTotal]
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexNode <- (abs(gvScaled4Cex) + cexMin)[(nHaplo + 1):nTotal]
pchNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, pchBase[1], pchBase[2])
colNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, colNodeBase[1], colNodeBase[2])
cexTip <- (abs(gvScaled4Cex) + cexMin)[1:nHaplo]
pchTip <- ifelse(gvScaled[1:nHaplo] > 0, pchBase[1], pchBase[2])
colorForNodeFac <- factor(gvScaled[(nHaplo + 1):nTotal] > 0,
levels = c("TRUE", "FALSE"))
colorForNodeDF <- data.frame(model.matrix(~ colorForNodeFac - 1))
rownames(colorForNodeDF) <- names(colorForNodeFac)
colnames(colorForNodeDF) <- c("gvNode +", "gvNode -")
colorForNodeDF$node <- rownames(colorForNodeDF)
gvScaled4CexForGG <- sign(gvScaled) * sqrt(abs(gvScaled)) *
(cexMaxForGG - cexMinForGG) / max(sqrt(abs(gvScaled)))
cexNodeForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[(nHaplo + 1):nTotal]
cexTipForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[1:nHaplo]
alphaTip <- ifelse(gvScaled[1:nHaplo] > 0, alphaBase[1], alphaBase[2])
alphaNode <- alphaBase[2]
}
} else {
plotNode <- FALSE
cexTip <- cexMax / 2
pchTip <- pchBase[2]
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
}
if (!is.null(subpopInfo)) {
if (length(colTipBase) != nGrp) {
stop("The length of 'colTipBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colTipNo <- as.numeric(subpopInfo)
colTip <- colTipBase[colTipNo]
names(colTipNo) <- names(colTip) <- lineNames
colTip <- tapply(colTip, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
edgeCol <- as.numeric(colTip[njRes$edge[, 2]])
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
clusterNosRatioDF$node <- 1:nHaplo
} else {
colTip <- rep("gray", nHaplo)
names(colTip) <- haploNames
colTipBase <- "gray"
edgeCol <- colTip[njRes$edge[, 2]]
clusterNosForHaplotype <- NA
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Tip"
clusterNosRatioDF$node <- 1:nHaplo
}
edgeCol[is.na(edgeCol)] <- "gray"
if (plotTree) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_tree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
if (edgeColoring) {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = edgeCol)
} else {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = "gray30")
}
if (plotNode) {
ape::nodelabels(pch = pchNode, cex = cexNode, col = colNode)
}
if (tipLabel) {
ape::tiplabels(pch = pchTip, cex = cexTip, col = colTip)
}
title(main = paste0(paste(c(traitName[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (plotNode) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Tip (gv:+)", "Tip (gv:-)",
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, 2))
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colTipBase,
pch = pchTip)
} else {
legend("topleft", legend = "Tip",
col = colTipBase,
pch = pchTip)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotTree) {
trPhylo41 <- as(njRes, 'phylo4')
if (edgeColoring) {
dataForTipCol <- data.frame(color = colTip)
} else {
dataForTipCol <- data.frame(color = rep("gray30", nHaplo))
}
rownames(dataForTipCol) <- njRes$tip.label
trPhylo42 <- phylobase::phylo4d(trPhylo41, dataForTipCol)
nodeData <- data.frame(randomTrait = gvEstTotal[(nHaplo + 1):nTotal],
color = rep("gray", phylobase::nNodes(trPhylo41)),
row.names = phylobase::nodeId(trPhylo41, "internal"))
phylobase::nodeData(trPhylo42) <- nodeData
plt <- ggtree::ggtree(tr = trPhylo42,
ggtree::aes(col = I(ggplot2::.data$color)),
layout = "equal_angle")
if (plotNode) {
piesNode <- ggtree::nodepie(colorForNodeDF, cols = 1:2,
color = colNodeBase,
alpha = alphaNode)
plt <- plt + ggtree::geom_inset(insets = piesNode,
width = cexNodeForGG,
height = cexNodeForGG)
}
if (tipLabel) {
if (!is.null(subpopInfo)) {
colTipBaseForPie <- colTipBase
} else {
colTipBaseForPie <- rep("gray", nGrp)
}
if (!all(is.na(EMMRes))) {
piesPlus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[1], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[1])
piesMinus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[2], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[2])
plt <- plt + ggtree::geom_inset(insets = piesPlus,
width = cexTipForGG[alphaTip == alphaBase[1]],
height = cexTipForGG[alphaTip == alphaBase[1]])
plt <- plt + ggtree::geom_inset(insets = piesMinus,
width = cexTipForGG[alphaTip == alphaBase[2]],
height = cexTipForGG[alphaTip == alphaBase[2]])
} else {
piesTip <- ggtree::nodepie(clusterNosRatioDF,
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = mean(alphaBase))
plt <- plt + ggtree::geom_inset(insets = piesTip,
width = cexTipForGG,
height = cexTipForGG)
}
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(traitName[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_ggtree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
}
#' Function to plot haplotype network from the estimated results
#'
#' @param estNetworkRes The estimated results of haplotype network by `estNetwork` function for one
#' @param traitName Name of trait of interest. This will be used in the title of the plots.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' This will be used in the title of the plots.
#' @param plotNetwork If TRUE, the function will return the plot of haplotype network.
#' @param subpopInfo The information of subpopulations.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param plotWhichMDS We will show the MDS (multi-dimensional scaling) plot,
#' and this argument is a vector of two integers specifying that will define which MDS dimension will be plotted.
#' The first and second integers correspond to the horizontal and vertical axes, respectively.
#' @param colConnection A vector of two integers or characters specifying the colors of connection between nodes for the original and complemented haplotypes, respectively.
#' @param ltyConnection A vector of two characters specifying the line types of connection between nodes for the original and complemented haplotypes, respectively.
#' @param lwdConnection A vector of two integers specifying the line widths of connection between nodes for the original and complemented haplotypes, respectively.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colCompBase A vector of two integers or characters specifying color of complemented haplotypes for the positive and negative genotypic values respectively.
#' @param colHaploBase A vector of integers or characters specifying color of original haplotypes for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param ggPlotNetwork If TRUE, the function will return the ggplot version of haplotype network.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for original haplotype with positive / negative effects.
#' alpha for complemented haplotype will be same as the alpha for original haplotype with negative effects.
#'
#' @return Draw plot of haplotype network.
#'
plotHaploNetwork <- function(estNetworkRes, traitName = NULL, blockName = NULL,
plotNetwork = TRUE, subpopInfo = estNetworkRes$subpopInfo,
saveName = NULL, saveStyle = "png",
plotWhichMDS = 1:2, colConnection = c("grey40", "grey60"),
ltyConnection = c("solid", "dashed"), lwdConnection = c(1.5, 0.8),
pchBase = c(1, 16), colCompBase = c(2, 4),
colHaploBase = c(3, 5, 6), cexMax = 2, cexMin = 0.7,
ggPlotNetwork = FALSE, cexMaxForGG = 0.025,
cexMinForGG = 0.008, alphaBase = c(0.9, 0.3)) {
if (requireNamespace("ggplot2", quietly = TRUE) &
requireNamespace("scatterpie", quietly = TRUE)) {
ggPlotNetwork <- ggPlotNetwork
} else {
warning(paste0("If you want to plot haplotype network, please install `ggplot2` and `scatterpie` packages! \n",
"We switched `ggPlotNetwork = FALSE`."))
ggPlotNetwork <- FALSE
}
haplotypeInfo <- estNetworkRes$haplotypeInfo
haploCluster <- haplotypeInfo$haploCluster
blockInterestUniqueSorted <- haplotypeInfo$haploBlock
blockInterestCompSorted <- haplotypeInfo$haploBlockCompSorted
nHaplo <- nrow(blockInterestUniqueSorted)
haploNames <- rownames(blockInterestUniqueSorted)
haploClusterNow <- match(haploCluster, haploNames)
lineNames <- names(haploCluster)
names(haploClusterNow) <- lineNames
distMat <- estNetworkRes$distMats$distMat
distMatComp <- estNetworkRes$distMats$distMatComp
nGrp <- length(levels(subpopInfo))
nMrkInBlock <- ncol(blockInterestUniqueSorted)
mstResults <- estNetworkRes$mstResults
mstResComp <- mstResults$mstResComp
nTotal <- nrow(distMatComp)
nComp <- nTotal - nHaplo
kernelTypes <- names(estNetworkRes$gvTotal)
namesBlockInterestComp <- rownames(distMatComp)
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
L <- estNetworkRes$distMats$laplacianMat
plotPlus <- !is.null(mstResCompPlus)
for (kernelType in kernelTypes) {
EMMResults <- estNetworkRes$EMMResults[[kernelType]]
EM3Res <- EMMResults$EM3Res
EMMRes <- EMMResults$EMMRes
gvEstTotal <- estNetworkRes$gvTotal[[kernelType]]
minuslog10p <- estNetworkRes$minuslog10p[kernelType]
nullPheno <- all(is.na(gvEstTotal))
if (!nullPheno) {
if (EM3Res$weights[2] <= 1e-06) {
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
} else {
if (existComp) {
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexComp <- (abs(gvScaled4Cex) + cexMin)[compNames]
pchComp <- ifelse(gvScaled[compNames] > 0, pchBase[1], pchBase[2])
colComp <- ifelse(gvScaled[compNames] > 0, colCompBase[1], colCompBase[2])
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexAll[compNames]
} else {
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
cexComp <- cexMax / 4
colComp <- colCompBase[2]
pchComp <- pchBase[2]
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexMaxForGG / 2
}
}
} else {
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
}
if (!is.null(subpopInfo)) {
if (length(colHaploBase) != nGrp) {
stop("The length of 'colHaploBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colHaploNo <- as.numeric(subpopInfo)
colHaplo <- colHaploBase[colHaploNo]
names(colHaploNo) <- names(colHaplo) <- lineNames
colHaplo <- tapply(colHaplo, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
} else {
colHaplo <- rep("gray", nHaplo)
names(colHaplo) <- haploNames
clusterNosForHaplotype <- NA
}
nDimMDS <- max(plotWhichMDS)
mdsResComp <- cmdscale(d = distMatComp,
k = nDimMDS,
eig = TRUE)
rangeX <- range(mdsResComp$points[, plotWhichMDS[1]])
rangeY <- range(mdsResComp$points[, plotWhichMDS[2]])
if (plotNetwork) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
rangeX[1] <- rangeX[1] - abs(diff(rangeX)) / 4
rangeY[2] <- rangeY[2] + abs(diff(rangeY)) / 3
plot(x = mdsResComp$points[haploNames, plotWhichMDS[1]],
y = mdsResComp$points[haploNames, plotWhichMDS[2]],
col = colHaplo,
pch = pchHaplo, cex = cexHaplo,
xlab = paste0("MDS", plotWhichMDS[1]),
ylab = paste0("MDS", plotWhichMDS[2]),
xlim = rangeX,
ylim = rangeY)
if (existComp) {
points(x = mdsResComp$points[compNames, plotWhichMDS[1]],
y = mdsResComp$points[compNames, plotWhichMDS[2]],
col = colComp,
pch = pchComp, cex = cexComp)
}
segments(x0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[2]],
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
segments(x0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[2]],
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
title(main = paste0(paste(c(traitName,
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (existComp) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)",
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, 2))
}
} else if (!nullPheno) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
col = rep(colHaploBase, each = 2),
pch = rep(pchBase, nGrp))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)"),
col = rep(colHaploBase, each = 2),
pch = pchBase)
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colHaploBase,
pch = pchHaplo)
} else {
legend("topleft", legend = "Haplotype",
col = colHaploBase,
pch = pchHaplo)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotNetwork) {
mdsPoints <- data.frame(mdsResComp$points[, plotWhichMDS[1:2]])
colnames(mdsPoints) <- paste0("MDS", plotWhichMDS[1:2])
mdsPoints$name <- rownames(mdsPoints)
if (!is.null(subpopInfo)) {
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
colHaploBaseForPie <- colHaploBase
} else {
nGrp <- 1
colHaploBaseForPie <- "gray"
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Haplotype"
}
if (existComp) {
if (!all(is.na(EMMRes))) {
clusterNosRatioDF$`gvComp +` <- 0
clusterNosRatioDF$`gvComp -` <- 0
colorForCompFac <- factor(gvScaled[compNames] > 0,
levels = c("TRUE", "FALSE"))
colorForCompDF <- data.frame(model.matrix(~ colorForCompFac - 1))
rownames(colorForCompDF) <- names(colorForCompFac)
colnames(colorForCompDF) <- c("gvComp +", "gvComp -")
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
clusterNosRatioDF$`Complement` <- 0
colorForCompDF <- data.frame(matrix(data = NA,
nrow = nComp, ncol = 1))
rownames(colorForCompDF) <- compNames
colnames(colorForCompDF) <- "Complement"
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
colCompBase <- "gray60"
}
colorForCompDFComp <- data.frame(matrix(data = 0,
nrow = nComp,
ncol = nGrp,
dimnames = list(rownames(colorForCompDF),
colnames(clusterNosRatioDF)[1:nGrp])))
colorForCompDF <- cbind(colorForCompDFComp,
colorForCompDF)
colorForAllCompDF <- data.frame(matrix(data = 0,
nrow = nTotal,
ncol = ncol(colorForCompDF)))
rownames(colorForAllCompDF) <- rownames(mdsPoints)
colnames(colorForAllCompDF) <- colnames(colorForCompDF)
colorForAllCompDF[haploNames, ] <- clusterNosRatioDF
colorForAllCompDF[compNames, ] <- colorForCompDF
alphaAll <- rep(NA, nTotal)
names(alphaAll) <- rownames(mdsPoints)
alphaAll[haploNames] <- alphaHaplo
alphaComp <- rep(alphaBase[2], nComp)
alphaAll[compNames] <- alphaComp
} else {
colorForAllCompDF <- clusterNosRatioDF
if (EM3Res$weights[2] > 1e-06) {
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
}
alphaAll <- alphaHaplo
colCompBase <- NULL
}
alpha2 <- alphaBase[2]
cexAll <- max(diff(rangeX), diff(rangeY)) * cexAll
mdsPointsForPlotDF <- cbind(mdsPoints,
colorForAllCompDF)
colnames(mdsPointsForPlotDF)[1:2] <- paste0("MDS", 1:2)
mdsPointsForPlotDF$cex <- cexAll
plt <- ggplot2::ggplot(data = mdsPointsForPlotDF,
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2))
if (any(alphaAll == alpha2)) {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha2, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha2) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
} else {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
}
mdsSegmentsDF <- data.frame(x1 = mdsPoints[mstResComp[, 1], 1],
y1 = mdsPoints[mstResComp[, 1], 2],
x2 = mdsPoints[mstResComp[, 2], 1],
y2 = mdsPoints[mstResComp[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsDF,
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
mdsSegmentsPlusDF <- data.frame(x1 = mdsPoints[mstResCompPlus[, 1], 1],
y1 = mdsPoints[mstResCompPlus[, 1], 2],
x2 = mdsPoints[mstResCompPlus[, 2], 1],
y2 = mdsPoints[mstResCompPlus[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsPlusDF,
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(traitName,
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network_ggplot")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 15.5, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 1300, height = 700)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 1300, height = 700)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 1300, height = 700)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
}
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Defines functions plotHaploNetwork plotPhyloTree estNetwork estPhylo convertBlockList MAF.cut See
Documented in convertBlockList estNetwork estPhylo MAF.cut plotHaploNetwork plotPhyloTree See
#' Function to view the first part of data (like head(), tail())
#'
#' @param data Your data. 'vector', 'matrix', 'array' (whose dimensions <= 4), 'data.frame' are supported format.
#' If other formatted data is assigned, str(data) will be returned.
#' @param fh From head. If this argument is TRUE, first part (row) of data will be shown (like head() function).
#' If FALSE, last part (row) of your data will be shown (like tail() function).
#' @param fl From left. If this argument is TRUE, first part (column) of data will be shown (like head() function).
#' If FALSE, last part (column) of your data will be shown (like tail() function).
#' @param rown The number of rows shown in console.
#' @param coln The number of columns shown in console.
#' @param rowst The start point for the direction of row.
#' @param colst The start point for the direction of column.
#' @param narray The number of dimensions other than row and column shown in console.
#' This argument is effective only your data is array (whose dimensions >= 3).
#' @param drop When rown = 1 or coln = 1, the dimension will be reduced if this argument is TRUE.
#' @param save.variable If you want to assign the result to a variable, please set this agument TRUE.
#' @param verbose If TRUE, print the first part of data.
#'
#' @return If save.variable is FALSE, NULL. If TRUE, the first part of your data will be returned.
#'
#'
See <- function(data, fh = TRUE, fl = TRUE, rown = 6, coln = 6,
rowst = 1, colst = 1, narray = 2, drop = FALSE,
save.variable = FALSE, verbose = TRUE) {
islist <- is.list(data)
isvec <- is.vector(data)
isfac <- is.factor(data)
if ((isvec | isfac) & (!islist)) {
n.data <- length(data)
if (fh) {
start <- min(rowst, n.data)
end <- min(rowst + rown - 1, n.data)
} else {
start <- max(n.data - rowst - rown + 2, 1)
end <- max(n.data - rowst + 1, 1)
}
data.show <- data[start:end]
dim.show <- length(data)
} else {
ismat <- is.matrix(data)
isdf <- is.data.frame(data)
if (ismat | isdf) {
n.data.row <- nrow(data)
if (fh) {
start.row <- min(rowst, n.data.row)
end.row <- min(rowst + rown - 1, n.data.row)
} else {
start.row <- max(n.data.row - rowst - rown + 2, 1)
end.row <- max(n.data.row - rowst + 1, 1)
}
n.data.col <- ncol(data)
if (fl) {
start.col <- min(colst, n.data.col)
end.col <- min(colst + coln - 1, n.data.col)
} else {
start.col <- max(n.data.col - colst - coln + 2, 1)
end.col <- max(n.data.col - colst + 1, 1)
}
class.each <- rep(NA, end.col - start.col + 1)
for (i in 1:(end.col - start.col + 1)) {
class.each[i] <- class(data[, i])
}
class.show <- paste0("<", class.each, ">")
data.show <-
data[start.row:end.row, start.col:end.col, drop = drop]
data.show <-
as.data.frame(rbind(class.show, apply(data.show, 2, as.character)))
data.rowname <- rownames(data)
if (!is.null(data.rowname)) {
rownames(data.show) <- c("class", rownames(data)[start.row:end.row])
} else {
rownames(data.show) <-
c("class", paste0("NULL_", start.row:end.row))
}
dim.show <- dim(data)
} else {
isarray <- is.array(data)
if (isarray) {
n.array <- length(dim(data))
n.data.row <- nrow(data)
if (fh) {
start.row <- min(rowst, n.data.row)
end.row <- min(rowst + rown - 1, n.data.row)
} else {
start.row <- max(n.data.row - rowst - rown + 2, 1)
end.row <- max(n.data.row - rowst + 1, 1)
}
n.data.col <- ncol(data)
if (fl) {
start.col <- min(colst, n.data.col)
end.col <- min(colst + coln - 1, n.data.col)
} else {
start.col <- max(n.data.col - colst - coln + 2, 1)
end.col <- max(n.data.col - colst + 1, 1)
}
if (n.array == 1) {
data.show <- data[start.row:end.row, drop = drop]
}
if (n.array == 2) {
data.show <- data[start.row:end.row, start.col:end.col, drop = drop]
}
if (n.array >= 3) {
start.other <- 1
end.other <- pmin(rep(narray, n.array - 2), dim(data)[-c(1:2)])
indices <- c(list(start.row:end.row, start.col:end.col),
lapply(X = end.other, FUN = function(end.other.now) {
start.other:end.other.now
}))
data.show <- R.utils::extract(x = data,
indices = indices,
dims = 1:n.array,
drop = drop)
}
dim.show <- dim(data)
} else {
warning("We cannot offer the simple view of your data. Instead we will offer the structure of your data.")
data.show <- str(data)
dim.show <- NULL
}
}
}
if (verbose) {
if (!is.null(data.show)) {
print(data.show)
}
print(paste0("class: ", paste(class(data), collapse = " & ")))
print(paste0("dimension: ", paste(dim.show, collapse = " x ")))
}
if (save.variable) {
return(data.show)
}
}
#' Function to remove the minor alleles
#'
#' @param x.0 A \eqn{n \times m} original marker genotype matrix.
#' @param map.0 Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' @param min.MAF Specifies the minimum minor allele frequency (MAF).
#' If a marker has a MAF less than min.MAF, it is removed from the original marker genotype data.
#' @param max.HE Specifies the maximum heterozygous rate (HE).
#' If a marker has a HE more than max.HE, it is removed from the original marker genotype data.
#' @param max.MS Specifies the maximum missing rate (MS).
#' If a marker has a MS more than max.MS, it is removed from the original marker genotype data.
#' @param return.MAF If TRUE, MAF will be returned.
#'
#' @return
#' \describe{
#' \item{$x}{The modified marker genotype data whose SNPs with MAF <= min.MAF were removed.}
#' \item{$map}{The modified map information whose SNPs with MAF <= min.MAF were removed.}
#' \item{$before}{Minor allele frequencies of the original marker genotype.}
#' \item{$after}{Minor allele frequencies of the modified marker genotype.}
#'}
#'
MAF.cut <- function(x.0, map.0 = NULL, min.MAF = 0.05, max.HE = 0.999,
max.MS = 0.05, return.MAF = FALSE) {
x.unique <- sort(unique(c(x.0)), decreasing = FALSE)
len.x.unique <- length(x.unique)
if (len.x.unique == 2) {
is.scoring1 <- all(x.unique == c(-1, 1))
is.scoring2 <- all(x.unique == c(0, 2))
} else {
if (len.x.unique == 3) {
is.scoring1 <- all(x.unique == c(-1, 0, 1))
is.scoring2 <- all(x.unique == c(0, 1, 2))
} else {
stop("Something wrong with your genotype data!!")
}
}
if (is.scoring1) {
freq <- apply(X = x.0 + 1, MARGIN = 2,
FUN = function(x) {
return(mean(x, na.rm = TRUE) / 2)
})
freq.hetero <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x == 0, na.rm = TRUE))
})
} else {
if (is.scoring2) {
freq <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x, na.rm = TRUE) / 2)
})
freq.hetero <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(x == 1, na.rm = TRUE))
})
} else {
stop("Genotype data should be scored with (-1, 0, 1) or (0, 1, 2)!!")
}
}
MAF.before <- pmin(freq, 1 - freq)
mark.remain.MAF <- MAF.before >= min.MAF
mark.remain.HE <- freq.hetero <= max.HE
MS.rate <- apply(X = x.0, MARGIN = 2,
FUN = function(x) {
return(mean(is.na(x)))
})
mark.remain.MS <- MS.rate <= max.MS
mark.remain <- mark.remain.MAF & mark.remain.HE & mark.remain.MS
x <- x.0[, mark.remain]
if (!is.null(map.0)) {
map <- map.0[mark.remain,]
} else {
map <- NULL
}
if (return.MAF) {
MAF.after <- MAF.before[mark.remain]
freq.hetero.after <- freq.hetero[mark.remain]
return(list(
data = list(x = x, map = map),
MAF = list(before = MAF.before, after = MAF.after),
HE = list(before = freq.hetero, after = freq.hetero.after)
))
} else {
return(list(x = x, map = map))
}
}
#' Function to convert haplotype block list from PLINK to RAINBOWR format
#'
#' @param fileNameBlocksDetPlink File name of the haplotype block list generated by PLINK (See reference).
#' The file names must contain ".blocks.det" in the tail.
#' @param map Data frame with the marker names in the first column.
#' The second and third columns contain the chromosome and map position.
#' @param blockNamesHead You can specify the header of block names for the returned data.frame.
#' @param imputeOneSNP As default, blocks including only one SNP will be discarded from the returned data.
#' If you want to include them when creating haplotype-block list for RAINBOWR,
#' please set `imputeOneSNP = TRUE`.
#' @param insertZeros When naming blocks, whether or not inserting zeros to the name of blocks.
#' For example, if there are 1,000 blocks in total, the function will name the block 1 as
#' "block_1" when `insertZeros = FALSE` and "block_0001" when `insertZeros = TRUE`.
#' @param n.core Setting n.core > 1 will enable parallel execution on a machine with multiple cores.
#' This argument is not valid when `parallel.method = "furrr"`.
#' @param parallel.method Method for parallel computation. We offer three methods, "mclapply", "furrr", and "foreach".
#'
#' When `parallel.method = "mclapply"`, we utilize \code{\link[pbmcapply]{pbmclapply}} function in the `pbmcapply` package
#' with `count = TRUE` and \code{\link[parallel]{mclapply}} function in the `parallel` package with `count = FALSE`.
#'
#' When `parallel.method = "furrr"`, we utilize \code{\link[furrr]{future_map}} function in the `furrr` package.
#' With `count = TRUE`, we also utilize \code{\link[progressr]{progressor}} function in the `progressr` package to show the progress bar,
#' so please install the `progressr` package from github (\url{https://github.com/HenrikBengtsson/progressr}).
#' For `parallel.method = "furrr"`, you can perform multi-thread parallelization by
#' sharing memories, which results in saving your memory, but quite slower compared to `parallel.method = "mclapply"`.
#'
#' When `parallel.method = "foreach"`, we utilize \code{\link[foreach]{foreach}} function in the `foreach` package
#' with the utilization of \code{\link[parallel]{makeCluster}} function in `parallel` package,
#' and \code{\link[doParallel]{registerDoParallel}} function in `doParallel` package.
#' With `count = TRUE`, we also utilize \code{\link[utils]{setTxtProgressBar}} and
#' \code{\link[utils]{txtProgressBar}} functions in the `utils` package to show the progress bar.
#'
#' We recommend that you use the option `parallel.method = "mclapply"`, but for Windows users,
#' this parallelization method is not supported. So, if you are Windows user,
#' we recommend that you use the option `parallel.method = "foreach"`.
#' @param count When count is TRUE, you can know how far RGWAS has ended with percent display.
#'
#' @return
#' \describe{
#' A data.frame object of
#' \item{$block}{Block names for SNP-set methods in RAINBOWR}
#' \item{$marker}{Marker names in each block for SNP-set methods in RAINBOWR}
#'}
#'
#' Purcell, S. and Chang, C. (2018). PLINK 1.9, www.cog-genomics.org/plink/1.9/.
#' Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4.
#' Gaunt T, RodrÃguez S, Day I (2007) Cubic exact solutions for the estimation of pairwise haplotype frequencies: implications for linkage disequilibrium analyses and a web tool 'CubeX'. BMC Bioinformatics, 8.
#' Taliun D, Gamper J, Pattaro C (2014) Efficient haplotype block recognition of very long and dense genetic sequences. BMC Bioinformatics, 15.
#'
convertBlockList <- function(fileNameBlocksDetPlink,
map,
blockNamesHead = "haploblock_",
imputeOneSNP = FALSE,
insertZeros = FALSE,
n.core = 1,
parallel.method = "mclapply",
count = FALSE) {
if (requireNamespace("data.table", quietly = TRUE)) {
blockDataRaw <- data.frame(data.table::fread(input = fileNameBlocksDetPlink))
} else {
blockDataRaw <- read.csv(fileNameBlocksDetPlink, check.names = FALSE, header = TRUE, sep = "")
}
nBlocks <- nrow(blockDataRaw)
blockNos <- rep(1:nBlocks, blockDataRaw[, 5])
if (insertZeros) {
blockNames <- sprintf(fmt = paste0(blockNamesHead,
"%0", floor(log10(max(blockNos))) + 1, "i"),
blockNos)
} else {
blockNames <- paste0(blockNamesHead, blockNos)
}
marker <- map[, 1]
blockSNPsList <- RAINBOWR::parallel.compute(vec = blockDataRaw[, 6],
func = function(x) {
stringr::str_split(string = x,
pattern = "\\|")[[1]]
},
n.core = n.core,
parallel.method = parallel.method,
count = count)
blockSNPs <- unlist(blockSNPsList)
if (!all(blockSNPs %in% marker)) {
chr <- map[, 2]
pos <- map[, 3]
chrPos <- paste0(chr, "_", pos)
chrPosBlockSt <- apply(X = blockDataRaw[, 1:2],
MARGIN = 1,
FUN = paste, collapse = "_")
chrPosBlockEd <- apply(X = blockDataRaw[, c(1, 3)],
MARGIN = 1,
FUN = paste, collapse = "_")
blockSNPsList <- RAINBOWR::parallel.compute(vec = 1:nBlocks,
func = function(x) {
markerSt <- which(chrPos == chrPosBlockSt[x])
markerEd <- which(chrPos == chrPosBlockEd[x])
return(markerSt:markerEd)
},
n.core = n.core,
parallel.method = parallel.method,
count = count)
blockSNPs <- marker[unlist(blockSNPsList)]
}
if (imputeOneSNP) {
mrkHaploBlockIds <- rep(NA, length(marker))
names(mrkHaploBlockIds) <- marker
mrkHaploBlockIds[blockSNPs] <- blockNos
mrkHaploBlockIds[is.na(mrkHaploBlockIds)] <- (max(mrkHaploBlockIds, na.rm = TRUE) + 1):
(max(mrkHaploBlockIds, na.rm = TRUE) + sum(is.na(mrkHaploBlockIds)))
mrkHaploBlockIdsFac <- factor(mrkHaploBlockIds, levels = unique(mrkHaploBlockIds))
mrkHaploBlockIds <- as.numeric(mrkHaploBlockIdsFac)
if (insertZeros) {
blockNames <- sprintf(fmt = paste0(blockNamesHead,
"%0", floor(log10(max(mrkHaploBlockIds))) + 1, "i"),
mrkHaploBlockIds)
} else {
blockNames <- paste0(blockNamesHead, mrkHaploBlockIds)
}
blockListDf <- data.frame(block = blockNames,
marker = marker)
} else {
blockListDf <- data.frame(block = blockNames,
marker = blockSNPs)
}
return(blockListDf)
}
#' Function to estimate & plot phylogenetic tree
#'
#' @param blockInterest A \eqn{n \times M} matrix representing the marker genotype that belongs to the haplotype block of interest.
#' If this argument is NULL, this argument will automatically be determined by `geno`,
#' @param gwasRes You can use the results (data.frame) of haplotype-based (SNP-set) GWAS by `RGWAS.multisnp` function.
#' @param nTopRes Haplotype blocks (or gene sets, SNP-sets) with top `nTopRes` p-values by `gwasRes` will be used.
#' @param gene.set If you have information of gene (or haplotype block), you can use it to perform kernel-based GWAS.
#' You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2).
#' In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
#' which correspond to the marker names of "geno" argument.
#' @param indexRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker index in `geno`.
#' @param chrInterest You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the chromosome number to this argument.
#' @param posRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the position in the chromosome to this argument.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' @param pheno Data frame where the first column is the line name (gid).
#' The remaining columns should be a phenotype to test.
#' @param geno Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' Columns 4 and higher contain the marker scores for each line, coded as {-1, 0, 1} = {aa, Aa, AA}.
#' @param ZETA A list of covariance (relationship) matrix (K: \eqn{m \times m}) and its design matrix (Z: \eqn{n \times m}) of random effects.
#' Please set names of list "Z" and "K"! You can use more than one kernel matrix.
#' For example,
#'
#' ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D))
#' \describe{
#' \item{Z.A, Z.D}{Design matrix (\eqn{n \times m}) for the random effects. So, in many cases, you can use the identity matrix.}
#' \item{K.A, K.D}{Different kernels which express some relationships between lines.}
#' }
#' For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
#' @param chi2Test If TRUE, chi-square test for the relationship between haplotypes & subpopulations will be performed.
#' @param thresChi2Test The threshold for the chi-square test.
#' @param plotTree If TRUE, the function will return the plot of phylogenetic tree.
#' @param distMat You can assign the distance matrix of the block of interest.
#' If NULL, the distance matrix will be computed in this function.
#' @param distMethod You can choose the method to calculate distance between accessions.
#' This argument corresponds to the `method` argument in the `dist` function.
#' @param evolutionDist If TRUE, the evolution distance will be used instead of the pure distance.
#' The `distMat` will be converted to the distance matrix by the evolution distance.
#' @param subpopInfo The information of subpopulations. This argument should be a vector of factor.
#' @param groupingMethod If `subpopInfo` argument is NULL, this function estimates subpopulation information from marker genotype.
#' You can choose the grouping method from `kmeans`, `kmedoids`, and `hclust`.
#' @param nGrp The number of groups (or subpopulations) grouped by `groupingMethod`.
#' If this argument is 0, the subpopulation information will not be estimated.
#' @param nIterClustering If `groupingMethod` = `kmeans`, the clustering will be performed multiple times.
#' This argument specifies the number of classification performed by the function.
#' @param kernelTypes In the function, similarlity matrix between accessions will be computed from marker genotype to estimate genotypic values.
#' This argument specifies the method to compute similarity matrix:
#' If this argument is `addNOIA` (or one of other options in `methodGRM` in `calcGRM`),
#' then the `addNOIA` (or corresponding) option in the `calcGRM` function will be used,
#' and if this argument is `phylo`, the gaussian kernel based on phylogenetic distance will be computed from phylogenetic tree.
#' You can assign more than one kernelTypes for this argument; for example, kernelTypes = c("addNOIA", "phylo").
#' @param nCores The number of cores used for optimization.
#' @param hOpt Optimized hyper parameter for constructing kernel when estimating haplotype effects.
#' If hOpt = "optimized", hyper parameter will be optimized in the function.
#' If hOpt = "tuned", hyper parameter will be replaced by the median of off-diagonal of distance matrix.
#' If hOpt is numeric, that value will be directly used in the function.
#' @param hOpt2 Optimized hyper parameter for constructing kernel when estimating haplotype effects of nodes.
#' If hOpt2 = "optimized", hyper parameter will be optimized in the function.
#' If hOpt2 = "tuned", hyper parameter will be replaced by the median of off-diagonal of distance matrix.
#' If hOpt2 is numeric, that value will be directly used in the function.
#' @param maxIter Max number of iterations for optimization algorithm.
#' @param rangeHStart The median of off-diagonal of distance matrix multiplied by rangeHStart will be used
#' as the initial values for optimization of hyper parameters.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colNodeBase A vector of two integers or chracters specifying color of nodes for the positive and negative genotypic values respectively.
#' @param colTipBase A vector of integers or chracters specifying color of tips for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param edgeColoring If TRUE, the edge branch of phylogenetic tree wiil be colored.
#' @param tipLabel If TRUE, lavels for tips will be shown.
#' @param ggPlotTree If TRUE, the function will return the ggplot version of phylogenetic tree.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for tip with positive / negative effects.
#' alpha for node will be same as the alpha for tip with negative effects.
#' @param verbose If this argument is TRUE, messages for the current step_s will be shown.
#'
#' @return
#' \describe{A list / lists of
#' \item{$haplotypeInfo}{\describe{A list of haplotype information with
#' \item{$haploCluster}{A vector indicating each individual belongs to which haplotypes.}
#' \item{$haploBlock}{Marker genotype of haplotype block of interest for the representing haplotypes.}
#' }
#' }
#' \item{$subpopInfo}{The information of subpopulations.}
#' \item{$distMats}{\describe{A list of distance matrix:
#' \item{$distMat}{Distance matrix between haplotypes.}
#' \item{$distMatEvol}{Evolutionary distance matrix between haplotypes.}
#' \item{$distMatNJ}{Phylogenetic distance matrix between haplotypes including nodes.}
#' }
#' }
#' \item{$pValChi2Test}{A p-value of the chi-square test for the dependency between haplotypes & subpopulations.
#' If `chi2Test = FALSE`, `NA` will be returned.}
#' \item{$njRes}{The result of phylogenetic tree by neighborhood-joining method}
#' \item{$gvTotal}{Estimated genotypic values by kernel regression for each haplotype.}
#' \item{$gvTotalForLine}{Estimated genotypic values by kernel regression for each individual.}
#' \item{$minuslog10p}{\eqn{-log_{10}(p)} for haplotype block of interest.
#' p is the p-value for the siginifacance of the haplotype block effect.}
#' \item{$hOpts}{Optimized hyper parameters, hOpt1 & hOpt2.}
#' \item{$EMMResults}{\describe{A list of estimated results of kernel regression:
#' \item{$EM3Res}{Estimated results of kernel regression for the estimation of haplotype effects. (1st step)}
#' \item{$EMMRes}{Estimated results of kernel regression for the estimation of haplotype effects of nodes. (2nd step)}
#' \item{$EMM0Res}{Estimated results of kernel regression for the null model.}
#' }
#' }
#' \item{$clusterNosForHaplotype}{A list of cluster Nos of individuals that belong to each haplotype.}
#'}
#'
estPhylo <- function(blockInterest = NULL, gwasRes = NULL, nTopRes = 1, gene.set = NULL,
indexRegion = 1:10, chrInterest = NULL, posRegion = NULL, blockName = NULL,
pheno = NULL, geno = NULL, ZETA = NULL,
chi2Test = TRUE, thresChi2Test = 5e-2, plotTree = TRUE,
distMat = NULL, distMethod = "manhattan", evolutionDist = FALSE,
subpopInfo = NULL, groupingMethod = "kmedoids",
nGrp = 3, nIterClustering = 100, kernelTypes = "addNOIA",
nCores = parallel::detectCores() - 1, hOpt = "optimized",
hOpt2 = "optimized", maxIter = 20, rangeHStart = 10 ^ c(-1:1),
saveName = NULL, saveStyle = "png",
pchBase = c(1, 16), colNodeBase = c(2, 4), colTipBase = c(3, 5, 6),
cexMax = 2, cexMin = 0.7, edgeColoring = TRUE, tipLabel = TRUE,
ggPlotTree = FALSE, cexMaxForGG = 0.12, cexMinForGG = 0.06,
alphaBase = c(0.9, 0.3), verbose = TRUE) {
if (requireNamespace("ggtree", quietly = TRUE) &
requireNamespace("phylobase", quietly = TRUE)) {
ggPlotTree <- ggPlotTree
} else {
warning(paste0("If you want to plot phylogenetic trees, please install `ggtree` and `phylobase` packages from Bioconductor! \n",
"We switched `ggPlotTree = FALSE`."))
ggPlotTree <- FALSE
}
if (!is.null(geno)) {
M <- t(geno[, -c(1:3)])
map <- geno[, 1:3]
nLine <- nrow(M)
lineNames <- rownames(M)
if (is.null(ZETA)) {
K <- calcGRM(M)
Z <- diag(nLine)
rownames(Z) <- colnames(Z) <- lineNames
ZETA <- list(A = list(Z = Z, K = K))
}
if (is.null(subpopInfo)) {
if (verbose) {
print("Now clustering...")
}
if (nGrp > 0) {
if (groupingMethod == "kmeans") {
bwSSRatios <- rep(NA, nIterClustering)
kmResList <- NULL
for (iterNo in 1:nIterClustering) {
kmResNow <- kmeans(x = M, centers = nGrp)
bwSSRatio <- kmResNow$betweenss / (kmResNow$betweenss + kmResNow$tot.withinss)
bwSSRatios[iterNo] <- bwSSRatio
kmResList <- c(kmResList, list(kmResNow))
}
maxNo <- which(bwSSRatios == max(bwSSRatios))
maxNoNow <- sample(maxNo, 1)
clusterNos <- match(kmResList[[maxNoNow]]$cluster, unique(kmResList[[maxNoNow]]$cluster))
} else if (groupingMethod == "kmedoids") {
kmResNow <- cluster::pam(x = M, k = nGrp, pamonce = 5)
clusterNos <- match(kmResNow$clustering, unique(kmResNow$clustering))
} else if (groupingMethod == "hclust") {
distMat <- Rfast::Dist(x = M, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(M)
tre <- hclust(as.dist(m = distMat))
cutreeRes <- cutree(tree = tre, k = nGrp)
clusterNos <- match(cutreeRes, unique(cutreeRes))
} else {
stop("We only offer 'kmeans', 'kmedoids', and 'hclust' methods for grouping methods.")
}
clusterRes <- factor(paste0("cluster_", clusterNos))
names(clusterRes) <- lineNames
subpopInfo <- clusterRes
} else {
subpopInfo <- NULL
}
} else {
if (!is.factor(subpopInfo)) {
subpopInfo <- as.factor(subpopInfo)
}
}
nGrp <- length(levels(subpopInfo))
if (is.null(blockInterest)) {
if (!is.null(gwasRes)) {
gwasResOrd <- gwasRes[order(gwasRes[, 4], decreasing = TRUE), ]
if (nTopRes == 1) {
blockName <- as.character(gwasResOrd[1, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterest <- M[, geno[, 1] %in% mrkInBlock]
} else {
blockInterest <- NULL
blockNames <- rep(NA, nTopRes)
for (topNo in 1:nTopRes) {
blockName <- as.character(gwasResOrd[topNo, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterestNow <- M[, geno[, 1] %in% mrkInBlock]
blockNames[topNo] <- blockName
blockInterest <- c(blockInterest, list(blockInterestNow))
}
names(blockInterest) <- blockNames
}
} else if (!is.null(posRegion)) {
if (is.null(chrInterest)) {
stop("Please input the chromosome number of interest!")
} else {
chrCondition <- map[, 2] %in% chrInterest
posCondition <- (posRegion[1] <= map[, 3]) & (posRegion[2] >= map[, 3])
indexRegion <- which(chrCondition & posCondition)
}
}
blockInterest <- M[, indexRegion]
}
} else {
blockInterestCheck <- !is.null(blockInterest)
ZETACheck <- !is.null(ZETA)
if (blockInterestCheck & ZETACheck) {
lineNames <- rownames(blockInterest)
nLine <- nrow(blockInterest)
} else {
stop("Please input 'geno', or both 'blockInterest' and 'ZETA'.")
}
}
estPhyloEachBlock <- function(blockInterest,
blockName = NULL) {
stringBlock <- apply(blockInterest, 1, function(x) paste0(x, collapse = ""))
blockInterestUnique <- blockInterest[!duplicated(stringBlock), ]
nHaplo <- length(unique(stringBlock))
haploClusterNow <- as.numeric(factor(stringBlock))
names(haploClusterNow) <- lineNames
blockInterestUniqueSorted <- blockInterestUnique[order(unique(stringBlock)), ]
haploNames <- paste0("haplo_", 1:nHaplo)
rownames(blockInterestUniqueSorted) <- haploNames
haploCluster <- haploNames[haploClusterNow]
names(haploCluster) <- lineNames
if (!is.null(subpopInfo)) {
tableRes <- table(haploCluster = haploCluster,
subpop = subpopInfo)[haploNames, ]
if (verbose) {
cat("Tabular for haplotype x subpopulation: \n")
print(head(tableRes))
cat("\nThe number of haplotypes: ", nHaplo, "\n")
}
if (chi2Test) {
chi2TestRes <- chisq.test(tableRes)
pValChi2Test <- chi2TestRes$p.value
if (verbose) {
cat("\n")
print(chi2TestRes)
cat("Haplotypes & Subpopulations are: \n")
if (pValChi2Test <= thresChi2Test) {
cat("\tSiginificantly dependent\n")
} else {
cat("\tindependent\n")
}
}
} else {
pValChi2Test <- NA
}
}
haplotypeInfo <- list(haploCluster = haploCluster,
haploBlock = blockInterestUniqueSorted)
nMrkInBlock <- ncol(blockInterest)
if (is.null(distMat)) {
distMat <- Rfast::Dist(x = blockInterestUniqueSorted, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(blockInterestUniqueSorted)
}
if (evolutionDist) {
inLog <- 1 - 4 * (distMat / (ncol(blockInterest) * 2)) / 3
inLog[inLog <= 0] <- min(inLog[inLog > 0]) / 10
dist4Nj <- - 3 * log(inLog) / 4
} else {
dist4Nj <- distMat
}
njRes <- ape::nj(X = as.dist(dist4Nj))
distNodes <- ape::dist.nodes(njRes) / sqrt(nMrkInBlock)
minuslog10ps <- c()
gvEstTotals <- gvEstTotalForLines <-
hOptsList <- EMMResultsList <- list()
hOptBase <- hOpt
hOptBase2 <- hOpt2
for (kernelType in kernelTypes) {
if (verbose) {
print(paste0("Now optimizing for kernelType: ", kernelType))
}
if (!is.null(pheno)) {
rownames(distNodes)[1:nHaplo] <- colnames(distNodes)[1:nHaplo] <- haploNames
nTotal <- nrow(distNodes)
nNode <- nTotal - nHaplo
ZgKernelPart <- as.matrix(Matrix::sparseMatrix(i = 1:nLine,
j = haploClusterNow,
x = rep(1, nLine),
dims = c(nLine, nHaplo),
dimnames = list(lineNames, haploNames)))
hInv <- median((distNodes ^ 2)[upper.tri(distNodes ^ 2)])
h <- 1 / hInv
hStarts <- h * rangeHStart
hStarts <- split(hStarts, factor(1:length(rangeHStart)))
if (kernelType %in% c("phylo", "gaussian", "exponential")) {
if (hOptBase == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects...")
}
maximizeFunc <- function(h) {
if (kernelType == "phylo") {
gKernel <- exp(- h * distNodes ^ 2)
gKernelPart <- gKernel[1:nHaplo, 1:nHaplo]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = h)
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- try(EM3.cpp(y = pheno[, 2], ZETA = ZETANow), silent = TRUE)
if (!("try-error" %in% class(EM3Res))) {
LL <- EM3Res$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo <- which.min(unlist(lapply(solnList, function(x) x$objective)))
soln <- solnList[[solnNo]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln <- nlminb(start = hStarts[[1]], objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt <- soln$par
} else if (hOptBase == "tuned") {
hOpt <- h
} else if (!is.numeric(hOptBase)) {
stop("`hOpt` should be either one of 'optimized', 'tuned', or numeric!!")
}
if (kernelType == "phylo") {
gKernel <- exp(- hOpt * distNodes)
gKernelPart <- gKernel[1:nHaplo, 1:nHaplo]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = hOpt)
}
} else {
hOpt <- NA
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType)
}
if (verbose) {
print("Now estimating genotypic values...")
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- EM3.cpp(y = pheno[, 2], ZETA = ZETANow)
LL <- EM3Res$LL
gvEst <- EM3Res$u.each[(nLine + 1):(nLine + nHaplo), ]
EMMRes0 <- EMM.cpp(y = pheno[, 2], ZETA = ZETA)
LL0 <- EMMRes0$LL
pVal <- pchisq(2 * (LL - LL0), df = 1, lower.tail = FALSE)
minuslog10p <- - log10(pVal)
if (EM3Res$weights[2] <= 1e-06) {
warning("This block seems to have no effect on phenotype...")
plotNode <- FALSE
gvEstTotal <- c(gvEst, rep(NA, nNode))
names(gvEstTotal) <- rownames(distNodes)
cexTip <- cexMax / 2
pchTip <- pchBase[2]
EMMRes <- NA
hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
} else {
plotNode <- TRUE
ZgKernel <- diag(nTotal)
rownames(ZgKernel) <- colnames(ZgKernel) <- rownames(distNodes)
gvEst2 <- matrix(c(gvEst, rep(NA, nNode)))
rownames(gvEst2) <- rownames(distNodes)
if (hOptBase2 == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects of nodes...")
}
maximizeFunc2 <- function(h) {
gKernel <- exp(- h * distNodes ^ 2)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel))
EMMRes <- try(EMM.cpp(y = gvEst2, ZETA = ZETA2), silent = TRUE)
if (!("try-error" %in% class(EMMRes))) {
LL <- EMMRes$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList2 <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList2 <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo2 <- which.min(unlist(lapply(solnList2, function(x) x$objective)))
soln2 <- solnList2[[solnNo2]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln2 <- nlminb(start = hStarts[[1]], objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt2 <- soln2$par
} else if (hOptBase2 == "tuned") {
hOpt2 <- h
} else if (!is.numeric(hOptBase2)) {
stop("`hOpt2` should be either one of 'optimized', 'tuned', or numeric!!")
}
gKernel2 <- exp(- hOpt2 * distNodes ^ 2)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel2))
EMMRes <- EMM.cpp(y = gvEst2, ZETA = ZETA2)
gvNode <- EMMRes$u[(nHaplo + 1):nTotal] + EMMRes$beta
gvEstTotal <- c(gvEst, gvNode)
names(gvEstTotal) <- rownames(distNodes)
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexNode <- (abs(gvScaled4Cex) + cexMin)[(nHaplo + 1):nTotal]
pchNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, pchBase[1], pchBase[2])
colNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, colNodeBase[1], colNodeBase[2])
cexTip <- (abs(gvScaled4Cex) + cexMin)[1:nHaplo]
pchTip <- ifelse(gvScaled[1:nHaplo] > 0, pchBase[1], pchBase[2])
colorForNodeFac <- factor(gvScaled[(nHaplo + 1):nTotal] > 0,
levels = c("TRUE", "FALSE"))
colorForNodeDF <- data.frame(model.matrix(~ colorForNodeFac - 1))
rownames(colorForNodeDF) <- names(colorForNodeFac)
colnames(colorForNodeDF) <- c("gvNode +", "gvNode -")
colorForNodeDF$node <- rownames(colorForNodeDF)
gvScaled4CexForGG <- sign(gvScaled) * sqrt(abs(gvScaled)) *
(cexMaxForGG - cexMinForGG) / max(sqrt(abs(gvScaled)))
cexNodeForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[(nHaplo + 1):nTotal]
cexTipForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[1:nHaplo]
alphaTip <- ifelse(gvScaled[1:nHaplo] > 0, alphaBase[1], alphaBase[2])
alphaNode <- alphaBase[2]
}
} else {
plotNode <- FALSE
nNode <- njRes$Nnode
nTotal <- nNode + nHaplo
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- rownames(distNodes)
minuslog10p <- NA
cexTip <- cexMax / 2
pchTip <- pchBase[2]
EM3Res <- EMMRes <- EMMRes0 <- NA
hOpt <- hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
}
gvEstTotalForLine <- (gvEstTotal[haploNames])[haploClusterNow]
names(gvEstTotalForLine) <- lineNames
hOpts <- c(hOpt = hOpt,
hOpt2 = hOpt2)
EMMResults <- list(EM3Res = EM3Res,
EMMRes = EMMRes,
EMMRes0 = EMMRes0)
gvEstTotals[[kernelType]] <- gvEstTotal
gvEstTotalForLines[[kernelType]] <- gvEstTotalForLine
minuslog10ps[kernelType] <- minuslog10p
hOptsList[[kernelType]] <- hOpts
EMMResultsList[[kernelType]] <- EMMResults
if (!is.null(subpopInfo)) {
if (length(colTipBase) != nGrp) {
stop("The length of 'colTipBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colTipNo <- as.numeric(subpopInfo)
colTip <- colTipBase[colTipNo]
names(colTipNo) <- names(colTip) <- lineNames
colTip <- tapply(colTip, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
edgeCol <- as.numeric(colTip[njRes$edge[, 2]])
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
clusterNosRatioDF$node <- 1:nHaplo
} else {
colTip <- rep("gray", nHaplo)
names(colTip) <- haploNames
colTipBase <- "gray"
edgeCol <- colTip[njRes$edge[, 2]]
clusterNosForHaplotype <- NA
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Tip"
clusterNosRatioDF$node <- 1:nHaplo
}
edgeCol[is.na(edgeCol)] <- "gray"
if (plotTree) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_tree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
if (edgeColoring) {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = edgeCol)
} else {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = "gray30")
}
if (plotNode) {
ape::nodelabels(pch = pchNode, cex = cexNode, col = colNode)
}
if (tipLabel) {
ape::tiplabels(pch = pchTip, cex = cexTip, col = colTip)
}
title(main = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (plotNode) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Tip (gv:+)", "Tip (gv:-)",
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, 2))
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colTipBase,
pch = pchTip)
} else {
legend("topleft", legend = "Tip",
col = colTipBase,
pch = pchTip)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotTree) {
trPhylo41 <- as(njRes, 'phylo4')
if (edgeColoring) {
dataForTipCol <- data.frame(color = colTip)
} else {
dataForTipCol <- data.frame(color = rep("gray30", nHaplo))
}
rownames(dataForTipCol) <- njRes$tip.label
trPhylo42 <- phylobase::phylo4d(trPhylo41, dataForTipCol)
nodeData <- data.frame(randomTrait = gvEstTotal[(nHaplo + 1):nTotal],
color = rep("gray", phylobase::nNodes(trPhylo41)),
row.names = phylobase::nodeId(trPhylo41, "internal"))
phylobase::nodeData(trPhylo42) <- nodeData
plt <- ggtree::ggtree(tr = trPhylo42,
ggtree::aes(col = I(ggplot2::.data$color)),
layout = "equal_angle")
if (plotNode) {
piesNode <- ggtree::nodepie(colorForNodeDF, cols = 1:2,
color = colNodeBase,
alpha = alphaNode)
plt <- plt + ggtree::geom_inset(insets = piesNode,
width = cexNodeForGG,
height = cexNodeForGG)
}
if (tipLabel) {
if (!is.null(subpopInfo)) {
colTipBaseForPie <- colTipBase
} else {
colTipBaseForPie <- rep("gray", nGrp)
}
if (!all(is.na(EMMRes))) {
piesPlus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[1], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[1])
piesMinus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[2], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[2])
plt <- plt + ggtree::geom_inset(insets = piesPlus,
width = cexTipForGG[alphaTip == alphaBase[1]],
height = cexTipForGG[alphaTip == alphaBase[1]])
plt <- plt + ggtree::geom_inset(insets = piesMinus,
width = cexTipForGG[alphaTip == alphaBase[2]],
height = cexTipForGG[alphaTip == alphaBase[2]])
} else {
piesTip <- ggtree::nodepie(clusterNosRatioDF,
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = mean(alphaBase))
plt <- plt + ggtree::geom_inset(insets = piesTip,
width = cexTipForGG,
height = cexTipForGG)
}
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_ggtree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
return(list(haplotypeInfo = haplotypeInfo,
subpopInfo = subpopInfo,
pValChi2Test = pValChi2Test,
distMats = list(distMat = distMat,
distMatEvol = dist4Nj,
distMatNJ = distNodes),
njRes = njRes,
gvTotal = gvEstTotals,
gvTotalForLine = gvEstTotalForLines,
minuslog10p = minuslog10ps,
hOpts = hOptsList,
EMMResults = EMMResultsList,
clusterNosForHaplotype = clusterNosForHaplotype))
}
if (is.matrix(blockInterest)) {
return(estPhyloEachBlock(blockInterest = blockInterest,
blockName = blockName))
} else if (is.list(blockInterest)) {
nTopRes <- length(blockInterest)
allResultsList <- rep(list(NULL), nTopRes)
blockNames <- names(blockInterest)
if (is.null(blockNames)) {
blockNames <- paste0("block_", 1:nTopRes)
}
names(allResultsList) <- blockNames
for (topNo in 1:nTopRes) {
allResultsList[[topNo]] <- estPhyloEachBlock(blockInterest = blockInterest[[topNo]],
blockName = blockNames[topNo])
}
return(allResultsList)
}
}
#' Function to estimate & plot haplotype network
#'
#' @param blockInterest A \eqn{n \times M} matrix representing the marker genotype that belongs to the haplotype block of interest.
#' If this argument is NULL, this argument will automatically be determined by `geno`,
#' @param gwasRes You can use the results (data.frame) of haplotype-based (SNP-set) GWAS by `RGWAS.multisnp` function.
#' @param nTopRes Haplotype blocks (or gene sets, SNP-sets) with top `nTopRes` p-values by `gwasRes` will be used.
#' @param gene.set If you have information of gene (or haplotype block), you can use it to perform kernel-based GWAS.
#' You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2).
#' In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
#' which correspond to the marker names of "geno" argument.
#' @param indexRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker index in `geno`.
#' @param chrInterest You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the chromosome number to this argument.
#' @param posRegion You can specify the haplotype block (or gene set, SNP-set) of interest by the marker position in `geno`.
#' Please assign the position in the chromosome to this argument.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' @param pheno Data frame where the first column is the line name (gid).
#' The remaining columns should be a phenotype to test.
#' @param geno Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
#' Columns 4 and higher contain the marker scores for each line, coded as {-1, 0, 1} = {aa, Aa, AA}.
#' @param ZETA A list of covariance (relationship) matrix (K: \eqn{m \times m}) and its design matrix (Z: \eqn{n \times m}) of random effects.
#' Please set names of list "Z" and "K"! You can use more than one kernel matrix.
#' For example,
#'
#' ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D))
#' \describe{
#' \item{Z.A, Z.D}{Design matrix (\eqn{n \times m}) for the random effects. So, in many cases, you can use the identity matrix.}
#' \item{K.A, K.D}{Different kernels which express some relationships between lines.}
#' }
#' For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
#' @param chi2Test If TRUE, chi-square test for the relationship between haplotypes & subpopulations will be performed.
#' @param thresChi2Test The threshold for the chi-square test.
#' @param plotNetwork If TRUE, the function will return the plot of haplotype network.
#' @param distMat You can assign the distance matrix of the block of interest.
#' If NULL, the distance matrix will be computed in this function.
#' @param evolutionDist If TRUE, the evolution distance will be used instead of the pure distance.
#' The `distMat` will be converted to the distance matrix by the evolution distance when you use `complementHaplo = "phylo"`.
#' @param distMethod You can choose the method to calculate distance between accessions.
#' This argument corresponds to the `method` argument in the `dist` function.
#' @param complementHaplo how to complement unobserved haplotypes.
#' When `complementHaplo = "all"`, all possible haplotypes will be complemented from the observed haplotypes.
#' When `complementHaplo = "never"`, unobserved haplotypes will not be complemented.
#' When `complementHaplo = "phylo"`, unobserved haplotypes will be complemented as nodes of phylogenetic tree.
#' When `complementHaplo = "TCS"`, unobserved haplotypes will be complemented by TCS methods (Clement et al., 2002).
#' @param subpopInfo The information of subpopulations. This argument should be a vector of factor.
#' @param groupingMethod If `subpopInfo` argument is NULL, this function estimates subpopulation information from marker genotype.
#' You can choose the grouping method from `kmeans`, `kmedoids`, and `hclust`.
#' @param nGrp The number of groups (or subpopulations) grouped by `groupingMethod`.
#' If this argument is 0, the subpopulation information will not be estimated.
#' @param nIterClustering If `groupingMethod` = `kmeans`, the clustering will be performed multiple times.
#' This argument specifies the number of classification performed by the function.
#' @param iterRmst The number of iterations for RMST (randomized minimum spanning tree).
#' @param networkMethod Either one of 'mst' (minimum spanning tree),
#' 'msn' (minimum spanning network), and 'rmst' (randomized minimum spanning tree).
#' 'rmst' is recommended.
#' @param autogamous This argument will be valid only when you use `complementHaplo = "all"` or `complementHaplo = "TCS"`.
#' This argument specifies whether the plant is autogamous or not. If autogamous = TRUE,
#' complemented haplotype will consist of only homozygous sites ({-1, 1}).
#' If FALSE, complemented haplotype will consist of both homozygous & heterozygous sites ({-1, 0, 1}).
#' @param probParsimony Equal to the argument `prob` in `haplotypes::parsimnet` function:
#'
#' A numeric vector of length one in the range [0.01, 0.99] giving the probability of parsimony as defined in Templeton et al. (1992).
#' In order to set maximum connection steps to Inf (to connect all the haplotypes in a single network), set the probability to NULL.
#' @param nMaxHaplo The maximum number of haplotypes. If the number of total (complemented + original) haplotypes are larger than `nMaxHaplo`,
#' we will only show the results only for the original haplotypes to reduce the computational time.
#' @param kernelTypes In the function, similarlity matrix between accessions will be computed from marker genotype to estimate genotypic values.
#' This argument specifies the method to compute similarity matrix:
#' If this argument is `addNOIA` (or one of other options in `methodGRM` in `calcGRM`),
#' then the `addNOIA` (or corresponding) option in the `calcGRM` function will be used,
#' and if this argument is `diffusion`, the diffusion kernel based on Laplacian matrix will be computed from network.
#' You can assign more than one kernelTypes for this argument; for example, kernelTypes = c("addNOIA", "diffusion").
#' @param nCores The number of cores used for optimization.
#' @param hOpt Optimized hyper parameter for constructing kernel when estimating haplotype effects.
#' If hOpt = "optimized", hyper parameter will be optimized in the function.
#' If hOpt is numeric, that value will be directly used in the function.
#' @param hOpt2 Optimized hyper parameter for constructing kernel when estimating complemented haplotype effects.
#' If hOpt2 = "optimized", hyper parameter will be optimized in the function.
#' If hOpt2 is numeric, that value will be directly used in the function.
#' @param maxIter Max number of iterations for optimization algorithm.
#' @param rangeHStart The median of off-diagonal of distance matrix multiplied by rangeHStart will be used
#' as the initial values for optimization of hyper parameters.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param plotWhichMDS We will show the MDS (multi-dimensional scaling) plot,
#' and this argument is a vector of two integers specifying that will define which MDS dimension will be plotted.
#' The first and second integers correspond to the horizontal and vertical axes, respectively.
#' @param colConnection A vector of two integers or characters specifying the colors of connection between nodes for the original and complemented haplotypes, respectively.
#' @param ltyConnection A vector of two characters specifying the line types of connection between nodes for the original and complemented haplotypes, respectively.
#' @param lwdConnection A vector of two integers specifying the line widths of connection between nodes for the original and complemented haplotypes, respectively.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colCompBase A vector of two integers or characters specifying color of complemented haplotypes for the positive and negative genotypic values respectively.
#' @param colHaploBase A vector of integers or characters specifying color of original haplotypes for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param ggPlotNetwork If TRUE, the function will return the ggplot version of haplotype network.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for original haplotype with positive / negative effects.
#' alpha for complemented haplotype will be same as the alpha for original haplotype with negative effects.
#' @param verbose If this argument is TRUE, messages for the current steps will be shown.
#'
#' @return
#' \describe{A list / lists of
#' \item{$haplotypeInfo}{\describe{A list of haplotype information with
#' \item{$haploCluster}{A vector indicating each individual belongs to which haplotypes.}
#' \item{$haploBlock}{Marker genotype of haplotype block of interest for the representing haplotypes.}
#' }
#' }
#' \item{$subpopInfo}{The information of subpopulations.}
#' \item{$pValChi2Test}{A p-value of the chi-square test for the dependency between haplotypes & subpopulations.
#' If `chi2Test = FALSE`, `NA` will be returned.}
#' \item{$mstResults}{\describe{A list of estimated results of MST / MSN / RMST:
#' \item{$mstRes}{Estimated results of MST / MSN / RMST for the data including original haplotypes.}
#' \item{$mstResComp}{Estimated results of MST / MSN / RMST for the data including both original and complemented haplotype.}
#' }
#' }
#' \item{$distMats}{\describe{A list of distance matrix:
#' \item{$distMat}{Distance matrix between haplotypes.}
#' \item{$distMatComp}{Distance matrix between haplotypes (including unobserved ones).}
#' \item{$laplacianMat}{Laplacian matrix between haplotypes (including unobserved ones).}
#' }
#' }
#' \item{$gvTotal}{Estimated genotypic values by kernel regression for each haplotype.}
#' \item{$gvTotalForLine}{Estimated genotypic values by kernel regression for each individual.}
#' \item{$minuslog10p}{\eqn{-log_{10}(p)} for haplotype block of interest.
#' p is the p-value for the siginifacance of the haplotype block effect.}
#' \item{$hOpts}{Optimized hyper parameters, hOpt1 & hOpt2.}
#' \item{$EMMResults}{\describe{A list of estimated results of kernel regression:
#' \item{$EM3Res}{Estimated results of kernel regression for the estimation of haplotype effects. (1st step)}
#' \item{$EMMRes}{Estimated results of kernel regression for the estimation of haplotype effects of nodes. (2nd step)}
#' \item{$EMM0Res}{Estimated results of kernel regression for the null model.}
#' }
#' }
#' \item{$clusterNosForHaplotype}{A list of cluster Nos of individuals that belong to each haplotype.}
#'}
#'
estNetwork <- function(blockInterest = NULL, gwasRes = NULL, nTopRes = 1, gene.set = NULL,
indexRegion = 1:10, chrInterest = NULL, posRegion = NULL, blockName = NULL,
pheno = NULL, geno = NULL, ZETA = NULL, chi2Test = TRUE,
thresChi2Test = 5e-2, plotNetwork = TRUE, distMat = NULL,
distMethod = "manhattan", evolutionDist = FALSE, complementHaplo = "phylo",
subpopInfo = NULL, groupingMethod = "kmedoids", nGrp = 3,
nIterClustering = 100, iterRmst = 100, networkMethod = "rmst",
autogamous = FALSE, probParsimony = 0.95, nMaxHaplo = 1000,
kernelTypes = "addNOIA", nCores = parallel::detectCores() - 1,
hOpt = "optimized", hOpt2 = "optimized", maxIter = 20,
rangeHStart = 10 ^ c(-1:1), saveName = NULL, saveStyle = "png",
plotWhichMDS = 1:2, colConnection = c("grey40", "grey60"),
ltyConnection = c("solid", "dashed"), lwdConnection = c(1.5, 0.8),
pchBase = c(1, 16), colCompBase = c(2, 4),
colHaploBase = c(3, 5, 6), cexMax = 2, cexMin = 0.7,
ggPlotNetwork = FALSE, cexMaxForGG = 0.025, cexMinForGG = 0.008,
alphaBase = c(0.9, 0.3), verbose = TRUE) {
if (requireNamespace("ggplot2", quietly = TRUE) &
requireNamespace("scatterpie", quietly = TRUE)) {
ggPlotNetwork <- ggPlotNetwork
} else {
warning(paste0("If you want to plot haplotype network, please install `ggplot2` and `scatterpie` packages! \n",
"We switched `ggPlotNetwork = FALSE`."))
ggPlotNetwork <- FALSE
}
if (!is.null(geno)) {
M <- t(geno[, -c(1:3)])
map <- geno[, 1:3]
nLine <- nrow(M)
lineNames <- rownames(M)
if (is.null(ZETA)) {
K <- calcGRM(M)
Z <- diag(nLine)
rownames(Z) <- colnames(Z) <- lineNames
ZETA <- list(A = list(Z = Z, K = K))
}
if (is.null(subpopInfo)) {
if (verbose) {
print("Now clustering...")
}
if (nGrp > 0) {
if (groupingMethod == "kmeans") {
bwSSRatios <- rep(NA, nIterClustering)
kmResList <- NULL
for (iterNo in 1:nIterClustering) {
kmResNow <- kmeans(x = M, centers = nGrp)
bwSSRatio <- kmResNow$betweenss / (kmResNow$betweenss + kmResNow$tot.withinss)
bwSSRatios[iterNo] <- bwSSRatio
kmResList <- c(kmResList, list(kmResNow))
}
maxNo <- which(bwSSRatios == max(bwSSRatios))
maxNoNow <- sample(maxNo, 1)
clusterNos <- match(kmResList[[maxNoNow]]$cluster, unique(kmResList[[maxNoNow]]$cluster))
} else if (groupingMethod == "kmedoids") {
kmResNow <- cluster::pam(x = M, k = nGrp, pamonce = 5)
clusterNos <- match(kmResNow$clustering, unique(kmResNow$clustering))
} else if (groupingMethod == "hclust") {
distMat <- Rfast::Dist(x = M, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(M)
tre <- hclust(as.dist(m = distMat))
cutreeRes <- cutree(tree = tre, k = nGrp)
clusterNos <- match(cutreeRes, unique(cutreeRes))
} else {
stop("We only offer 'kmeans', 'kmedoids', and 'hclust' methods for grouping methods.")
}
clusterRes <- factor(paste0("cluster_", clusterNos))
names(clusterRes) <- lineNames
subpopInfo <- clusterRes
} else {
subpopInfo <- NULL
}
} else {
if (!is.factor(subpopInfo)) {
subpopInfo <- as.factor(subpopInfo)
}
}
nGrp <- length(levels(subpopInfo))
if (is.null(blockInterest)) {
if (!is.null(gwasRes)) {
gwasResOrd <- gwasRes[order(gwasRes[, 4], decreasing = TRUE), ]
if (nTopRes == 1) {
blockName <- as.character(gwasResOrd[1, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterest <- M[, geno[, 1] %in% mrkInBlock]
} else {
blockInterest <- NULL
blockNames <- rep(NA, nTopRes)
for (topNo in 1:nTopRes) {
blockName <- as.character(gwasResOrd[topNo, 1])
mrkInBlock <- gene.set[gene.set[, 1] %in% blockName, 2]
blockInterestNow <- M[, geno[, 1] %in% mrkInBlock]
blockNames[topNo] <- blockName
blockInterest <- c(blockInterest, list(blockInterestNow))
}
names(blockInterest) <- blockNames
}
} else if (!is.null(posRegion)) {
if (is.null(chrInterest)) {
stop("Please input the chromosome number of interest!")
} else {
chrCondition <- map[, 2] %in% chrInterest
posCondition <- (posRegion[1] <= map[, 3]) & (posRegion[2] >= map[, 3])
indexRegion <- which(chrCondition & posCondition)
}
}
blockInterest <- M[, indexRegion]
}
} else {
blockInterestCheck <- !is.null(blockInterest)
ZETACheck <- !is.null(ZETA)
if (blockInterestCheck & ZETACheck) {
lineNames <- rownames(blockInterest)
nLine <- nrow(blockInterest)
} else {
stop("Please input 'geno', or both 'blockInterest' and 'ZETA'.")
}
}
estNetworkEachBlock <- function(blockInterest,
blockName = NULL) {
stringBlock <- apply(blockInterest, 1, function(x) paste0(x, collapse = ""))
blockInterestUnique <- blockInterest[!duplicated(stringBlock), ]
nHaplo <- length(unique(stringBlock))
haploClusterNow <- as.numeric(factor(stringBlock))
lineNames <- rownames(blockInterest)
names(haploClusterNow) <- lineNames
blockInterestUniqueSorted <- blockInterestUnique[order(unique(stringBlock)), ]
haploNames <- paste0("h", 1:nHaplo)
rownames(blockInterestUniqueSorted) <- haploNames
stringBlockUniqueSorted <- apply(blockInterestUniqueSorted, 1,
function(x) paste0(x, collapse = ""))
haploCluster <- haploNames[haploClusterNow]
names(haploCluster) <- lineNames
if (!is.null(subpopInfo)) {
tableRes <- table(haploCluster = haploCluster,
subpop = subpopInfo)[haploNames, ]
if (verbose) {
cat("Tabular for haplotype x subpopulation: \n")
print(head(tableRes))
cat("\nThe number of haplotypes: ", nHaplo, "\n")
}
if (chi2Test) {
chi2TestRes <- chisq.test(tableRes)
pValChi2Test <- chi2TestRes$p.value
if (verbose) {
cat("\n")
print(chi2TestRes)
cat("Haplotypes & Subpopulations are: \n")
if (pValChi2Test <= thresChi2Test) {
cat("\tSiginificantly dependent\n")
} else {
cat("\tindependent\n")
}
}
} else {
pValChi2Test <- NA
}
}
haplotypeInfo <- list(haploCluster = haploCluster,
haploBlock = blockInterestUniqueSorted)
nMrkInBlock <- ncol(blockInterest)
if (is.null(distMat)) {
distMat <- Rfast::Dist(x = blockInterestUniqueSorted, method = distMethod)
rownames(distMat) <- colnames(distMat) <- rownames(blockInterestUniqueSorted)
}
if (networkMethod == "rmst") {
mstRes <- pegas::rmst(d = distMat, B = iterRmst)
mstResPlus <- attr(mstRes, "alter.links")
mstResAll <- rbind(mstRes,
mstResPlus)
} else if (networkMethod == "mst") {
mstRes <- pegas::mst(d = distMat)
mstResPlus <- NULL
mstResAll <- mstRes
} else if (networkMethod == "msn") {
mstRes <- pegas::msn(d = distMat)
mstResPlus <- attr(mstRes, "alter.links")
mstResAll <- rbind(mstRes,
mstResPlus)
} else {
stop("We only offer 'rmst', 'mst', and 'msn' for `networkMethod`!!")
}
if (complementHaplo %in% c("all", "never")) {
if (complementHaplo == "all") {
blockInterestComp <- do.call(
what = rbind,
args = sapply(1:nrow(mstResAll), function(eachComb) {
blocksNow <- blockInterestUniqueSorted[mstResAll[eachComb, 1:2], ]
diffBlocks <- diff(blocksNow)
whichDiff <- which(diffBlocks != 0)
nDiff <- length(whichDiff)
blocksPart <- blocksNow[, whichDiff, drop = FALSE]
gridList <- lapply(apply(blocksPart, 2, function(eachMrk) {
gridCand <- min(eachMrk):max(eachMrk)
if (autogamous) {
gridCand <- gridCand[gridCand != 0]
}
return(list(gridCand))
}), function(x) x[[1]])
newBlocksPart <- as.matrix(expand.grid(gridList))
nNewBlocks <- nrow(newBlocksPart)
newBlocks <- matrix(data = rep(blocksNow[1, ], nNewBlocks),
nrow = nNewBlocks,
ncol = ncol(blocksNow),
byrow = TRUE)
colnames(newBlocks) <- colnames(blocksNow)
newBlocks[, whichDiff] <- newBlocksPart
return(newBlocks)
}, simplify = FALSE)
)
if (nrow(blockInterestComp) > nMaxHaplo) {
warning("There are too many complemented haplotypes... We will show the results only for the original haplotypes.")
blockInterestComp <- blockInterestUniqueSorted
}
} else if (complementHaplo == "never") {
blockInterestComp <- blockInterestUniqueSorted
}
blockInterestComp <- blockInterestComp[!duplicated(blockInterestComp), ]
stringBlockComp <- apply(blockInterestComp, 1,
function(x) paste0(x, collapse = ""))
nHaploComp <- nrow(blockInterestComp)
blockInterestCompSorted <- blockInterestComp[order(stringBlockComp), ]
stringBlockCompSorted <- apply(blockInterestCompSorted, 1,
function(x) paste0(x, collapse = ""))
matchString <- match(stringBlockCompSorted, stringBlockUniqueSorted)
namesBlockInterestComp <- rownames(blockInterestUniqueSorted)[matchString]
nComp <- sum(is.na(namesBlockInterestComp))
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp[is.na(namesBlockInterestComp)] <- compNames
rownames(blockInterestCompSorted) <- namesBlockInterestComp
haplotypeInfo$haploBlockCompSorted <- blockInterestCompSorted
distMatComp <- Rfast::Dist(x = blockInterestCompSorted, method = distMethod)
rownames(distMatComp) <- colnames(distMatComp) <- rownames(blockInterestCompSorted)
} else if (complementHaplo == "phylo") {
haplotypeInfo$haploBlockCompSorted <- NULL
if (evolutionDist) {
inLog <- 1 - 4 * (distMat / (ncol(blockInterest) * 2)) / 3
inLog[inLog <= 0] <- min(inLog[inLog > 0]) / 10
dist4Nj <- - 3 * log(inLog) / 4
} else {
dist4Nj <- distMat
}
njRes <- ape::nj(X = as.dist(dist4Nj))
distMatComp <- ape::dist.nodes(njRes)
nHaploComp <- nrow(distMatComp)
nComp <- nHaploComp - nHaplo
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp <- c(haploNames, compNames)
rownames(distMatComp) <- colnames(distMatComp) <- namesBlockInterestComp
} else if (complementHaplo == "TCS") {
extractParsimnetRes <- function (parsimnetRes) {
nHapEachNet <- parsimnetRes@nhap
distForEachNet <- parsimnetRes@d
nNet <- length(distForEachNet)
nTotalEachNet <- unlist(lapply(distForEachNet, nrow))
nCompEachNet <- nTotalEachNet - nHapEachNet
haploNamesByTCS <- unlist(sapply(1:nNet, function(netNo) {
distNow <- distForEachNet[[netNo]]
haploNamesNow <- rownames(distNow)
nHapNow <- nHapEachNet[netNo]
nTotalNow <- nTotalEachNet[netNo]
if (nCompEachNet[netNo] >= 1) {
for (compNo in (nHapNow + 1):nTotalNow) {
compNameNow <- haploNamesNow[compNo]
x <- stringr::str_split(string = compNameNow, pattern = "_")[[1]]
haploNos <- as.numeric(x[1:2])
if (is.na(x[3])) {
compNameNew <- paste(haploNamesNow[haploNos],
collapse = "_")
} else {
compNameNew <- paste(c(haploNamesNow[haploNos],
x[3]), collapse = "_")
}
haploNamesNow[compNo] <- compNameNew
}
}
return(haploNamesNow)
}, simplify = FALSE))
return(list(nCompEachNet = nCompEachNet,
distForEachNet = distForEachNet,
haploNamesByTCS = haploNamesByTCS))
}
haplotypeInfo$haploBlockCompSorted <- NULL
if (requireNamespace("haplotypes", quietly = TRUE)) {
if (autogamous) {
parsimnetRes <- haplotypes::parsimnet(x = distMat / 2,
seqlength = nMrkInBlock,
prob = probParsimony)
parsimnetResAll <- haplotypes::parsimnet(x = distMat / 2,
seqlength = nMrkInBlock,
prob = NULL)
} else {
parsimnetRes <- haplotypes::parsimnet(x = distMat,
seqlength = nMrkInBlock)
parsimnetResAll <- haplotypes::parsimnet(x = distMat,
seqlength = nMrkInBlock,
prob = NULL)
}
} else {
stop("R package `haplotypes` should be correctly installed when you use the option `complementHaplo = 'TCS'` !")
}
parsimnetResults <- list(parsimnetRes = parsimnetRes,
parsimnetResForOneNet = parsimnetResAll)
haplotypeInfo$parsimnetResults <- parsimnetResults
extractInfoByTCS <- extractParsimnetRes(parsimnetRes = parsimnetRes)
extractInfoByTCSAll <- extractParsimnetRes(parsimnetRes = parsimnetResAll)
haploNamesByTCS <- extractInfoByTCS$haploNamesByTCS
distMatByTCSAll <- extractInfoByTCSAll$distForEachNet$net1
distMatComp <- distMatByTCSAll[haploNamesByTCS, haploNamesByTCS]
if (autogamous) {
distMatComp <- distMatComp * 2
}
nComp <- sum(extractInfoByTCS$nCompEachNet)
nHaploComp <- nHaplo + nComp
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
namesBlockInterestComp <- haploNamesByTCS
namesBlockInterestComp[!(namesBlockInterestComp %in% haploNames)] <- compNames
rownames(distMatComp) <- colnames(distMatComp) <- namesBlockInterestComp
}
if (networkMethod == "rmst") {
mstResComp <- pegas::rmst(d = distMatComp, B = iterRmst)
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
} else if (networkMethod == "mst") {
mstResComp <- pegas::mst(d = distMatComp)
mstResCompPlus <- NULL
mstResCompAll <- mstResComp
} else if (networkMethod == "msn") {
mstResComp <- pegas::msn(d = distMatComp)
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
} else {
stop("We only offer 'rmst', 'mst', and 'msn' for `networkMethod`!!")
}
L <- as.matrix(Matrix::sparseMatrix(i = mstResCompAll[, 1],
j = mstResCompAll[, 2],
x = rep(-1, nrow(mstResCompAll)),
dims = c(nHaploComp, nHaploComp),
dimnames = list(namesBlockInterestComp,
namesBlockInterestComp)))
L <- L + t(L)
diag(L) <- - apply(L, 2, sum)
nTotal <- nrow(L)
plotPlus <- !is.null(mstResCompPlus)
minuslog10ps <- c()
gvEstTotals <- gvEstTotalForLines <-
hOptsList <- EMMResultsList <- list()
hOptBase <- hOpt
hOptBase2 <- hOpt2
for (kernelType in kernelTypes) {
if (verbose) {
print(paste0("Now optimizing for kernelType: ", kernelType))
}
if (!is.null(pheno)) {
ZgKernelPart <- as.matrix(Matrix::sparseMatrix(i = 1:nLine,
j = haploClusterNow,
x = rep(1, nLine),
dims = c(nLine, nHaplo),
dimnames = list(lineNames, haploNames)))
h <- 1
hStarts <- h * rangeHStart
hStarts <- split(hStarts, factor(1:length(rangeHStart)))
if (kernelType %in% c("diffusion", "gaussian", "exponential")) {
if (hOptBase == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating haplotype effects...")
}
maximizeFunc <- function(h) {
if (kernelType == "diffusion") {
gKernel <- expm::expm(- h * L)
gKernelPart <- gKernel[haploNames, haploNames]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = h)
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- try(EM3.cpp(y = pheno[, 2], ZETA = ZETANow), silent = TRUE)
if (!("try-error" %in% class(EM3Res))) {
LL <- EM3Res$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo <- which.min(unlist(lapply(solnList, function(x) x$objective)))
soln <- solnList[[solnNo]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln <- nlminb(start = hStarts[[1]], objective = maximizeFunc, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt <- soln$par
} else if (!is.numeric(hOptBase)) {
stop("`hOpt` should be either one of 'optimized' or numeric!!")
}
if (kernelType == "diffusion") {
gKernel <- expm::expm(- hOpt * L)
gKernelPart <- gKernel[haploNames, haploNames]
} else {
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType,
kernel.h = hOpt)
}
} else {
hOpt <- NA
gKernelPart <- calcGRM(blockInterestUniqueSorted,
methodGRM = kernelType)
}
if (verbose) {
print("Now estimating genotypic values...")
}
ZETANow <- c(ZETA, list(Part = list(Z = ZgKernelPart, K = gKernelPart)))
EM3Res <- EM3.cpp(y = pheno[, 2], ZETA = ZETANow)
gvEst <- EM3Res$u.each[(nLine + 1):(nLine + nHaplo), ]
LL <- EM3Res$LL
EMMRes0 <- EMM.cpp(y = pheno[, 2], ZETA = ZETA)
LL0 <- EMMRes0$LL
pVal <- pchisq(2 * (LL - LL0), df = 1, lower.tail = FALSE)
minuslog10p <- - log10(pVal)
if ((EM3Res$weights[2] <= 1e-06)) {
warning("This block seems to have no effect on phenotype...")
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- namesBlockInterestComp
gvEstTotal[haploNames] <- gvEst
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
EMMRes <- NA
hOpt2 <- NA
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
} else {
if (existComp) {
ZgKernel <- diag(nTotal)
rownames(ZgKernel) <- colnames(ZgKernel) <- namesBlockInterestComp
gvEst2 <- matrix(rep(NA, nTotal))
rownames(gvEst2) <- namesBlockInterestComp
gvEst2[haploNames, ] <- gvEst
if (hOptBase2 == "optimized") {
if (verbose) {
print("Now optimizing hyperparameter for estimating complemented haplotype effects...")
}
maximizeFunc2 <- function(h) {
gKernel <- expm::expm(- h * L)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel))
EMMRes <- try(EMM.cpp(y = gvEst2, ZETA = ZETA2), silent = TRUE)
if (!("try-error" %in% class(EMMRes))) {
LL <- EMMRes$LL
} else {
LL <- -1e12
}
return(-LL)
}
if (length(hStarts) >= 2) {
if (verbose) {
solnList2 <- pbmcapply::pbmclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
} else {
solnList2 <- parallel::mclapply(X = hStarts,
FUN = function(h) {
soln <- nlminb(start = h, objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(iter.max = maxIter))
return(soln)
}, mc.cores = nCores)
}
solnNo2 <- which.min(unlist(lapply(solnList2, function(x) x$objective)))
soln2 <- solnList2[[solnNo2]]
} else {
traceInside <- ifelse(verbose, 1, 0)
soln2 <- nlminb(start = hStarts[[1]], objective = maximizeFunc2, gradient = NULL, hessian = NULL,
lower = 0, upper = 1e06, control = list(trace = traceInside, iter.max = maxIter))
}
hOpt2 <- soln2$par
} else if (!is.numeric(hOptBase2)) {
stop("`hOpt2` should be either one of 'optimized' or numeric!!")
}
gKernel2 <- expm::expm(- hOpt2 * L)
ZETA2 <- list(Part = list(Z = ZgKernel, K = gKernel2))
EMMRes <- EMM.cpp(y = gvEst2, ZETA = ZETA2)
u <- EMMRes$u
names(u) <- namesBlockInterestComp
gvPlus <- u[compNames] + EMMRes$beta
gvEstTotal <- gvEst2[, 1]
gvEstTotal[compNames] <- gvPlus
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexComp <- (abs(gvScaled4Cex) + cexMin)[compNames]
pchComp <- ifelse(gvScaled[compNames] > 0, pchBase[1], pchBase[2])
colComp <- ifelse(gvScaled[compNames] > 0, colCompBase[1], colCompBase[2])
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexAll[compNames]
} else {
gvEstTotal <- gvEst
names(gvEstTotal) <- haploNames
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
cexComp <- cexMax / 4
colComp <- colCompBase[2]
pchComp <- pchBase[2]
EMMRes <- NA
hOpt2 <- NA
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexMaxForGG / 2
}
}
} else {
gvEstTotal <- rep(NA, nTotal)
names(gvEstTotal) <- namesBlockInterestComp
minuslog10p <- NA
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
EM3Res <- EMMRes <- EMMRes0 <- NA
hOpt <- hOpt2 <- NA
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
}
gvEstTotalForLine <- (gvEstTotal[haploNames])[haploClusterNow]
names(gvEstTotalForLine) <- lineNames
hOpts <- c(hOpt = hOpt,
hOpt2 = hOpt2)
EMMResults <- list(EM3Res = EM3Res,
EMMRes = EMMRes,
EMMRes0 = EMMRes0)
gvEstTotals[[kernelType]] <- gvEstTotal
gvEstTotalForLines[[kernelType]] <- gvEstTotalForLine
minuslog10ps[kernelType] <- minuslog10p
hOptsList[[kernelType]] <- hOpts
EMMResultsList[[kernelType]] <- EMMResults
if (!is.null(subpopInfo)) {
if (length(colHaploBase) != nGrp) {
stop("The length of 'colHaploBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colHaploNo <- as.numeric(subpopInfo)
colHaplo <- colHaploBase[colHaploNo]
names(colHaploNo) <- names(colHaplo) <- lineNames
colHaplo <- tapply(colHaplo, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
} else {
colHaplo <- rep("gray", nHaplo)
names(colHaplo) <- haploNames
clusterNosForHaplotype <- NA
}
nDimMDS <- max(plotWhichMDS)
mdsResComp <- cmdscale(d = distMatComp,
k = nDimMDS,
eig = TRUE)
rangeX <- range(mdsResComp$points[, plotWhichMDS[1]])
rangeY <- range(mdsResComp$points[, plotWhichMDS[2]])
if (plotNetwork) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
rangeX[1] <- rangeX[1] - abs(diff(rangeX)) / 4
rangeY[2] <- rangeY[2] + abs(diff(rangeY)) / 3
plot(x = mdsResComp$points[haploNames, plotWhichMDS[1]],
y = mdsResComp$points[haploNames, plotWhichMDS[2]],
col = colHaplo,
pch = pchHaplo, cex = cexHaplo,
xlab = paste0("MDS", plotWhichMDS[1]),
ylab = paste0("MDS", plotWhichMDS[2]),
xlim = rangeX,
ylim = rangeY)
if (existComp) {
points(x = mdsResComp$points[compNames, plotWhichMDS[1]],
y = mdsResComp$points[compNames, plotWhichMDS[2]],
col = colComp,
pch = pchComp, cex = cexComp)
}
segments(x0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[2]],
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
segments(x0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[2]],
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
title(main = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (existComp) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)",
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, 2))
}
} else if (!is.null(pheno)) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
col = rep(colHaploBase, each = 2),
pch = rep(pchBase, nGrp))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)"),
col = rep(colHaploBase, each = 2),
pch = pchBase)
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colHaploBase,
pch = pchHaplo)
} else {
legend("topleft", legend = "Haplotype",
col = colHaploBase,
pch = pchHaplo)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotNetwork) {
mdsPoints <- data.frame(mdsResComp$points[, plotWhichMDS[1:2]])
colnames(mdsPoints) <- paste0("MDS", plotWhichMDS[1:2])
mdsPoints$name <- rownames(mdsPoints)
if (!is.null(subpopInfo)) {
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
colHaploBaseForPie <- colHaploBase
} else {
nGrp <- 1
colHaploBaseForPie <- "gray"
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Haplotype"
}
if (existComp) {
if (!all(is.na(EMMRes))) {
clusterNosRatioDF$`gvComp +` <- 0
clusterNosRatioDF$`gvComp -` <- 0
colorForCompFac <- factor(gvScaled[compNames] > 0,
levels = c("TRUE", "FALSE"))
colorForCompDF <- data.frame(model.matrix(~ colorForCompFac - 1))
rownames(colorForCompDF) <- names(colorForCompFac)
colnames(colorForCompDF) <- c("gvComp +", "gvComp -")
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
clusterNosRatioDF$`Complement` <- 0
colorForCompDF <- data.frame(matrix(data = NA,
nrow = nComp, ncol = 1))
rownames(colorForCompDF) <- compNames
colnames(colorForCompDF) <- "Complement"
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
colCompBase <- "gray60"
}
colorForCompDFComp <- data.frame(matrix(data = 0,
nrow = nComp,
ncol = nGrp,
dimnames = list(rownames(colorForCompDF),
colnames(clusterNosRatioDF)[1:nGrp])))
colorForCompDF <- cbind(colorForCompDFComp,
colorForCompDF)
colorForAllCompDF <- data.frame(matrix(data = 0,
nrow = nTotal,
ncol = ncol(colorForCompDF)))
rownames(colorForAllCompDF) <- rownames(mdsPoints)
colnames(colorForAllCompDF) <- colnames(colorForCompDF)
colorForAllCompDF[haploNames, ] <- clusterNosRatioDF
colorForAllCompDF[compNames, ] <- colorForCompDF
alphaAll <- rep(NA, nTotal)
names(alphaAll) <- rownames(mdsPoints)
alphaAll[haploNames] <- alphaHaplo
alphaComp <- rep(alphaBase[2], nComp)
alphaAll[compNames] <- alphaComp
} else {
colorForAllCompDF <- clusterNosRatioDF
if (EM3Res$weights[2] > 1e-06) {
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
}
alphaAll <- alphaHaplo
colCompBase <- NULL
}
alpha2 <- alphaBase[2]
cexAll <- max(diff(rangeX), diff(rangeY)) * cexAll
mdsPointsForPlotDF <- cbind(mdsPoints,
colorForAllCompDF)
colnames(mdsPointsForPlotDF)[1:2] <- paste0("MDS", 1:2)
mdsPointsForPlotDF$cex <- cexAll
plt <- ggplot2::ggplot(data = mdsPointsForPlotDF,
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2))
if (any(alphaAll == alpha2)) {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha2, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha2) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
} else {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
}
mdsSegmentsDF <- data.frame(x1 = mdsPoints[mstResComp[, 1], 1],
y1 = mdsPoints[mstResComp[, 1], 2],
x2 = mdsPoints[mstResComp[, 2], 1],
y2 = mdsPoints[mstResComp[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsDF,
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
mdsSegmentsPlusDF <- data.frame(x1 = mdsPoints[mstResCompPlus[, 1], 1],
y1 = mdsPoints[mstResCompPlus[, 1], 2],
x2 = mdsPoints[mstResCompPlus[, 2], 1],
y2 = mdsPoints[mstResCompPlus[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsPlusDF,
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(colnames(pheno)[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network_ggplot")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 15.5, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 1300, height = 700)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 1300, height = 700)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 1300, height = 700)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
return(list(haplotypeInfo = haplotypeInfo,
subpopInfo = subpopInfo,
mstResults = list(mstRes = mstRes,
mstResComp = mstResComp),
distMats = list(distMat = distMat,
distMatComp = distMatComp,
laplacianMat = L),
pValChi2Test = pValChi2Test,
gvTotal = gvEstTotals,
gvTotalForLine = gvEstTotalForLines,
minuslog10p = minuslog10ps,
hOpts = hOptsList,
EMMResults = EMMResultsList,
clusterNosForHaplotype = clusterNosForHaplotype))
}
if (is.matrix(blockInterest)) {
return(estNetworkEachBlock(blockInterest = blockInterest,
blockName = blockName))
} else if (is.list(blockInterest)) {
nTopRes <- length(blockInterest)
allResultsList <- rep(list(NULL), nTopRes)
blockNames <- names(blockInterest)
if (is.null(blockNames)) {
blockNames <- paste0("block_", 1:nTopRes)
}
names(allResultsList) <- blockNames
for (topNo in 1:nTopRes) {
allResultsList[[topNo]] <- estNetworkEachBlock(blockInterest = blockInterest[[topNo]],
blockName = blockNames[topNo])
}
return(allResultsList)
}
}
#' Function to plot phylogenetic tree from the estimated results
#'
#' @param estPhyloRes The estimated results of phylogenetic analysis by `estPhylo` function for one
#' @param traitName Name of trait of interest. This will be used in the title of the plots.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' This will be used in the title of the plots.
#' @param plotTree If TRUE, the function will return the plot of phylogenetic tree.
#' @param subpopInfo The information of subpopulations.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colNodeBase A vector of two integers or chracters specifying color of nodes for the positive and negative genotypic values respectively.
#' @param colTipBase A vector of integers or chracters specifying color of tips for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param edgeColoring If TRUE, the edge branch of phylogenetic tree wiil be colored.
#' @param tipLabel If TRUE, lavels for tips will be shown.
#' @param ggPlotTree If TRUE, the function will return the ggplot version of phylogenetic tree.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for ggtree,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for tip with positive / negative effects.
#' alpha for node will be same as the alpha for tip with negative effects.
#'
#' @return Draw plots of phylogenetic tree.
#'
plotPhyloTree <- function(estPhyloRes, traitName = NULL, blockName = NULL, plotTree = TRUE,
subpopInfo = estPhyloRes$subpopInfo, saveName = NULL, saveStyle = "png",
pchBase = c(1, 16), colNodeBase = c(2, 4), colTipBase = c(3, 5, 6),
cexMax = 2, cexMin = 0.7, edgeColoring = TRUE, tipLabel = TRUE,
ggPlotTree = FALSE, cexMaxForGG = 0.12, cexMinForGG = 0.06, alphaBase = c(0.9, 0.3)) {
if (requireNamespace("ggtree", quietly = TRUE) &
requireNamespace("phylobase", quietly = TRUE)) {
ggPlotTree <- ggPlotTree
} else {
warning(paste0("If you want to plot phylogenetic trees, please install `ggtree` and `phylobase` packages from Bioconductor! \n",
"We switched `ggPlotTree = FALSE`."))
ggPlotTree <- FALSE
}
haplotypeInfo <- estPhyloRes$haplotypeInfo
haploCluster <- haplotypeInfo$haploCluster
blockInterestUniqueSorted <- haplotypeInfo$haploBlock
nHaplo <- nrow(blockInterestUniqueSorted)
haploNames <- rownames(blockInterestUniqueSorted)
haploClusterNow <- match(haploCluster, haploNames)
lineNames <- names(haploCluster)
names(haploClusterNow) <- lineNames
nGrp <- length(levels(subpopInfo))
nMrkInBlock <- ncol(blockInterestUniqueSorted)
njRes <- estPhyloRes$njRes
distNodes <- estPhyloRes$distMats$distMatNJ
nTotal <- nrow(distNodes)
nNode <- nTotal - nHaplo
kernelTypes <- names(estPhyloRes$gvTotal)
for (kernelType in kernelTypes) {
EMMResults <- estPhyloRes$EMMResults[[kernelType]]
EM3Res <- EMMResults$EM3Res
EMMRes <- EMMResults$EMMRes
gvEstTotal <- estPhyloRes$gvTotal[[kernelType]]
minuslog10p <- estPhyloRes$minuslog10p[kernelType]
nullPheno <- all(is.na(gvEstTotal))
if (!nullPheno) {
if (EM3Res$weights[2] <= 1e-06) {
plotNode <- FALSE
cexTip <- cexMax / 2
pchTip <- pchBase[2]
hOpt2 <- NA
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
} else {
plotNode <- TRUE
gvNode <- gvEstTotal[(nHaplo + 1):nTotal]
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexNode <- (abs(gvScaled4Cex) + cexMin)[(nHaplo + 1):nTotal]
pchNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, pchBase[1], pchBase[2])
colNode <- ifelse(gvScaled[(nHaplo + 1):nTotal] > 0, colNodeBase[1], colNodeBase[2])
cexTip <- (abs(gvScaled4Cex) + cexMin)[1:nHaplo]
pchTip <- ifelse(gvScaled[1:nHaplo] > 0, pchBase[1], pchBase[2])
colorForNodeFac <- factor(gvScaled[(nHaplo + 1):nTotal] > 0,
levels = c("TRUE", "FALSE"))
colorForNodeDF <- data.frame(model.matrix(~ colorForNodeFac - 1))
rownames(colorForNodeDF) <- names(colorForNodeFac)
colnames(colorForNodeDF) <- c("gvNode +", "gvNode -")
colorForNodeDF$node <- rownames(colorForNodeDF)
gvScaled4CexForGG <- sign(gvScaled) * sqrt(abs(gvScaled)) *
(cexMaxForGG - cexMinForGG) / max(sqrt(abs(gvScaled)))
cexNodeForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[(nHaplo + 1):nTotal]
cexTipForGG <- (abs(gvScaled4CexForGG) + cexMinForGG)[1:nHaplo]
alphaTip <- ifelse(gvScaled[1:nHaplo] > 0, alphaBase[1], alphaBase[2])
alphaNode <- alphaBase[2]
}
} else {
plotNode <- FALSE
cexTip <- cexMax / 2
pchTip <- pchBase[2]
cexTipForGG <- rep(cexMaxForGG / sqrt(2), nHaplo)
}
if (!is.null(subpopInfo)) {
if (length(colTipBase) != nGrp) {
stop("The length of 'colTipBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colTipNo <- as.numeric(subpopInfo)
colTip <- colTipBase[colTipNo]
names(colTipNo) <- names(colTip) <- lineNames
colTip <- tapply(colTip, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
edgeCol <- as.numeric(colTip[njRes$edge[, 2]])
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
clusterNosRatioDF$node <- 1:nHaplo
} else {
colTip <- rep("gray", nHaplo)
names(colTip) <- haploNames
colTipBase <- "gray"
edgeCol <- colTip[njRes$edge[, 2]]
clusterNosForHaplotype <- NA
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Tip"
clusterNosRatioDF$node <- 1:nHaplo
}
edgeCol[is.na(edgeCol)] <- "gray"
if (plotTree) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_tree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
if (edgeColoring) {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = edgeCol)
} else {
ape::plot.phylo(njRes, type = "u", show.tip.label = F, edge.color = "gray30")
}
if (plotNode) {
ape::nodelabels(pch = pchNode, cex = cexNode, col = colNode)
}
if (tipLabel) {
ape::tiplabels(pch = pchTip, cex = cexTip, col = colTip)
}
title(main = paste0(paste(c(traitName[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (plotNode) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Tip (gv:+)", "Tip (gv:-)",
"Node (gv:+)", "Node (gv:-)"),
col = c(rep(colTipBase, each = 2), colNodeBase),
pch = rep(pchBase, 2))
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colTipBase,
pch = pchTip)
} else {
legend("topleft", legend = "Tip",
col = colTipBase,
pch = pchTip)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotTree) {
trPhylo41 <- as(njRes, 'phylo4')
if (edgeColoring) {
dataForTipCol <- data.frame(color = colTip)
} else {
dataForTipCol <- data.frame(color = rep("gray30", nHaplo))
}
rownames(dataForTipCol) <- njRes$tip.label
trPhylo42 <- phylobase::phylo4d(trPhylo41, dataForTipCol)
nodeData <- data.frame(randomTrait = gvEstTotal[(nHaplo + 1):nTotal],
color = rep("gray", phylobase::nNodes(trPhylo41)),
row.names = phylobase::nodeId(trPhylo41, "internal"))
phylobase::nodeData(trPhylo42) <- nodeData
plt <- ggtree::ggtree(tr = trPhylo42,
ggtree::aes(col = I(ggplot2::.data$color)),
layout = "equal_angle")
if (plotNode) {
piesNode <- ggtree::nodepie(colorForNodeDF, cols = 1:2,
color = colNodeBase,
alpha = alphaNode)
plt <- plt + ggtree::geom_inset(insets = piesNode,
width = cexNodeForGG,
height = cexNodeForGG)
}
if (tipLabel) {
if (!is.null(subpopInfo)) {
colTipBaseForPie <- colTipBase
} else {
colTipBaseForPie <- rep("gray", nGrp)
}
if (!all(is.na(EMMRes))) {
piesPlus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[1], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[1])
piesMinus <- ggtree::nodepie(clusterNosRatioDF[alphaTip == alphaBase[2], ],
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = alphaBase[2])
plt <- plt + ggtree::geom_inset(insets = piesPlus,
width = cexTipForGG[alphaTip == alphaBase[1]],
height = cexTipForGG[alphaTip == alphaBase[1]])
plt <- plt + ggtree::geom_inset(insets = piesMinus,
width = cexTipForGG[alphaTip == alphaBase[2]],
height = cexTipForGG[alphaTip == alphaBase[2]])
} else {
piesTip <- ggtree::nodepie(clusterNosRatioDF,
cols = 1:nGrp,
color = colTipBaseForPie,
alpha = mean(alphaBase))
plt <- plt + ggtree::geom_inset(insets = piesTip,
width = cexTipForGG,
height = cexTipForGG)
}
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(traitName[2],
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_phylogenetic_ggtree")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
}
#' Function to plot haplotype network from the estimated results
#'
#' @param estNetworkRes The estimated results of haplotype network by `estNetwork` function for one
#' @param traitName Name of trait of interest. This will be used in the title of the plots.
#' @param blockName You can specify the haplotype block (or gene set, SNP-set) of interest by the name of haplotype block in `geno`.
#' This will be used in the title of the plots.
#' @param plotNetwork If TRUE, the function will return the plot of haplotype network.
#' @param subpopInfo The information of subpopulations.
#' @param saveName When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
#' When saveName = NULL, the plot is not saved.
#' @param saveStyle This argument specifies how to save the plot of phylogenetic tree.
#' The function offers `png`, `pdf`, `jpg`, and `tiff`.
#' @param plotWhichMDS We will show the MDS (multi-dimensional scaling) plot,
#' and this argument is a vector of two integers specifying that will define which MDS dimension will be plotted.
#' The first and second integers correspond to the horizontal and vertical axes, respectively.
#' @param colConnection A vector of two integers or characters specifying the colors of connection between nodes for the original and complemented haplotypes, respectively.
#' @param ltyConnection A vector of two characters specifying the line types of connection between nodes for the original and complemented haplotypes, respectively.
#' @param lwdConnection A vector of two integers specifying the line widths of connection between nodes for the original and complemented haplotypes, respectively.
#' @param pchBase A vector of two integers specifying the plot types for the positive and negative genotypic values respectively.
#' @param colCompBase A vector of two integers or characters specifying color of complemented haplotypes for the positive and negative genotypic values respectively.
#' @param colHaploBase A vector of integers or characters specifying color of original haplotypes for the positive and negative genotypic values respectively.
#' The length of the vector should equal to the number of subpopulations.
#' @param cexMax A numeric specifying the maximum point size of the plot.
#' @param cexMin A numeric specifying the minimum point size of the plot.
#' @param ggPlotNetwork If TRUE, the function will return the ggplot version of haplotype network.
#' It offers the precise information on subgroups for each haplotype.
#' @param cexMaxForGG A numeric specifying the maximum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param cexMinForGG A numeric specifying the minimum point size of the plot for the ggplot version of haplotype network,
#' relative to the range of x and y-axes (0 < cexMaxForGG <= 1).
#' @param alphaBase alpha (parameter that indicates the opacity of a geom) for original haplotype with positive / negative effects.
#' alpha for complemented haplotype will be same as the alpha for original haplotype with negative effects.
#'
#' @return Draw plot of haplotype network.
#'
plotHaploNetwork <- function(estNetworkRes, traitName = NULL, blockName = NULL,
plotNetwork = TRUE, subpopInfo = estNetworkRes$subpopInfo,
saveName = NULL, saveStyle = "png",
plotWhichMDS = 1:2, colConnection = c("grey40", "grey60"),
ltyConnection = c("solid", "dashed"), lwdConnection = c(1.5, 0.8),
pchBase = c(1, 16), colCompBase = c(2, 4),
colHaploBase = c(3, 5, 6), cexMax = 2, cexMin = 0.7,
ggPlotNetwork = FALSE, cexMaxForGG = 0.025,
cexMinForGG = 0.008, alphaBase = c(0.9, 0.3)) {
if (requireNamespace("ggplot2", quietly = TRUE) &
requireNamespace("scatterpie", quietly = TRUE)) {
ggPlotNetwork <- ggPlotNetwork
} else {
warning(paste0("If you want to plot haplotype network, please install `ggplot2` and `scatterpie` packages! \n",
"We switched `ggPlotNetwork = FALSE`."))
ggPlotNetwork <- FALSE
}
haplotypeInfo <- estNetworkRes$haplotypeInfo
haploCluster <- haplotypeInfo$haploCluster
blockInterestUniqueSorted <- haplotypeInfo$haploBlock
blockInterestCompSorted <- haplotypeInfo$haploBlockCompSorted
nHaplo <- nrow(blockInterestUniqueSorted)
haploNames <- rownames(blockInterestUniqueSorted)
haploClusterNow <- match(haploCluster, haploNames)
lineNames <- names(haploCluster)
names(haploClusterNow) <- lineNames
distMat <- estNetworkRes$distMats$distMat
distMatComp <- estNetworkRes$distMats$distMatComp
nGrp <- length(levels(subpopInfo))
nMrkInBlock <- ncol(blockInterestUniqueSorted)
mstResults <- estNetworkRes$mstResults
mstResComp <- mstResults$mstResComp
nTotal <- nrow(distMatComp)
nComp <- nTotal - nHaplo
kernelTypes <- names(estNetworkRes$gvTotal)
namesBlockInterestComp <- rownames(distMatComp)
if (nComp >= 1) {
existComp <- TRUE
compNames <- paste0("c", 1:nComp)
} else {
existComp <- FALSE
compNames <- NULL
}
mstResCompPlus <- attr(mstResComp, "alter.links")
mstResCompAll <- rbind(mstResComp,
mstResCompPlus)
L <- estNetworkRes$distMats$laplacianMat
plotPlus <- !is.null(mstResCompPlus)
for (kernelType in kernelTypes) {
EMMResults <- estNetworkRes$EMMResults[[kernelType]]
EM3Res <- EMMResults$EM3Res
EMMRes <- EMMResults$EMMRes
gvEstTotal <- estNetworkRes$gvTotal[[kernelType]]
minuslog10p <- estNetworkRes$minuslog10p[kernelType]
nullPheno <- all(is.na(gvEstTotal))
if (!nullPheno) {
if (EM3Res$weights[2] <= 1e-06) {
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
} else {
if (existComp) {
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexComp <- (abs(gvScaled4Cex) + cexMin)[compNames]
pchComp <- ifelse(gvScaled[compNames] > 0, pchBase[1], pchBase[2])
colComp <- ifelse(gvScaled[compNames] > 0, colCompBase[1], colCompBase[2])
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexAll[compNames]
} else {
gvCentered <- gvEstTotal - mean(gvEstTotal)
gvScaled <- gvCentered / sd(gvCentered)
gvScaled4Cex <- gvScaled * (cexMax - cexMin) / max(abs(gvScaled))
cexHaplo <- (abs(gvScaled4Cex) + cexMin)[haploNames]
pchHaplo <- ifelse(gvScaled[haploNames] > 0, pchBase[1], pchBase[2])
cexComp <- cexMax / 4
colComp <- colCompBase[2]
pchComp <- pchBase[2]
gvScaled4CexForGG <- gvScaled * (cexMaxForGG - cexMinForGG) / max(abs(gvScaled))
cexAll <- abs(gvScaled4CexForGG) + cexMinForGG
cexHaploForGG <- cexAll[haploNames]
cexCompForGG <- cexMaxForGG / 2
}
}
} else {
cexHaplo <- cexMax / 2
cexComp <- cexMax / 4
pchHaplo <- pchComp <- pchBase[2]
colComp <- colCompBase[2]
cexHaploForGG <- cexMaxForGG / sqrt(2)
cexCompForGG <- cexMaxForGG / 2
cexAll <- rep(NA, nTotal)
names(cexAll) <- namesBlockInterestComp
cexAll[haploNames] <- cexHaploForGG
if (existComp) {
cexAll[compNames] <- cexCompForGG
}
}
if (!is.null(subpopInfo)) {
if (length(colHaploBase) != nGrp) {
stop("The length of 'colHaploBase' should be equal to 'nGrp' or the number of subpopulations!")
}
colHaploNo <- as.numeric(subpopInfo)
colHaplo <- colHaploBase[colHaploNo]
names(colHaploNo) <- names(colHaplo) <- lineNames
colHaplo <- tapply(colHaplo, INDEX = haploCluster, FUN = function(x) {
as.numeric(names(which.max(table(x) / sum(table(x)))))
})[haploNames]
clusterNosForHaplotype <- tapply(subpopInfo, INDEX = haploCluster,
FUN = table)[haploNames]
} else {
colHaplo <- rep("gray", nHaplo)
names(colHaplo) <- haploNames
clusterNosForHaplotype <- NA
}
nDimMDS <- max(plotWhichMDS)
mdsResComp <- cmdscale(d = distMatComp,
k = nDimMDS,
eig = TRUE)
rangeX <- range(mdsResComp$points[, plotWhichMDS[1]])
rangeY <- range(mdsResComp$points[, plotWhichMDS[2]])
if (plotNetwork) {
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 12, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 800, height = 600)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 800, height = 600)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 800, height = 600)
}
}
rangeX[1] <- rangeX[1] - abs(diff(rangeX)) / 4
rangeY[2] <- rangeY[2] + abs(diff(rangeY)) / 3
plot(x = mdsResComp$points[haploNames, plotWhichMDS[1]],
y = mdsResComp$points[haploNames, plotWhichMDS[2]],
col = colHaplo,
pch = pchHaplo, cex = cexHaplo,
xlab = paste0("MDS", plotWhichMDS[1]),
ylab = paste0("MDS", plotWhichMDS[2]),
xlim = rangeX,
ylim = rangeY)
if (existComp) {
points(x = mdsResComp$points[compNames, plotWhichMDS[1]],
y = mdsResComp$points[compNames, plotWhichMDS[2]],
col = colComp,
pch = pchComp, cex = cexComp)
}
segments(x0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResComp[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResComp[, 2], plotWhichMDS[2]],
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
segments(x0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[1]],
y0 = mdsResComp$points[mstResCompPlus[, 1], plotWhichMDS[2]],
x1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[1]],
y1 = mdsResComp$points[mstResCompPlus[, 2], plotWhichMDS[2]],
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
title(main = paste0(paste(c(traitName,
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (existComp) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = c(paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, nGrp + 1))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)",
"Complement (gv:+)", "Complement (gv:-)"),
col = c(rep(colHaploBase, each = 2), colCompBase),
pch = rep(pchBase, 2))
}
} else if (!nullPheno) {
if (!is.null(subpopInfo)) {
legend("topleft", legend = paste0(rep(levels(subpopInfo), each = 2),
rep(c(" (gv:+)", " (gv:-)"), nGrp)),
col = rep(colHaploBase, each = 2),
pch = rep(pchBase, nGrp))
} else {
legend("topleft", legend = c("Haplotype (gv:+)", "Haplotype (gv:-)"),
col = rep(colHaploBase, each = 2),
pch = pchBase)
}
} else {
if (!is.null(subpopInfo)) {
legend("topleft", legend = levels(subpopInfo),
col = colHaploBase,
pch = pchHaplo)
} else {
legend("topleft", legend = "Haplotype",
col = colHaploBase,
pch = pchHaplo)
}
}
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
if (ggPlotNetwork) {
mdsPoints <- data.frame(mdsResComp$points[, plotWhichMDS[1:2]])
colnames(mdsPoints) <- paste0("MDS", plotWhichMDS[1:2])
mdsPoints$name <- rownames(mdsPoints)
if (!is.null(subpopInfo)) {
clusterNosDF <- data.frame(do.call(what = rbind,
args = clusterNosForHaplotype))
clusterNosRatioDF <- data.frame(t(apply(clusterNosDF, 1,
function(x) x / sum(x))))
colHaploBaseForPie <- colHaploBase
} else {
nGrp <- 1
colHaploBaseForPie <- "gray"
clusterNosRatioDF <- data.frame(matrix(data = 1,
nrow = nHaplo,
ncol = 1))
rownames(clusterNosRatioDF) <- haploNames
colnames(clusterNosRatioDF) <- "Haplotype"
}
if (existComp) {
if (!all(is.na(EMMRes))) {
clusterNosRatioDF$`gvComp +` <- 0
clusterNosRatioDF$`gvComp -` <- 0
colorForCompFac <- factor(gvScaled[compNames] > 0,
levels = c("TRUE", "FALSE"))
colorForCompDF <- data.frame(model.matrix(~ colorForCompFac - 1))
rownames(colorForCompDF) <- names(colorForCompFac)
colnames(colorForCompDF) <- c("gvComp +", "gvComp -")
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
clusterNosRatioDF$`Complement` <- 0
colorForCompDF <- data.frame(matrix(data = NA,
nrow = nComp, ncol = 1))
rownames(colorForCompDF) <- compNames
colnames(colorForCompDF) <- "Complement"
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
colCompBase <- "gray60"
}
colorForCompDFComp <- data.frame(matrix(data = 0,
nrow = nComp,
ncol = nGrp,
dimnames = list(rownames(colorForCompDF),
colnames(clusterNosRatioDF)[1:nGrp])))
colorForCompDF <- cbind(colorForCompDFComp,
colorForCompDF)
colorForAllCompDF <- data.frame(matrix(data = 0,
nrow = nTotal,
ncol = ncol(colorForCompDF)))
rownames(colorForAllCompDF) <- rownames(mdsPoints)
colnames(colorForAllCompDF) <- colnames(colorForCompDF)
colorForAllCompDF[haploNames, ] <- clusterNosRatioDF
colorForAllCompDF[compNames, ] <- colorForCompDF
alphaAll <- rep(NA, nTotal)
names(alphaAll) <- rownames(mdsPoints)
alphaAll[haploNames] <- alphaHaplo
alphaComp <- rep(alphaBase[2], nComp)
alphaAll[compNames] <- alphaComp
} else {
colorForAllCompDF <- clusterNosRatioDF
if (EM3Res$weights[2] > 1e-06) {
alphaHaplo <- ifelse(gvScaled[haploNames] > 0, alphaBase[1], alphaBase[2])
alpha1 <- alphaBase[1]
} else {
alpha1 <- mean(alphaBase)
alphaHaplo <- rep(alpha1, nHaplo)
names(alphaHaplo) <- haploNames
}
alphaAll <- alphaHaplo
colCompBase <- NULL
}
alpha2 <- alphaBase[2]
cexAll <- max(diff(rangeX), diff(rangeY)) * cexAll
mdsPointsForPlotDF <- cbind(mdsPoints,
colorForAllCompDF)
colnames(mdsPointsForPlotDF)[1:2] <- paste0("MDS", 1:2)
mdsPointsForPlotDF$cex <- cexAll
plt <- ggplot2::ggplot(data = mdsPointsForPlotDF,
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2))
if (any(alphaAll == alpha2)) {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha2, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha2) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
} else {
plt <- plt +
scatterpie::geom_scatterpie(data = mdsPointsForPlotDF[alphaAll == alpha1, ],
ggplot2::aes(x = ggplot2::.data$MDS1,
y = ggplot2::.data$MDS2,
r = ggplot2::.data$cex),
cols = colnames(colorForAllCompDF),
col = NA, alpha = alpha1) +
ggplot2::scale_fill_manual(values = c(colHaploBaseForPie,
colCompBase)[order(colnames(colorForAllCompDF))]) +
ggplot2::coord_equal() +
ggplot2::xlab(paste0("MDS", plotWhichMDS[1])) +
ggplot2::ylab(paste0("MDS", plotWhichMDS[2]))
}
mdsSegmentsDF <- data.frame(x1 = mdsPoints[mstResComp[, 1], 1],
y1 = mdsPoints[mstResComp[, 1], 2],
x2 = mdsPoints[mstResComp[, 2], 1],
y2 = mdsPoints[mstResComp[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsDF,
col = colConnection[1],
lty = ltyConnection[1],
lwd = lwdConnection[1])
if (plotPlus) {
mdsSegmentsPlusDF <- data.frame(x1 = mdsPoints[mstResCompPlus[, 1], 1],
y1 = mdsPoints[mstResCompPlus[, 1], 2],
x2 = mdsPoints[mstResCompPlus[, 2], 1],
y2 = mdsPoints[mstResCompPlus[, 2], 2])
plt <- plt + ggplot2::geom_segment(ggplot2::aes(x = ggplot2::.data$x1,
y = ggplot2::.data$y1,
xend = ggplot2::.data$x2,
yend = ggplot2::.data$y2),
data = mdsSegmentsPlusDF,
col = colConnection[2],
lty = ltyConnection[2],
lwd = lwdConnection[2])
}
plt <- plt + ggplot2::ggtitle(label = paste0(paste(c(traitName,
blockName, kernelType), collapse = "_"),
" (-log10p: ", round(minuslog10p, 2), ")"))
if (!is.null(saveName)) {
savePlotNameBase <- paste0(paste(c(saveName, blockName, kernelType), collapse = "_"),
"_network_ggplot")
if (saveStyle == "pdf") {
savePlotName <- paste0(savePlotNameBase, ".pdf")
pdf(file = savePlotName, width = 15.5, height = 9)
} else if (saveStyle == "jpg") {
savePlotName <- paste0(savePlotNameBase, ".jpg")
jpeg(filename = savePlotName, width = 1300, height = 700)
} else if (saveStyle == "tiff") {
savePlotName <- paste0(savePlotNameBase, ".tiff")
tiff(filename = savePlotName, width = 1300, height = 700)
} else {
savePlotName <- paste0(savePlotNameBase, ".png")
png(filename = savePlotName, width = 1300, height = 700)
}
}
print(plt)
if (!is.null(saveName)) {
dev.off()
} else {
Sys.sleep(0.5)
}
}
}
}
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