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getQTLseqCI.R
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getQTLseqCI.R
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#!/bin/env Rscript
# parse parameter ---------------------------------------------------------
library(argparser, quietly=TRUE)
# Create a parser
p <- arg_parser("a program for calculating QTLseq confidence interval")
## Add command line arguments
#
p <- add_argument(p, "--popType", help = "Population type, 'F2' or 'RIL'", type = "character")
p <- add_argument(p, "--bulkSizeH", help = "Bulk size with high phenotype", type = "numeric")
p <- add_argument(p, "--bulkSizeL", help = "Bulk size with low phenotype", type = "numeric")
p <- add_argument(p, "--minDepth", help = "Minimum depth for simulation", type = "numeric", default = 5)
p <- add_argument(p, "--maxDepth", help = "Maxmum depth for simulation", type = "numeric", default = 150)
p <- add_argument(p, "--repN", help = "how many times for simulation", type = "numeric", default = 10000)
# Parse the command line arguments
argv <- parse_args(p)
popType <- argv$popType
bulkSizeH <- argv$bulkSizeH
bulkSizeL <- argv$bulkSizeL
minDepth <- argv$minDepth
maxDepth <- argv$maxDepth
repN <- argv$repN
if (FALSE) {
popType <- "RIL"
bulkSizeH <- 30
bulkSizeL <- 30
minDepth <- 5
maxDepth <- 150
repN <- 10000
}
library(tidyverse)
library(cowplot)
if (FALSE) {
# 第一个版本
getQTLseqCI <- function(popType, bulkSizeH, bulkSizeL, minDepth = 5, maxDepth = 150, repN = 10000){
#set.seed(123)
dltIndex_CI <- data.frame(HB.DP = integer(0), LB.DP = integer(0), CI95upper = numeric(0), CI95lower = numeric(0), CI99upper = numeric(0), CI99lower = numeric(0))
cat(date(), ", program start runing ...\n", sep = "")
##
{
n <- 1
for (depthH in minDepth:maxDepth) {
for (depthL in minDepth:maxDepth) {
indexH <- vector(length = repN)
indexL <- vector(length = repN)
dltIndex <- vector(length = repN)
# 以循环方式速度太慢
#for (i in 1:repN) {
# if (popType == "RIL") {
# PH <- mean(sample(c(0,1), bulkSizeH, replace = TRUE))
# PL <- mean(sample(c(0,1), bulkSizeL, replace = TRUE))
# }else if (popType == "F2") {
# PH <- mean(sample(c(0, 0.5, 1), bulkSizeH, replace = TRUE, prob = c(0.25, 0.5, 0.25)))
# PL <- mean(sample(c(0, 0.5, 1), bulkSizeL, replace = TRUE, prob = c(0.25, 0.5, 0.25)))
# }
# indexH[i] <- mean(sample(c(0, 1), depthH, replace = TRUE, prob = c(1-PH, PH)))
# indexL[i] <- mean(sample(c(0, 1), depthL, replace = TRUE, prob = c(1-PL, PL)))
# dltIndex[i] <- indexL[i] - indexH[i]
#}
# 改为取随机数方式
if (popType == "RIL") {
PH <- rbinom(repN, bulkSizeH, 0.5) / bulkSizeH
PL <- rbinom(repN, bulkSizeL, 0.5) / bulkSizeL
#PH <- apply(rmultinom(repN, bulkSizeH, c(1, 1)) * c(1, 0) / bulkSizeH, 2, sum)
#PL <- apply(rmultinom(repN, bulkSizeL, c(1, 1)) * c(1, 0) / bulkSizeL, 2, sum)
} else if (popType == "F2") {
PH <- apply(rmultinom(repN, bulkSizeH, c(1, 2, 1)) * c(1, 0.5, 0) / bulkSizeH, 2, sum)
PL <- apply(rmultinom(repN, bulkSizeL, c(1, 2, 1)) * c(1, 0.5, 0) / bulkSizeL, 2, sum)
}
indexH <- rbinom(repN, depthH, PH) / depthH
indexL <- rbinom(repN, depthL, PL) / depthL
dltIndex <- indexH - indexL
dltIndex_CI[n, ] <- c(depthH, depthL,
quantile(dltIndex, 0.975), quantile(dltIndex, 0.025),
quantile(dltIndex, 0.995), quantile(dltIndex, 0.005))
n = n + 1
}
cat(date(), ", ", paste(depthH, maxDepth, sep = "/"), " have been done ...\n", sep = "")
}
}
cat(date(), ", finish, export data ...\n", sep = "")
df <- gather(dltIndex_CI, type, CI, -HB.DP, -LB.DP) %>%
mutate(level = if_else(str_detect(type, "95"), "95 CI", "99 CI"),
direction = if_else(str_detect(type, "upper"), "Upper CI", "Lower CI"))
df$direction <- factor(df$direction, levels = c("Upper CI", "Lower CI"))
P_CI <- ggplot(df, aes(x = HB.DP, y = LB.DP, fill = CI)) +
geom_tile() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_distiller(palette = "RdBu") +
labs(title = paste("CI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"), sep = "."),
fill = NULL) +
facet_grid(direction ~ level) +
theme_half_open() +
theme(strip.background = element_rect(fill = "#90EE90"),
plot.title = element_text(hjust = 0.5))
ggsave(P_CI, filename = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"pdf", sep = "."),
width = 6.5, height = 6)
ggsave(P_CI, filename = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"png", sep = "."),
width = 6.5, height = 6, units = "in", dpi = 500)
save(dltIndex_CI,
file = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"RData", sep = "."))
}
# 以上版本在“RIL 30 30 5 200 10000” “执行时间：278.360000000015秒”
}
# 第二个版本
getQTLseqCI <- function(popType, bulkSizeH, bulkSizeL, minDepth = 5, maxDepth = 150, repN = 10000){
dltIndex_CI <- tibble(HB.DP = rep(minDepth:maxDepth, times = maxDepth-minDepth+1),
LB.DP = rep(minDepth:maxDepth, each = maxDepth-minDepth+1),
CI95upper = NA, CI95lower = NA, CI99upper = NA, CI99lower = NA)
cat(date(), ", program start runing ...\n", sep = "")
# 定义一个进度条
width <- options()$width
pb <- progress::progress_bar$new(
format = 'Progress [:bar] :percent eta: :eta',
total = nrow(dltIndex_CI), clear = FALSE, width = width
)
for (i in 1:nrow(dltIndex_CI)) {
depthH <- dltIndex_CI[i, 1][[1]]
depthL <- dltIndex_CI[i, 2][[1]]
if (popType == "RIL") {
PH <- rbinom(repN, bulkSizeH, 0.5) / bulkSizeH
PL <- rbinom(repN, bulkSizeL, 0.5) / bulkSizeL
#PH <- apply(rmultinom(repN, bulkSizeH, c(1, 1)) * c(1, 0) / bulkSizeH, 2, sum)
#PL <- apply(rmultinom(repN, bulkSizeL, c(1, 1)) * c(1, 0) / bulkSizeL, 2, sum)
} else if (popType == "F2") {
PH <- apply(rmultinom(repN, bulkSizeH, c(1, 2, 1)) * c(1, 0.5, 0) / bulkSizeH, 2, sum)
PL <- apply(rmultinom(repN, bulkSizeL, c(1, 2, 1)) * c(1, 0.5, 0) / bulkSizeL, 2, sum)
}
indexH <- rbinom(repN, depthH, PH) / depthH
indexL <- rbinom(repN, depthL, PL) / depthL
dltIndex <- indexH - indexL
dltIndex_CI[i, c("CI95upper", "CI95lower", "CI99upper", "CI99lower")] <- t(
c(quantile(dltIndex, 0.975), quantile(dltIndex, 0.025),
quantile(dltIndex, 0.995), quantile(dltIndex, 0.005))
)
# 打印进度条
pb$tick()
}
cat(date(), ", finish, export data ...\n", sep = "")
df <- gather(dltIndex_CI, type, CI, -HB.DP, -LB.DP) %>%
mutate(level = if_else(str_detect(type, "95"), "95 CI", "99 CI"),
direction = if_else(str_detect(type, "upper"), "Upper CI", "Lower CI"))
df$direction <- factor(df$direction, levels = c("Upper CI", "Lower CI"))
P_CI <- ggplot(df, aes(x = HB.DP, y = LB.DP, fill = CI)) +
geom_tile() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_distiller(palette = "RdBu") +
#scale_fill_gradientn(colors = c("#4682B4", "white", "white", "#FF4500"),
# values = c(0, (max(dltIndex_CI$CI95lower, dltIndex_CI$CI99lower) - min(dltIndex_CI$CI95lower, dltIndex_CI$CI99lower)) / (max(dltIndex_CI$CI95upper, dltIndex_CI$CI99upper) - min(dltIndex_CI$CI95lower, dltIndex_CI$CI99lower)),
# (min(dltIndex_CI$CI95upper, dltIndex_CI$CI99upper) - min(dltIndex_CI$CI95lower, dltIndex_CI$CI99lower)) / (max(dltIndex_CI$CI95upper, dltIndex_CI$CI99upper) - min(dltIndex_CI$CI95lower, dltIndex_CI$CI99lower)), 1)) +
labs(title = paste("CI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"), sep = "."),
fill = NULL) +
facet_grid(direction ~ level) +
theme_half_open() +
theme(strip.background = element_rect(fill = "#90EE90"),
plot.title = element_text(hjust = 0.5))
ggsave(P_CI, filename = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"pdf", sep = "."),
width = 6.5, height = 6)
ggsave(P_CI, filename = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"png", sep = "."),
width = 6.5, height = 6, units = "in", dpi = 500)
save(dltIndex_CI,
file = paste("QTLseqCI",
paste(paste("indH", bulkSizeH, sep = ""),
paste("indL", bulkSizeL, sep = ""),
popType, sep = "_"),
paste("Depth", minDepth, maxDepth, sep = "_"),
paste("Rep", repN, sep = "_"),
"RData", sep = "."))
}
# 以上版本在“RIL 30 30 5 200 10000” “执行时间：264.410000000003秒”
getQTLseqCI(popType, bulkSizeH, bulkSizeL, minDepth, maxDepth, repN)