R/filters.R
In mmollina/MAPPoly: Genetic Linkage Maps in Autopolyploids
Defines functions
filter_aneuploid
edit_order
rf_snp_filter
filter_individuals
filter_segregation
filter_missing_ind
filter_missing_mrk
filter_missing
filter_non_conforming_classes
Documented in
edit_order
filter_aneuploid
filter_individuals
filter_missing
filter_missing_ind
filter_missing_mrk
filter_non_conforming_classes
filter_segregation
rf_snp_filter
#' Filter non-conforming classes in F1, non double reduced population.
#' @keywords internal
filter_non_conforming_classes <- function(input.data, prob.thres = NULL)
{
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
ploidy <- input.data$ploidy
dp <- input.data$dosage.p1
dq <- input.data$dosage.p2
Ds <- array(NA, dim = c(ploidy+1, ploidy+1, ploidy+1))
for(i in 0:ploidy)
for(j in 0:ploidy)
Ds[i+1,j+1,] <- segreg_poly(ploidy = ploidy, dP = i, dQ = j)
Dpop <- cbind(dp,dq)
#Gathering segregation probabilities given parental dosages
M <- t(apply(Dpop, 1, function(x) Ds[x[1]+1, x[2]+1,]))
M[M != 0] <- 1
dimnames(M) <- list(input.data$mrk.names, 0:ploidy)
##if no prior probabilities
if(!is.prob.data(input.data)){
for(i in 1:nrow(M)){
id0 <- !as.numeric(input.data$geno.dose[i,])%in%(which(M[i,] == 1)-1)
if(any(id0))
input.data$geno.dose[i,id0] <- (ploidy+1)
}
return(input.data)
}
## 1 represents conforming classes/ 0 represents non-conforming classes
dp <- rep(dp, input.data$n.ind)
dq <- rep(dq, input.data$n.ind)
M <- M[rep(seq_len(nrow(M)), input.data$n.ind),]
R <- input.data$geno[,-c(1:2)] - input.data$geno[,-c(1:2)]*M
id1 <- apply(R, 1, function(x) abs(sum(x))) > 0.3 # if the sum of the excluded classes is greater than 0.3, use segreg_poly
N <- matrix(NA, sum(id1), input.data$ploidy+1)
ct <- 1
for(i in which(id1)){
N[ct,] <- Ds[dp[i]+1, dq[i]+1, ]
ct <- ct+1
}
input.data$geno[id1,-c(1:2)] <- N
# if the sum of the excluded classes is greater than zero
# and smaller than 0.3, assign zero to those classes and normalize the vector
input.data$geno[,-c(1:2)][R > 0] <- 0
input.data$geno[,-c(1:2)] <- sweep(input.data$geno[,-c(1:2)], 1, rowSums(input.data$geno[,-c(1:2)]), FUN = "/")
if(is.null(prob.thres))
prob.thres <- input.data$prob.thres
geno.dose <- dist_prob_to_class(geno = input.data$geno, prob.thres = prob.thres)
if(geno.dose$flag)
{
input.data$geno <- geno.dose$geno
input.data$geno.dose <- geno.dose$geno.dose
} else {
input.data$geno.dose <- geno.dose$geno.dose
}
input.data$geno.dose[is.na(input.data$geno.dose)] <- ploidy + 1
input.data$n.ind <- ncol(input.data$geno.dose)
input.data$ind.names <- colnames(input.data$geno.dose)
return(input.data)
}
#' Filter missing genotypes
#'
#' Excludes markers or individuals based on their proportion of missing data.
#'
#' @param input.data an object of class \code{mappoly.data}.
#'
#' @param type one of the following options:
#' \enumerate{
#' \item \code{"marker"}: filter out markers based on their percentage of missing data (default).
#' \item \code{"individual"}: filter out individuals based on their percentage of missing data.
#' }
#' Please notice that removing individuals with certain amount of data can change some marker parameters
#' (such as depth), and can also change the estimated genotypes for other individuals.
#' So, be careful when removing individuals.
#'
#' @param filter.thres maximum percentage of missing data (default = 0.2).
#'
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering.
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}.
#'
#' @examples
#' plot(tetra.solcap)
#' dat.filt.mrk <- filter_missing(input.data = tetra.solcap,
#' type = "marker",
#' filter.thres = 0.1,
#' inter = TRUE)
#' plot(dat.filt.mrk)
#'
#' @export
#' @importFrom magrittr "%>%"
#' @importFrom dplyr filter
#' @importFrom graphics axis
filter_missing <- function(input.data,
type = c("marker", "individual"),
filter.thres = 0.2,
inter = TRUE)
{
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
type <- match.arg(type)
switch(type,
marker = filter_missing_mrk(input.data,
filter.thres = filter.thres,
inter = inter),
individual = filter_missing_ind(input.data,
filter.thres = filter.thres,
inter = inter)
)
}
#' Filter markers based on missing genotypes
#'
#' @param input.data an object of class \code{"mappoly.data"}
#' @param filter.thres maximum percentage of missing data
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#' @keywords internal
filter_missing_mrk <- function(input.data, filter.thres = 0.2, inter = TRUE)
{
op <- par(pty="s")
on.exit(par(op))
ANSWER <- "flag"
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
na.num <- apply(input.data$geno.dose, 1, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.ind
x <- sort(perc.na)
plot(x,
xlab = "markers",
ylab = "frequency of missing data",
col = ifelse(x <= filter.thres, 4, 2),
pch =ifelse(x <= filter.thres, 1, 4))
abline(h = filter.thres, lty = 2)
f<-paste0("Filtered out: ", sum(perc.na > filter.thres))
i<-paste0("Included: ", sum(perc.na <= filter.thres))
legend("topleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the filter threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("Stop function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
filter.thres <- as.numeric(ANSWER)
}
mrks.id <- which(perc.na <= filter.thres)
if(length(mrks.id) == input.data$n.mrk){
return(input.data)
}
out.dat <- sample_data(input.data, type = "markers", selected.mrk = names(mrks.id))
return(out.dat)
}
else{
na.num <- apply(input.data$geno.dose, 1, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.ind
mrks.id <- which(perc.na <= filter.thres)
if(length(mrks.id) == input.data$n.mrk){
return(input.data)
}
out.dat <- sample_data(input.data, type = "markers", selected.mrk = names(mrks.id))
return(out.dat)
}
}
#' Filter individuals based on missing genotypes
#'
#' @param input.data an object of class \code{"mappoly.data"}
#' @param filter.thres maximum percentage of missing data
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#' @keywords internal
#' @importFrom magrittr "%>%"
#' @importFrom dplyr filter
#' @importFrom graphics axis
filter_missing_ind <- function(input.data, filter.thres = 0.2, inter = TRUE)
{
op <- par(pty="s")
on.exit(par(op))
ANSWER <- "flag"
ind <- NULL
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
na.num <- apply(input.data$geno.dose, 2, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.mrk
x <- sort(perc.na)
plot(x,
xlab = "offspring",
ylab = "frequency of missing data",
col = ifelse(x <= filter.thres, 4, 2),
pch =ifelse(x <= filter.thres, 1, 4));
abline(h = filter.thres, lty = 2)
f<-paste0("Filtered out: ", sum(perc.na > filter.thres))
i<-paste0("Included: ", sum(perc.na <= filter.thres))
legend("topleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the filter threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("You decided to stop the function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
filter.thres <- as.numeric(ANSWER)
}
ind.id <- which(perc.na <= filter.thres)
if(length(ind.id) == input.data$n.ind){
return(input.data)
}
ind <- names(ind.id)
out.dat <- sample_data(input.data, type = "individual", selected.ind = names(ind.id))
return(out.dat)
}
else{
na.num <- apply(input.data$geno.dose, 2, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.mrk
ind.id <- which(perc.na <= filter.thres)
if(length(ind.id) == input.data$n.ind){
return(input.data)
}
ind <- names(ind.id)
out.dat <- sample_data(input.data, type = "individual", selected.ind = names(ind.id))
return(out.dat)
}
}
#' Filter markers based on chi-square test
#'
#' This function filter markers based on p-values of a chi-square test.
#' The chi-square test assumes that markers follow the expected segregation
#' patterns under Mendelian inheritance, random chromosome bivalent
#' pairing and no double reduction.
#'
#' @param input.obj name of input object (class \code{mappoly.data})
#'
#' @param chisq.pval.thres p-value threshold used for chi-square tests
#' (default = Bonferroni aproximation with global alpha of 0.05, i.e.,
#' 0.05/n.mrk)
#'
#' @param inter if TRUE (default), plots distorted vs. non-distorted markers
#'
#' @return An object of class \code{mappoly.chitest.seq} which contains a list with the following components:
#' \item{keep}{markers that follow Mendelian segregation pattern}
#' \item{exclude}{markers with distorted segregation}
#' \item{chisq.pval.thres}{threshold p-value used for chi-square tests}
#' \item{data.name}{input dataset used to perform the chi-square tests}
#'
#'@examples
#' mrks.chi.filt <- filter_segregation(input.obj = tetra.solcap,
#' chisq.pval.thres = 0.05/tetra.solcap$n.mrk,
#' inter = TRUE)
#' seq.init <- make_seq_mappoly(mrks.chi.filt)
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}
#'
#' @importFrom graphics axis
#' @export
filter_segregation <- function(input.obj, chisq.pval.thres = NULL, inter = TRUE){
op <- par(pty="s")
on.exit(par(op))
if(inherits(input.obj, c("mappoly.data"))){
chisq.val <- input.obj$chisq.pval
n.mrk <- input.obj$n.mrk
data.name <- as.character(sys.call())[2]
} else if (inherits(input.obj, c("mappoly.sequence"))){
chisq.val <- input.obj$chisq.pval[input.obj$seq.mrk.names]
n.mrk <- length(input.obj$seq.num)
data.name <- input.obj$data.name
} else {
stop(deparse(substitute(input.obj)),
" is not an object of class 'mappoly.data' or 'mappoly.sequence'")
}
##Bonferroni approx
if(is.null(chisq.pval.thres))
chisq.pval.thres <- 0.05/n.mrk
ANSWER <- "flag"
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
x <- log10(sort(chisq.val, decreasing = TRUE))
th <- log10(chisq.pval.thres)
plot(x,
xlab = "markers",
ylab = bquote(log[10](P)),
col = ifelse(x <= th, 2, 4),
pch =ifelse(x <= th, 4, 1))
abline(h = th, lty = 2)
f<-paste0("Filtered out: ", sum(x < th))
i<-paste0("Included: ", sum(x >= th))
legend("bottomleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the p value threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("You decided to stop the function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
chisq.pval.thres <- as.numeric(ANSWER)
}
}
keep <- names(which(chisq.val >= chisq.pval.thres))
exclude <- names(which(chisq.val < chisq.pval.thres))
structure(list(keep = keep, exclude = exclude, chisq.pval.thres = chisq.pval.thres,
data.name = data.name),
class = "mappoly.chitest.seq")
}
#' Filter out individuals
#'
#' This function removes individuals from the data set. Individuals can be
#' user-defined or can be accessed via interactive kinship analysis.
#'
#' @param input.data name of input object (class \code{mappoly.data})
#'
#' @param ind.to.remove individuals to be removed. If \code{NULL} it opens
#' an interactive graphic to proceed with the individual
#' selection
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#'
#' @param type A character string specifying the procedure to be used for
#' detecting outlier offspring. Options include "Gmat", which
#' utilizes the genomic kinship matrix, and "PCA", which employs
#' principal component analysis on the dosage matrix.
#'
#' coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
#'
#' @param verbose if \code{TRUE} (default), shows the filtered out individuals
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}
#'
#' @importFrom stats prcomp
#' @export
filter_individuals <- function(input.data,
ind.to.remove = NULL,
inter = TRUE,
type = c("Gmat", "PCA"),
verbose = TRUE){
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
type <- match.arg(type)
op <- par(pty="s")
on.exit(par(op))
D <- t(input.data$geno.dose)
D[D == input.data$ploidy+1] <- NA
D <- rbind(input.data$dosage.p1, input.data$dosage.p2, D)
rownames(D)[1:2] <- c("P1", "P2")
if(type == "Gmat"){
G <- AGHmatrix::Gmatrix(D, method = "VanRaden",ploidy = input.data$ploidy)
x <- G[1,]
y <- G[2,]
df <- data.frame(x = x, y = y, type = c(2, 2, rep(4, length(x)-2)))
plot(df[,1:2], col = df$type, pch = 19,
xlab = "relationships between the offspring and P1",
ylab = "relationships between the offspring and P2")
abline(c(0,1), lty = 2)
abline(c(-0.4,1), lty = 2, col = "gray")
abline(c(0.4,1), lty = 2, col = "gray")
legend("topright", c("Parents", "Offspring") , col = c(2,4), pch = 19)
if(!is.null(ind.to.remove)){
out.data <- sample_data(input.data, selected.ind = setdiff(input.data$ind.names, ind.to.remove))
return(out.data)
}
if(interactive() && inter)
{
# if (!require(gatepoints))
# stop("Please install package 'gatepoints' to proceed")
ANSWER <- readline("Enter 'Y/n' to proceed with interactive filtering or quit: ")
if(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == "")
{
ind.to.remove <- gatepoints::fhs(df, mark = TRUE)
ind.to.remove <- setdiff(ind.to.remove, c("P1", "P2"))
ind.to.include <- setdiff(rownames(df)[-c(1:2)], ind.to.remove)
if(verbose){
cat("Removing individual(s): \n")
print(ind.to.remove)
cat("...\n")
}
if(length(ind.to.remove) == 0){
warning("No individuals removed. Returning original data set.")
return(input.data)
}
out.data <- sample_data(input.data, selected.ind = ind.to.include)
return(out.data)
} else{
warning("No individuals removed. Returning original data set.")
return(input.data)
}
}
}
else{
row_means <- rowMeans(D, na.rm = TRUE)
for (i in 1:nrow(D))
D[i, is.na(D[i, ])] <- row_means[i]
pc <- prcomp(D)
x <- pc$x[,"PC1"]
y <- pc$x[,"PC2"]
a <- diff(range(x))*0.05
b <- diff(range(y))*0.05
df <- data.frame(x = x, y = y, type = c(2, 2, rep(4, length(x)-2)))
plot(df[,1:2], col = df$type, pch = 19,
xlab = "PC1",
ylab = "PC2",
xlim = c(min(x)-a, max(x)+a),
ylim = c(min(y)-b, max(y)+b))
points(df[1:2,1:2], col = 2, pch = 19)
legend("bottomleft", c("Parents", "Offspring") , col = c(2,4), pch = 19)
if(!is.null(ind.to.remove)){
out.data <- sample_data(input.data, selected.ind = setdiff(input.data$ind.names, ind.to.remove))
return(out.data)
}
if(interactive() && inter)
{
ANSWER <- readline("Enter 'Y/n' to proceed with interactive filtering or quit: ")
if(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == "")
{
ind.to.remove <- gatepoints::fhs(df, mark = TRUE)
ind.to.remove <- setdiff(ind.to.remove, c("P1", "P2"))
ind.to.include <- setdiff(rownames(df)[-c(1:2)], ind.to.remove)
if(verbose){
cat("Removing individual(s): \n")
print(ind.to.remove)
cat("...\n")
}
if(length(ind.to.remove) == 0){
warning("No individuals removed. Returning original data set.")
return(input.data)
}
out.data <- sample_data(input.data, selected.ind = ind.to.include)
return(out.data)
} else{
warning("No individuals removed. Returning original data set.")
return(input.data)
}
}
}
}
#' Remove markers that do not meet a LOD criteria
#'
#' Remove markers that do not meet a LOD and recombination fraction
#' criteria for at least a percentage of the pairwise marker
#' combinations. It also removes markers with strong evidence of
#' linkage across the whole linkage group (false positive).
#'
#' \code{thresh.LOD.ph} should be set in order to only select
#' recombination fractions that have LOD scores associated to the
#' linkage phase configuration higher than \code{thresh_LOD_ph}
#' when compared to the second most likely linkage phase configuration.
#' That action usually eliminates markers that are unlinked to the
#' set of analyzed markers.
#'
#' @param input.twopt an object of class \code{mappoly.twopt}
#'
#' @param thresh.LOD.ph LOD score threshold for linkage phase configuration
#' (default = 5)
#'
#' @param thresh.LOD.rf LOD score threshold for recombination fraction
#' (default = 5)
#'
#' @param thresh.rf threshold for recombination fractions (default = 0.15)
#'
#' @param probs indicates the probability corresponding to the filtering
#' quantiles. (default = c(0.05, 1))
#'
#' @param diag.markers A window where marker pairs should be considered.
#' If NULL (default), all markers are considered.
#'
#' @param mrk.order marker order. Only has effect if 'diag.markers' is not NULL
#'
#' @param ncpus number of parallel processes (i.e. cores) to spawn
#' (default = 1)
#'
#' @param diagnostic.plot if \code{TRUE} produces a diagnostic plot
#'
#' @param breaks number of cells for the histogram
#'
#' @return A filtered object of class \code{mappoly.sequence}.
#' See \code{\link[mappoly]{make_seq_mappoly}} for details
#'
#' @examples
#' all.mrk <- make_seq_mappoly(hexafake, 1:20)
#' red.mrk <- elim_redundant(all.mrk)
#' unique.mrks <- make_seq_mappoly(red.mrk)
#' all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
#' ncpus = 1,
#' verbose = TRUE)
#'
#' ## Full recombination fraction matrix
#' mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
#' plot(mat.full)
#'
#' ## Removing disruptive SNPs
#' tpt.filt <- rf_snp_filter(all.pairs, 2, 2, 0.07, probs = c(0.15, 1))
#' p1.filt <- make_pairs_mappoly(input.seq = tpt.filt, input.twopt = all.pairs)
#' m1.filt <- rf_list_to_matrix(input.twopt = p1.filt)
#' plot(mat.full, main.text = "LG1")
#' plot(m1.filt, main.text = "LG1.filt")
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu} with updates by Gabriel Gesteira, \email{gdesiqu@ncsu.edu}
#'
#' @references
#' Mollinari, M., and Garcia, A. A. F. (2019) Linkage
#' analysis and haplotype phasing in experimental autopolyploid
#' populations with high ploidy level using hidden Markov
#' models, _G3: Genes, Genomes, Genetics_.
#' \doi{10.1534/g3.119.400378}
#'
#' @export rf_snp_filter
#' @importFrom ggplot2 ggplot geom_histogram aes scale_fill_manual xlab ggtitle
#' @importFrom graphics hist
rf_snp_filter <- function(input.twopt,
thresh.LOD.ph = 5,
thresh.LOD.rf = 5,
thresh.rf = 0.15,
probs = c(0.05, 1),
diag.markers = NULL,
mrk.order = NULL,
ncpus = 1L,
diagnostic.plot = TRUE,
breaks = 100)
{
input_classes <- c("mappoly.twopt", "mappoly.twopt2")
if (!inherits(input.twopt, input_classes)) {
stop(deparse(substitute(input.twopt)), paste0(" is not an object of class ", paste0(input_classes, collapse = " or ")))
}
probs <- range(probs)
## Getting filtered rf matrix
rf_mat <- rf_list_to_matrix(input.twopt = input.twopt, thresh.LOD.ph = thresh.LOD.ph,
thresh.LOD.rf = thresh.LOD.rf, thresh.rf = thresh.rf,
ncpus = ncpus, verbose = FALSE)
M <- rf_mat$rec.mat
if(!is.null(mrk.order))
M <- M[mrk.order, mrk.order]
if(!is.null(diag.markers))
M[abs(col(M) - row(M)) > diag.markers] <- NA
x <- apply(M, 1, function(x) sum(!is.na(x)))
w <- hist(x, breaks = breaks, plot = FALSE)
th <- quantile(x, probs = probs)
rem <- c(which(x < th[1]), which(x > th[2]))
ids <- names(which(x >= th[1] & x <= th[2]))
value <- type <- NULL
if(diagnostic.plot){
d <- rbind(data.frame(type = "original", value = x),
data.frame(type = "filtered", value = x[ids]))
p <- ggplot2::ggplot(d, ggplot2::aes(value)) +
ggplot2::geom_histogram(ggplot2::aes(fill = type),
alpha = 0.4, position = "identity", binwidth = diff(w$mids)[1]) +
ggplot2::scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
ggplot2::ggtitle( paste0("Filtering probs: [", probs[1], " : ", probs[2], "] - Non NA values by row in rf matrix - b width: ", diff(w$mids)[1])) +
ggplot2::xlab(paste0("Non 'NA' values at LOD.ph = ", thresh.LOD.ph, ", LOD.rf = ", thresh.LOD.rf, ", and thresh.rf = ", thresh.rf))
print(p)
}
## Returning sequence object
ch_filt <- make_seq_mappoly(input.obj = get(input.twopt$data.name, pos = 1), arg = ids, data.name = input.twopt$data.name)
return(ch_filt)
}
#' Edit sequence ordered by reference genome positions
#' comparing to another set order
#'
#' @param input.seq object of class mappoly.sequence with alternative order (not genomic order)
#' @param invert vector of marker names to be inverted
#' @param remove vector of marker names to be removed
#'
#' @author Cristiane Taniguti, \email{chtaniguti@tamu.edu}
#'
#' @examples
#' \donttest{
#' dat <- filter_segregation(tetra.solcap, inter = FALSE)
#' seq_dat <- make_seq_mappoly(dat)
#' seq_chr <- make_seq_mappoly(seq_dat, arg = seq_dat$seq.mrk.names[which(seq_dat$chrom=="1")])
#'
#' tpt <- est_pairwise_rf(seq_chr)
#' seq.filt <- rf_snp_filter(tpt, probs = c(0.05, 0.95))
#' mat <- rf_list_to_matrix(tpt)
#' mat2 <- make_mat_mappoly(mat, seq.filt)
#'
#' seq_test_mds <- mds_mappoly(mat2)
#' seq_mds <- make_seq_mappoly(seq_test_mds)
#' edit_seq <- edit_order(input.seq = seq_mds)
#' }
#'
#' @return object of class \code{mappoly.edit.order}: a list containing
#' vector of marker names ordered according to editions (`edited_order`);
#' vector of removed markers names (`removed`);
#' vector of inverted markers names (`inverted`).
#'
#' @export
edit_order <- function(input.seq, invert= NULL, remove = NULL){
if (!inherits(input.seq, "mappoly.sequence")) {
stop(deparse(substitute(input.seq)), " is not an object of class 'mappoly.sequence'")
}
get_weird <- data.frame(x = 1:length(input.seq$genome.pos),
y = input.seq$genome.pos)
rownames(get_weird) <- input.seq$seq.mrk.names
get_weird <- get_weird[order(get_weird$y),]
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
inverted <- removed <- vector()
if(!is.null(invert) | !is.null(remove)){
if(!is.null(invert)){
inverted <- c(inverted, as.vector(invert))
repl <- get_weird[rev(match(as.vector(invert),rownames(get_weird))),]
get_weird[match(as.vector(invert),rownames(get_weird)),2] <- repl[,2]
rownames(get_weird)[match(as.vector(invert), rownames(get_weird))] <- rownames(repl)
}
if(!is.null(remove)){
removed <- c(removed, as.vector(remove))
get_weird <- get_weird[-match(remove, rownames(get_weird)),]
}
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
} else {
cat("Mark at least three points on the plot and press `Esc` to continue.")
if(interactive()){
ANSWER <- "Y"
while(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == ""){
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
mks.to.remove <- gatepoints::fhs(get_weird, mark = TRUE)
if(length(which(rownames(get_weird) %in% mks.to.remove)) > 0){
ANSWER2 <- readline("Enter 'invert/remove' to proceed with the edition: ")
if(ANSWER2 == "invert"){
inverted <- c(inverted, as.vector(mks.to.remove))
repl <- get_weird[rev(match(as.vector(mks.to.remove),rownames(get_weird))),]
get_weird[match(as.vector(mks.to.remove),rownames(get_weird)),2] <- repl[,2]
rownames(get_weird)[match(as.vector(mks.to.remove), rownames(get_weird))] <- rownames(repl)
} else {
removed <- c(removed, as.vector(mks.to.remove))
get_weird <- get_weird[-match(mks.to.remove, rownames(get_weird)),]
}
}
ANSWER <- readline("Enter 'Y/n' to proceed with interactive edition or quit: ")
}
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
}
}
return(structure(list(edited_order = rownames(get_weird),
removed = removed,
inverted = inverted,
data.name = input.seq$data.name), class = "mappoly.edit.order"))
}
#' Filter aneuploid chromosomes from progeny individuals
#'
#' @param input.data name of input object (class \code{mappoly.data})
#'
#' @param aneuploid.info data.frame with ploidy information by chromosome (columns) for each individual
#' in progeny (rows). The chromosome and individuals names must match the ones in the file used as input
#' in mappoly.
#'
#' @param ploidy main ploidy
#'
#' @param rm_missing remove also genotype information from chromosomes with missing data (NA) in the aneuploid.info file
#'
#' @examples
#' aneuploid.info <- matrix(4, nrow=tetra.solcap$n.ind, ncol = 12)
#' set.seed(8080)
#' aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 3
#' aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 5
#'
#' colnames(aneuploid.info) <- paste0(1:12)
#' aneuploid.info <- cbind(inds = tetra.solcap$ind.names, aneuploid.info)
#'
#' filt.dat <- filter_aneuploid(input.data = tetra.solcap,
#' aneuploid.info = aneuploid.info, ploidy = 4)
#'
#' @author Cristiane Taniguti, \email{chtaniguti@tamu.edu}
#'
#' @return object of class \code{mappoly.data}
#'
#' @export
filter_aneuploid <- function(input.data, aneuploid.info, ploidy, rm_missing = TRUE){
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
aneu.chroms <- colnames(aneuploid.info)[-1]
keep.ind <- match(input.data$ind.names, aneuploid.info[,1])
if(length(keep.ind) != length(input.data$ind.names) | any(is.na(keep.ind)))
stop("The aneuploid information ploidy is missing at least one sample.")
aneuploid.info.filt <- aneuploid.info[keep.ind,]
idx.list <- apply(aneuploid.info.filt[,-1], 1, function(x) which(x != ploidy))
names(idx.list) <- aneuploid.info.filt[,1]
idx.list <- idx.list[-which(!sapply(idx.list, function(x) length(x) > 0))]
if(rm_missing){
idx.list.na <- apply(aneuploid.info.filt[,-1], 1, function(x) which(is.na(x)))
if(length(idx.list.na) > 0){
names(idx.list.na) <- aneuploid.info.filt[,1]
idx.list.na <- idx.list.na[-which(!sapply(idx.list.na, function(x) length(x) > 0))]
idx.list <- c(idx.list, idx.list.na)
}
cat(round((length(unlist(idx.list))/(dim(aneuploid.info.filt)[1]*(dim(aneuploid.info.filt)[2]-1)))*100,2), "% of the chromosomes x individuals are aneuploids or has missing ploidy information\n")
} else
cat(round((length(unlist(idx.list))/(dim(aneuploid.info.filt)[1]*(dim(aneuploid.info.filt)[2]-1)))*100,2), "% of the chromosomes x individuals are aneuploids\n")
for(i in 1:length(idx.list)){
idx <- which(input.data$chrom %in% names(idx.list[[i]]))
input.data$geno.dose[idx,names(idx.list[i])] <- ploidy + 1
}
return(input.data)
}
mmollina/MAPPoly documentation built on March 8, 2024, 2:04 a.m.
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Defines functions filter_aneuploid edit_order rf_snp_filter filter_individuals filter_segregation filter_missing_ind filter_missing_mrk filter_missing filter_non_conforming_classes
Documented in edit_order filter_aneuploid filter_individuals filter_missing filter_missing_ind filter_missing_mrk filter_non_conforming_classes filter_segregation rf_snp_filter
#' Filter non-conforming classes in F1, non double reduced population.
#' @keywords internal
filter_non_conforming_classes <- function(input.data, prob.thres = NULL)
{
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
ploidy <- input.data$ploidy
dp <- input.data$dosage.p1
dq <- input.data$dosage.p2
Ds <- array(NA, dim = c(ploidy+1, ploidy+1, ploidy+1))
for(i in 0:ploidy)
for(j in 0:ploidy)
Ds[i+1,j+1,] <- segreg_poly(ploidy = ploidy, dP = i, dQ = j)
Dpop <- cbind(dp,dq)
#Gathering segregation probabilities given parental dosages
M <- t(apply(Dpop, 1, function(x) Ds[x[1]+1, x[2]+1,]))
M[M != 0] <- 1
dimnames(M) <- list(input.data$mrk.names, 0:ploidy)
##if no prior probabilities
if(!is.prob.data(input.data)){
for(i in 1:nrow(M)){
id0 <- !as.numeric(input.data$geno.dose[i,])%in%(which(M[i,] == 1)-1)
if(any(id0))
input.data$geno.dose[i,id0] <- (ploidy+1)
}
return(input.data)
}
## 1 represents conforming classes/ 0 represents non-conforming classes
dp <- rep(dp, input.data$n.ind)
dq <- rep(dq, input.data$n.ind)
M <- M[rep(seq_len(nrow(M)), input.data$n.ind),]
R <- input.data$geno[,-c(1:2)] - input.data$geno[,-c(1:2)]*M
id1 <- apply(R, 1, function(x) abs(sum(x))) > 0.3 # if the sum of the excluded classes is greater than 0.3, use segreg_poly
N <- matrix(NA, sum(id1), input.data$ploidy+1)
ct <- 1
for(i in which(id1)){
N[ct,] <- Ds[dp[i]+1, dq[i]+1, ]
ct <- ct+1
}
input.data$geno[id1,-c(1:2)] <- N
# if the sum of the excluded classes is greater than zero
# and smaller than 0.3, assign zero to those classes and normalize the vector
input.data$geno[,-c(1:2)][R > 0] <- 0
input.data$geno[,-c(1:2)] <- sweep(input.data$geno[,-c(1:2)], 1, rowSums(input.data$geno[,-c(1:2)]), FUN = "/")
if(is.null(prob.thres))
prob.thres <- input.data$prob.thres
geno.dose <- dist_prob_to_class(geno = input.data$geno, prob.thres = prob.thres)
if(geno.dose$flag)
{
input.data$geno <- geno.dose$geno
input.data$geno.dose <- geno.dose$geno.dose
} else {
input.data$geno.dose <- geno.dose$geno.dose
}
input.data$geno.dose[is.na(input.data$geno.dose)] <- ploidy + 1
input.data$n.ind <- ncol(input.data$geno.dose)
input.data$ind.names <- colnames(input.data$geno.dose)
return(input.data)
}
#' Filter missing genotypes
#'
#' Excludes markers or individuals based on their proportion of missing data.
#'
#' @param input.data an object of class \code{mappoly.data}.
#'
#' @param type one of the following options:
#' \enumerate{
#' \item \code{"marker"}: filter out markers based on their percentage of missing data (default).
#' \item \code{"individual"}: filter out individuals based on their percentage of missing data.
#' }
#' Please notice that removing individuals with certain amount of data can change some marker parameters
#' (such as depth), and can also change the estimated genotypes for other individuals.
#' So, be careful when removing individuals.
#'
#' @param filter.thres maximum percentage of missing data (default = 0.2).
#'
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering.
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}.
#'
#' @examples
#' plot(tetra.solcap)
#' dat.filt.mrk <- filter_missing(input.data = tetra.solcap,
#' type = "marker",
#' filter.thres = 0.1,
#' inter = TRUE)
#' plot(dat.filt.mrk)
#'
#' @export
#' @importFrom magrittr "%>%"
#' @importFrom dplyr filter
#' @importFrom graphics axis
filter_missing <- function(input.data,
type = c("marker", "individual"),
filter.thres = 0.2,
inter = TRUE)
{
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
type <- match.arg(type)
switch(type,
marker = filter_missing_mrk(input.data,
filter.thres = filter.thres,
inter = inter),
individual = filter_missing_ind(input.data,
filter.thres = filter.thres,
inter = inter)
)
}
#' Filter markers based on missing genotypes
#'
#' @param input.data an object of class \code{"mappoly.data"}
#' @param filter.thres maximum percentage of missing data
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#' @keywords internal
filter_missing_mrk <- function(input.data, filter.thres = 0.2, inter = TRUE)
{
op <- par(pty="s")
on.exit(par(op))
ANSWER <- "flag"
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
na.num <- apply(input.data$geno.dose, 1, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.ind
x <- sort(perc.na)
plot(x,
xlab = "markers",
ylab = "frequency of missing data",
col = ifelse(x <= filter.thres, 4, 2),
pch =ifelse(x <= filter.thres, 1, 4))
abline(h = filter.thres, lty = 2)
f<-paste0("Filtered out: ", sum(perc.na > filter.thres))
i<-paste0("Included: ", sum(perc.na <= filter.thres))
legend("topleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the filter threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("Stop function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
filter.thres <- as.numeric(ANSWER)
}
mrks.id <- which(perc.na <= filter.thres)
if(length(mrks.id) == input.data$n.mrk){
return(input.data)
}
out.dat <- sample_data(input.data, type = "markers", selected.mrk = names(mrks.id))
return(out.dat)
}
else{
na.num <- apply(input.data$geno.dose, 1, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.ind
mrks.id <- which(perc.na <= filter.thres)
if(length(mrks.id) == input.data$n.mrk){
return(input.data)
}
out.dat <- sample_data(input.data, type = "markers", selected.mrk = names(mrks.id))
return(out.dat)
}
}
#' Filter individuals based on missing genotypes
#'
#' @param input.data an object of class \code{"mappoly.data"}
#' @param filter.thres maximum percentage of missing data
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#' @keywords internal
#' @importFrom magrittr "%>%"
#' @importFrom dplyr filter
#' @importFrom graphics axis
filter_missing_ind <- function(input.data, filter.thres = 0.2, inter = TRUE)
{
op <- par(pty="s")
on.exit(par(op))
ANSWER <- "flag"
ind <- NULL
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
na.num <- apply(input.data$geno.dose, 2, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.mrk
x <- sort(perc.na)
plot(x,
xlab = "offspring",
ylab = "frequency of missing data",
col = ifelse(x <= filter.thres, 4, 2),
pch =ifelse(x <= filter.thres, 1, 4));
abline(h = filter.thres, lty = 2)
f<-paste0("Filtered out: ", sum(perc.na > filter.thres))
i<-paste0("Included: ", sum(perc.na <= filter.thres))
legend("topleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the filter threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("You decided to stop the function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
filter.thres <- as.numeric(ANSWER)
}
ind.id <- which(perc.na <= filter.thres)
if(length(ind.id) == input.data$n.ind){
return(input.data)
}
ind <- names(ind.id)
out.dat <- sample_data(input.data, type = "individual", selected.ind = names(ind.id))
return(out.dat)
}
else{
na.num <- apply(input.data$geno.dose, 2, function(x,ploidy) sum(x == ploidy+1), ploidy = input.data$ploidy)
perc.na <- na.num/input.data$n.mrk
ind.id <- which(perc.na <= filter.thres)
if(length(ind.id) == input.data$n.ind){
return(input.data)
}
ind <- names(ind.id)
out.dat <- sample_data(input.data, type = "individual", selected.ind = names(ind.id))
return(out.dat)
}
}
#' Filter markers based on chi-square test
#'
#' This function filter markers based on p-values of a chi-square test.
#' The chi-square test assumes that markers follow the expected segregation
#' patterns under Mendelian inheritance, random chromosome bivalent
#' pairing and no double reduction.
#'
#' @param input.obj name of input object (class \code{mappoly.data})
#'
#' @param chisq.pval.thres p-value threshold used for chi-square tests
#' (default = Bonferroni aproximation with global alpha of 0.05, i.e.,
#' 0.05/n.mrk)
#'
#' @param inter if TRUE (default), plots distorted vs. non-distorted markers
#'
#' @return An object of class \code{mappoly.chitest.seq} which contains a list with the following components:
#' \item{keep}{markers that follow Mendelian segregation pattern}
#' \item{exclude}{markers with distorted segregation}
#' \item{chisq.pval.thres}{threshold p-value used for chi-square tests}
#' \item{data.name}{input dataset used to perform the chi-square tests}
#'
#'@examples
#' mrks.chi.filt <- filter_segregation(input.obj = tetra.solcap,
#' chisq.pval.thres = 0.05/tetra.solcap$n.mrk,
#' inter = TRUE)
#' seq.init <- make_seq_mappoly(mrks.chi.filt)
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}
#'
#' @importFrom graphics axis
#' @export
filter_segregation <- function(input.obj, chisq.pval.thres = NULL, inter = TRUE){
op <- par(pty="s")
on.exit(par(op))
if(inherits(input.obj, c("mappoly.data"))){
chisq.val <- input.obj$chisq.pval
n.mrk <- input.obj$n.mrk
data.name <- as.character(sys.call())[2]
} else if (inherits(input.obj, c("mappoly.sequence"))){
chisq.val <- input.obj$chisq.pval[input.obj$seq.mrk.names]
n.mrk <- length(input.obj$seq.num)
data.name <- input.obj$data.name
} else {
stop(deparse(substitute(input.obj)),
" is not an object of class 'mappoly.data' or 'mappoly.sequence'")
}
##Bonferroni approx
if(is.null(chisq.pval.thres))
chisq.pval.thres <- 0.05/n.mrk
ANSWER <- "flag"
if(interactive() && inter)
{
while(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
{
x <- log10(sort(chisq.val, decreasing = TRUE))
th <- log10(chisq.pval.thres)
plot(x,
xlab = "markers",
ylab = bquote(log[10](P)),
col = ifelse(x <= th, 2, 4),
pch =ifelse(x <= th, 4, 1))
abline(h = th, lty = 2)
f<-paste0("Filtered out: ", sum(x < th))
i<-paste0("Included: ", sum(x >= th))
legend("bottomleft", c(f, i) , col = c(2,4), pch = c(4,1))
ANSWER <- readline("Enter 'Y/n' to proceed or update the p value threshold: ")
if(substr(ANSWER, 1, 1) == "n" | substr(ANSWER, 1, 1) == "no" | substr(ANSWER, 1, 1) == "N")
stop("You decided to stop the function.")
if(substr(ANSWER, 1, 1) != "y" && substr(ANSWER, 1, 1) != "yes" && substr(ANSWER, 1, 1) != "Y" && ANSWER != "")
chisq.pval.thres <- as.numeric(ANSWER)
}
}
keep <- names(which(chisq.val >= chisq.pval.thres))
exclude <- names(which(chisq.val < chisq.pval.thres))
structure(list(keep = keep, exclude = exclude, chisq.pval.thres = chisq.pval.thres,
data.name = data.name),
class = "mappoly.chitest.seq")
}
#' Filter out individuals
#'
#' This function removes individuals from the data set. Individuals can be
#' user-defined or can be accessed via interactive kinship analysis.
#'
#' @param input.data name of input object (class \code{mappoly.data})
#'
#' @param ind.to.remove individuals to be removed. If \code{NULL} it opens
#' an interactive graphic to proceed with the individual
#' selection
#' @param inter if \code{TRUE}, expects user-input to proceed with filtering
#'
#' @param type A character string specifying the procedure to be used for
#' detecting outlier offspring. Options include "Gmat", which
#' utilizes the genomic kinship matrix, and "PCA", which employs
#' principal component analysis on the dosage matrix.
#'
#' coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
#'
#' @param verbose if \code{TRUE} (default), shows the filtered out individuals
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu}
#'
#' @importFrom stats prcomp
#' @export
filter_individuals <- function(input.data,
ind.to.remove = NULL,
inter = TRUE,
type = c("Gmat", "PCA"),
verbose = TRUE){
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
type <- match.arg(type)
op <- par(pty="s")
on.exit(par(op))
D <- t(input.data$geno.dose)
D[D == input.data$ploidy+1] <- NA
D <- rbind(input.data$dosage.p1, input.data$dosage.p2, D)
rownames(D)[1:2] <- c("P1", "P2")
if(type == "Gmat"){
G <- AGHmatrix::Gmatrix(D, method = "VanRaden",ploidy = input.data$ploidy)
x <- G[1,]
y <- G[2,]
df <- data.frame(x = x, y = y, type = c(2, 2, rep(4, length(x)-2)))
plot(df[,1:2], col = df$type, pch = 19,
xlab = "relationships between the offspring and P1",
ylab = "relationships between the offspring and P2")
abline(c(0,1), lty = 2)
abline(c(-0.4,1), lty = 2, col = "gray")
abline(c(0.4,1), lty = 2, col = "gray")
legend("topright", c("Parents", "Offspring") , col = c(2,4), pch = 19)
if(!is.null(ind.to.remove)){
out.data <- sample_data(input.data, selected.ind = setdiff(input.data$ind.names, ind.to.remove))
return(out.data)
}
if(interactive() && inter)
{
# if (!require(gatepoints))
# stop("Please install package 'gatepoints' to proceed")
ANSWER <- readline("Enter 'Y/n' to proceed with interactive filtering or quit: ")
if(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == "")
{
ind.to.remove <- gatepoints::fhs(df, mark = TRUE)
ind.to.remove <- setdiff(ind.to.remove, c("P1", "P2"))
ind.to.include <- setdiff(rownames(df)[-c(1:2)], ind.to.remove)
if(verbose){
cat("Removing individual(s): \n")
print(ind.to.remove)
cat("...\n")
}
if(length(ind.to.remove) == 0){
warning("No individuals removed. Returning original data set.")
return(input.data)
}
out.data <- sample_data(input.data, selected.ind = ind.to.include)
return(out.data)
} else{
warning("No individuals removed. Returning original data set.")
return(input.data)
}
}
}
else{
row_means <- rowMeans(D, na.rm = TRUE)
for (i in 1:nrow(D))
D[i, is.na(D[i, ])] <- row_means[i]
pc <- prcomp(D)
x <- pc$x[,"PC1"]
y <- pc$x[,"PC2"]
a <- diff(range(x))*0.05
b <- diff(range(y))*0.05
df <- data.frame(x = x, y = y, type = c(2, 2, rep(4, length(x)-2)))
plot(df[,1:2], col = df$type, pch = 19,
xlab = "PC1",
ylab = "PC2",
xlim = c(min(x)-a, max(x)+a),
ylim = c(min(y)-b, max(y)+b))
points(df[1:2,1:2], col = 2, pch = 19)
legend("bottomleft", c("Parents", "Offspring") , col = c(2,4), pch = 19)
if(!is.null(ind.to.remove)){
out.data <- sample_data(input.data, selected.ind = setdiff(input.data$ind.names, ind.to.remove))
return(out.data)
}
if(interactive() && inter)
{
ANSWER <- readline("Enter 'Y/n' to proceed with interactive filtering or quit: ")
if(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == "")
{
ind.to.remove <- gatepoints::fhs(df, mark = TRUE)
ind.to.remove <- setdiff(ind.to.remove, c("P1", "P2"))
ind.to.include <- setdiff(rownames(df)[-c(1:2)], ind.to.remove)
if(verbose){
cat("Removing individual(s): \n")
print(ind.to.remove)
cat("...\n")
}
if(length(ind.to.remove) == 0){
warning("No individuals removed. Returning original data set.")
return(input.data)
}
out.data <- sample_data(input.data, selected.ind = ind.to.include)
return(out.data)
} else{
warning("No individuals removed. Returning original data set.")
return(input.data)
}
}
}
}
#' Remove markers that do not meet a LOD criteria
#'
#' Remove markers that do not meet a LOD and recombination fraction
#' criteria for at least a percentage of the pairwise marker
#' combinations. It also removes markers with strong evidence of
#' linkage across the whole linkage group (false positive).
#'
#' \code{thresh.LOD.ph} should be set in order to only select
#' recombination fractions that have LOD scores associated to the
#' linkage phase configuration higher than \code{thresh_LOD_ph}
#' when compared to the second most likely linkage phase configuration.
#' That action usually eliminates markers that are unlinked to the
#' set of analyzed markers.
#'
#' @param input.twopt an object of class \code{mappoly.twopt}
#'
#' @param thresh.LOD.ph LOD score threshold for linkage phase configuration
#' (default = 5)
#'
#' @param thresh.LOD.rf LOD score threshold for recombination fraction
#' (default = 5)
#'
#' @param thresh.rf threshold for recombination fractions (default = 0.15)
#'
#' @param probs indicates the probability corresponding to the filtering
#' quantiles. (default = c(0.05, 1))
#'
#' @param diag.markers A window where marker pairs should be considered.
#' If NULL (default), all markers are considered.
#'
#' @param mrk.order marker order. Only has effect if 'diag.markers' is not NULL
#'
#' @param ncpus number of parallel processes (i.e. cores) to spawn
#' (default = 1)
#'
#' @param diagnostic.plot if \code{TRUE} produces a diagnostic plot
#'
#' @param breaks number of cells for the histogram
#'
#' @return A filtered object of class \code{mappoly.sequence}.
#' See \code{\link[mappoly]{make_seq_mappoly}} for details
#'
#' @examples
#' all.mrk <- make_seq_mappoly(hexafake, 1:20)
#' red.mrk <- elim_redundant(all.mrk)
#' unique.mrks <- make_seq_mappoly(red.mrk)
#' all.pairs <- est_pairwise_rf(input.seq = unique.mrks,
#' ncpus = 1,
#' verbose = TRUE)
#'
#' ## Full recombination fraction matrix
#' mat.full <- rf_list_to_matrix(input.twopt = all.pairs)
#' plot(mat.full)
#'
#' ## Removing disruptive SNPs
#' tpt.filt <- rf_snp_filter(all.pairs, 2, 2, 0.07, probs = c(0.15, 1))
#' p1.filt <- make_pairs_mappoly(input.seq = tpt.filt, input.twopt = all.pairs)
#' m1.filt <- rf_list_to_matrix(input.twopt = p1.filt)
#' plot(mat.full, main.text = "LG1")
#' plot(m1.filt, main.text = "LG1.filt")
#'
#' @author Marcelo Mollinari, \email{mmollin@ncsu.edu} with updates by Gabriel Gesteira, \email{gdesiqu@ncsu.edu}
#'
#' @references
#' Mollinari, M., and Garcia, A. A. F. (2019) Linkage
#' analysis and haplotype phasing in experimental autopolyploid
#' populations with high ploidy level using hidden Markov
#' models, _G3: Genes, Genomes, Genetics_.
#' \doi{10.1534/g3.119.400378}
#'
#' @export rf_snp_filter
#' @importFrom ggplot2 ggplot geom_histogram aes scale_fill_manual xlab ggtitle
#' @importFrom graphics hist
rf_snp_filter <- function(input.twopt,
thresh.LOD.ph = 5,
thresh.LOD.rf = 5,
thresh.rf = 0.15,
probs = c(0.05, 1),
diag.markers = NULL,
mrk.order = NULL,
ncpus = 1L,
diagnostic.plot = TRUE,
breaks = 100)
{
input_classes <- c("mappoly.twopt", "mappoly.twopt2")
if (!inherits(input.twopt, input_classes)) {
stop(deparse(substitute(input.twopt)), paste0(" is not an object of class ", paste0(input_classes, collapse = " or ")))
}
probs <- range(probs)
## Getting filtered rf matrix
rf_mat <- rf_list_to_matrix(input.twopt = input.twopt, thresh.LOD.ph = thresh.LOD.ph,
thresh.LOD.rf = thresh.LOD.rf, thresh.rf = thresh.rf,
ncpus = ncpus, verbose = FALSE)
M <- rf_mat$rec.mat
if(!is.null(mrk.order))
M <- M[mrk.order, mrk.order]
if(!is.null(diag.markers))
M[abs(col(M) - row(M)) > diag.markers] <- NA
x <- apply(M, 1, function(x) sum(!is.na(x)))
w <- hist(x, breaks = breaks, plot = FALSE)
th <- quantile(x, probs = probs)
rem <- c(which(x < th[1]), which(x > th[2]))
ids <- names(which(x >= th[1] & x <= th[2]))
value <- type <- NULL
if(diagnostic.plot){
d <- rbind(data.frame(type = "original", value = x),
data.frame(type = "filtered", value = x[ids]))
p <- ggplot2::ggplot(d, ggplot2::aes(value)) +
ggplot2::geom_histogram(ggplot2::aes(fill = type),
alpha = 0.4, position = "identity", binwidth = diff(w$mids)[1]) +
ggplot2::scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
ggplot2::ggtitle( paste0("Filtering probs: [", probs[1], " : ", probs[2], "] - Non NA values by row in rf matrix - b width: ", diff(w$mids)[1])) +
ggplot2::xlab(paste0("Non 'NA' values at LOD.ph = ", thresh.LOD.ph, ", LOD.rf = ", thresh.LOD.rf, ", and thresh.rf = ", thresh.rf))
print(p)
}
## Returning sequence object
ch_filt <- make_seq_mappoly(input.obj = get(input.twopt$data.name, pos = 1), arg = ids, data.name = input.twopt$data.name)
return(ch_filt)
}
#' Edit sequence ordered by reference genome positions
#' comparing to another set order
#'
#' @param input.seq object of class mappoly.sequence with alternative order (not genomic order)
#' @param invert vector of marker names to be inverted
#' @param remove vector of marker names to be removed
#'
#' @author Cristiane Taniguti, \email{chtaniguti@tamu.edu}
#'
#' @examples
#' \donttest{
#' dat <- filter_segregation(tetra.solcap, inter = FALSE)
#' seq_dat <- make_seq_mappoly(dat)
#' seq_chr <- make_seq_mappoly(seq_dat, arg = seq_dat$seq.mrk.names[which(seq_dat$chrom=="1")])
#'
#' tpt <- est_pairwise_rf(seq_chr)
#' seq.filt <- rf_snp_filter(tpt, probs = c(0.05, 0.95))
#' mat <- rf_list_to_matrix(tpt)
#' mat2 <- make_mat_mappoly(mat, seq.filt)
#'
#' seq_test_mds <- mds_mappoly(mat2)
#' seq_mds <- make_seq_mappoly(seq_test_mds)
#' edit_seq <- edit_order(input.seq = seq_mds)
#' }
#'
#' @return object of class \code{mappoly.edit.order}: a list containing
#' vector of marker names ordered according to editions (`edited_order`);
#' vector of removed markers names (`removed`);
#' vector of inverted markers names (`inverted`).
#'
#' @export
edit_order <- function(input.seq, invert= NULL, remove = NULL){
if (!inherits(input.seq, "mappoly.sequence")) {
stop(deparse(substitute(input.seq)), " is not an object of class 'mappoly.sequence'")
}
get_weird <- data.frame(x = 1:length(input.seq$genome.pos),
y = input.seq$genome.pos)
rownames(get_weird) <- input.seq$seq.mrk.names
get_weird <- get_weird[order(get_weird$y),]
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
inverted <- removed <- vector()
if(!is.null(invert) | !is.null(remove)){
if(!is.null(invert)){
inverted <- c(inverted, as.vector(invert))
repl <- get_weird[rev(match(as.vector(invert),rownames(get_weird))),]
get_weird[match(as.vector(invert),rownames(get_weird)),2] <- repl[,2]
rownames(get_weird)[match(as.vector(invert), rownames(get_weird))] <- rownames(repl)
}
if(!is.null(remove)){
removed <- c(removed, as.vector(remove))
get_weird <- get_weird[-match(remove, rownames(get_weird)),]
}
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
} else {
cat("Mark at least three points on the plot and press `Esc` to continue.")
if(interactive()){
ANSWER <- "Y"
while(substr(ANSWER, 1, 1) == "y" | substr(ANSWER, 1, 1) == "yes" | substr(ANSWER, 1, 1) == "Y" | ANSWER == ""){
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
mks.to.remove <- gatepoints::fhs(get_weird, mark = TRUE)
if(length(which(rownames(get_weird) %in% mks.to.remove)) > 0){
ANSWER2 <- readline("Enter 'invert/remove' to proceed with the edition: ")
if(ANSWER2 == "invert"){
inverted <- c(inverted, as.vector(mks.to.remove))
repl <- get_weird[rev(match(as.vector(mks.to.remove),rownames(get_weird))),]
get_weird[match(as.vector(mks.to.remove),rownames(get_weird)),2] <- repl[,2]
rownames(get_weird)[match(as.vector(mks.to.remove), rownames(get_weird))] <- rownames(repl)
} else {
removed <- c(removed, as.vector(mks.to.remove))
get_weird <- get_weird[-match(mks.to.remove, rownames(get_weird)),]
}
}
ANSWER <- readline("Enter 'Y/n' to proceed with interactive edition or quit: ")
}
plot(get_weird$x, get_weird$y, xlab="input sequence order", ylab = "genomic position (bp)")
}
}
return(structure(list(edited_order = rownames(get_weird),
removed = removed,
inverted = inverted,
data.name = input.seq$data.name), class = "mappoly.edit.order"))
}
#' Filter aneuploid chromosomes from progeny individuals
#'
#' @param input.data name of input object (class \code{mappoly.data})
#'
#' @param aneuploid.info data.frame with ploidy information by chromosome (columns) for each individual
#' in progeny (rows). The chromosome and individuals names must match the ones in the file used as input
#' in mappoly.
#'
#' @param ploidy main ploidy
#'
#' @param rm_missing remove also genotype information from chromosomes with missing data (NA) in the aneuploid.info file
#'
#' @examples
#' aneuploid.info <- matrix(4, nrow=tetra.solcap$n.ind, ncol = 12)
#' set.seed(8080)
#' aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 3
#' aneuploid.info[sample(1:length(aneuploid.info), round((4*length(aneuploid.info))/100),0)] <- 5
#'
#' colnames(aneuploid.info) <- paste0(1:12)
#' aneuploid.info <- cbind(inds = tetra.solcap$ind.names, aneuploid.info)
#'
#' filt.dat <- filter_aneuploid(input.data = tetra.solcap,
#' aneuploid.info = aneuploid.info, ploidy = 4)
#'
#' @author Cristiane Taniguti, \email{chtaniguti@tamu.edu}
#'
#' @return object of class \code{mappoly.data}
#'
#' @export
filter_aneuploid <- function(input.data, aneuploid.info, ploidy, rm_missing = TRUE){
if (!inherits(input.data, "mappoly.data")) {
stop(deparse(substitute(input.data)), " is not an object of class 'mappoly.data'")
}
aneu.chroms <- colnames(aneuploid.info)[-1]
keep.ind <- match(input.data$ind.names, aneuploid.info[,1])
if(length(keep.ind) != length(input.data$ind.names) | any(is.na(keep.ind)))
stop("The aneuploid information ploidy is missing at least one sample.")
aneuploid.info.filt <- aneuploid.info[keep.ind,]
idx.list <- apply(aneuploid.info.filt[,-1], 1, function(x) which(x != ploidy))
names(idx.list) <- aneuploid.info.filt[,1]
idx.list <- idx.list[-which(!sapply(idx.list, function(x) length(x) > 0))]
if(rm_missing){
idx.list.na <- apply(aneuploid.info.filt[,-1], 1, function(x) which(is.na(x)))
if(length(idx.list.na) > 0){
names(idx.list.na) <- aneuploid.info.filt[,1]
idx.list.na <- idx.list.na[-which(!sapply(idx.list.na, function(x) length(x) > 0))]
idx.list <- c(idx.list, idx.list.na)
}
cat(round((length(unlist(idx.list))/(dim(aneuploid.info.filt)[1]*(dim(aneuploid.info.filt)[2]-1)))*100,2), "% of the chromosomes x individuals are aneuploids or has missing ploidy information\n")
} else
cat(round((length(unlist(idx.list))/(dim(aneuploid.info.filt)[1]*(dim(aneuploid.info.filt)[2]-1)))*100,2), "% of the chromosomes x individuals are aneuploids\n")
for(i in 1:length(idx.list)){
idx <- which(input.data$chrom %in% names(idx.list[[i]]))
input.data$geno.dose[idx,names(idx.list[i])] <- ploidy + 1
}
return(input.data)
}
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